November 2018, Volume 24, Number 4

ENVIRONMENTAL & ENGINEERING GEOSCIENCE Volume XXIV, Number 4, November 2018

Environmental & Engineering Geoscience NOVEMBER 2018

VOLUME XXIV, NUMBER 4

THE JOINT PUBLICATION OF THE ASSOCIATION OF ENVIRONMENTAL AND ENGINEERING GEOLOGISTS AND THE GEOLOGICAL SOCIETY OF AMERICA SERVING PROFESSIONALS IN ENGINEERING GEOLOGY, ENVIRONMENTAL GEOLOGY, AND HYDROGEOLOGY

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EDITORIAL BOARD Jerome V. DeGraff CSU Fresno Chester (skip) f. Watts Radford University thomas oommen Michigan Technological Univ. syeD e. hasan University of Missouri

ira D. sasoWsky University of Akron abDul shakoor Kent State University brian G. katz Florida Department of Environmental Protection

ASSOCIATE EDITORS John W. bell Nevada Bureau of Mines and Geology riCharD e. JaCkson Geofirma Engineering, Ltd. Jeffrey r. keaton AMEC Americas paul G. marinos National Technical University of Athens, Greece June e. mireCki U.S. Army Corps of Engineers peter pehme Waterloo Geophysics, Inc niCholas pinter Southern Illinois University

paul m. santi Colorado School of Mines robert l. sChuster U.S. Geological Survey roy J. shlemon R. J. Shlemon & Associates, Inc. GreG m. stoCk National Park Service resat ulusay Hacettepe University, Turkey Chester f. “skip” Watts Radford University terry r. West Purdue University

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Cover photo A view of the Mt. Rushmore National Memorial, Keystone, SD. Photo courtesy of S. Lindsay Poluga. See article on page 385.

Environmental & Engineering Geoscience Volume 24, Number 4, November 2018 Table of Contents 357

Challenging Geostatistical Methods to Represent Heterogeneity in CO2 Reservoirs Under Residual Trapping James R. Damico, Robert W. Ritzi, Naum I. Gershenzon, and Roland T. Okwen

375

Off-Fault Deformation Associated with Strike-Slip Faults Jeffrey A. Johnson

385

Rock Mass Characterization and Stability Evaluation of Mount Rushmore National Memorial, Keystone, South Dakota S. Lindsay Poluga, Abdul Shakoor, and Eric L. Bilderback

413

Characteristics and Numerical Runout Modeling Analysis of the Jiweishan Landslide, Chongqing, China Gao Yang, Yin Yueping, Li Bin, Feng Zhen, and He Kai

425

Application of Hydraulic Flushing in Coal Seams to Reduce Hazardous Outbursts in the Mengjin Mine, China Jingyu Jiang, Weihua Yang, Yuanping Cheng, Baomin Lv, Kai Zhang, and Ke Zhao

441

Phytoremediation Ability of the New Heavy Metal Accumulator Plants Fariba Mohsenzadeh and Roghayeh Mohammadzadeh

451

Book Review: Geology Applied to Engineering (Terry R. West and Abdul Shakoor)

Challenging Geostatistical Methods to Represent Heterogeneity in CO2 Reservoirs Under Residual Trapping JAMES R. DAMICO Illinois State Geological Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign, Champaign, IL 61820

ROBERT W. RITZI* NAUM I. GERSHENZON Department of Earth and Environmental Sciences, Wright State University, Dayton, OH 45435

ROLAND T. OKWEN Illinois State Geological Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign, Champaign, IL 61820

Key Terms: Geostatistics, Reservoir Engineering, Carbon Geosequestration, Heterogeneity, Capillary Trapping ABSTRACT Geostatistical methods based on two-point spatialbivariate statistics have been used to model heterogeneity within computational studies of the dispersion of contaminants in groundwater reservoirs and the trapping of CO2 in geosequestration reservoirs. The ability of these methods to represent fluvial architecture, commonly occurring in such reservoirs, has been questioned. We challenged a widely used two-point spatial-bivariate statistical method to represent fluvial heterogeneity in the context of representing how reservoir heterogeneity affects residual trapping of CO2 injected for geosequestration. A more rigorous model for fluvial architecture was used as the benchmark in these studies. Both the geostatistically generated model and the benchmark model were interrogated, and metrics for the connectivity of high-permeability preferential flow pathways were quantified. Computational simulations of CO2 injection were performed, and metrics for CO2 dynamics and trapping were quantified. All metrics were similar between the two models. The percentage of high-permeability cells in spanning connected clusters (percolating clusters) was similar because percolation is strongly dependent upon proportions, and the same proportion of higher permeability cross-strata was specified in generating both models. The CO2 plume dynamics and residual trapping

*Corresponding author email: robert.ritzi@wright.edu

metrics were similar because they are largely controlled by the occurrence of percolating clusters. The benchmark model represented more features of the fluvial architecture and, depending on context, representing those features may be quite important, but the simpler geostatistical model was able to adequately represent fluvial reservoir architecture within the context and within the scope of the parameters represented here.

INTRODUCTION Modeling Heterogeneity The workflow for reservoir engineers and hydrogeologists developing flow models for reservoir or aquifer systems commonly includes developing a geocellular model representing the heterogeneity in petrophysical attributes within the reservoir or aquifer. In this step, heterogeneity is modeled at the scales considered to be relevant to flow dynamics on a grid matching the gridding scheme (e.g., orthogonal grid) of the numerical method (e.g., finite volume method) upon which the flow simulator is based. Geostatistical methods based on the two-point spatial-bivariate (TPSB) structure of petrophysical attributes are commonly used by practitioners to interpolate and extrapolate information between boreholes as a part of constructing the geocellular model of heterogeneity (e.g., Deutsch and Journel, 1998). TPSB methods represent the probability of similarity in an attribute between two points as a function of their separation distance in space.

Geocellular models for heterogeneity are also important in computational research seeking to understand, at a basic-science level, how particular types

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and scales of heterogeneity fundamentally affect processes associated with subsurface fluid flow. In this vein, accurate representations of heterogeneity are important, and advances in TPSB geostatistical methods have been proposed (e.g., Carle, 1998), alternatives to TPSB have been proposed (Deutsch and Tran, 2002; Caers and Zhang, 2004; Ramanathan et al., 2010; and Zagayevskiy and Deutsch, 2016), and various methods have been compared and contrasted in the literature (e.g., Lee et al., 2007). The efficacy of any particular method is context dependent: A given method may be quite appropriate for a particular scale, type of heterogeneity, and its influence on a particular process, but not for others.

The efficacy of geostatistical TPSB methods has been questioned in the context of representing heterogeneity in fluvial reservoirs (i.e., sedimentary formations with internal architecture representing fluvial deposition, hereafter referred to as “fluvial architecture”). What has been specifically questioned is the ability of TPSB methods to represent the continuity of preferential flow pathways formed by river channel deposits (via the connectedness of higher permeability cells in the geocellular model), important because of the influence of those pathways on flow dynamics (e.g., Western et al., 2001). Lee et al. (2007) reviewed the literature containing criticism of TPSB methods in this context and evaluated the effectiveness of two alternate TPSB methods for representing channel deposits in simulating the hydraulic response to a groundwater pumping test in the aquifer system at the Lawrence Livermore National Laboratory. It was shown that one of the methods, one based on transition probability indicator simulation, was effective in representing the continuity of channel deposits and the effect of that continuity on the hydraulic response. Importantly, the prior criticism of TPSB methods had been based on evaluating them using two-dimensional (2-D) models and highly simplified hypothetical conceptual models for river deposits, and the results of Lee et al. (2007) demonstrated the importance of using a fully threedimensional (3-D) model and an advanced conceptual model of the reservoir architecture.

Here we continue the evaluation of the efficacy of the TPSB method based on transition probability indicator simulation to represent fluvial architecture in geocellular models. Our ultimate interest is in how fluvial architecture will fundamentally affect the residual trapping of CO2 in sandstone formations targeted for the geologic sequestration of CO2 . Small-scale fluvial architecture has been shown to have a primary effect on CO2 flow dynamics and on residual trapping, and thus on how CO2 is ultimately distributed within the reservoir (Krevor et al., 2015; Gershenzon et al., 2015, 2016a, 2016b, 2017a, 2017b). These scales of fluvial 358

architecture are smaller than what was represented in prior work by Lee et al. (2007), and the processes of interest here (capillary trapping processes) are much different than the processes of interest to Lee et al. (hydraulic response to pumping). Thus, the efficacy of the transition probability indicator simulation approach for creating geocellular models is being evaluated in a new and different context.

This study has similarities with the work of Lee et al.

(2007) in that we use a fully 3-D approach and an advanced conceptual model of the reservoir architecture. The focus is again on the ability of the method to represent complex connected pathways for preferential flow in fluvial deposits via the connectivity of highpermeability cells within the geocellular model. 3-D modeling is critical because the probability of having connected pathways is significantly higher in 3-D geocellular models relative to otherwise-equivalent 2-D models (e.g., Stauffer and Aharony, 1994). Related to this fact is the importance of the relationship between the proportion of the geologic facies being simulated in the model and the resulting connectivity. To explain, consider the face-to-face connectivity of high-permeability cells when they are placed randomly in realizations of an infinite grid, without any geologic structure. As per the percolation theory, an infinite connected pathway will exist (percolation) when those cells exceed a proportion (percolation threshold) of 31.16 percent of the cells, but it will not form in an otherwise-equivalent 2-D lattice until the proportion exceeds 59.28 percent. (Note that, accordingly, at 31.16 percent, the continuous connected pathways in the 3-D model cannot be discerned in 2-D cross sections taken from it [e.g., Proce et al., 2004; Guin and Ritzi, 2008].) When adding geologic structure to the placement of cells, in grids of finite extent, connectivity spanning opposing boundaries of the grid will occur at lower proportions than occurs with random placement (Harter, 2005; Guin and Ritzi, 2008). Our hypothesis is that the transition probability indicator simulation method will be able to represent the continuity of preferential pathways for flow in this study, given that the model is created in three dimensions, that it is based on a good conceptual model of the fluvial architecture, and that the proportions of the fluvial facies are properly represented.

Newer gridless approaches for creating geocellular models have been developed that directly and more rigorously simulate geologic architecture (Ramanathan et al., 2010; Zagayevskiy and Deutsch, 2016). Some may require a computational effort that exceeds what would be considered acceptable in practical application by reservoir engineers and hydrogeologists, and thus practitioners are still likely to use a TPSB method.

The newer gridless approach by Ramanathan et al.

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Challenging Geostatistical Methods

(2010, implemented in the GEOSIM code, described further below) is used in this study to create a highly realistic digital reservoir, which is used as a benchmark model against which the TPSB method is compared. Specifically, we challenge the ability of the transition probability indicator simulation method to reproduce the preferential flow pathways in the benchmark model by comparing metrics on connectivity.

We also use both models in multi-phase flow simulations of CO2 injection and further evaluate the efficacy of the TPSB method by comparing metrics for CO2 plume dynamics and residual trapping between the simulations.

The following sections of the introduction review the transition probability indicator simulation method being evaluated, the scales of fluvial architecture targeted for simulation and how they may affect residual trapping of CO2 , and the benchmark model.

Geostatistical Simulation Based on Two-Point Spatial-Bivariate Statistics (T-PROGS) TPSB statistics consider the spatial relationship of an attribute at two locations, x and x� , separated in space by lag vector h. In indicator geostatics (Johnson and Dreiss, 1989) the attribute is the presence or absence of geologic unit k within an assemblage of distinct unit types. Carle and Fogg (1996) have discussed the advantages of using transition probabilities to represent the relationship through the conditional probability: t jk (h) = Pr {k occurs at x + h | j occurs at x} ,

where k and j refer to the geologic unit type in the head and tail of the lag vector, respectively. For a system with K unit types, they used a Markov Chain model to mathematically represent the system of K × K transition probabilities:

T (hφ ) = exp Rφ hφ ,

where φ denotes direction and Rφ is a matrix of transition rates with entries rjk,φ describing the rate of change from unit type j to k in direction φ and the autotransition rate defined by r j j,φ = −

1 l j,φ

,

where lj,φ is the mean length of unit j.

The T-PROGS geostatistical package (Carle, 1998) facilitates developing a Markov Chain model in an elegant canonical form based on input of the proportions and mean lengths of unit types and using it in a transition probability-based cokriging algorithm in order to generate the geocellular model for the spatial distribution of the geologic units. A simulated quenching step

adjusts the final model to help ensure the input parameters are honored.

The T-PROGS package has been widely used, both to create geocellular modes for specific sites conditioned to real data (e.g., Weissmann et al., 1999a, 1999b, 2002; Proce et al., 2004) and for representing the general aspects of geologic architecture within computation research on subsurface fluid flow (e.g., Huang et al., 2011; Zhou et al., 2013). The specific implementation in this study is presented in the “Methods” section below.

Reservoirs with Fluvial Architecture and Residual Trapping of CO2 The sedimentary architecture created by fluvial deposition has been comprehensively described and quantified in three dimensions by Lunt (2002), Lunt et al. (2004), and Bridge (2006). The focus here is on braided systems with mid-channel bars, although many aspects of the architecture can also be found in meandering systems with point bars (Bridge, 2006).

Here the most relevant points are summarized.

As shown in Figure 1, channel-belt deposits are characterized by a large volume-fraction of convexup compound-bar deposits formed within channels, which comprise unit-bar deposits which, in turn, comprise mostly sets of cross strata (hereafter referred to as “cross sets”) formed by migrating dune and ripple bedforms. There is a corresponding hierarchy of stratification in preserved fluvial deposits. When channels are abandoned and filled, concave-up channel fills are formed comprising finer grained sediment. In a reservoir, the preserved bar deposits are the permeable facies, and channel fills are low-permeability baffles. This sedimentary architecture is found in modern fluvial settings and in ancient fluvial reservoirs that range in mean grain size from sand (e.g., lower Mt. Simon Sandstone) to gravel (e.g., the Ivishak Formation conglomerate) (Bridge, 2006). Importantly, these deposits and reservoirs are dominated by an assemblage of finer and coarser grained cross sets, as illustrated in Figure 1, which have distinct differences in their petrophysical attributes, such as capillary entry pressures, intrinsic permeability, saturation relationships with capillary pressure and relative permeability, and porosity. Coarser grained cross sets are known to create preferential flow pathways within fluvial reservoirs (Tye et al., 1999, 2003). They have a strong influence on residual capillary trapping of CO2 , as shown by Gershenzon et al. (2015, 2016a, 2016b, 2017a, 2017b).

As reviewed by Krevor et al. (2015), a significant body of evidence, including results from laboratory studies, computational studies, and field pilot injection tests, now indicates that residual trapping in the

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Figure 1. Fluvial deposition and internal architecture of fluvial deposits. (a) A modern fluvial channel belt system comprising active channels and compound bars. The compound bars, in turn, comprise unit bars. From Sambrook Smith, G. H.; Ashworth, P. J.; Best, J. L.; Woodward, J.; and Simpson, C. J., 2006, The sedimentology and alluvial architecture of the sandy braided South Saskatchewan River, Canada: Sedimentology, Vol. 53, pp. 413–434. International Association of Sedimentologists. Used by permission. (b) Conceptual diagram of a compound bar with unit bars outlined in green. (c) Cross section through (b) showing that unit-bar deposits comprise sets of cross strata with different textures. Ideas for (b) and (c) adapted from Lunt et al. (2004).

permeable part of the reservoir will be a primary mechanism for physically immobilizing CO2 until it dissolves and mineralizes. There are two main residual capillary trapping mechanisms (Figure 2): (a) CO2 bubbles are trapped within pore spaces because of “snap-off,” a process in which counter-imbibition of brine behind the advancing plume traps residual CO2 bubbles within the intervening pore bodies (Hunt et al., 1988; Iglauer et al., 2011) and (b) trapping due to heterogeneity in the capillary pressure between adjacent reservoir rock types (i.e., CO2 is pinned below local contacts between an underlying reservoir rock type with larger pores and an overlying reservoir rock type with smaller pores and thus larger capillary pressure) (Bryant et al., 2006; Ide et al., 2007; Saadatpoor et al., 2009; Zhou et al., 2009; and Gershenzon et al., 2015, 2016a, 2016b, 2017a, 2017b). We will use the terms “snap-off” and “capillary pinning” to designate these two processes. As shown in Figures 2b and 2c, the heterogeneity inside of bar deposits creates a 360

significant amount of capillary pinning and retardation. CO2 preferentially enters a connected pathway of coarser grained cross sets during injection because of the relatively higher permeability and lower entry pressure. It will not buoyantly rise up into the overlying FG cross strata unless a critical capillary pressure is exceeded. If finite entry pressures exist and are not exceeded the CO2 is immobilized. If they do not exist or otherwise are exceeded, the flux will be rate limited, and capillary retardation will occur (Figure 2c). In addition to immobilizing a significant amount of CO2 , snap-off trapping and capillary pinning also increase the surface area of the plume and thus enhance dissolution trapping. Gershenzon et al. (2016b) have shown how the connectedness of coarser grained cross sets affects the ultimate distribution of capillary trapped CO2 . When coarser grained cross sets are abundant enough to form spanning preferential flow paths more of the CO2 is trapped by pinning than by snap-off, and the opposite is true without the spanning preferen-

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The methodology section below provides details on how T-PROGS was used to create a geocellular model for the fluvial architecture described above. A subsequent section gives details on how the geocellular model was used within a multi-phase flow numerical model to simulate CO2 injection, plume dynamics, and residual trapping processes.

The Benchmark Model for Fluvial Architecture (GEOSIM)

Figure 2. (a) Snap-off trapping. Counter-imbibition of brine (the wetting fluid, light gray) behind the advancing plume occurs preferentially through smaller pores and pore throats between grains of sediment (dark gray), trapping residual CO2 bubbles (green) within the intervening pore bodies. (b) Capillary pinning: CO2 is pinned below local contacts between an underlying rock type with larger pores (coarser grained cross beds, individual grains and pores within not shown) and an overlying reservoir rock type with smaller pores and thus larger entry pressure (e.g., finer grained cross beds). This occurs where the capillary pressure in the finer grained cross beds (Pe ) exceeds the buoyant pressure of CO2 in the coarser grained cross beds (Pb ). (c) Capillary retardation: a transient pinning effect in which CO2 preferentially enters regions with larger pores during injection and leaks through networks of pores with sufficiently small capillary pressure in the overlying, finer grained rock. Increase in CO2 saturation in the finer grained rock is ratelimited because of the difference in capillary pressure between rock types, retarding plume movement. From Gershenzon et al. (2016a).

tial flow paths. In either case, these trapping processes can immobilize appreciable amounts of CO2 within the reservoir well below caprock seal and add to the overall efficacy of CO2 storage (e.g., Juanes et al., 2006; Saadatpoor et al., 2009; Gershenzon et al., 2015; and Krevor et al., 2015).

The code GEOSIM (Ramanathan et al., 2010; Ritzi, 2013) generates gridless models of hierarchical fluvial architecture using a geometric- and depositional-based approach. The basic idea is that given that the hierarchy of 3-D stratification in fluvial deposits and the geobodies delineated therein are well understood, then fluvial architecture can be simulated directly by using polyhedra to represent the geobodies and then assembling the polyhedra following rules of deposition. Details are given in Ramanathan et al. (2010). The main points are summarized here.

Geobodies defined across different scales (e.g., cross sets, unit-bar deposits, compound-bar deposits and channel fills) are created starting with piecewise-planar polyhedra. The general idea is conveyed in Figure 3.

Sets of trough-cross strata are created starting with scoop-shaped polyhedra representing their lower erosive boundaries. They are simulated with variable dip angle and direction, depending on their location with a unit bar. For example, cross sets dip more steeply at the head of unit bars and less steeply in the tail.

At a larger scale, unit-bar deposits are created starting with serpentine-shaped polyhedra (reflecting the shape of active unit bars) and modified with curvature and to reflect erosion, depending on their location within compound-bar deposits. At a still larger scale, compound-bar deposits are created starting with polyhedra that reflect their broad lower erosive boundary and prolate plan-view shape. Active channels migrate with concomitant growth in compound bars until a channel is blocked and becomes filled with finer grained sediment. The major channel fills that form lateral boundaries of compound bars and cross-bar channel fills are created with convex-up channel-shaped polyhedra, as shown. The polyhedra at all scales are defined from a parsimonious set of lengths, which are drawn from statistical distributions, with mean and variance defined in input files, along with proportions. As discussed by Bride (2006), the lengths of geobodies in fluvial deposits scale together with the width of the formative river channel, providing guidance for scaling the model to a specific site (e.g., Guin et al., 2010).

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Figure 3. Scales of stratification created with GEOSIM. Lower right shows creation of trough cross-set stratification with comparison to natural deposits. Lower left shows creation of unit-bar scale stratification with comparison to a radar image of natural deposits. Upper diagram shows creation of stratification comprises larger scales defining compound bar and channel-fill deposits, compared to natural abandoned channel belt. Stratification at any one scale comprises smaller scale stratification, as indicated.

The polyhedra are merged into a global coordinate system according to rules of deposition, and generally only a piece of each ends up preserved in the digital preserved deposit. Because it only takes four numbers to define each plane in the global coordinate system, this gridless model for the hierarchy of stratification in a fluvial deposit is efficiently stored. A geocellular model is required by reservoir flow simulators, and for this purpose, the gridless model can be sampled with any desired grid resolution, as shown in Figure 4, and petrophysical properties can be mapped in from appropriate statistical distributions. Obviously, the grid resolution should be chosen to be fine enough so that the smallest-scale features in the model are appropriately represented.

Guin et al. (2010) used GEOSIM to simulate preserved fluvial deposits in the well-studied 362

Sagavanirktok River system (Alaska). Quantitative metrics for geobody proportions and lengths sampled from the GEOSIM model matched well with field data quantified from the natural deposits.

The Sagavanirktok system is a good analog for the Ivishak Formation, a candidate CO2 reservoir on the Alaska North Slope. Accordingly, Gershenzon et al. (2015, 2016a, 2016b, 2017a, 2017b) used the Guin et al. (2010) model in interpretive studies of how fluvial reservoir architecture affects CO2 injection, plume dynamics, and capillary trapping. The same model is used as a benchmark model in this study.

The reader is referred to the afore-cited studies for full details, and some essential points are summarized here. Guin et al. (2010) showed the percolation threshold for cross-domain spanning connectivity of coarser grained cross sets in the model is 20 percent.

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Figure 4. Example of sampling the gridless model of stratification in Figure 3 with a chosen grid resolution and mapping in a petrophysical property such as intrinsic permeability. Scale bar represents natural log of permeability, with warmer colors indicating higher values. Note lower permeability channel fills stand out as light blue in the upper-right figure. The higher permeability pathways in the model are created by the coarser grained cross sets within the bar deposits.

For the benchmark model, a realization was chosen with a proportion of 24 percent so that cross-domain spanning connectivity exists (quantified below). This is similar to the proportion of coarser grained cross sets in the Ivishak Formation, in which they are known to be well connected and form preferential flow pathways controlling injected gas migration (Tye et al., 1999, 2003). In the model, consistent with natural deposits, cross-set dip and lengths vary with position in unit-bar deposits. In the heads of unit-bar deposits the dips range from 12° to 22°, horizontal lengths from 0.14 to 1.09 m, and thicknesses from 0.03 to 0.3 m. In the tails dips range from 1° to 5°, horizontal lengths from 2.0 to 13.0 m, and thicknesses from 0.03 to 0.16 m.

Unit-bar deposits have lengths ranging from 41 to 161 m in the long axis, widths ranging from 7.5 to 52 m, and thicknesses ranging from 0.13 to 1.33 m.

Compound-bar deposits have lengths ranging from 209 to 1,104 m in the long axis, widths measuring from

98 to 405 m, and thicknesses ranging from 0.36 to 3.45 m. A piece of the kilometer-scale model domain was sampled for the computational experiments below.

METHODS A consistent grid scheme was used for the creation of the geostatistical (GS) geocellular model with T-PROGS, the benchmark (BM) geocellular model with GEOSIM, and the finite-volume-based numerical model for simulating multi-phase fluid flow. Consistent with prior work by Gershenzon et al. (2015, 2016a, 2016b, 2017a, 2017b), the geocellular model grids and finite-volume grid were chosen to have a domain size of 200 m by 200 m by 5 m, comprising 100 by 100 by 100 cells (1 million total), each cell with dimensions of 2 m by 2 m by 0.05 m. As discussed further below, the domain is limited to this size by the challenge of obtaining numerically convergent solutions for

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CO2 saturations when heterogeneity and hysteresis in saturation relationships are included in multi-phase flow simulations. The simulations at this scale represent the effect that heterogeneities within compound bars have on residual trapping in reservoirs with fluvial architecture. Investigations of grid-cell size have shown that the adopted grid-cell size gives sufficient resolution for adequately representing the connectivity of the smallest-scale features of the model (Guin et al., 2010; Gershenzon et al., 2015, 2016a, 2016b, 2017a,b).

GS Model Created with T-PROGS The GS model was created utilizing the T-PROGS package in the following way. Smaller domain simulations representing preserved pieces of unit-bar deposits were created individually and independently and aggregated to create a compound-bar deposit. Each was created with an initial, pre-aggregated dimension of 20 m by 20 m by 1.25 m. Within each, coarser grained cross sets were simulated using the T-PROGS code with a target proportion of coarser grained cells, pc , of 24 percent. The background finer grained cross sets, by default, had a target proportion, pf , of 76 percent. The mean lengths, l, of the coarser grained cross sets were specified to be 5 m in the paleoflow (i.e., dip) direction, 2 m along strike, and 0.33 m in the vertical. In order to honor probability laws, the background category in a two-facies Markov Chain model will have mean length greater than that of coarser grained cross sets by the ratio pf /pc . The mean lengths were defined in the Markov Chain modeling package of the T-PROGS code by specifying proportions and a transition rate matrix with autotransition rates of −1/li for cross-set type i in each direction.

The cross sets were rotated as they were created in each indicator simulation (within the tsim module of T-PROGS) so as to have a uniform dip of 10° in the paleoflow direction. The individual unit-bar deposits were horizontal as they were aggregated, and thus the upstream dip of unit bars is not represented in the GS model. The unit-bar deposits were aggregated into one compound-bar deposit by randomly stacking 100 individual sub-domain simulations to fill the larger 200 m × 200 m × 5 m domain. Adding together the sub-domains that each have pc of approximately 24 percent gives a domain with pc of approximately 24 percent. Images are given in the next section in making comparisons to the BM model.

Summary of Similarities and Differences between the Two Geocellular Models It is helpful to summarize the similarities and differences between the two geocellular models. Both the GS and BM models represent coarser and finer grained 364

cross sets aggregated within unit bars and unit bars aggregated within a compound bar(s). In both models, the coarser grained cross strata occur as 24 percent of the domain volume and would be expected a priori to have spanning connectivity. Figure 5 shows vertical and horizontal slices through each model, with the coarser grained cross strata imaged as red and all other unit types as blue. Note that the 3-D connectivity is not evident in 2-D slices, as has been thoroughly discussed in prior literature (Guin et al., 2008, 2010).

The 3-D connectivity indeed exists and is quantified in the results given below. Within the GS model the dip of the cross sets is uniform in angle and in direction, as is the size of the unit-bar deposits aggregated within a single compound bar. Within the BM model, the dip angle is highly variable, steeper at the head of unit-bar deposits and less steep in the tail; the dip direction varies with mean direction following the curvature of unit bars, and only pieces may be preserved. Accordingly, the coarser grained cross sets are more varied in shape within the BM model in Figure 5 as compared to those in the GS model. In the BM model, the size of preserved unit-bar deposits is highly variable, and they have a geometry and curvature not represented in the GS model (the unit-bar boundaries are not imaged with a separate color, as in Figure 5, and thus are not immediately evident but are indicated by lines of discontinuity, between groups of coarser grained cross sets, dipping to the right).

The BM model represents an aggregation of preserved pieces of more than 10 compound-bar deposits. These are aggregated with preserved pieces of finer grained channel fills and cross-bar fills, which are finer grained baffles within the domain. A preserved piece of a channel fill is evident by the absence of cross strata near the left edge of the horizontal slice imaged in Figure 5.

Channel fills are not represented in the GS model.

Numerical Simulation of CO2 Dynamics and Trapping In computational research on residual capillary trapping in which both hysteresis and heterogeneity in the nonlinear constitutive relationships have been represented, it is challenging to achieve numerically convergent solutions. The commercial reservoir simulator ECLIPSE (trademark Schlumberger) has been demonstrated to be useful in achieving convergent solutions (Juanes et al., 2006; Gershenzon et al., 2015, 2016a, 2016b, 2017a, 2017b). ECLIPSE-300 was used here with the CO2STORE option. As in Gershenzon et al. (2017a), the simulations included three components: H2 O, CO2 , and NaCl, with initial total phase mole fractions of 0.9109, 0.0, and 0.0891, respectively. The partitioning of CO2 and H2 O in the liquid

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Figure 5. The spatial distribution of coarser grained (red) and finer grained (blue) cross sets in the geocellular models. (top) BM model. (bottom) GS model. Panels on the left show cross sections along paleoflow, with paleoflow from right to left, as indicated by the mean dip direction of the cross sets. Vertical exaggeration is 15×. In the BM model (top left) some of the boundaries between unit-bar deposits are indicated (1) by lines of discontinuity, with upper and lower boundaries having an average dip (left to right) opposite the paleoflow direction.

Variation in unit-bar orientation and cross-set dip direction is indicated (2) by cross sets dipping toward the viewer in some smaller preserved pieces of unit bars. Panels on the right show planar slices through the middle of each model. Finer grained channel fills are indicated (3) by the absence of coarser grained cross sets.

and gas phase and their respective solubilities were calculated according to the method of Spycher and Pruess (2005). The maximum NaCl solubility in the aqueous phase was calculated according to the method of Potter et al. (1977). The water compressibility and viscosity were specified at 4.35 × 10−5 1/bar and 0.813 cP, respectively. Hysteresis in history-dependent relative permeability functions was determined according to the method of Killough (1976). The CO2 viscosity was calculated according to the method of Fenghour et al. (1998) and Vesovic et al. (1990). Water fugacity was obtained by Henry’s law, while CO2 fugacity was calculated using a modified RedlichKwong equation of state. The gas density was obtained by a cubic equation of state tuned to accurately give the density of the compressed gas phase, according to the method of Spycher and Pruess (2005).

The diffusion flow in terms of liquid mole fraction was specified by the water phase diffusion coefficients for each component. The values of all three coefficients were 10−4 m2 /d. The gas phase diffusion coefficients for both H2 O and CO2 were 10−3 . The simulations are isothermal, with initial temperature of 45°C and initial pressure of 89.2 bar. The Adaptive IMplicit method (AIM) solver was used. Run times were

similar between the stand-alone and parallel processing versions of ECLIPSE and are on the order of weeks.

The reservoir simulation requires the cells of each model to be populated, per coarser grained (CG) and finer grained (FG) regions, with intrinsic permeability and saturation relationships. This was done consistent with the method of Gershenzon et al. (2015, 2016a, 2016b, 2017a, 2017b) and in the same way for both the GS and BM models. In the context of CO2 trapping in fluvial reservoir architecture, Gershenzon et al. (2016b) explored sensitivity to the contrast in intrinsic permeability between CG and FG facies and showed that residual trapping is enhanced with contrasts exceeding one order of magnitude. A three order of magnitude contrast is common in reservoirs, and a contrast of roughly that magnitude was used in this work, given the focus on residual trapping. CG cells were randomly assigned intrinsic permeability, k, with a geometric mean of 3,828 mD and ln(k) variance of 1.

Among FG cells the geometric mean was 58 mD and ln(k) variance was 1. This included FG cross sets and the major and cross-bar-channel fills. The resulting distribution is shown in slices of the model rendered in Figure 6.

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Figure 6. The spatial distribution of permeability in the geocellular models. (top) BM model. (bottom) GS model. Panels on the left show cross sections along paleoflow. Vertical exaggeration is 15×. Panels on the right show planar slices through the middle of each model. See Figure 5 caption for additional description.

Previous work by Gershenzon et al. (2015, 2016a, 2016b, 2017a, 2017b) has shown that plume dynamics and capillary trapping in this context are sensitive to hysteresis in the relative permeability relationships and are not sensitive to hysteresis in the capillary pressure relationships, as long as a different relationship has been defined between CG and FG cell types. Because the results are not sensitive to hysteresis in the capillary pressure relationships and because including hysteresis in capillary pressure relationships greatly impedes computational efficiency in what is already a computationally challenging problem, only hysteresis in the relative permeability relationships was included. Importantly, the saturation relationships for capillary pressure and relative permeability are defined differently for CG and FG cell types. Gershenzon et al. (2016a, 2016b, 2017a, 2017b) explored the sensitivity of residual capillary trapping to the functional form of the relationship (e.g., Brooks-Corey vs. van Genuchten) and to the parameters defining those relationships (e.g., entry pressures, irreducible brine saturation, etc.). Here, relationships from that prior work shown to give rise to a significant residual trapping effect, and thus that are appropriate to the goals of this study, were chosen and are shown in Figures 7 and 8.

CO2 was injected at a rate of 3.6 (standard) sm3 /s during 100 days into the bottom of a vertical well at a depth equivalent to 2,360 m and monitored for 1,000 days. The vertical well is placed in the middle of the reservoir. The total amount of injected CO2 was 5,940 kg-mol (∼261 tons). The boundary conditions are (1) 366

no-flow at the top and the bottom of the reservoir and (2) a Carter-Tracy aquifer boundary (Carter and Tracy, 1960) at all other reservoir boundaries.

RESULTS AND DISCUSSION Comparing Connectivity in the Geocellular Models The differences between the BM model and the GS model were explored on a quantitative basis via analysis of the connectivity of the CG cells. Connectivity is here defined based on face-to-face cell adjacency (not corners). This definition is consistent with the equations solved in the finite-volume numerical scheme used for the CO2 simulations and thus is consistent with the hydraulic connectivity among cells in the simulations. A group of connected CG cells is referred to as a cluster, and spanning clusters contain cells on all pairs of opposing boundaries of the domain. The number of clusters were counted and the geometry was analyzed using the search code CONNECT3D (Pardo-Igúzquiza and Dowd, 2003). Table 1 contains summary statistics from the analysis. The results for the BM model are consistent with those determined by Gershenzon et al. (2015) with an alternate search code.

Figure 9 shows a logarithmic inverse scaling of cluster size with the number of clusters. The scaling in each geocellular model is similar. Importantly, in each geocellular model there is one large spanning cluster that includes a large majority (i.e., at least 70 percent) of the CG cells (Table 1). This result is consistent with the

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Figure 7. Relative permeability (kr ) of CO2 vs. water saturation (Sw ) for FG (red) and CG (blue) cells, under drainage (solid) and imbibition (dotted), and of brine vs. saturation for FG and CG (both green).

results of other published work (e.g., Harter, 2005; Guin and Ritzi, 2008; and Gershenzon et al., 2015).

Thus, though the CG cross sets were simulated as if they were individual units of deposition, and within bar deposits that are individually created and juxtaposed without intentional alignment of CG cells, there is a lot of adjacency and resulting connectivity between CG cells in the end result in both models. The connectivity is mainly governed by the proportions and mean size of the CG cells relative to the domain size (Harter, 2005; Guin and Ritzi, 2008) and not by the method used to simulate them. While the connectivity was not visually evident in the 2-D slices through the models

Figure 8. Capillary pressure (Pc) as function of water saturation (Sw) for FG (red) and CG (blue) cells.

shown in Figure 5, the 3-D rendering in Figure 10 gives a sense of the 3-D connectivity of the larger spanning clusters.

Comparing CO2 Plume Dynamics and Trapping between Simulations Using the BM and GS Models Figure 11 shows the growth of mobile CO2 with time during the 100-day injection period (the dynamics beyond 100 days are discussed further below). The increase is similar in both models. The plume reached

Figure 9. Frequency vs. number of cells in a cluster for the BM and GS models.

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Damico, Ritzi, Gershenzon, and Okwen Table 1. Summary statistics on clusters of CG cells.

Model BM GS

Number of Clusters

Mean Cluster Size (cells)

Maximum Cluster Size (cells)

Percentage of Cells in Spanning Largest Cluster

8,141 3,048

30.0 78.6

173,648 206,054

71.2 86.1

CG = coarser grained; BM = benchmark; GS = geostatistical.

the top of the reservoir in 60 days in the BM model and in 40 days in the GS model. Figure 12 shows cross sections through each simulation indicating the distribution of CO2 at 100 days (the end of injection). The

regions of higher CO2 saturation define the shape of CG cross sets in both models. It is clear that during injection the CO2 has preferentially distributed into a 3-D connected network of CG cross sets (again, 3-D

Figure 10. Identification of connected clusters in the BM model (top) and GS model (bottom). The legend indicates the number of cells contained within a cluster of given color. Note the large red spanning cluster occurring in each model.

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Figure 11. Mobile CO2 as a function of time for the BM model (red) and the GS model (green).

connections are not discernable in 2-D sections) and to a lesser extent has mobilized upward from the CG cross sets into overlying FG regions under injection pressure. It is clear that in both models the large connected network of CG cells is a preferential flow pathway controlling the distribution of CO2 during the injection period. Figure 13 shows that snap-off trapping occurs during the injection period. Gershenzon et al.

(2017b) demonstrated that it occurs as a result of heterogeneity (it does not occur in homogeneous simulations during injection). The brine imbibition causing trapping of CO2 bubbles occurs because of the differences in relative permeability between CG and FG cell types. Figure 14 shows that dissolution is also occurring during the injection period.

Figure 11 also shows the change in mobile CO2 with time during the post-injection period, from 100 days to 1,000 days, for both models. In both models there is a similar, rapid decline in mobile CO2 as capillary pinning, snap-off trapping, and dissolution trapping immobilize the plume. Figures 13 and 14 show the concomitant increase of CO2 trapped by snap-off and by dissolution, respectively. The behavior is generally similar in both models, with somewhat more dissolution trapping in the BM model (Figure 14) at any given time, and, accordingly, there is somewhat less mobile CO2 in the BM model (Figure 11). The slightly enhanced dissolution trapping in the BM model occurs because the plume has a relatively wider distribution (Figures 12 and 15), increasing the surface area. As can be discerned in Figure 10, the tortuous pathways of connectivity are more widely spread in the BM model than in the GS model, giving the plume a wider distribution. This may ultimately be due to the finer grained channel fills and variability in dip angle and direction that were included in the BM model but not in the GS model. Multiple realizations would need to be undertaken to see if this would be a consistently observed difference. Such realizations are outside the scope of this article. There is more capillary trapping in the BM

Figure 12. Cross section showing the distribution of CO2 at 100 days (end of injection) in the BM model (top panel) and the GS model (bottom panel).

model during injection, but the amount trapped during injection in either model is small relative to the amount of trapping during the post-injection period.

Cross sections through each simulation in Figure 15 show the distribution of CO2 at 1,000 days. As per Figure 11, almost all CO2 is immobilized by this time.

Capillary pinning of CO2 is evident by the fact that CG cross sets have retained a higher saturation long after the injection period. Upward movement of CO2 into overlying FG regions was largely checked by the higher capillary pressure in the FG regions. The BM and GS models are similar in exhibiting pronounced capillary pinning. Importantly, the majority of CO2 has been trapped well below the top of the reservoir by the combined residual trapping processes of capillary pinning, snap-off trapping, and dissolution in both models.

Figure 13. Snap-off capillary trapping of CO2 as function of time for the BM model (red) and the GS model (green).

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Figure 14. Dissolution of CO2 as function of time for the BM model (red) and the GS model (green).

In the context of representing plume dynamics and residual trapping within fluvial deposits, and within the scope of the parameters used here, the GS model appears to give an adequate representation of the smaller scale heterogeneity. The same interpretation of how fluvial heterogeneity affects residual trapping processes, with residual trapping being a primary CO2 storage mechanism, would be derived from the result using either model. The indication is that commercial simulators could adopt the simpler geostatistical approach in the practice of modelling geosequestration reservoirs within the context of representing the scale and specific type of reservoir heterogeneity examined here. These results are not universal, but rather are context dependent, and future work should be done to expand on the evaluation of geologic modelling approaches and should include a broader range of

parameters and scenarios, including modelling of other scales, other processes, and other types of deposits (e.g., Lu et al., 2012; Soltanian et al., 2016a, 2016b, 2017).

The focus of this study was on the ability of the geostatistical method to represent connectivity. Accordingly, the scenario examined here had a proportion of CG cross sets known to be above the threshold required for spanning connectivity in the BM modelling approach. Using the BM model, Gershenzon et al.

(2016b) showed that when CG cross sets occur at proportions below the threshold for spanning, and, thus, when connectivity is reduced, and spanning clusters do not exist, residual trapping is still a primary CO2 storage mechanism. However, more CO2 is trapped by snap-off in FG cross sets and less is pinned in CG cross sets relative to the results when CG cross sets have spanning connectivity. There is likely a small range for the proportion of CG cross set cells within which one of the two approaches, BM or GS, would have spanning connectivity and the other would not (likely to be in the vicinity of 20 percent). The metrics quantified here would show larger differences between the models if a proportion within that range was used. However, the important issue to be raised is not how well the geostatistical model compares to the benchmark model but rather the fact that if the threshold for spanning connectivity of CG cross sets in a real georeservoir is not precisely known, and if the quantified proportion from borehole data is thought to be close to the threshold, there will be uncertainty as to the connectivity in the actual reservoir. In that case, it is likely best to undertake interpretive simulations to assess what happens under alternate scenarios of spanning connectivity vs. non-spanning connectivity, regardless of the geocellular modeling approach being used.

SUMMARY AND CONCLUSIONS

Figure 15. Cross section showing the distribution of CO2 after 1,000 days in the BM model (top panel) and the GS model (bottom panel).

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We challenged a widely used two-point spatialbivariate geostatistical method that uses a Markov Chain process model and indicator simulation implemented through the T-PROGS package to represent fluvial heterogeneity in the context of simulating how reservoir heterogeneity affects residual trapping of CO2 injected for geosequestration. A more rigorously created, depositional-based, multi-scale, and hierarchical geometric model for fluvial architecture generated with the GEOSIM package was used as the benchmark in these studies. Both the geostatistically generated model and the benchmark model were interrogated to quantify metrics for the connectivity of high-permeability preferential flow pathways. Furthermore, computational simulations of CO2 injection were undertaken, and metrics for CO2 dynamics and

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residual trapping (capillary pinning, snap-off, and dissolution) were quantified and were used as the basis for evaluating how well the geostatistical model performed within the context of representing plume dynamics and residual trapping. The findings were as follows: 1. All of the metrics for connectivity, CO2 plume dynamics, and residual trapping were similar between the geostatistically generated model and the benchmark model.

2. The percent of high-permeability cells in spanning connected clusters was similar between the models because the connectivity is strongly dependent upon proportions of coarser grained cross sets, and the same proportion of higher permeability cross sets was specified in generating both models. Both models had a large spanning cluster containing greater than 70 percent of the high-permeability cells.

3. The CO2 mobility and distribution during injection were similar between the models because they are largely controlled by the occurrence of the large spanning cluster and the consequent preferential flow pathway through the domain.

4. The post-injection CO2 mobility and residual trapping, including capillary pinning, snap-off trapping, and dissolution trapping, were all similar between the models. The plume was largely immobilized by residual trapping in both models. The same conclusion would be drawn from the results from either model, which is that residual trapping processes can have a primary effect on CO2 storage in fluvial reservoirs. There was slightly less plume spreading within the geostatistical model and, consequently, slightly less dissolution. It is uncertain if the small differences would be consistently observed in replicate realizations, so no conclusions can be drawn from the small differences.

Thus, in the context of representing plume dynamics and residual trapping within fluvial deposits, and within the scope of the parameters used here, the simpler geostatistical model of braided fluvial deposits appears to give an adequate representation of the smaller scale heterogeneity. The depositional- and geometricbased benchmark models represented more features of the fluvial architecture, including variability in the dip of cross sets, variability in the geometry and orientation of unit bars, and the occurrence of channel fills. Depending on context, representing those features may be quite important to understanding some multiphase flow processes in aquifers and reservoirs. However, the simpler geostatistical model is able to capture the important aspects of fluvial architecture within the context of understanding the general effect of smaller scale heterogeneity on residual trapping of CO2 in geosequestration reservoirs, within the scope of the pa-

rameters used here. The indication is that commercial simulators could adopt the simpler geostatistical approach in the practice of modeling geosequestration in target reservoirs. The efficacy of a geostatistical approach depends upon context, and thus the results and conclusions arrived at here are not considered universal. Accordingly, building on these results, future work should expand on the evaluation of geologic modeling approaches and include a broader range of parameters and scenarios, including representing reservoirs with other types of sedimentary architecture.

ACKNOWLEDGMENTS This work was supported as part of the Center for Geologic Storage of CO2 , an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under award DE-SC0C12504. We acknowledge Schlumberger Limited for the donation of ECLIPSE Reservoir Simulation Software. This work was supported in part by the Ohio Supercomputer Center, which provided an allocation of computing time and technical support.

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Off-Fault Deformation Associated with Strike-Slip Faults JEFFREY A. JOHNSON* 422 Northwest 13th Avenue, #807, Portland, OR 97209

Key Terms: Off-Fault Deformation, Design Buffer ABSTRACT Habitable buildings can be protected from surface fault rupture by establishing structure “setback zones” similar in purpose to legally mandated zones in California and Utah. But post-earthquake surveys of offset and warped linear cultural features, believed to have been straight prior to the event, demonstrate that potentially damaging inelastic strains or off-fault deformation can extend tens of meters beyond the principal slip zone of strike-slip surface fault ruptures. Setback zones designed to also mitigate off-fault deformation are likely to be prohibitively wide, indicating the need for structural and geotechnical engineering solutions to accommodate the potentially damaging strains within adequate design buffers. This study analyzes nine strike-slip surface fault ruptures between 1906 and 2014 and develops a simplified procedure to quantify off-fault deformation based on earthquake magnitude and distance from the principal slip zone of strike-slip faults.

INTRODUCTION California and Utah passed laws legally mandating “setback zones” (Bryant and Hart, 2007; Lund et al., 2016) to protect structures for human occupancy from surface fault rupture. Depending on its width, a setback zone can also mitigate off-fault deformation (OFD; Bryant, 2010). OFD is the total horizontal brittle and non-brittle deformation minus principal slip zone (PSZ) and secondary fault brittle deformation (Rockwell et al., 2002). Setback zones that also incorporated OFD are likely to be excessively wide because potentially damaging strains may extend tens of meters beyond the PSZ of a strike-slip fault (Lawson, 1908; Haeussler et al., 2004). For example, following the 1906 M7.8 San Francisco earthquake surveys of offset and warped linear fences, tree lines, roads, and a train tunnel documented OFD up to 400 m from the San Andreas Fault (Prentice and Ponti, 1997). Lawson (1908) suggested the warping or drag of cultural features was likely due to unnoticed “auxiliary cracks” *Corresponding author email: jjohnson@jajgeological.com

that did not rupture the surface because of “yielding” of the near-surface sediments.

Following the 2002 M7.9 Denali, AK, earthquake (Haeussler et al., 2004), OFD importance was documented where the Denali fault crossed the TransAlaskan Pipeline System (TAPS). Total slip on the Denali fault was 5.8 m, of which 1.3 m was measured at the PSZ. The 4.8 m of OFD extended symmetrically 500 m from the PSZ (Figure 1). The Denali fault was not located at TAPS during the original fault study (Woodward-Lundgren and Associates, 1974). The trace was interpolated between two known locations (Woodward-Lundgren and Associates, 1974; Haeussler et al., 2004). Honegger et al. (2004, p. 715) noted: “A width of 76.2 m (250 ft.) was assigned to the active fault zone at the pipeline. The zone was extended 152.4 m (500 ft.) to the north and to the south to provide a margin of safety because no fault scarp could be identified in the bottom of the Delta River valley. The northern margin was then extended another 198.1 m (650 ft.) to include an escarpment that was a natural limit to the zone. The recommended width of the design displacement zone at the Denali fault crossing was 579.1 m (1,900 ft.).” If Woodward-Lundgren and Associates (1974) had located the fault, the length of pipeline designed to accommodate surface rupture would have been reduced 87 percent to 76.2 m (Honegger et al., 2004). Approximately 80 percent of the OFD occurred within a zone 100 m wide centered on the PSZ (Figure 1), suggesting TAPS would have been exposed to potentially damaging strains if the fault had been located.

This article describes a simplified procedure to estimate brittle and non-brittle OFD as a function of earthquake magnitude and distance from the PSZ of a single strike-slip fault (Figure 2). OFD is estimated separately for each fault of concern. The procedure therefore differs from the method developed by Petersen et al. (2011) and Chen and Petersen (2011) to estimate the probability of surface rupture on secondary faults. A site-specific investigation is required to identify all active strike-slip faults and to assign design earthquake magnitude and slip to each identified structure. Estimated OFD can be used in conjunction with currently available structural and geotechnical engineering practice (Lazarte et al., 1994; Bray, 2001, 2009) to safely mitigate the inelastic strains within a

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Johnson

Figure 1. Plot of off-fault deformation (OFD), Trans-Alaska Pipeline System (TAPS), Denali fault crossing, M7.9, 2002 Denali, AK, earthquake (Reference: Haeussler et al., 2004).

design buffer (Treiman, 2010). The establishment and width of a design buffer are based on mitigation economics and failure consequences.

The following describes the conceptual fault model used in the study (Figure 2), analyzes the collected OFD data, and provides an example of the simplified procedure.

Fault Model The conceptual strike-slip fault model illustrates the relationships between the PSZ, core, and damage zone and host rock and Riedel and tension fractures expected in future coseismic surface ruptures (Figure 2A). Not shown are the effects of multiple slip events on fracture type and orientation. For example, physical analog models (Tchalenko, 1970; Rahe et al., 1998), paleo-fault studies (Rockwell and Ben-Zion, 2007), and analysis of surface fault ruptures (Brown et al., 1967; Tchalenko and Ambraseys, 1970; Wesnousky, 1988; and Johnson et al., 1994) demonstrate that strike-slip faults evolve with increasing slip (Gudmundsson, 2011). Early-formed fractures, such as R1 shears, are abandoned as they link, and slip is concentrated or localized within the PSZ (Tchalenko, 1970; Scholz, 2002; Rockwell and Ben-Zion, 2007; and Gudmundsson, 2011), resulting in a smoother trace with fewer discontinuities (Wesnousky, 1988; Scholz, 2002).

376

Also illustrated is total deformation (DT ), idealized warping, inelastic strain on a Riedel shear (Figure 2B), and clockwise rotation in the core and damage zones consistent with the modeled right lateral fault (Johnson et al., 2001). DT is defined as brittle slip measured in the PSZ and along secondary faults plus surveyed OFD due to distributed brittle deformation, most likely on synthetic (R1 ) and antithetic (R2 ) Riedel shear fractures and warping (Rockwell et al., 2002).

An example of DT is shown in Figure 1 (Haeussler et al., 2004). Core width is a function of the length of R1 and R2 Riedel shears and/or tension fractures (T) multiplied by the sine of the acute angle (Rp ) between the fractures and the PSZ (Tchalenko, 1970).

Damage zone width cannot be estimated based on the fault model.

Inelastic OFD is concentrated within the fault core and, to a lesser degree, in the fault damage zone (Tchalenko, 1970; Scholz, 2002; Sibson, 2003; and Gudmundsson, 2011). For example, inelastic strains, which start at the edge of the PSZ, consist primarily of distributed brittle deformation on synthetic R1 shear fractures (Figure 2B). Ridged body structure rotation is believed to be caused by heterogeneous R1 slip.

Simple shear is most likely the cause of non-brittle warping at the edge of the core and in the damage zone (Bray, 2001).

OFD Data In this article I compile OFD data from nine strikeslip earthquakes that occurred between 1906 and 2014 (Table 1). The data set includes 51 surveys conducted at 31 sites where linear cultural features, believed to have been straight prior to the earthquake, were offset and warped. Two of the 1906 San Francisco surveys noted by Lawson (1908), fence “A” and southeast of Mussel Rock, were not included in this study. Reid (1910) considered the fence “A” measurements to be inaccurate and the Mussel Rock deformation diagram to be incorrect.

Table 1 also includes two additional OFD surveys that were conducted at hundreds of locations along the entire lengths of the 1992 Landers (Milliner et al., 2015) and 1999 Hector Mine surface ruptures (Milliner et al., 2016) in the Mojave Desert of southern California. The Landers “all fault” data included measurements at complex zones such as step overs and bends as well as relatively straight segments on 69 distinct faults. Milliner et al. (2015) noted OFD is markedly larger in areas of structural complexity. The Milliner et al. (2015, 2016) data were not plotted on the figures or used in the regression calculations because of the inclusion of measurements at complex zones.

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Off-Fault Deformation

Figure 2. (A) Conceptual strike-slip fault model (not to scale), after Tchalenko (1970), Rockwell, et al. (2002), and Sibson (2003). (B) Idealized distributed slip (not to scale) Riedel fracture assuming slip trends to zero at the end of a shear fracture (Scholz, 2002; Gudmundsson, 2011).

Tables 1 through 3 present the DT , percent DT in the PSZ, the total width of the zone of deformation (WT ), core width (WC ), the general geology at the 31 sites, OFD symmetry relative to the PSZ at 10 locations, and building rotation angles resulting from the 1992 Landers and the two 1999 Turkey earthquakes.

WT is defined as the entire width of the zone brittle and non-brittle deformation (DT ) was measured. WT increases as a function of magnitude, although the correlation is not strong (Figure 3). Core width based on two methods (WC [Table 1] and WC [Table 2]) is also shown on Figure 3. I recorded WC , in Table 1, if there was no evidence of a damage zone. I estimated WC , listed in Table 2, based on either the recorded width of zones of en echelon fractures or the length of en echelon fractures multiplied by the sine of the acute angle between the fractures and the PSZ (Tchalenko, 1970). The correlations are not strong, but the data

suggest that WC increases with magnitude. WC estimated by the second method (Table 2) results in larger core widths for M > 6.5.

DT vs. magnitude and the maximum measured slip (Max Slip) and average computed slip (Ave Slip) for each of the nine earthquakes are plotted in Figure 4.

Based on limited data, DT increases with magnitude and is approximately equal to the average slip for magnitudes 7.0. Correlation coefficients for DT , maximum, and average slip are 0.4, 0.8, and 0.86, respectively.

Percent OFD as a function of distance from the PSZ is shown on Figure 5. Surveys from three different earthquakes are shown including (1) the Clos du Val Vineyards; M6.0 2014 South Napa, CA (Beyzaei et al., 2014); (2) the single transmission tower; M7.3 1992 Landers, CA (Johnson et al., 1994); and (3) the TAPS Denali fault crossing; M7.9 2002 Denali, AK (Haeussler et al., 2004).

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378 6

35.56ºN 120.31ºW

Environmental & Engineering Geoscience, Vol. XXIV, No. 4, November 2018, pp. 375–384 Site Number 7 Site Number 8 Site Number 9

Site Number 6

Site Number 5

Transmission tower All Fault 1999.08.17 Izmit, Turkey Site Number 4 40.81ºN

29.98ºE

7.6

7.3

6.9

32.73ºN 115.5ºW

Latitude Longitude Magnitude 37.75ºN 122.55ºW 7.8

Fences N and S Highway 46 Fence N side Turkey Flat Rd 1992.06.28 Landers, CA 34.217ºN 116.433ºW

Date General Location 1906.04.18 San Francisco, CA Fence “C” SE San Andreas Lake Fort Ross Historic Park Head of Bolinas Lagoon Wright Station tunnel 1940.05.18 Imperial Valley, CA North of USA/Mexico border 1966.06.27 Parkfield, CA

5.5

6

0.4

7

Slip 6.4

4

2.95

0.31

1.5

Slip 5

3.3 3.2 3.8 3.7 4.4 4.45 4.55 4.3 4.2 4.4 5.1 2.85 2.8 2.75 3.76 3.8

3.3 3.1 3.7 3.3 4.4 4.25 4.1 3.9 3.9 3.2 3.8 1.55 1.5 2 3.05 3.35

2.7

0.064

0.064

3.5

0.18

5.5

0.18

5.5

1.05–1.49 60–85 400

Alluvium

Sedimentary rock

Rock and Quaternary alluvium

Sedimentary rock

General Geology

66

1.8

100 38 97 38 97 37 89 37 100 32 94 36 90 36 91 48 93 57 73 56 75 69 54 122 54 116 73 76 81 57 88 94

1.8

Alluvium Alluvium Alluvium

Alluvium

Alluvium

Alluvium

Alluvium

Alluvium/ rock

3.05 3.05 Alluvium

5

54 154

77

100

100

100

14

1.75

74

3.05

81

4.1

44

5

Width Width

42 200

Slip PSZ

1.6

2.2

PSZ

3.6

5.2

Deformation

Largest Average Total Hort. Hort. Slip % Total Total Core

Table 1. Earthquake, fault, and off-fault data.

No

No

No

No

Yes

OFD Symmetrical

Rockwell et al. (2002) Rockwell et al. (2002) Rockwell et al. (2002)

Rockwell et al. (2002)

Rockwell et al. (2002)

Milliner et al. (2015) Lettis et al. (2000) Rockwell et al. (2002)

Wells and Coppersmith (1994) Johnson et al. (1994)

Brown et al. (1967)

Lienkaemper and Prescott (1989) Brown et al. (1967)

Prentice and Ponti (1997) Rockwell and Klinger (2013) Rockwell and Klinger (2013)

Lawson (1908)

Lawson (1908)

Reference Haeussler et al.

(2004) Lawson (1908)

Johnson

Site Number 11

Site Number 10

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0.46

0.25 0.41–0.46

6.9

Tok Cutoff 2014.08.24 South Napa, CA 38.22ºN 122.31ºW Clos du Val Vineyards

3.9

4.9

3.8 3.45 3.8 1.9 1.98 4.05

Richardson Hwy

8.8

7.9

3

.20–.25

5

3.4

1.3

2.8 2.45 2 1.15 1.98 4.05

3.5

4.5 3.4 3.6 3.8 3.6 3.7 3.95 4.3 5.2 2.3 2.8 1.85 1.8 1.9

4.5 3.4 4.4 4.4 4.6 4.2 4.7 5.15 5.2 3.85 3.65 2.25 1.8 1.9 3.9

PSZ

Deformation

5.8

5

7.2

2.5

Slip

TAPS

5.25

Slip

7.1

General Location Latitude Longitude Magnitude

1999.10.16 Hector Mine, CA 34.6ºN 116.27ºW N Bullion Mts All fault 1999.11.12 Duzce, Turkey 40.82ºN 31.2ºE Site Number 1 Site Number 2 Site Number 3 Kaynasli Viaduct Town of Yeni Town of Cinarlikoyu 2002.11.03 Denali, AK 63.31ºN 147.36ºW

Date

Alluvium

Alluvium

General Geology

520

75

1000

113 36 66 110 2.5 4.5

3

20

>25

Alluvial fan

Alluvial

Fluvial

Fluvial

Alluvium Alluvium Alluvium Alluvium 2.5 Alluvium 4.5 Alluvium

14–20 14–20 Alluvium 121

116 138 141 66 144 129 112 81 66 43 39 63 77 67

Width Width

43–61 10–14

72

87

22

74 71 53 61 100 100

90 61

100 100 82 86 78 88 84 83 100 60 77 82 100 100

Slip PSZ

Largest Average Total Hort. Hort. Slip % Total Total Core

Table 1. Continued.

Yes

No

Yes

No

Yes

OFD Symmetrical

Haeussler et al.

(2004) Haeussler et al.

(2004) Haeussler et al.

(2004) Haeussler et al.

(2004) Beyzaei et al. (2014) Beyzaei et al. (2014)

Rockwell et al. (2002) Treiman et al. (2002) Treiman et al. (2002) Milliner et al. (2016) Akyuz et al. (2002) Rockwell et al. (2002) Rockwell et al. (2002) Rockwell et al. (2002) Johnson et al. (2001) Hartleb et al. (2002) Hartleb et al. (2002)

Rockwell et al. (2002)

Rockwell et al. (2002)

Reference

Off-Fault Deformation

379

3 3 5º–35º 3–12

0.27–1.08 1.71–6.84

15º–20º, 45º–50º 38.22N

122.31W

6

2–10 5

5–10

1.3–7.7

>25 20

14–20

2014.08.24

Imperial Valley, CA Parkfield, CA Borrego Mountain Hector Mine, CA Denali, AK, TAPS Tok Cutoff South Napa, CA Poseidon Vineyard NW Thompson Ave NW Thompson Ave 1940.05.18 1966.06.28 1968.04.09 1999.10.16 2002.11.03

32.73ºN 35.56 N 33.15N 34.6N 63.31ºN

115.5ºW 120.31 W 116.125W 116.27W 147.36ºW

6.9 6 6.5 7.1 7.9

3–6 1–10 10–50

3–6

30º

1.5–4.6

5 1.8–3.05

Haeussler et al. (2004) Lawson (1908) Lawson (1908) Rockwell and Klinger (2013) Brown et al. (1967) Clark (1972) Treiman et al. (2002) Haeussler et al. (2004) Haeussler et al. (2004) Haeussler et al. (2004) Beyzaei et al. (2014) Beyzaei et al. (2014) Beyzaei et al. (2014) Beyzaei et al. (2014) 52 91.44 7.8 122.55W San Francisco, CA 1906.04.18

37.75N

Matnitude Longitude General Location Date

Latitude

380

35º–40º 45º

Angle with PSZ Length (m) En Echelon Fractures Width (m) En Echelon Zone

Table 2. Estimated core width.

Estimated Core Width

1–27

Core Width Table 1

Reference

Johnson

OFD is relatively common; however, it was not recorded at 24 percent of the surveyed sites (Hartleb et al., 2002; Rockwell et al., 2002; and Rockwell and Klinger, 2013). For example, Rockwell and Klinger (2013), studying slip on the 1940 Imperial fault rupture in southern California, analyzed 15 km of highresolution aerial photographs. The 15 km represented the total extent of available photographs. Nearly 650 displacements were measured, mostly on linear cultural features and crop rows. According to Rockwell and Klinger (2013), OFD was not observed but could not be precluded.

OFD symmetry, relative to the PSZ (Sibson, 2003), was noted at 10 sites (Table 1). For six of the 10 sites that were asymmetrical the earthquake magnitude was M 7.2. Gilbert, as reported by Lawson (1908, p. 70), noted following the 1906 San Francisco earthquake that “three-fourths of the whole displacement occurred on the main plane of the fracture, and the remainder was diffused thru the ground adjoining on the southwest.” Prentice and Ponti (1997) analyzed a 1906 damage survey conducted in Wrights Tunnel. They concluded that the total horizontal deformation was confined to a zone that was <400 m wide, that 60–85 percent of the slip occurred at a single narrow zone, and that the OFD was asymmetrical, occurring southwest of the San Andreas Fault. Sibson (2003) concluded asymmetry is a function of the differing mechanical properties of the rocks juxtaposed by the fault.

Structure rotations were reported at nine alluvial locations (Table 3) following the 1992 Landers and the two 1999 Turkey earthquakes (Lazarte et al., 1994; Lund, 1994; Lettis et al., 2000; Johnson et al., 2001; Hartleb et al., 2002; and Rockwell et al., 2002). Core rotations accounted for seven of the 10 observations and ranged from 3º to 12º relative to damage zone rotations of 0.5º to 5º. Rotation angles were generally larger in the M7.6 Izmit, Turkey earthquake (3º–12º) relative to the M7.2 Duzce, Turkey earthquake (0.5º to 5º). Clockwise rotation, consistent with dextral slip (Johnson et al., 2001), was observed at all locations except the Golcuk Naval Base, where the rotation was reported to be small and the surface rupture complex (Lettis et al., 2000). Foundations ranged from the pilesupported Kaynasli Viaduct I (Johnson et al., 2001) to slabs on grade at the Lannon and Batdorf sites (Lazarte et al., 1994; Murbach et al., 1999). I was unable to determine if the structures that rotated during the 1999 Turkey earthquakes were attached to their foundations.

Simplified Procedure The simplified procedure consists of estimating (1) the total width of the zone of brittle and non-brittle

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Off-Fault Deformation Table 3. Structure rotation data.

Earthquake

Magnitude

General Location

Core

Single Tower Lannon Batdorf Yeni Mosque S. of Golyaka S. of Golyaka Golcuk Naval Base Kaynasli Viaduct I Eften Golu

X X X

1992.06.28

Landers, CA

7.3

1999.08.17

Izmit, Turkey

7.6

1999.11.12

Duzce, Turkey

7.2

Damage Zone

3º–5º?

10º–12º

deformation (WT ); (2) the core width (WC ) (Figure 3); (3) DT (Figure 4); (4) OFD (DT − Sd ); (5) core OFD percent based on core width and distance from the PSZ (Figure 5); and (6) whether or not the inelastic strains are expected to be symmetrical or asymmetrical relative to the PSZ. Non-core OFD is distributed in the damage zone over a distance equal to WT − WC . Prior to estimating OFD a site-specific study is required to identify active fault(s), and a design earthquake magnitude (Md ) and average slip (Sd ) are assigned to each fault of concern.

To illustrate the simplified procedure, an Md of 7.3 and an average Sd of 2.0 m (Wells and Coppersmith, 1994, their figure 11b) were selected for a single hypothetical strike-slip fault. WT and WC are estimated to be 40 m and 20 m, respectively (Figure 3). The width of the damage zone is 20 m, assuming a narrow PSZ. DT was estimated to be 3.5 m (Figure 4). Total OFD

Figure 3. Combined total width (WT ) of core plus damage zone vs. magnitude. Also shown are core widths (WC ) based on data listed in Tables 1 and 2.

5º X 3º X

0.5º?

Rotation

Geology

Reference

Clockwise Clockwise Clockwise Clockwise Clockwise Clockwise Counter Cclockwise Clockwise

Alluvium Alluvium Alluvium Alluvium Alluvium Alluvium Alluvium Alluvium Alluvium

Lund (1994) Lazarte et al. (1994) Lazarte et al. (1994) Hartleb et al. (2002) Hartleb et al. (2002) Hartleb et al. (2002) Lettis et al. (2000) Johnson et al. (2001) Rockwell et al. (2002)

(DT − Sd ) is 1.5 m. Assuming the OFD is asymmetrical, 60 percent of OFD, or 0.9 m, is distributed within the 20-m core width and the 0.6-m balance in the damage zone (Figure 5). Structure rotation should be considered because Md 7.2 (Table 3).

DISCUSSION OFD quantification provides data needed for analyzing structural and geotechnical engineering mitigation solutions for establishing a design buffer and determining the width of the setback zone. However, data variability and procedure uncertainty, due to limited data and incomplete knowledge, can affect OFD estimates. I suggest parameters with low correlation coefficients, such as WT and WC , and whether strains

Figure 4. Total deformation (DT ), including the principal slip zone PSZ, core, and damage zone, vs. magnitude. Also shown is the maximum measured slip (Max Slip) and computed average slip (Ave Slip) for each earthquake.

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Figure 5. Estimated percent off-fault deformation (OFD) as a function of distance from the principal slip zone (PSZ) surveyed at three locations during three different earthquakes: Clos du Val Vineyards, M6.0 2014 South Napa, CA (Beyzaei et al., 2014); single transmission tower, M7.3 1992 Landers, CA (Johnson et al., 1994); and the Trans-Alaskan Pipeline System (TAPS) Denali fault crossing, M7.9 2002 Denali, AK (Haeussler et al., 2004).

will be symmetrical or asymmetrical should be addressed during the site-specific fault study. For example, WT and WC are dependent on how well the fault is defined, and the thickness of sediments deposited subsequent to the most recent event and symmetry is a function of the mechanical properties of the tectonically juxtaposed rocks (Clark, 1972; Sibson, 2003; Bray, 2001, 2009; and Gudmundsson, 2011). Of the 10 locations where symmetry was observed, 60 percent were asymmetrical, and of those, 100 percent occurred on surface ruptures caused by M 7.2 earthquakes.

Uncertainty due to a small data set is also problematic. However, two of the OFD surveys are a near upper and lower bound for earthquakes of M7.9. For example, the M7.9 2002 Denali earthquake is one of the largest historic North American earthquakes. Inelastic strains, measured by high-resolution GPS and photogrammetric surveys (Haeussler et al., 2004), extended over a zone 1,000 m wide at TAPS, which is underlain by 240 m of saturated alluvium. Seventy-eight percent of the DT was OFD and, importantly, 80 percent occurred within 50 m of the PSZ (Figures 1 and 5).

The remaining 20 percent (or 0.45 m) was distributed over a distance of 450 m. In contrast, OFD measured at the Clos du Val Vineyards following the 2014 M6.0 South Napa, CA, earthquake, is likely a lower bound for an alluvial site (Figure 5 and Table 1).

OFD estimated by the simplified procedure is a function of Md , Sd , WT , WC , and DT . Wells and Coppersmith (1994) or others should be used to estimate 382

Md and Sd . An average value of Sd is recommended.

DT , maximum slip, and average slip and respective regressions are shown in Figure 4. DT is generally larger but approximately equal to average slip for M 7.0.

DT should be considered an average value for use in the simplified procedure, even though the data are limited.

Maximum slip, rather than DT (Figure 4), should be considered depending on the importance of the structure(s) and/or the consequences of failure. Maximum slip, according to Wells and Coppersmith (1994), is approximately twice the average slip.

Table 2 WC should be used, rather than Table 1 WC (Figure 3). The correlation with magnitude is better, although not strong, indicating the benefit of determining core width during the site-specific fault investigation.

Guidance regarding the style of brittle and nonbrittle OFD as a function of distance from the PSZ is shown on Figure 2. The general shape of the OFD curve is likely similar to that shown on Figure 1 and can be scaled based on the simplified procedure results.

Within the core, outside of the PSZ, distributed brittle deformation will occur primarily on synthetic R1 shears during the early stages of the rupture process.

The brittle slip will transition to non-brittle deformation near the edge of the core (Figure 2B) and continue into the damage zone.

Limited data suggest that foundation rotation due to heterogeneous slip on R1 shears should be considered, depending upon the type of proposed structure or lifeline and if the fault is capable of a M 7.0 earthquake (Table 3). Rotation is less likely in the damage zone but should also be considered for M 7.0. Clockwise rotation will occur if the fault is right-lateral (Figure 2), and counterclockwise rotation will occur if the fault is left-lateral.

OFD mitigation options, discussed by Bray (2001, 2009), Bray and Kelson (2006), Lazarte et al. (1994), and Lazarte and Bray (1996), include (1) decoupling by placement of plastic sheets between layers of sand or gravel directly below the structure; (2) strong, ductile foundations and post-tensioned floor slabs that can accommodate tilting and rotation and bridge tension fractures; (3) ridged foundations capable of deflecting the rupture; (4) limiting lateral earth pressures by backfilling foundation and utility trenches with Styrofoam or a similar material; and (5) earth fills designed to absorb or spread the fault rupture.

CONCLUSIONS The likelihood of OFD can be determined by analyzing the geology exposed in a fault trench, but it is difficult, if not impossible, to quantify future strains. OFD determined by the simplified procedure

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addresses the problem and allows structural and geotechnical engineers to determine (1) if the proposed structure(s) can be economically designed to safely accommodate the estimated strains within a design buffer and (2) if the setback zone can be limited to the width of the PSZ. ACKNOWLEDGMENTS I would like to acknowledge Roy Shlemon and Eldon Gath and a third reviewer for their time and expertise and for providing suggestions for improving the article.

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Rock Mass Characterization and Stability Evaluation of Mount Rushmore National Memorial, Keystone, South Dakota S. LINDSAY POLUGA Menard Group USA, 9930 Johnnycake Ridge Road, Suite 5G, Concord Township, OH 44060

ABDUL SHAKOOR* Department of Geology, Kent State University, Kent, OH 44240

ERIC L. BILDERBACK National Park Service, Geologic Resources Division, Lakewood, CO 80228

Key Terms: Mount Rushmore National Memorial, Rock Mass Characterization, Kinematic Analysis, Probabilistic Analysis, Mean Factor of Safety, Reliability Index, Probability of Failure ABSTRACT The purpose of this study was to characterize the rock mass at Mount Rushmore National Memorial (MORU) and to evaluate the stability of the presidential sculptures. The sculptures are carved in granite, but quartzmica schist and minor outcrops of pegmatite are also present within the site area. We divided the MORU area into four “regions” to collect discontinuity data. Since the sculptures were not accessible during this study, we used light detection and ranging (LiDAR) data and Split-FX software to determine the orientations of both the discontinuities and the slopes on the sculptures. The rock mass characterization results, using both the Rock Mass Rating system and the Q-system, indicate the granite, schist, and pegmatite classify as fair to good rock. Kinematic analysis results indicate that the potential for planar, wedge, and toppling failures exists for various slopes on each of the sculptures. The factor of safety (FS) values against planar and wedge sliding, ignoring cohesion, range from 0.1 to 0.8 and from 0.2 to 1.3, respectively. Since failures have not been observed at the memorial, we back-calculated the amount of cohesion required to raise the FS values to >1. The backcalculation results show that both cohesion and friction contribute to stability of the sculptures. Using the Slide program, we performed an overall slope probabilistic analysis for the slopes on which the MORU sculptures are located. The analysis determines the mean factor of *Corresponding author email: ashakoor@kent.edu

safety (FSM), reliability index (RI), and probability of failure (PF) for the slopes. For the static condition, the analysis resulted in FSM, RI, and PF values ranging from 3.3 to 4.5 percent, 3.3 to 7.8 percent, and 0 percent, respectively. With a seismic load coefficient of 0.14 applied to the slopes, the corresponding values were: 2.6 to 4.1 percent, 2.9 to 4.7 percent, and 0 percent. For both the static and seismic conditions, the results indicate that, overall, the slopes of the sculptures are stable.

INTRODUCTION Mount Rushmore National Memorial (MORU), Keystone, South Dakota, represents the first 150 years of American history through the carvings of the four well-known presidents: George Washington, Thomas Jefferson, Theodore Roosevelt, and Abraham Lincoln (Figure 1) (Graham, 2008; Jones, 2011). Washington represents the foundation of America, Jefferson signifies the expansion of America, as the Louisiana

Figure 1. Presidential sculptures of Mount Rushmore National Memorial (view to the northwest). The red box shows a schist band present within the bust area of Washington’s sculpture.

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Figure 2. The location of MORU in the Black Hills of South Dakota (Google Earth Pro, 2013).

Purchase occurred during his presidency, Roosevelt symbolizes the development of America, as he supported establishing the Panama Canal, and Lincoln represents the preservation of America due to his leadership and accomplishments during the Civil War (Jones, 2011; National Park Service [NPS], 2014a).

Work on the sculptures, led by Gutzon Borglum, began on August 10, 1927, and finished on October 31, 1941 (Graham, 2008; Jones, 2011). The sculptures are 18 m high and have an elevation of 1,745 m (Graham, 2008). MORU has become a symbol of America that attracts about two million visitors annually from all around the world (Jones, 2011; NPS, 2014b).

Geologic Setting Geologically, MORU is located in the Black Hills of South Dakota (Figure 2) (Graham, 2008). The Black Hills, a 200 km × 105 km dome structure, elongated in a north-south direction, form the core of a prominent uplift associated with the Laramide Orogeny, a mountain-building episode that occurred about 65– 45 million years ago (Ma) (Graham, 2008). The Precambrian core of the Black Hills dome is surrounded by concentric rings of Paleozoic and Mesozoic sedimentary rock (Graham, 2008; NPS, 2016), and it is composed mainly of metasedimentary, metavolcanic, and intrusive rocks (Dahl et al., 1993). About 1.7 billion years ago, the Harney Peak Granite, in which the sculptures are carved, intruded the country rock 386

(NPS, 2005). MORU is located in the southern half, on the easterly dipping side of the Black Hills dome, such that the foliation and remnant bedding in the rocks near the western boundary of the memorial dip about 30°E, and near the eastern boundary, they dip about 65°E (Powell et al., 1973; Graham, 2008). Within the Harney Peak Granite, there are very coarse-grained granitic pegmatite sills and dikes, some of which can be seen as lighter-color bands across the faces of the sculptures (Figure 3) (RESPEC, Inc., 1989; Graham, 2008). The granite contains discontinuities that range from open joints to fine fractures (RESPEC, Inc., 2000) The NPS uses a silicon solution to seal the rock fractures within the area of the sculptures (Graham, 2008). The other rocks within the memorial area are mostly schists into which the granite intruded (Powell et al., 1973). Figure 1 shows a dark, horizontal band of schist above Washington’s waist.

The granite within the MORU area is highly variable in its composition, but it is composed mainly of potassium feldspar, albite, and quartz with smaller amounts of perthite and muscovite (Powell et al., 1973; Graham, 2008). The average grain size of the minerals in the granite is about 3 mm, with the exception of perthite, which can be several centimeters or longer (Powell et al., 1973; Graham, 2008). The primary minerals in the pegmatite are quartz, albite, microcline, and muscovite (Graham, 2008). The schistose rocks at MORU are also highly variable, but they are composed primarily of quartz, biotite, and muscovite. The typical

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Figure 3. Pegmatitic sills and dikes traversing Lincoln’s sculpture.

grain size in schist ranges from 0.3 to 1.0 mm (Graham, 2008), and the rock cleaves easily (Powell et al., 1973). Figure 4 shows examples of the Harney Peak Granite, pegmatite, and schist outcrops, the focus of rock mass characterization at MORU.

Previous Investigations on the Stability of Mount Rushmore RESPEC, Inc., conducted a study from 1989 to 1992 on the structural stability of the sculptures. During the span of their study, RESPEC, Inc., prepared multiple reports and papers, including RESPEC, Inc. (1989), Boyle and Vogt (1992), and RESPEC, Inc. (2000).

The company mapped 144 discontinuities traversing the sculptures (Boyle and Vogt, 1992; RESPEC, Inc., 2000). The orientations of the discontinuities were utilized in a key block analysis, a method developed by Goodman and Shi (1985) “for the geometrical analysis of rigid blocks” (Priest, 1993, p.246). A key block analysis identifies the key blocks for a given slope orientation and assumes that, if the key blocks are stable, then the entire slope face is stable (Priest, 1993).

Figure 4. (a) Harney Peak Granite, (b) a pegmatitic area within the Harney Peak Granite, and (c) a schist outcrop.

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The study by RESPEC, Inc. (2000), revealed the presence of 22 distinct rock blocks exposed on the sculptures’ surfaces. Ten of the 22 blocks were considered as removable blocks; i.e., the bounding discontinuities of the blocks are orientated such that the blocks are removable from the slope (Boyle and Vogt, 1992). Two of these blocks were identified as possible key blocks (the blocks are removable, but they are considered stable due to sufficient friction/cohesion along the bounding discontinuities) (Boyle and Vogt, 1992). The first key block is located in the forehead and frontal hair area of Roosevelt’s sculpture, and the second key block is in the forehead and face of Lincoln’s sculpture. There is also a thin block located on the southwestern side of Washington’s sculpture that has the potential to dislodge, requiring special attention (Boyle and Vogt, 1992; RESPEC, Inc., 2000).

A rock block monitoring system, capable of measuring movements of less than 0.003 mm, was installed between 1998 and 1999 for monitoring the three critical blocks (RESPEC, Inc., 2000; Graham, 2008). In September 2010, the NPS replaced the aging, lightning-prone electrical sensor system with an updated monitoring system based on fiber Bragg gratings (Micron Optics, Inc., 2010).

Study Objectives The structural integrity of the MORU sculptures is of utmost importance. The NPS is interested in the effect of anthropogenic vibrations, such as those caused by the Fourth of July fireworks, on the stability of the MORU sculptures. Because discontinuities are areas of weakness where movements of the rock blocks are most likely to occur, the impact of vibrations on the sculptures will depend on the response of discontinuities traversing the sculptures. The specific objectives of this study were to: 1. determine the engineering properties of the rock at MORU, including density, absorption, unconfined compressive strength, tensile strength, basic friction angle, slake durability index, and freeze-thaw durability; 2. characterize the rock mass at the MORU site, based on different aspects of the discontinuities, using both the Rock Mass Rating (RMR) system proposed by Bieniawski (1989) and the Rock Mass Quality system (Q-system) developed by Barton et al. (1974); 3. evaluate the potential modes of failure for the sculptures and determine the factor of safety for the potential failures; and 388

4. perform static and seismic overall slope probabilistic analyses of the MORU sculptures to determine their global stability.

RESEARCH METHODS Site Selection We divided the area at MORU into four “regions,” each with multiple locations for collecting discontinuity data (Figure 5). Table 1, to be used in conjunction with Figure 5, shows the rock types present at different data collection locations in the four regions. Because the MORU sculptures were restricted for discontinuity data collection during this study, light detection and ranging (LiDAR) data, provided by the NPS, were used to obtain orientations of discontinuities within the sculptures.

Field Investigations We conducted field investigations to collect the data required to apply rock mass classification systems and to evaluate the stability of the sculptures. During these investigations, we evaluated the degree of weathering, mapped discontinuities, estimated rock quality designation (RQD), and collected block samples of granite and schist for laboratory testing.

We documented the degree of weathering of the rocks at MORU according to the classification chart

by Dearman (1976), which uses the following scale: I (fresh), II (slightly weathered), III (moderately weathered), IV (highly weathered), V (completely weathered), VI (residual soil).

We used the window mapping method (Priest, 1993; Wyllie and Mah, 2004), with a window size of 10 m × 10 m, to collect discontinuity data within the four selected regions at MORU, reducing the orientation bias produced by linear sampling (Park and West, 2002). In total, 695 discontinuities were mapped: 386 for granite, 279 for schist, and 30 for pegmatite. The mapping also recorded the following aspects of discontinuities: orientation, continuity, spacing, aperture, surface irregularities, nature of the infilling material, presence of water, and degree of weathering. We used a Brunton compass to measure orientations of discontinuities, and visual inspection to estimate aperture and continuity of discontinuities. Spacing of the discontinuities was determined by stretching a tape measure across the slope face, recording the distances between all discontinuities intercepted by the tape, and calculating the average spacing. Surface irregularities were documented as joint roughness coefficient (JRC) using the chart by Barton (1982), found in Singh and Goel (1999). The

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Figure 5. Locations of the selected sites for discontinuity mapping at MORU. Each location (L) number represents the general area within each region where data were collected. The location numbers are not significant and were only used for recording purposes. Some locations have more than one pin, indicating that more than one GPS reading was taken at those locations during mapping. The red star is the approximate location of rock block 5 (Table 2). The general area of the sculptures is southeast of L21. Map was created using the ArcGIS® software by ESRI (2014); point locations were imported from Google Earth (Google Earth Pro, 2013). Note: 1 foot = 0.3048 m.

Table 1. Rock types present at data collection locations in the four regions at MORU (see Figure 5).

Granite Schist Pegmatite

Region 1

Region 2

Region 3

Region 4

L2, L10–13 L1–2, L10–13 L13

L8–9, L14–16, L8–9, L14 L9

L21–24, L27 L23–26, L28 L21

L3–1, L3–2, L4–8, L17–20, L29, L31 L3–3, L4, L8, L17–18, L20, L29–31 L4–7, L19, L31

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Poluga, Shakoor, and Bilderback Table 2. Rock block sample designations and locations.

Rock Block Designation No. 1a 1b 2 3 4 5

Rock Type

Approximate Location (Figure 5)

Schist Schist Granite Granite Granite Granite

L12 L1 L11 L12 L20 Talus slope below the MORU sculptures

JRC values range from 0 to 20 and are determined by assessing the amplitude of the asperities and the length of the joint profile (Singh and Goel, 1999). The asperities and length of the joint profile were based on visual inspection, as was the presence or absence of infilling material. Water presence within the discontinuity was recorded as completely dry, damp, wet, dripping, or flowing (following the RMR system parameters for groundwater). The degree of weathering of joint surfaces was evaluated using the classification chart by Dearman (1976).

Since core samples were not available, we used Palmstrom’s method (Palmstrom, 1982) to estimate RQD. The method suggests counting the number of joints within 1 m3 of the rock volume and using the following equation to calculate RQD: RQD = 115 − 3.3Jv, where Jv = total number of joints/m3 . RQD = 100 percent for Jv < 4.5 (Barton et al., 1974). Palmstrom and Broch (2006) reported that for Jv = 4 to 44, the equation RQD = 110 − 2.5Jv gives a better correlation, and, thus, it was used when applicable.

For laboratory testing, we collected six rock block samples, two of schist and four of granite. Table 2 summarizes the sample designations and locations.

Laboratory Investigations In the laboratory, we determined density, absorption, unconfined compressive strength (UCS), tensile strength, basic friction angle, slake durability index, and freeze-thaw durability of the intact rock samples collected in the field, using the American Society for Testing and Materials (ASTM) standardized procedures when applicable.

Density (mass/volume) was determined using core specimens of granite and disc specimens of schist, obtained by coring the rock blocks in the laboratory.

All specimens were oven dried for 24 hours at 105°C, cooled to room temperature, and weighed to the nearest tenth of a gram. Five measurements taken of the diameter and length of each specimen were averaged to calculate the volume. These data were used to calculate density.

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ASTM method D6473-10 (ASTM, 2010) was used to measure absorption by the following equation: Absorption = (B − A/A) × 100, where A is the dry weight of the specimen, and B is the saturated surface dried weight.

UCS is an input parameter for the RMR system.

Both the standard method (ASTM D7012-13; ASTM, 2013) and the point load test method (ASTM D5731; ASTM, 2008b) were used to determine UCS. The Brazilian test, as specified by ASTM method D3967-08 (ASTM, 2008a), was used to measure tensile strength. Three discs were tested for each rock block sample (Table 2). Tensile strength, although not used in any of the analyses, was determined for general characterization of the intact rock at MORU. The method suggested by Stimpson (1981) was used to estimate the basic friction angle of each rock block sample, except for schist 1b (Table 2). This rock block sample could not be cored successfully due to its schistose nature. Three cores of each sample were placed on the tilt table: Two cores were placed next to each other, restrained and unable to slide, and the third core was placed on top of the other two cores, free to slide. The base of the tilt table was lifted slowly, and the angle at which the third core moved was measured with a Brunton compass and recorded. The cores were rotated to a fresh surface, and the test was repeated three times to obtain the average tilt angle, which was used in the following equation to calculate the basic friction angle: Basic friction angle (�) = tan−1 (1.155 tan α),

(1)

where α = tilt angle of the base at sliding (Stimpson, 1981). The basic friction angle was used for kinematic analysis.

The slake durability test was conducted following ASTM method D4644-08 (ASTM, 2008c). This test is used to evaluate the durability of weak rocks after two cycles of wetting and drying. A band of schist is present within the granite rock comprising MORU sculptures (Figure 1). The slake durability test was used to evaluate the potential for differential weathering between these two rock types.

ASTM method C666/C666M-03 (ASTM, 2003) was used to perform the freeze-thaw test. The specimens were oven dried for 24 hours, weighed, and then saturated for 24 hours before the freeze-thaw test began. The test was performed at BASF in Beachwood, Ohio, a concrete testing facility. The freeze-thaw apparatus at the BASF facility automatically freezes and thaws the samples throughout the test. After the completion of the freeze-thaw test, the specimens were dried for 24 hours at 105°C and weighed when cool.

The total mass lost and the percent change in mass were recorded for each specimen. These data were used to determine the average values for each specimen.

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Data Analysis Discontinuity Data Analysis Discontinuity orientations could not be measured manually on the MORU presidential sculptures in the field due to safety and monument visitor experience concerns. Therefore, we used a LiDAR point cloud and Split-FX software (Split Engineering, 2014) to extract discontinuity orientation data. Figure 6 shows an example of the procedure followed to extract discontinuity orientation data from the LiDAR point cloud.

Figure 6a is a photographic image showing two discontinuities, designated as A and B, on the right side of Washington’s forehead. These were used as a guide to locate the same discontinuities on the LiDAR point cloud of Washington’s forehead, generated by SplitFX software, as seen in Figure 6b. Figure 6c shows the discontinuity planes in purple color. The Split-FX software calculates the best-fit plane that fits the points along the trace of the discontinuity (Split Engineering, 2014) and the orientation of this plane in terms of dip and dip direction. Previous studies (Kemeny and Donovan, 2005; Lato et al., 2009; Gigli and Casagli, 2011; and Fisher et al., 2014) have documented the usefulness of LiDAR point cloud for extracting reliable discontinuity orientation data.

We also used Split-FX software to determine slope orientations of the foreheads and noses of all four sculptures for use in the kinematic analyses. The slope orientations were obtained by drawing patches on the slopes of interest. Using the insert patch function of the program, three points, forming a triangle, were selected on a slope. The program then calculated the dip and dip direction of the plane of the inserted patch, which is the orientation of the slope. This process was repeated three times for each slope, and the values were averaged.

Microsoft Excel was used to create histograms of the following quantitative data for use in the rock mass classification systems: degree of weathering, RQD, continuity, spacing, JRC and roughness, infilling material, and aperture. The highest frequency of each parameter (unless otherwise noted) was used for the calculations in the rock mass classification systems.

Stereonet Analysis Discontinuity orientation data, from both LiDAR and field measurements, were plotted on a stereonet, using the Dips software program (Rocscience, 2016), to determine the principal joint sets (pole concentrations >4 percent). Because the rock mass at MORU was divided into four “regions,” with multiple locations where discontinuity data were collected, we

Figure 6. Procedure for extracting discontinuity orientations from LiDAR point cloud using Split-FX software.

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Poluga, Shakoor, and Bilderback Table 3. Input parameters for the RMR and Q-systems, rating calculation procedure, and rating-based categorization of the rock mass.

Input parameters

Calculation Classification

RMR System

Q-System

UCS of intact rock RQD Spacing of discontinuities Condition of discontinuities Groundwater conditions Orientation of discontinuities Sum of input parameters Very poor, poor, fair, good, and very good

RQD Joint set number (Jn ) Joint roughness number (Jr ) Joint alteration number (Ja ) Joint water reduction factor (Jw ) Stress reduction factor (SRF) Q = (RQD/Jn ) × (Jr /Ja ) × (Jw /SRF) Exceptionally poor, extremely poor, very poor, poor, fair, good, very good, extremely good, and exceptionally good

determined the principal joints for each rock type separately within the four regions as well as for the three rock types as a whole with all the discontinuity data combined from each region (note: pegmatite rock data were collected only in regions 1 and 2).

Rock Mass Characterization We used both the RMR system (Bieniawski, 1989) and the Q-system (Barton et al., 1974) to characterize the rock mass at MORU. Table 3 shows the input parameters, rating calculation procedure, and categorization of the rock mass based on calculated ratings. We assigned ratings for each parameter according to the RMR system classification chart by Bieniawski (1989).

During field investigations, we measured and recorded the discontinuity parameters required for the two classification systems. Since we recorded roughness as JRC (Barton et al., 1974), and because a correlation between JRC values and the RMR roughness categories is not available, we used Table 4 to approximate roughness from JRC values. In the Q-system equation (Table 3), (RQD/Jn ) represents the structure of the rock mass, (Jr /Ja ) represents the shear strength along the discon-

tinuities, and (Jw /SRF) represents the “active stress” (Bieniawski, 1989). We estimated parameter Jr based on JRC values and adjusted it based on field observations. Since the Q-system was developed based on studies involving tunnels rather than surface excavations, we used estimated values of SRF. Kinematic Analysis The Dips software program (Rocscience, 2016) was used to perform a kinematic analysis on the foreheads and noses of each MORU sculpture. The kinematic analysis indicated whether there was a potential for the occurrence of plane, wedge, or toppling failures.

The requirements for these three types of failure can be found in Hudson and Harrison (1997) and Wyllie and Mah (2004). For the kinematic analysis, the slopes of the sculptures’ foreheads were divided into sections, including the right side, left side, and center. The nose of each sculpture was also divided into sections, including the right side, left side, and front. The average slope orientations obtained using the Split-FX software, the principal joints obtained from the stereonet analysis, and the basic friction angle of the granite rock obtained by laboratory tests were used to complete the kinematic analyses.

It should be noted that we approximated the curved slopes of noses and forehead using planar surfaces for use in kinematic analysis. This approximation is valid, because it does not affect the potential modes of failure; it only changes the anticipated volume of the failure. Also, each forehead was divided into right, left, and center to account for the curved surface and justify the planar approximation of the slopes for analysis purposes.

Factor of Safety Calculations for the Potential Failures The software programs RocPlane and Swedge (Rocscience, 2016) were used to calculate the factor of safety (FS) values for the potential plane and wedge failures indicated by the kinematic analysis. FS values for the potential toppling failures were not calculated due to lack of information such as the overall base inclination and the width of the toppling blocks.

Table 4. Joint roughness categories, based on JRC values, used for RMR.

Back-Analysis for Planar and Wedge Sliding with FS <1

JRC Value

Many of the FS values for planar sliding and wedge sliding for various slopes on each of the sculptures were <1. FS values <1 indicate unstable slopes; however, failures have not been observed on the sculptures.

In the FS calculations, we assumed cohesion to be zero and used a basic friction angle of 40° (assuming

0–1 1–2 2–12 12–18 18–20

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Assigned RMR Roughness Slickensided Smooth Slightly Rough Rough Very Rough

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smooth discontinuities). Because the cohesive strength of discontinuities in granite could not be determined in this study due to lack of core samples containing discontinuities (drilling is not permitted on the monument), we performed back-analysis to estimate the cohesion value required for FS just >1 (mostly 1.01; the range being 1.01–1.4). This was done using friction angle values of 40°, 45°, and 50° to consider not only the basic friction angle, but also possible effective friction angle values that considered roughness along the discontinuities (Wyllie and Mah, 2004). Back-analysis was also conducted to estimate the value of friction angle required along the discontinuities for FS just >1 (assuming cohesion = 0). The computer programs RocPlane and Swedge (Rocscience, 2016) were used for the back-analysis.

Overall Slope Probabilistic Analysis of the MORU Sculptures (Static and Seismic Conditions) We used the Slide 7.0 software program (Rocscience, 2016) to complete a probabilistic slope stability analysis (PSSA) for the entire slope on which the MORU sculptures are located. A traditional slope stability analysis assumes that all of the parameters of the model (e.g. UCS, geologic strength index [GSI], seismic coefficients, etc.) are exactly known, whereas a PSSA takes into account the uncertainty of the model parameters by allowing the user to assign a statistical distribution to one or more of the parameters (Rocscience, 2016). In PSSA, the input parameters are considered as random variables and are generated by assigning a probability distribution function (PDF) to each parameter (Admassu, 2010). A PDF is defined by the type of statistical distribution (normal, uniform, triangular, beta, exponential, lognormal, gamma) and the statistical parameters of the distribution (mean, standard deviation, minimum, and maximum values) (Rocscience, 2016). The PDF describes the range of potential values for the random variables in the analysis (Rocscience, 2016). These variable values, used in the FS calculations, are assigned random numbers generated by the user-defined sampling method, which is usually the Monte Carlo simulation (Admassu, 2010).

We used the overall slope (OS) method to perform the PSSA analysis. The OS method locates multiple global minimum slip surfaces on which the PSSA is completed. A new set of random variables for the input parameters is used for each search of a global minimum slip surface.

The PSSA results in a distribution of FS values that is used to calculate the mean factor of safety (FSM), reliability index (RI), and probability of failure (PF) for the slope (Rocscience, 2016). The RI represents the number of standard deviations that separate the FSM

Table 5. Random variables used in the probabilistic analysis.

Random Variable

Mean

Standard Deviation

GSI Hoek-Brown mi constant UCS (kPa) Horizontal and vertical seismic coefficient

62 32 33,000 0.14

7 1 3,500 0.02

from the critical factor of safety (i.e., when FS = 1) (Rocscience, 2016). It assumes the FS values from the probabilistic analysis are distributed either normally or lognormally (Rocscience, 2016). The PF is a measure of safety for the slope and is calculated by dividing the number of slip surfaces that have FS <1 by the total number of samples (Rocscience, 2016). The results of the PSSA indicate the distribution that is the best fit for the FS data. Details of PSSA can be found in the Rocscience manual (Rocscience, 2016).

We divided the rock mass containing the four presidential sculptures into four sections, one for each sculpture, for which four different slope profiles were generated and analyzed. We used MORU LiDAR data in ArcGIS® ArcMapTM (ESRI, 2014) to generate the slope profiles. These slope profiles were generalized and drawn in the Slide program (Rocscience, 2016).

The PSSA for each slope section was completed with and without the application of a seismic load coefficient. The seismic condition was evaluated because the NPS is interested in the effect of anthropogenic vibrations, such as those caused by Fourth of July fireworks, on the MORU sculptures. This study assumes that if the MORU sculptures are safe against failure when an earthquake force is applied, the air blast seismic energy resulting from fireworks will not be a concern to their structural stability. The seismic load coefficient of 0.14g (acceleration due to gravity in the horizontal and vertical directions) used in the stability analysis was estimated using the 2014 U.S. Geological Survey (USGS) map titled “Two-Percent Probability of Exceedance in 50 Years Map of Peak Ground Acceleration” (USGS, 2014). Hynes-Griffin and Franklin (1984) suggested using a seismic coefficient corresponding to 50 percent of the peak ground acceleration (PGA), but we used the full value of PGA in order to be more conservative.

Since granite is a hard rock with well-defined sets of discontinuities, a non-circular slip surface was chosen for the analysis. In all, we analyzed 5,000 slip surfaces.

The material parameters used in the analysis were the density and UCS of the granite, as determined in the laboratory, the GSI value of the granite, the strength type (the generalized Hoek-Brown type), the Hoek-Brown intact rock constant (mi ) for the granite, and the disturbance factor (D). A GSI value of

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62 was used, as calculated by the computer software program RocData (Rocscience, 2016) with discontinuity and RQD data from the granite rock in region 3 (closest to the sculptures) at MORU as input parameters. An mi value of 32 was estimated using a chart in RocData (Rocscience, 2016). The Slide program (Rocscience, 2016) indicates that a disturbance factor should be applied only to damaged rock around excavations or behind the slope but not to the entire rock mass. The analyses in this study considered the entire rock mass, and so the disturbance factor was considered as zero. Groundwater effects were not considered, because most of the slopes studied were dry in the field.

The GSI, mi , and UCS values were assigned as random variables because the analysis results were sensitive to these input parameters, and/or their mean values were not known precisely. The horizontal and vertical seismic coefficients were assigned as random variables in the seismic analysis as well. Normal distributions were assigned to the random variables. Rocscience (2016) states that the standard deviation can be estimated for a normal distribution using the following equation: Standard Deviation (σ) = (HCV − LCV) /6,

Results of Field Investigations Table 6 summarizes the average discontinuity data, except the orientation, and the degree of rock weathering. The table shows that the discontinuities in the granite and schist in region 3, the area closest to the sculptures, are, on average, more continuous than in other regions. The table also shows that the average discontinuity spacing in the granite is the largest in region 2, while the average spacing in the schist is the largest in region 3. Furthermore, the average JRC for the discontinuities, in both the granite and schist, is the highest in region 4. Generally, the discontinuities in granite and schist did not have any infilling material and were dry. Table 6 shows that, on average, the granite is moderately weathered in all regions except for region 1, where it is moderately to highly weathered. The schist in regions 3 and 4 is, on average, less weathered than in regions 1 and 2. The pegmatite is less weathered in region 2 as compared to region 1.

For the four regions studied, the RQD ranges from 93 to 97 percent for granite, it ranges from 70 to 85 percent for schist, and it is 78 percent for pegmatite, which was studied only in region 2. According to the RQD classification (Deere, 1964; West 1995), the high values of RQD (>90 percent) for granite, the rock in which the sculptures are carved, indicate excellent quality rock.

(2)

where HCV is highest conceivable value of the random variable, and LCV is lowest conceivable value of the random variable.

This equation was used to calculate the standard deviation values of the random variables. The HCV and LCV values were estimated from data collected in region 3 at MORU, data determined in the laboratory for the granite rock, the mi values chart in RocData (Rocscience, 2016), and the USGS (2014) map. Table 5 lists the random variable data used in the analysis.

Results of Laboratory Investigations Laboratory test results indicate the average values of density, absorption, UCS, tensile strength, and basic friction angle for granite are 2.6 Mg/m3 , 0.9 percent, 33.4 MPa, 2.4 MPa, and 41°, respectively, whereas for

Table 6. Average discontinuity data and degree of rock weathering for each region studied at MORU. Region

Rock Type

Continuity (m)

Spacing (m)

Aperture (cm)

JRC

Infilling

Water

Degree of Weathering (Dearman, 1976)

1

Granite Schist Pegmatite Granite Schist Pegmatite Granite Schist Pegmatite Granite Schist Pegmatite

6.1 1.2 2.3 4.0 0.9 2.6 7.9 3.5 NA 6.7 3.3 NA

1.0 0.3 — 1.8 0.7 1.1 1.4 1.1 NA 1.1 0.4 NA

8.9 1.3 0.5 9.1 0.5 1.3 14.5 1.8 NA 21.3 2.5 NA

11–12 6–7 16 10–11 4 10 8 6–8 NA 14–15 9–10 NA

None None None None None None None None NA None None NA

Dry Dry Dry Dry Dry Dry Dry Dry NA Dry Dry NA

III–IV (moderate–high) IV–V (high–complete) IV–V (high–complete) III (moderate) V (high) V (high) III (moderate) III (moderate) NA III (moderate) III (moderate) NA

2 3 4

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The freeze-thaw test results indicate a relatively low average percent change in mass for each of the granite specimens after 300 freeze-thaw cycles (<2 percent) (Poluga, 2017). This low percent change in mass of the granite is corroborated by the average absorption of granite, i.e., <1 percent, which indicates low susceptibility to freeze-thaw damage. The schist samples exhibit higher average change in mass after 300 freeze-thaw cycles (>5 percent) (Poluga, 2017), indicating that schist has lower freeze-thaw durability than granite.

Results of Data Analysis

Figure 7. SEM scan of a granite sample from MORU showing the presence of micro-fractures. Note: Not all micro-fractures are shown with arrows.

schist, these values are 2.8 Mg/m3 , 1.1 percent, 58.0 MPa, 5.1 MPa, and 40°, respectively (Poluga, 2017).

For both granite and schist, the point load test resulted in slightly higher values of UCS. The UCS of pegmatite was not determined in the laboratory due to lack of sample but was assumed to be 90 percent of the value for granite, as the strength decreases with increasing grain size (West, 1995). The relatively high density and low absorption values suggest that these rocks, in their intact form, are not subject to freezethaw damage. The UCS values for granite, obtained by both methods, are unexpectedly low. Thin-section and scanning electron microscope (SEM) analyses indicated that the low strength may be attributed to the presence of micro-fractures in granite (Figure 7).

The second-cycle slake durability index (Id2 ) values for granite and schist are 96 percent and 61 percent, respectively (Poluga, 2017), indicating that, according the International Society for Rock Mechanics (ISRM) classification (ISRM, 1979), granite has high durability against wetting and drying, and schist has medium durability. This suggests a slight potential for differential weathering between granite and schist.

Analysis of the 53 discontinuities traversing the MORU sculptures, obtained using LiDAR data and the Split-FX software (Split Engineering, 2014), indicates that 57 percent of them are steeper than 70°, 24 percent are between 70° and 45°, and 19 percent are gentler than 45°. Table 7 lists the average slope orientations of the sculptures’ foreheads and noses, obtained using the same software. Table 8 presents a summary of the highest frequency data, as determined from histograms (Poluga, 2017), for use in rock mass characterization. Tables 9, 10, and 11 list the principal joint set (PJS) data for granite, schist, and pegmatite, respectively, as derived from stereonet analysis, for each of the four regions individually, as well as all discontinuity data combined for the four regions (note: pegmatite data were collected only in regions 1 and 2). Tables 9 through 11 indicate joint sets A and B to be the most dominant discontinuities.

Region 3 is the closest area to the sculptures among the four areas where discontinuity data were collected in the field. In total, 59 discontinuity orientations were measured in the field in region 3, and 53 discontinuity orientations were traced on the MORU sculptures using the LiDAR point cloud and Split-FX software (Split Engineering, 2014). Figure 8 shows that the discontinuity data clusters for the two data sets are generally similar, but their concentration levels are different.

Joint set B appears more prominently in region 3 than in the sculpture data. Because of the proximity of region 3 to the sculptures and the general similarity of

Table 7. Average slope orientations for the MORU sculptures, obtained using LiDAR and Split-FX software (Split Engineering, 2014).

Nose (Average) Sculpture Washington Jefferson Roosevelt Lincoln

Forehead (Average)

Right

Left

Front

Right

Left

Center

80°/211° 75°/182° 78°/156° 82°/232°

78°/066° 72°/023° 68°/013° 80°/081°

72°/139° 43°/098° 66°/094° 71°/145°

76°/195° 69°/145° 80°/140° 84°/222°

79°/070° 68°/042° 84°/062° 78°/099°

72°/136° 59°/099° 79°/083° 78°/146°

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Poluga, Shakoor, and Bilderback Table 8. Highest frequency data, obtained from histograms, for use in rock mass characterization.

Region at MORU 1 2 3 4

Rock Type

Degree of Weathering*

RQD** (%)

Continuity**

Granite Schist Pegmatite Granite Schist Pegmatite Granite

Moderate–high 90–100 3–10 m High–complete 25–50; 90–100 <1 m High–complete NA 1–3 m Moderate 90–100 3–10 High 50–75 <1 m High 75–90 <1 m; 1–3 m; 3–10 m Moderate 90–100 3–10 m

Schist Granite Schist

Moderate Moderate Moderate

50–75; 75–90 90–100 75–90

1–3 m 3–10 m 1–3 m

Spacing**

JRC***

0.6–2 m 200–600 mm NA 0.6–2 m 0.6–2 m 0.6–2 m 0.6–2 m

2–12 (slightly rough) 2–12 (slightly rough) 12–18 (rough) 2–12 (slightly rough) 1–2 (smooth) 2–12 (slightly rough) 1–2 (smooth); 2–12 (slightly rough)**** 0.6–2 m 1–2 (smooth) 0.6–2 m 18–20 (very rough) 200–600 mm 2–12 (slightly rough)

Infill**

Average Aperture**

None >5 mm None 0 mm None 0 mm None >5 mm None 0 mm None 0 mm–>5 mm None >5 mm None None None

0 mm >5 mm >5 mm

*Dearman (1976) classification. **Bieniawski (1989) classification. ***The JRC values were categorized based on the RMR roughness classifications. This study classified the JRC values based on judgement and field observations. The roughness categories by Bieniawski (1989) are in parentheses next to the JRC values. JRC values of 2 fell in the slightly rough category based on field observations. ****The slightly rough category was included for region 3 because even though it was not the highest frequency for the data, its frequency was very close to that of the smooth category.

their orientation data, we combined the two data sets to determine the principal joints, which were used in the kinematic analysis of the MORU sculptures.

To further validate the representativeness of the discontinuity data used for kinematic analysis, we compared our combined data with the data manually colTable 9. Principal joint set data for the granite rock in each region studied at MORU.

All regions Region 1 Region 2

Region 3

Region 4 Sculptures (using Split-FX)

Sculptures (Split-FX and region 3 data combined)

396

Principal Joint Set

Dip/Dip Direction

A B A B A B C D A B E F A B G A E H I A B E F I

83°/320° 78°/254° 85°/323° 74°/241° 82°/318° 76°/258° 86°/181° 31°/061° 82°/329° 80°/254° 75°/299° 88°/028° 84°/315° 78°/245° 81°/354° 82°/337° 60°/295° 27°/237° 80°/114° 83°/337° 82°/256° 71°/302° 89°/029° 74°/116°

lected by RESPEC, Inc. (Boyle and Vogt, 1992), as shown in Figure 9. Joint sets A, B, and F are common in both data sets. Table 12 compares the orientations of the joint sets determined by the two studies. Notice that the dip directions of joint sets B and F can be in opposite directions in the two studies because of their vertical nature (dip = 89°). Joint set N, which is nearly horizontal, appears prominently in RESPEC, Inc. (Boyle and Vogt, 1992), data, but its presence is subdued in the LiDAR-derived data (Figure 8b) beTable 10. Principal joint set data for the schist rock in each region studied at MORU.

All regions

Region 1

Region 2

Region 3 Region 4

Principal Joint Set

Dip/Dip Direction

A B D E A B D J K A D E J A B D A E J

83°/324° 68°/262° 39°/071° 77°/292° 86°/330° 68°/261° 41°/078° 85°/239° 32°/358° 83°/320° 30°/048° 82°/292° 88°/066° 83°/317° 63°/268° 40°/077° 77°/321° 78°/287° 75°/229°

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Regions 1 & 2

Region 1 Region 2

Principal Joint Set

Dip/Dip Direction

A E J L M B J A B E L M

69°/321° 84°/297° 72°/225° 69°/166° 87°/095° 81°/265° 72°/225° 68°/320° 49°/257° 84°/297° 74°/167° 87°/095°

cause the resolution capability of LiDAR data is not adequate to capture horizontal surfaces (Fisher et al., 2014). However, the nearly horizontal discontinuities at the sculptures site are not of concern with respect to slope instability. This study also shows the presence of joint sets E and I, which are not indicated by the RESPEC, Inc., data.

ROCK MASS CHARACTERIZATION OF MORU We used both the RMR system and the Q-system to characterize the rock mass at MORU in each of the four regions. For the RMR system, we assigned the ratings for each parameter based on the data in Table 8 and the UCS values determined in the laboratory.

The presence of water was negligible in the field; therefore, the rock was rated as dry. If there was a range in the data, we used an average value to assign the rating value. Table 13 presents the individual scores for the RMR parameters, summation of scores, and the final ratings for all four regions. All of the rocks studied in each of the four regions at MORU classify as good rock. For a rock classified as “good,” the estimated rock mass cohesion ranges from 300 kPa to 400 kPa, and the rock mass angle of internal friction ranges from 35° to 40° (Bieniawski, 1989). Table 14 summarizes the Q-system ratings and results, indicating the rock mass is of fair to good quality. Again, the data from Table 8 were used to assign the ratings for each parameter. The categorization of granite as “good” rock by both systems explains the longevity of this important monument since its construction and suggests its continued stability in the future, with proper maintenance and monitoring. The difference in the classifications between the two systems may be due to the fact that the SRF values in the Q-system calculations were based on the “low stress, near surface” value, as

there is not a value for surface excavations because the Q-system was developed for tunneling.

STABILITY EVALUATION OF MORU Kinematic Analysis Results We used Dips software (Rocscience, 2016) to conduct a kinematic analysis to determine the potential modes of failure along the slopes of the foreheads and noses of the MORU presidential sculptures. Input parameters for the analysis included the PJS orientation data, the slope orientations determined from the LiDAR, and the basic angle of friction.

For the sake of brevity, in this article, we present the detailed results of kinematic analysis for only the Washington sculpture. Tables 15 through 18 present the results of the analysis regarding planar sliding, wedge sliding, direct/oblique toppling, and flexural toppling for Washington’s forehead and nose. Figures 10 through 13 provide examples of stereoplots of kinematic analysis for the four types of failure for the right side of the nose of Washington’s sculpture. Considering other slopes, the analysis resulted in 24 stereoplots for Washington’s sculpture. Stereoplots for the remaining slopes on Washington’s sculpture were provided in Poluga (2017). Because of the large number of stereoplots involved, we summarize the results for Washington’s sculpture in the form of tables (Tables 15 through 18). Results for the Jefferson, Roosevelt, and Lincoln sculptures can also be found in Poluga (2017).

For the Washington sculpture, the results of kinematic analysis for the planar sliding (Table 15) and flexural toppling (Table 18) are presented as a percentage of poles within the individual PJS, whereas the results of kinematic analysis for the wedge sliding (Table 16) and direct/oblique toppling (Table 17) are presented as a percentage of the total intersections between the PJS planes. The percentages are an estimate of the probability of failure with respect to all of the individual PJS (in the case of planar sliding or flexural toppling), or with respect to the total number of intersections between the PJS planes (in the case of wedge sliding or direct/oblique toppling) (Rocscience, 2016). It should be noted that the designation of “right” or “left” in the tables and figures pertaining to kinematic analysis refers to the sculpture’s right or left (the opposite of the viewer’s perspective).

The results of kinematic analysis, which ignores cohesion, indicate the probability of planar sliding is the highest (20 percent) along the central slope of Washington’s forehead, the probability of wedge sliding is the highest (20 percent) along the slopes on the right side of the nose and on the left side of the forehead,

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Figure 8. Stereonet plots of the contoured discontinuity pole data at MORU: (a) manually collected data for region 3 and (b) LiDAR-derived data for the sculptures. The principal joint sets are labeled. The cluster analysis function in the Dips program (Rocscience, 2016) was used to determine the joint sets in order to account for scatter in the data.

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Figure 9. Stereonet plots of the contoured discontinuity pole data at MORU: (a) combined data from region 3 and the sculptures, and (b) manually collected data by RESPEC, Inc. (Boyle and Vogt, 1992). The principal joint sets are labeled. The cluster analysis function in the Dips program (Rocscience, 2016) was used to determine the joint sets in order to account for scatter in the data. Notice that joint sets A, B, and F are common in both sets.

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Poluga, Shakoor, and Bilderback Table 12. Comparison of the principal joint sets in granite determined using the RESPEC, Inc., data (Boyle and Vogt, 1992) and the principal joint sets determined from the data collected in this study.

Principal Joint Sets Determined for the Sculptures using RESPEC Data Principal Joint Set

Principal Joint Sets Determined for the Sculptures by This Study

Dip/Dip Direction

Principal Joint Set

Dip/Dip Direction

84°/334° 89°/078° — 89°/216° — 2°/325°

A B E F I —

83°/337° 82°/256° 71°/302° 89°/029° 74°/116° —

A B — F — N

Table 13. Rock Mass Rating (RMR) System parameter ratings and results.

RMR Parameter Ratings and Results Region 1 RMR Parameter

Granite

1 Strength of intact rock material 2 RQD 3 Spacing of discontinuities 4 Condition of discontinuities Continuity Aperture Roughness Infilling Weathering 5 Water RMR rating Class number Rock description

Schist

Region 2 Pegmatite

Granite

Schist

Region 3 Pegmatite

Granite

Schist

Region 4 Granite

Schist

7 20 15

7 12.5 10

7 17* 15*

7 20 15

7 13 15

7 17 15

7 20 15

7 15 15

7 20 15

7 17 10

2 0 3 6 2 15 70 II Good

6 6 3 6 0.5 15 66 II Good

4 6 5 6 0.5 15 76 II Good

2 0 3 6 3 15 71 II Good

6 6 1 6 1 15 70 II Good

4 4 3 6 1 15 72 II Good

2 0 2 6 3 15 70 II Good

4 6 1 6 3 15 72 II Good

2 0 6 6 3 15 74 II Good

4 0 3 6 3 15 65 II Good

*Ratings assigned based on region 2. These parameters were not measured in the field for pegmatite in region 1.

the probability of direct toppling is the highest (50 percent) along the slopes on the front and central areas of the nose and forehead, and the probability of oblique toppling is the highest (50 percent to 60 percent) along the right and left sides of the nose and forehead (Tables 15 through 18).

For the Jefferson sculpture,there is no probability of planar or wedge sliding, or it is quite low (<10 percent). However, the probability for direct and oblique toppling is high for the slopes along the front of the nose and the right side of the forehead (50 percent and 40 percent, respectively), whereas the probability

Table 14. Rock Mass Quality (Q) System parameter ratings and results.

Q-System Ratings and Results Region 1 Q-System Parameter 1 RQD 2 Joint set number (Jn) 3 Joint roughness number (Jr) 4 Joint alteration number (Ja) 5 Joint water reduction 6 Stress reduction factor Q-value Rock classification

Region 2

Region 3

Granite

Schist

Pegmatite

Granite

Schist

Pegmatite

Granite

Schist

Granite

Schist

100 4 2.75 1 1 2.5 28 Good

70 15 2.75 1 1 2.5 5 Fair

80* 4 3 1 1 2.5 24 Good

90 15 2.75 1 1 2.5 7 Fair

75 15 2 1 1 2.5 4 Fair

80 15 2.75 1 1 2.5 6 Fair

100 15 2.5 1 1 2.5 7 Fair

80 9 2 1 1 2.5 7 Fair

90 9 3.5 1 1 2.5 14 Good

85 9 2.75 1 1 2.5 10 Good

*Ratings assigned based on region 2. These parameters were not measured in the field for pegmatite in region 1.

400

Region 4

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Stability Evaluation of Mount Rushmore Table 15. Kinematic analysis results for planar sliding on the slopes of the Washington sculpture at MORU. “Set” refers to the PJS. The planes are plotted as pole vectors on the stereonet.

Kinematic Analysis Planar sliding

Washington Sculpture Slope Nose (right) Nose (left) Nose (front) Forehead (right) Forehead (left) Forehead (center)

Critical PJS Pole Vectors Set: F NA Set: I Set: F NA Set: I

of flexural toppling is the highest (60 percent) for the slopes along the right side of the nose and the left side of the forehead (Poluga, 2017).

The Roosevelt sculpture has little to no risk of planar sliding along any of the slopes analyzed, except for the slope along the right side of the forehead. The risk for wedge sliding exists for all slopes, but the probability of failure is <20 percent. The probability for direct and oblique toppling is high for the slopes along the front of the nose and the right side of the forehead (50 percent and 40 percent, respectively). The slopes along

2 0 1 1 0 2

Total Number of Pole Vectors in the Set

%

18 NA 10 18 NA 10

11 0 10 6 0 20

the right side of the nose and the left side of the forehead show the highest probability of flexural toppling (60 percent) (Poluga, 2017).

Kinematic analysis results for the Lincoln sculpture indicate the highest probability (40 percent) of planar sliding for the left side of the forehead, whereas the probability for wedge sliding is present for all slopes but is <20 percent. All of the slopes analyzed for the Lincoln sculpture have a potential for direct and oblique toppling. The slope along the left side of the nose has the lowest probability (10 percent) of direct

Figure 10. Stereonet plot of the kinematic analysis evaluating the potential for planar sliding along the slope on the right side of Washington’s nose. The probability of planar sliding is low along this slope. Dips program (Rocscience, 2016) was used to create the plot.

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Poluga, Shakoor, and Bilderback Table 16. Kinematic analysis results for wedge sliding on the slopes of the Washington sculpture at MORU. “Set” refers to the PJS. The planes are plotted as pole vectors on the stereonet.

Kinematic Analysis Wedge sliding

Washington Sculpture Slope Nose (right) Nose (left) Nose (front) Forehead (right) Forehead (left) Forehead (center)

Critical Intersections (PJS Planes) Sets: I & B; E & A Sets: I & A Sets: I & B Sets: I & B Sets: I & A; I & F Sets: I & B

toppling failure. The slopes along the right side of the nose and forehead have higher probability (20 percent), and the slopes along the front of the sculpture’s nose, the left side of the forehead, and the central slope of the forehead have the highest probabilities of direct toppling failure (40 percent, 50 percent, and 40 percent, respectively) with respect to the total number of intersections of the PJS. The probability of failure for oblique toppling along the slopes on the left side of the nose and the central part of the forehead

2 1 1 1 2 1

Total Number Intersections (PJS Planes)

%

10 10 10 10 10 10

20 10 10 10 20 10

is the highest (60 percent and 30 percent, respectively) with respect to the total number of intersections of the PJS. These results of kinematic analysis, as well as those provided in Poluga (2017), indicate that the potential for planar sliding, wedge sliding, and toppling failure exists for each of the MORU sculptures if cohesion along the discontinuities is ignored, but the potential varies for each of the slopes analyzed on the individual sculptures.

Figure 11. Stereonet plot of the kinematic analysis evaluating the potential for wedge sliding along the slope on the right side of Washington’s nose using the intersections of the principal joint set planes. There is a risk for wedge sliding on this slope as two intersections (sets A and E, and sets B and I) fall within the critical zone for wedge sliding. Dips program (Rocscience, 2016) was used to create the plot.

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Stability Evaluation of Mount Rushmore Table 17. Kinematic analysis results for direct and oblique toppling along the slopes of the Washington sculpture at MORU. “Set” refers to the PJS. The planes are plotted as pole vectors on the stereonet.

Kinematic Analysis

Washington Sculpture Slope

Direct & oblique toppling

Nose (right)

Nose (left) Nose (front)

Forehead (right)

Forehead (left) Forehead (center)

Total Critical Intersections (PJS Planes) & Base Plane Pole Vectors Direct toppling Oblique toppling Base plane (all pole vectors) Base plane (set: F) Direct toppling Oblique toppling Base plane (all pole vectors) Direct toppling Oblique toppling Base plane (all pole vectors) Base plane (set: I) Direct toppling Oblique toppling Base plane (all pole vectors) Base plane (set: F) Direct toppling Oblique toppling Base plane (all pole vectors) Direct toppling Oblique toppling Base plane (all pole vectors) Base plane (set: I)

1 5 15 2 1 6 7 5 1 11 4 0 6 16 2 1 6 6 5 1 11 4

Total Intersections (PJS Planes) & Base Plane Pole Vectors

%

10 10 112 18 10 10 112 10 10 112 10 10 10 112 18 10 10 112 10 10 112 10

10 50 13 11 10 60 6 50 10 10 40 0 60 14 11 10 60 5 50 10 10 40

Figure 12. Stereonet plot of the kinematic analysis evaluating the potential for direct and oblique toppling along the slope on the right side of Washington’s nose. There is a risk for direct and oblique toppling, as there are intersections between the principal joint set planes in the critical areas. There are both sliding and non-sliding release planes, as there are critical pole vectors that fall both within the friction cone (non-sliding release planes) and outside of the friction cone limits (sliding release planes). Dips program (Rocscience, 2016) was used to create the plot.

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Poluga, Shakoor, and Bilderback Table 18. Kinematic analysis results for flexural toppling along the slopes of the Washington sculpture at MORU. “Set” refers to the PJS. The planes are plotted as pole vectors on the stereonet.

Kinematic Analysis Flexural toppling

Washington Sculpture Slope Nose (right) Nose (left) Nose (front) Forehead (right) Forehead (left) Forehead (center)

Critical PJS Pole Vectors Set: F Set: B Set: F Set: A Set: E Set: I Set: A Set: F Set: B Set: F Set: A Set: E Set: I

Factor of Safety Calculations for Potential Failures The FS values against planar sliding and wedge sliding for the slopes on each of the sculptures, with the potential for these two types of failure, were calculated

8 15 2 16 10 1 7 7 15 1 13 10 1

Total Number of Pole Vectors in the Set

%

18 18 18 25 11 10 25 18 18 18 25 11 10

44 83 11 64 91 10 28 39 83 6 52 91 10

using the software programs RocPlane and Swedge, respectively (Rocscience, 2016). For the Washington sculpture, the FS values against planar sliding ranged from 0.3 to 0.4 (Table 19), and those for wedge sliding ranged from 0.2 to 1.3 (Table 20). There is only one

Figure 13. Stereonet plot of the kinematic analysis evaluating the potential for flexural toppling along the slope on the right side of Washington’s nose. Principal joint set I has a 44 percent probability of failure with respect to the PJS planes in the set. Dips program (Rocscience, 2016) was used to create the plot.

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Stability Evaluation of Mount Rushmore Table 19. Factor of safety (FS) against planar sliding along the slopes of the Washington sculpture.

Planar Sliding (Considering Principal Joint Sets)

Slope Analyzed

Sliding Potential

Principal Joint Set

Critical Plane Dip (degrees)

Slope Face Dip (degrees)

Upper Slope Dip (degrees)

FS

Nose (right) Nose (right) Nose (left) Nose (front) Forehead (right) Forehead (left) Forehead (center) Forehead (center)

Yes Yes No Yes Yes No Yes Yes

F F NA I F NA I I

71 73 NA 67 71 NA 67 68

80 80 78 72 76 79 72 72

57 57 57 57 5 5 5 5

0.3 0.3 NA 0.4 0.3 NA 0.4 0.3

potential wedge failure that had a FS <1, and it is on the slope on the left side of Washington’s forehead.

Tables containing FS values for various slopes of the Jefferson, Roosevelt, and Lincoln sculptures are available in Poluga (2017). For the Jefferson sculpture, the FS is 0.8 against planar sliding, and it ranges from 1.2 to 1.3 against wedge sliding. For the Roosevelt sculpture, the FS values range from 0.2 to 0.8 against planar sliding, and they range from 0.2 to 1.3 against wedge sliding. The values <1 are along the slopes on the right side of the nose and forehead, and on the left and center of the forehead. Lincoln’s sculpture has FS values ranging from 0.1 to 0.8 against planar sliding and from 0.2 to 1.3 against wedge sliding. The values <1 are along the slopes on the left side of the nose and forehead, and on the center of the forehead (Poluga, 2017).

We did not consider the effect of water on FS values because we did not observe significant water seeps during field studies conducted during the month of July 2014. Monthly precipitation averages for MORU from 1981 to 2011, based on data from National Oceanic and Atmospheric Administration (2014) and the Weather Channel (2014), indicate that May and June are the wettest months (10.7 cm and 8.7 cm precipitation, respectively), followed by July (7.4 cm).

Because discontinuities traversing the memorial are sealed, and those away from the memorial are generally open, water does not appear to affect the stability

of the sculptures and was not considered in FS calculations.

The results of the FS calculations show that many FS values against planar and wedge sliding are <1, and sometimes <<1 (as low as 0.1), indicating unstable to highly unstable conditions. Yet, there have been no failures observed in the sculptures, indicating that the factor of safety is >1. The low FS values against planar and wedge sliding are due to: (1) the steep nature of discontinuities at the MORU site, with nearly 65 percent of the discontinuities being steeper than 70°, (2) ignoring cohesion in the FS calculations, and (3) ignoring the roughness of the discontinuities.

Drilling is not allowed at the MORU site, nor is collection of hand specimens using a geologic hammer. Because of these constraints, it was not possible for us to determine the cohesion and friction angle values along the actual discontinuity surfaces using laboratory tests.

The fact that no failures have occurred at the MORU site clearly shows that both cohesion and friction act along the discontinuities to contribute to the stability of the MORU sculptures. Therefore, we performed a back-analysis, using RocPlane and Swedge programs (Rocscience, 2016), to determine the strength parameters values required to maintain stability. The purpose was to determine the required values of cohesion to yield a FS >1 (1.01–1.4) for varying values of friction angle (40°, 45°, 50°) or to determine the required val-

Table 20. Factor of safety (FS) against wedge sliding along the slopes of the Washington sculpture.

Wedge Sliding (Considering Principal Joint Sets) Slope Analyzed Nose (right) Nose (right) Nose (left) Nose (front) Forehead (right) Forehead (left) Forehead (left) Forehead (center)

Sliding Potential

Principal Joint Sets (Critical Planes 1 & 2)

Yes Yes Yes Yes Yes Yes Yes Yes

I&B E&A I&A I&B I&B I&A I&F I&B

Critical Plane 1 Critical Plane 2 Upper Slope Dip/Dip Direction Dip/Dip Direction Dip/Dip Direction (degrees) (degrees) (degrees) FS 74/116 71/302 74/116 74/116 74/116 74/116 74/116 74/116

82/256 83/337 83/337 82/256 82/256 83/337 89/029 82/256

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57/139 57/139 57/139 57/139 5/136 5/136 5/136 5/136

1.3 1.0 1.2 1.3 1.3 1.2 0.2 1.3

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Poluga, Shakoor, and Bilderback Table 21. Input data for the back-analysis calculations for planar sliding. The numbers in the slope column are for reference and use in Table 22.

Sculpture Washington

Jefferson Roosevelt

Lincoln

Slope

Principal Joint Set

Critical Plane Dip (degrees)

Slope Face Dip (degrees)

Upper Slope Dip (degrees)

Slope Height (m)

Nose (right)-1 Nose (right)-2 Nose (front)-1 Forehead (right)-1 Forehead (center)-1 Forehead (center)-2 Forehead (center)-1 Nose (front)-1 Forehead (right)-1 Forehead (right)-2 Forehead (right)-3 Forehead (right)-4 Forehead (center)-1 Nose (right)-1 Nose (right)-2 Nose (right)-3 Nose (right)-4 Forehead (right)-1 Forehead (right)-2 Forehead (right)-3 Forehead (left)-1 Forehead (left)-2 Forehead (left)-3 Forehead (left)-4

F F I F I I I I A I I I I B B B F F F F I I I I

73 71 67 71 67 68 48 48 67 74 77 78 73 68 73 76 78 71 73 83 48 68 72 73

80 80 72 76 72 72 59 66 80 80 80 80 79 82 82 82 82 84 84 84 78 78 78 78

57 57 57 5 5 5 5 45 11 11 11 11 11 61 61 61 61 13 13 13 13 13 13 13

4.0 4.0 4.0 7.0 7.0 7.0 6.7 2.1 5.5 5.5 5.5 5.5 5.5 4.3 4.3 4.3 4.3 10.7 10.7 10.7 10.7 10.7 10.7 10.7

Table 22. Back-analysis results for planar sliding. The table also shows the FS values when c = 0. Note: c = cohesion, Ф = friction angle.

Sculpture Washington

Jefferson Roosevelt

Lincoln

406

Slope Nose (right)-1 Nose (right)-2 Nose (front)-1 Forehead (right)-1 Forehead (center)-1 Forehead (center)-2 Forehead (center)-1 Nose (front)-1 Forehead (right)-1 Forehead (right)-2 Forehead (right)-3 Forehead (right)-4 Forehead (center)-1 Nose (right)-1 Nose (right)-2 Nose (right)-3 Nose (right)-4 Forehead (right)-1 Forehead (right)-2 Forehead (right)-3 Forehead (left)-1 Forehead (left)-2 Forehead (left)-3 Forehead (left)-4

FS When c = 0 MPa; Ф = 40o

c Required for FS > 1 When Ф = 40° (kPa)

c required for FS > 1 When Ф = 45° (kPa)

c required for FS > 1 When Ф = 50° (kPa)

0.3 0.3 0.4 0.3 0.4 0.3 0.8 0.8 0.4 0.2 0.2 0.2 0.3 0.3 0.3 0.2 0.2 0.3 0.3 0.1 0.8 0.3 0.3 0.3

4.56 5.52 2.83 5.52 5.99 4.08 3.65 1.78 9.74 5.52 2.97 2.06 5.42 8.35 6.24 4.51 3.17 21.12 18.82 2.16 13.44 15.22 10.27 8.8

4.27 5.09 2.54 5.09 4.46 3.74 1.58 0.77 8.69 5.23 2.83 1.97 5.04 7.54 5.81 4.27 3.02 19.44 17.62 2.11 5.95 13.68 9.55 8.21

3.89 4.61 2.16 4.61 3.48 3.26 0.00 0.00 7.49 4.80 2.69 1.87 6.58 6.58 5.38 4.03 2.88 17.52 16.32 2.06 0.00 11.81 8.64 7.58

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Ф required for FS > 1 When c = 0 MPa (degrees) 74 72 68 72 68 69 49 49 68 75 78 79 74 69 74 77 79 72 74 84 49 69 73 74

Stability Evaluation of Mount Rushmore Table 23. Input data (excluding slope orientation) for the back-analysis calculations for wedge sliding.

Sculpture Washington Roosevelt

Lincoln

Slope Analyzed

Principal Joint Sets (Critical Planes 1 & 2)

Critical Plane 1 Dip/Dip Direction (degrees)

Critical Plane 2 Dip/Dip Direction (degrees)

Upper Slope Dip/Dip Direction (degrees)

Slope Height (m)

Nose (right) Forehead (left) Nose (right) Forehead (right) Forehead (left) Forehead (center) Nose (right) Nose (left) Forehead (right) Forehead (left) Forehead (center)

E&A I&F I&F I&F I&F I&F E&A I&F E&A I&F I&F

71/302 74/116 74/116 74/116 74/116 74/116 71/302 74/116 71/302 74/116 74/116

83/337 89/029 89/029 89/029 89/029 89/029 83/337 89/029 83/337 89/029 89/029

57/139 5/136 45/094 11/083 11/083 11/083 61/145 61/145 13/146 13/146 13/146

4.0 7.0 2.1 5.5 5.5 5.5 4.3 4.3 10.7 10.7 10.7

ues of friction angle when cohesion = 0. Tables 21 and 22 present the input data and results of back-analysis calculations for planar sliding, respectively, whereas Tables 23 and 24 present the input data and results of back-analysis for wedge sliding, respectively.

Table 22 shows that when assuming friction angles of 40°, 45°, and 50° along the discontinuities, the highest cohesion values required to increase the FS >1 against planar sliding are 21.12 kPa, 19.44 kPa, and 17.52 kPa, respectively. A figure in Hoek and Bray (1981) provided cohesive strength (c) values obtained by back-analysis of slopes in different rock types. From this figure, the c value mobilized at failure for weathered granite slopes is 24.00 kPa. This value suggests that the highest c value from the back-analysis is a reasonable value of c for the granite rock at MORU. When considering cohesion = 0, the friction angle required for FS >1 against planar sliding ranges from 49° to 84° (Table 22). The majority of the friction angle values are very high and improbable, indicating that both

cohesion and friction along the discontinuities are required for a FS >1.

The results in Table 24 indicate that at friction angle values of 40°, 45°, and 50° along the discontinuity planes, the highest cohesion values required to increase the FS >1 against wedge sliding are 3.22 kPa, 3.02 kPa, and 2.78 kPa, respectively. These are reasonable cohesion values, as they are less than 24.00 kPa, referred to previously for weathered granites (Hoek and Bray, 1981). When considering cohesion = 0, the friction angle values required for FS >1 range from 41° to 75°. The majority of the friction angle values are very high (Table 24) and improbable, again indicating that both cohesion and friction along the discontinuities are required for FS >1 against wedge sliding.

Overall, the back-analysis results indicate that our use of cohesion = 0 and friction angle = 40° in the FS calculations underestimates the strength parameters because failures on MORU sculptures have not

Table 24. Back-analysis results for wedge sliding. The table also shows the FS values when c = 0. Note: c = cohesion, Ф = friction angle.

Sculpture Washington Roosevelt

Lincoln

Slope

Wedge Contact

FS When c = 0 MPa; Ф = 40°

Nose (right) Forehead (left) Nose (right) Forehead (right) Forehead (left) Forehead (center) Nose (right) Nose (left) Forehead (right) Forehead (left) Forehead (center)

Both planes Both planes Plane 1 only Plane 1 only Both planes Both planes Both planes Both planes Both planes Both planes Plane 1 only

1 0.2 0.2 0.2 0.2 0.2 1 0.2 1 0.2 0.2

c Required for FS > 1 When Ф = 40° (kPa)

c Required for FS > 1 When Ф = 45° (kPa)

c Required for FS > 1 When Ф = 50° (kPa)

0.05 0.24 0.10 2.93 1.63 1.2 0.10 0.91 0.19 3.22 2.54

0.00 0.25 0.10 2.74 1.54 1.15 0.00 0.86 0.00 3.02 2.35

0.00 0.20 0.10 2.54 1.44 1.06 0.00 0.82 0.00 2.78 2.21

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Ф Required for FS > 1 When c = 0 MPa (degrees) 41 74 75 75 74 74 41 74 41 74 75

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Figure 14. Overall slope probabilistic analysis for the Washington sculpture, considering the static condition. The legend indicates the factor of safety (FS) values, and the various colored slip surfaces are the global minimum slip surfaces generated by this method. The best fit for the FS values is a lognormal distribution. Scale is in meters.

been observed, i.e., FS cannot be <1. The results imply that either the cohesion along discontinuities is >0, or the friction angle is >40°, and most likely both cohesion and friction angle play a role in maintaining the stability of the MORU sculptures.

considered acceptable. The other two measures of safety for the overall slopes of the MORU sculptures indicate that the slopes are safe, as all of the FS values are >1, and the PF values were all 0 percent.

Overall Slope Probabilistic Analysis of the MORU Sculptures

DISCUSSION

Figures 14 and 15 display the results of the overall slope probabilistic analysis, for static and seismic conditions, respectively, for the Washington slope. Similar figures for other sculptures are available in Poluga (2017). Table 25 summarizes the results in terms of FSM, RI, and PF for all four of the MORU sculptures.

An RI value >3 indicates a safe slope (Rocscience, 2016). The results show that most of the RI values are >3; however, one falls slightly below this value for the seismic condition evaluated for the Lincoln sculpture (Table 25). Since the value is very close to 3, it is

The second-cycle slake durability index values between schist sample 1a and granite sample 2 (see Table 2 for sample designations) indicated no potential for undercutting, whereas the values between schist sample 1b and granite sample 2 indicated a slight potential for undercutting. Undercutting is not prominent at MORU; however, considering the importance of the sculptures, we recommended that differential weathering between schist and granite rock from which the sculptures are carved be monitored on a longterm basis by periodically measuring the depth of

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Stability Evaluation of Mount Rushmore

Figure 15. Overall slope probabilistic analysis for the Washington sculpture, considering the seismic condition (horizontal and vertical seismic load coefficients = 0.14). The legend indicates the FS values, and the various colored slip surfaces are the global minimum slip surfaces generated by this method. The best fit for the FS values is a lognormal distribution. Scale is in meters.

freeze-thaw test results confirmed this for the granite rock, as the specimens had relatively low average percent change in mass (<2 percent) after 300 freeze-thaw cycles. The schist rock, however, had a higher average percent change in mass (>5 percent). The results con-

undercutting using a tape measure and by maintaining a photographic record.

The relatively high density (2.6 Mg/m3 ) and low absorption (<1 percent) values suggest that the intact granite is not subject to freeze-thaw damage. The

Table 25. Results of the overall slope probabilistic analysis for the MORU sculptures. The Slide program (Rocscience, 2016) indicates the statistical distribution that best fits the factor of safety distribution for the reliability index calculations. Therefore, only the results for the best-fit statistical distribution are listed, and the others are marked as NA. Reliability Index (RI)

Mean Factor of Safety (FSM) Sculpture Washington Jefferson Roosevelt Lincoln

Normal

Probability of Failure (PF; %)

Lognormal

Static

Seismic

Static

Seismic

Static

Seismic

Static

Seismic

3.3 4.5 5.2 4.4

2.6 3.6 4.1 3.4

NA NA NA 3.3

NA 3.7 NA 2.9

6.0 7.8 5.7 NA

4.6 NA 4.7 NA

0 0 0 0

0 0 0 0

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firm that the granite, in its intact form, is not subject to freeze-thaw damage, whereas the schist is (especially schist sample 1b).

The kinematic analysis results indicate that the potential for planar sliding, wedge sliding, and toppling failure exists for various slopes on each of the MORU sculptures. Many FS values against planar and wedge sliding are <1, indicating unstable conditions. However, failures have not been observed on the sculptures.

This may be due to several reasons: (1) FS calculations did not consider cohesion or roughness characteristics of discontinuities, both of which would result in higher shear strength and, hence, higher FS values; (2) there may be areas along the discontinuities where there are still intact rock segments, causing the slopes to remain stable. Back-analysis results demonstrate that both cohesion and friction are required to maintain stability.

The overall slope probabilistic analysis, evaluating the effects of seismic loading on the stability of the entire slopes on which the MORU sculptures are located, indicates that all of the slopes are safe against failure. This study assumes that if the slopes are stable against the earthquake force modeled, the vibrations produced by air blasts from fireworks will not cause failures along the slopes. In light of these results, vibrations caused by the Fourth of July fireworks celebrations at MORU do not pose a threat to the stability of the sculptures.

This study had the following limitations: (1) Access to the sculptures for examination and measurement of discontinuities was not available; (2) samples containing discontinuity surfaces from the MORU area were not available for strength testing because of sampling restrictions; and (3) there are other slopes on the sculptures, such as eyebrows, cheeks, and lips, that were not considered for stability evaluation in this study because of time constraints. Additional research, involving direct access to the sculptures, is required to validate the results of this study.

CONCLUSIONS The main conclusions of this research are: 1. The second-cycle slake durability results indicate a slight potential for undercutting of granite by schist at MORU. Due to the importance of the sculptures, it is recommended that the schist band that is present in the bust area of Washington’s sculpture be monitored for the amount of undercutting on a yearly basis.

2. The intact granite rock, due to its relatively high average density (2.6 Mg/m3 ), low average absorption (0.9 percent), and low average percent mass 410

3.

4.

5.

6.

change after ∼300 freeze-thaw cycles (<2 percent), is not subject to freeze-thaw damage. The intact schist rock has relatively high average density (2.8 Mg/m3 ) and low average absorption (1.1 percent).

However, the freeze-thaw durability tests indicated the rock is subject to freeze-thaw damage due to its relatively high average percent mass change after ∼300 freeze-thaw cycles (>5 percent).

The granite, schist, and pegmatite rock masses studied in the four regions at MORU classify as good rock according to the RMR system and fair to good rock according to the Q-system.

The kinematic analysis results indicate that the potential for planar sliding, wedge sliding, direct/oblique toppling, and flexural toppling exists for various slopes on each of the MORU sculptures.

Wedge sliding and toppling failures have the highest potentials. The FS values against planar and wedge sliding, ignoring cohesion, range from 0.1 to 0.8 and from 0.2 to 1.3, respectively. Since failures have not occurred on the sculptures, ignoring cohesion for the steeply dipping discontinuities is not an appropriate approach for evaluating stability of MORU sculptures. Back-analysis completed for the slopes with FS <1 indicates that both cohesion and friction, acting along the discontinuities, contribute to the stability of the sculptures.

The areas on the MORU sculptures that have the potential for planar sliding include the slope on the central area of the forehead of the Washington sculpture and the slope on the right side of the forehead of the Roosevelt sculpture. Similarly, areas with a potential for wedge sliding include the slope on the left side of the forehead of the Washington sculpture, the right side of the nose on the Roosevelt sculpture, and the right, left, and central slopes on the forehead of the Roosevelt sculpture. These areas would benefit from further studies of the condition of the discontinuities to determine if monitoring is necessary.

The overall slope probabilistic analyses results for static and seismic conditions indicate that the entire slopes on which MORU sculptures are located are stable based on the FSM, RI, and PF values.

ACKNOWLEDGMENTS

This research was funded by the U.S. Department of the Interior, National Park Service Grant No. USDI/NPS 810795473. We greatly appreciate the help provided by Bruce A. Weisman, Integrated Resource Program Manager, Mount Rushmore National Memorial, throughout the field investigations stage of this study. We would also like to thank Dr. Yonathan Admassu, James Madison University, VA, for his help

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during the field work stage of the study. The constructive comments from three anonymous reviewers were very helpful in improving the quality of this manuscript. We extend our gratitude to them.

Disclaimers The authors are solely responsible for the contents of this article. The conclusions drawn from this study are based on the data derived from field and laboratory investigations, and subsequent computer analyses, and may change as additional data become available. Also, the conclusions pertain to the existing conditions of MORU and do not take into account the temporal changes that may occur with the aging of the monument.

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Characteristics and Numerical Runout Modeling Analysis of the Jiweishan Landslide, Chongqing, China GAO YANG Institute of Geo-Mechanics, Chinese Academy of Geological Sciences (CAGS), Beijing, 100081, China; China University of Geosciences, Beijing 100083, China; Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences

YIN YUEPING1 China Institute of Geo-Environment Monitoring, Beijing 100081, China

LI BIN FENG ZHEN HE KAI Institute of Geo-Mechanics, Chinese Academy of Geological Sciences (CAGS), Beijing 100037, China, and MLR Key Laboratory of Neotectonic Movement and Geohazards

Key Terms: Rapid Long Runout, Landslide, DANW, Numerical Simulation ABSTRACT In this paper, we conduct a numerical simulation analysis of the catastrophic 2009 Jiweishan landslide in Wulong, Chongqing, China, which was characterized by its rapid velocity and long runout. The study is based on detailed geological field survey data and employed DANW (Dynamic Analysis of Landslides) runout analysis software to simulate the debris-flow process and accumulation characteristics of the landslide. The best match to the actual characteristics of the debris-flow process was obtained by applying the friction rheological model in the source and accumulation areas, and the Voellmy model in the scraping area. The simulation estimated the duration of the landslide to be approximately 101 seconds, with a maximum velocity of 39.2 m/s. The initial source volume, total accumulation volume, and runout distance obtained from the DANW numerical simulation results were in good agreement with the corresponding characteristics of the actual event. The modeling scheme and parameters employed here are expected to help clarify the motion of the Jiweishan landslide and improve the accuracy of future hazard assessments in areas with similar geological, topographical, and climatic features.

1

Corresponding author email: yyueping@mail.cgs.gov.cn

INTRODUCTION Debris flows have caused significant damage worldwide. Rapid and long-runout debris flows, in particular, have resulted in extensive infrastructural damage and loss of life. Therefore, the analysis and risk assessment of debris-flow geohazards have been subjects of intense interest worldwide for many years (Eisbacher, 1979; Sassa, 1988; Hungr and Evans, 1996; McDougall and Hungr, 2004; Boultbee, 2005; Wong et al., 2006; Wooten et al., 2006, 2008; Sosio et al., 2008; Bruckno, 2011; Xing et al., 2016). As early as the end of the 19th century, Buss and Heim (1881) investigated the 1881 Elm landslide in Switzerland and proposed the grain flow model. Kent (1966) proposed the air lubrication model after analyzing several rapid and long-runout debris flows, including the Saidmarreh landslide in Iran, the Frank landslide in Canada, and the Madison Canyon landslide in the United States.

Eisbacher (1979) derived the earliest energy transfer model. Later, Sassa (1988) presented a model based on excess pore-water pressure at the slip plane. These four models have played a vital role in studies focused on rapid and long-runout landslides (Zhang et al., 2010).

Hungr and Evans (1996, 2004) established the dynamic analysis (DAN) numerical computing method based on the Lagrange difference method, which enables the selection of different debris-flow constitutive equations during the debris-flow runout process and within the flow mass itself, and so it can simulate debris-flow motion and accumulation. Sosio et al. (2008) modified the DAN three-dimensional (3D) model by combining the two most common debris-flow constitutive

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rheological models (frictional rheology and Voellmy rheology) and applied the model to a dynamic analysis of the 2004 Punta Thurwieser rock avalanche in Italy.

Evans et al. (2001) simulated the 1984 Mount Cayley rock avalanche in Canada with the DAN model, employing three constitutive equations, including the friction, Bingham, and Voellmy rheological models. According to the results, the Voellmy rheological model provided the best representation of the debris-flow and accumulation processes of that particular debris flow.

The present study presents a numerical simulation analysis of the catastrophic 2009 Jiweishan landslide in Wulong, Chongqing, China, which was characterized by high velocity and a long runout distance. DANW runout analysis software was used to simulate the motion of the landslide based on detailed geological field survey data. In preliminary work, we compared the characteristics of several similar rapid and longrunout landslides occurring in the mountains of Southwest China, i.e., the Touzhai landslide (Xing and Yin, 2009), the Wenjiagou landslide (Xing et al., 2017), the Donghekou landslide (Yin et al., 2011b), and the Sanxicun landslide (Yin et al., 2016; Gao et al., 2017). In addition, we analyzed the selection of rheological models in the published literature. Our analysis suggests that the frictional model and the Voellmy model perform better for the back analysis of landslide movement, and that combinations of these models are more suitable for simulating the overall landslide process. Accordingly, different rheological models were employed in the landslide source area, dynamic scraping area, and debris accumulation areas to best capture the debris-flow characteristics of the actual event. The modeling scheme and parameters employed are expected to help clarify the motion of this landslide and improve the accuracy of future hazard assessments in areas with similar geological, topographical, and climatic features. The proposed model selection method is expected improve the precision of hazard zonation mapping so as to better predict the landslide hazard level of un-failed slopes.

JIWEISHAN LANDSLIDE On 5 June 2009, a catastrophic landslide occurred in Wulong County, Chongqing, in the southwest of China. Of those people in the direct path of the landslide, 74 were killed, and 8 were injured. Its geographical location was latitude 29◦ 14� N and longitude 107◦ 26� E. The site of the landslide is illustrated in Figure 1a, and its general appearance is indicted by the images in Figures 1b and 1c. The landslide involved approximately 5 million cubic meters of displaced material from the source area. The total accumulation represented about 7 million cubic meters of material and covered an area of 0.48 million square meters. This in414

dicates that the volume of the accumulation was about 1.2–1.4 times the volume of the original rock mass of the source area (Yin et al., 2016), i.e., a volume expansion coefficient of 1.24. This represents an original rock mass volume of about 6.2 million cubic meters and about 0.8 million cubic meters of scraped material.

The Jiweishan landslide initiated as a rock slide in the source area and transitioned into a debris flow in the scraping and accumulation areas. The length of the accumulated body was 2,200 m (Xu et al., 2009), with a fall height of 700 m, which represents a Fahrböschung (reach) angle of 18◦ . According to Fahrböschung angle, this landslide is categorized as a long-runout landslidedebris flow (Zhang et al., 2010). The original surface of the accumulation area before the landslide was covered by unconsolidated Quaternary sediments with a thickness of less than 2 m, and the soil layer consisted of clay interbedded with fragmented rocks.

The main causes of the initial instability of the Jiweishan landslide have been attributed to a geological structure dominated by weak interlayers, underground mining activities, and karst (Feng et al., 2012). The failure mode of the Jiweishan landslide was thick-layer rockslide failure, where the slope failure was confined within four planes and moved in the direction of an apparent bedding dip (Yin et al., 2011a; Feng et al., 2016). This represents a typical debris-flow disaster in the mountainous areas of southwestern China, which often involve mining activities leading to rapid and long-runout debris flows.

Geologic Background The site of the Jiweishan landslide is located in the Qiyaoshan Fault Belt, extending from the Chengdu Plain to the Yungui Plateau in an area of highly eroded low mountains and hills. The top of Jiweishan Hill was steep, while the lower elevations had relatively gentle slopes, and the overall failure formed along a fissure oriented in a NE-SW direction (Yin et al., 2011a). The geological structure is divided into three sections, as shown in Figure 2. The top-most section, denoted as P1 m, is a gray pelmicrite layer with a thickness of about 50 m, which included karst gap formations and distributed cave formations having diameters about tens of meters.

The middle section, denoted as P1 q, is composed of a dark gray bituminous limestone about 90–130 m in thickness, divided into upper, middle, and lower layers, denoted as P1 q3 , P1 q2 , and P1 q1 , respectively. Soft rock strata of carbonaceous shale are also observable in P1 q. The lower section, denoted as P1 l, is about 10–14 m in thickness and is composed of hydromica clay rock containing hematite. The exploitation of the hematite by mining activities forced adjustments in the mountain stress and accelerated the level of instabil-

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Figure 1. Site of the 2009 Jiweishan landslide: (a) location of Jiweishan landslide; (b) aerial image of the landslide; (c) image of the landslide from the tail of the accumulation area.

ity leading up to this landslide. The substrate is S3 hj celadon chiltern shale (Xu et al., 2009). In this landslide, the Permian limestone of P1 m was the main body material at the source, and the soft carbonaceous shale at the bottom of P1 q3 was the dominant material in the rupture surface. Underground mining of hematite had been conducted in the Jiweishan area since the 1920s, and long wall panels had been excavated beneath the rockfall source area about 130 m below the ground surface and 140 m behind the cliff face. Several layers of muddy and carbonaceous shale were sandwiched in the

Figure 2. Geological structure of the Jiweishan landslide (the topmost section is Permian Maokou gray pelmicrite layer, denoted as P1 m; the middle section is Permian Qixia dark gray bituminous limestone, denoted as P1 q; the lower section is Permian Liangshan hydromica clay rock, denoted as P1 l).

limestone during formation under a marine sedimentary environment. The rock mass included two sets of dominant tectonic joints, denoted as T0 and T1 , with dip directions and dip angles of 77◦ ∠80◦ and 185◦ ∠75◦ , respectively.

POST-FAILURE BEHAVIOR OF THE JIWEISHAN LANDSLIDE The basic model of the Jiweishan landslide involves the instant failure of key barrier rock blocks owing to the incessant creep of rear blocks. The displaced material at the source then slides along the soft carbon and pyrobituminous shale layer and rapidly disintegrates.

This material then gains considerable kinetic energy after falling from a scarp having about a 50 m height.

During this process, the extruding ridge and abundant surface rubble above the rupture collapse, adding to the landslide mass. Subsequently, the rockfall material crosses the Tiejiang gully with high velocity, is redirected downstream owing to resistance from the steep slope on the opposite side of the ravine, and then forms the accumulation area (Yin et al., 2011a). Based on comprehensive mapping and geological field surveys, in addition to information obtained from satellite remote sensing and unmanned aerial vehicle data, the Jiweishan landslide area was divided into the source area, the scraping area, the accumulation area, and the landslide body sprinkling area, as shown in Figure 3.

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Figure 3. (a) Jiweishan landslide plan, showing longitudinal section I–I� and cross sections II–II� , III–III� , and IV–IV� , and (b) landslide debris-flow longitudinal section (section I–I� ).

Landslide Source Area

Scraping Area

The source area of the Jiweishan landslide is shown in Figure 4a. In the field investigation, a laser range finder and global positioning system handset were employed to measure the geometrical characteristics of the landslide source area. Meanwhile, satellite remote sensing image technology was used in the laboratory.

The source area consisted of a triangular area about 240 m in length in the front and a quadrangular area about 480 m in length in the rear, with a slope angle of approximately 24◦ . As such, the total basal surface of the landslide source area was about 720 m in length.

The quadrangular area was about 152 m in width on the south side and 125 m in width on the north side.

The rear elevation of the rupture surface was approximately 1,400 m, while the toe of the rupture surface was approximately 1,280 m in elevation, for a height difference of 120 m. The main body had an average thickness of 60 m.

According the sliding direction of this landslide, the rear elevation of the scraping area was approximately 1,280 m, while the front elevation was approximately 980 m, for a height difference of 300 m. The slope angle was as great as 29◦ for a distance of about 530 m.

The landform was thus favorable for initiating a rapid landslide. The debris flow of the rockslide directly impacted the west side of the Tiejiang gully in 30◦ direction angle and turned with a 40◦ deflection at an elevation of 1050 m, as discussed previously. Based on these field measurements of the scraping area and considerations of kinetic energy, we estimated that the maximum rock flow velocity at the collision point was 43.5 m/s using a frictional coefficient of 0.2. The displaced source material at high elevation collided with the surrounding lower elevation material in the scraping area and removed a 13-m-thick surface layer of soil and rock, along with vegetation, and formed a triangular

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Figure 6. Cross section of the landslide scraping area (section III– III� in Figure 3).

Figure 4. Source area of the Jiweishan landslide: (a) overhead image of the source area; (b) cross-sectional illustration of the source area (section II–II� ).

scraping slope with a length of 500 m (Figures 5 and 6). Approximately 0.8 million cubic meters of scraped debris was thereby added to the sliding body volume.

The scraping area served as a transition zone between the initial rock landslide and the resulting debris flow and greatly contributed to its disastrous effects.

Accumulation Area The Jiweishan landslide debris flow formed an inclined and distorted “funnel”-shaped accumulation

area (Figures 1b and 7). The accumulation area was about 1,000 m in length and was at most 470 m wide, and the debris field was 55 m thick at its greatest accumulation thickness. In the accumulation area, the debris flow moved downwards in a NE direction along the ravine, then slightly NW, and then turned to the NE again. The debris flow deposited material along the ravine path and formed the accumulation area. The rear elevation of the accumulation area was approximately 980 m, and the front elevation was approximately 750 m, for a height difference of approximately 230 m. The slope angle was as great as 13◦ . Site investigation revealed that the deposit mass mainly consisted of boulders, very large block stones, gravel, and silty clay. Among these, the content of boulders and block stones was approximately 25 percent, and the sizes of the block stones ranged between 0.5 m and 15 m, as indicated by Figure 8.

DYNAMIC ANALYSIS DANW treats the slip mass as a fluid with variable rheological properties on the basis of a continuous medium. DANW divides the slip mass into N blocks

Figure 5. Source and scraping areas of the Jiweishan landslide: (a) before the event; and (b) after the event.

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Figure 7. Accumulation depth contour lines of the Jiweishan landslide debris flow obtained from remote sensing (the units of the accumulation depth contours are meters).

and obtains the position, thickness, and velocity of each block by solving the equilibrium and kinematic equations established for each block using the Lagrange finite difference method.

Dynamic Model In DANW analysis, the substrate resistance is obtained according to the rheological properties of the landslide materials. Using DANW, semi-empirical dynamic back-analysis can be employed using historical data such as volume, fall height, and runout, or, alter-

natively, numerical modeling can be employed directly for runout analysis (Hungr et al., 2005). The 3D analytical model shown in Figure 9 was established in accordance with the two-dimensional conditions illustrated by section I–I in Figure 3. Variable path widths were imposed according to aerial views of the landslide. The dynamic model was governed by internal and basal rheological relationships. The frictional and Voellmy rheologies were found to most accurately represent the recorded events (Francis and Baker, 1977; Boultbee, 2005; Xing et al., 2016). These rheological models are presented as follows.

Frictional Model The frictional model is usually employed to represent the initial motion of a debris flow, as could be expected given the granular nature of both the rock material and most of the foundation debris (Hungr et al., 2002). As such, frictional rheology is suitable for the simulation of open hillside failures where turbulent flow has not developed and the landslide mass has good integrity.

This opinion has also been expressed by the Geotechnical Engineering Office Report of Hong Kong (GEO,

Figure 8. Accumulation area of the Jiweishan landslide: (a) photography of the remaining debris; and (b) cross section of the ravine deposits (section IV–IV ).

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Figure 9. Schematic of the three-dimensional DANW model employed for the Jiweishan landslide debris flow along the twodimensional pathway illustrated by section I–I in Figure 3.

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2011, 2012, 2013). Assuming a resisting shear force, the frictional rheology is expressed as follows: = (1 â&#x2C6;&#x2019; ru ) tan ,

(1)

where stands for shear stress at the bottom of the sliding mass in frictional rheology, and the pore pressure ratio, ru , and the dynamic friction angle, , are the rheological parameters introduced in the model. The pore pressure ratio was derived from the pore pressure, u, normalized by the total bed normal stress at the base, . There was no rainfall in this landslide disaster, so the pore-water pressure was not considered in this study.

Voellmy Model Voellmy rheology pertains to a velocity-dependent friction, and it is able to simulate the energy loss of turbulent ďŹ&#x201A;ows. It is presumably caused by a greater proportion of fully liqueďŹ ed material, including loose soil overridden in the ďŹ&#x201A;ow path, which is mobilized by rapid loading and entrained within the slide debris.

Voellmy rheology is considered suitable for simulation of channelized debris ďŹ&#x201A;ows. Voellmy rheology describes the total resistance as the sum of a frictional term and a turbulence term: = f +

v2 .

Table 1. Model combinations applied to the debris-ďŹ&#x201A;ow stages of the Jiweishan landslide.

Model Combination

Source Area

Scraping Area

Accumulation Area

Frictional model Voellmy model FFV model FVV model FVF model

Frictional Voellmy Frictional Frictional Frictional

Frictional Voellmy Frictional Voellmy Voellmy

Frictional Voellmy Voellmy Voellmy Frictional

each combination were compared to the characteristics of the actual landslide. From the results, we can conclude that the FVF model is the more practical scheme. The Voellmy model (Eq. 2) provides a positive correlation between the motion velocity and the substrate resistance, whereas the frictional model (Eq. 1) has no relationship with velocity. The Voellmy model can, therefore, provide greater substrate resistance and sufďŹ cient scraping force for removal of the surface soil layer. Therefore, the Voellmy model is more suitable than the frictional model in the scraping area. The velocity decreased gradually when the debris ďŹ&#x201A;ow moved into the ďŹ nal stage of accumulation area, which weakened the effect of velocity. Therefore, either model can be employed in the accumulation area.

(2)

Here, the frictional term relates the shear stress to through a friction coefďŹ cient f, which has values in the range 0.05â&#x20AC;&#x201C;0.2 according to published data. Alternatively, we can also refer to the ring shear test results in the actual landslide. The turbulence term summarizes all velocity-dependent factors of the ďŹ&#x201A;ow resistance and is expressed as the product of the square of the velocity v and the density through a turbulence coefďŹ cient .

Model Selection Different rheology models were selected for the numerical simulation of the Jiweishan landslide in the source area, scraping area, and accumulation area. The models were combined to obtain simulation results that best matched the actual landslide characteristics. Because the sliding mass had good integrity and slower speed in the source area, the frictional model (denoted as F) was deemed most appropriate in this area. The models selected for the scraping area and the accumulation area were either the Voellmy model (denoted as V) or F. Therefore, the simulation through the three regions followed the order frictional model, Voellmy model, FFV model, FVV model, or FVF model, as listed in Table 1. The simulation results obtained for

Input Parameters To establish the model parameters for the FVF modeling scheme, we ďŹ rst established appropriate ranges for empirical parameter values based on the rheological parameter values employed in earlier simulation studies of long-runout debris ďŹ&#x201A;ows (Hungr and Evans, 1996; McDougall and Hungr, 2004; Sosio et al., 2008; Yin et al., 2016; Xing et al., 2016). Values of ru in the range 0.5â&#x20AC;&#x201C;0.8 were used in the areas with water inďŹ&#x201A;uence, and values in the range 10â&#x2014;Ś â&#x20AC;&#x201C;30â&#x2014;Ś were adopted.

For the Voellmy model, values of f in the range 0.05â&#x20AC;&#x201C; 0.2 and in the range 200â&#x20AC;&#x201C;500 m/s2 were investigated.

Previous experience guided the initial choice for values, and then individual values were selected that appropriately duplicated the actual runout behavior of the Jiweishan landslide based on the available data. The rockslide mass internal friction angle was selected as i = 35â&#x2014;Ś , and the density was selected as = 27 kN/m3 .

The frictional parameters were selected by trial-anderror as = 18â&#x2014;Ś and pore pressure ratio ru = 0. The Voellmy parameters for the investigated cases were selected as f = 0.2 and = 200 m/s2 . The maximum erosion depth was set as 13 m in scraping area. The parameters employed in the FVF modeling scheme are listed in Table 2.

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Yang, Yueping, Bin, Zhen, and Kai Table 2. FVF model parameters of the Jiweishan landslide.

Model Frictional Voellmy Frictional

Internal Friction Angle, i (◦ )

Debris Density, (kN/m3 )

Friction Coefficient, f

Friction Angle, (◦ )

Turbulence Coefficient, (m/s2 )

Maximum Erosion Depth (m)

35 — 35

27 27 27

— 0.20 —

18 — 18

— 200 —

0 13 0

SIMULATION RESULTS AND ANALYSIS Landslide Speed Results The front and rear edge velocities of the landslide obtained during simulation with respect to time are presented in Figure 10. In this simulation, the speed of the landslide main body increased rapidly after the landslide failure in the source area. Then, the speed of the sliding mass decreased due to resistance during scraping and impact, when the sliding mass entered the scraping area. The sliding mass or debris subsequently entered the accumulation area, and the speed increased again due to the terrain factors. In the final stage of the accumulation, the speed of the sliding mass decreased gradually and stopped due to energy consumption. Through the simulation, the maximum velocity of the Jiweishan landslide attained a value of 39.2 m/s, which was at the collision point. We note that this value is approximately consistent with our field estimate value of 43.5 m/s. These results indicate that the event lasted a total of 101 seconds, and that the forward motion of the front edge occurred about 30 seconds earlier than that of the rear edge, while the rear edge continued moving for 74 seconds after the front edge had stopped. The front and rear edge velocities of the landslide obtained during simulation with respect to distance along the sliding path are presented in Figure 11. Here, the runout length obtained from the simulation was 2,213 m. In addition, the front edge velocity is observed to fluctuate over the entire sliding

Figure 10. Simulated front and rear edge velocities with respect to time (the maximum velocity is 39.2 m/s, and the total time is 101 seconds).

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path and is abruptly reduced to zero after 2,000 m. The simulation results indicate that the average velocity was 21.9 m/s, and a maximum velocity of 39.2 m/s was obtained after about 33 seconds, at which time the front edge of the debris flow had extended to about 1,294 m, indicating that the landslide velocity reached its peak within the accumulation area.

Landslide Thickness Results Figure 12 presents cross-sectional contours of the debris flow mass at 20 second intervals over the sliding path substrate during the simulated landslide movement. Figure 13 presents the data in Figure 12 according to the flow mass thickness above the substrate. The simulated accumulation body had an average thickness of about 21.4 m, and a maximum accumulation thickness of 31.9 m was obtained at a distance of about 1,350 m along the sliding direction. From the simulation results, we estimate that the accumulation body was about 1,600 m in horizontal length. The simulated thickness of the accumulation body was obviously less than the actual measured values, which included a maximum thickness of 55 m. The primary reason for this discrepancy is that the measured value was obtained in a V-shaped ravine, while the model employed in DANW involved a perfectly flat bottom profile owing to the extension of the two-dimensional model into three dimensions (Figure 9). As a result, the simula-

Figure 11. Simulated front and rear edge velocities with respect to distance along the sliding path (the runout length is 2,213 m, and the average velocity is 21.9 m/s).

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Numerical Modeling of Jiweishan Landslide, China

(3) The speed of the debris flow decreases gradually when the debris flow moves into the accumulation area, and either model could be selected when the effects of debris-flow speed begin to weaken. (4) We selected the Voellmy model when the landslide moved in a fluidized and turbulent state. These conclusions can be expected to help improve the precision of hazard zonation mapping in the analysis of potential rapid and long-runout landslide hazards.

Figure 12. Simulated contours of the debris flow at 20 second intervals (the average thickness of the accumulation body is about 21.4 m, and the maximum accumulation thickness is 31.9 m).

tion underestimated the thickness of the accumulation body.

Analysis of Simulation Results The FVF modeling scheme employed in the DANW simulation appropriately simulated the basic characteristics of the three debris-flow stages in the Jiweishan landslide. The results obtained from the simulation agree well with the overall characteristics of the Jiweishan landslide, where the initial sliding volume was estimated to be 5.3 million cubic meters, the final accumulation volume was 7.1 million cubic meters, the average scraping depth was 13 m, and the Fahrböschung angle was 18◦ . The simulated results revealed that the FVF modeling scheme was able to provide the best performance in the source, scraping, and accumulation areas, respectively. We note that, in analyses of the Sanxicun landslide (Gao et al., 2017), the Touzhai landslide (Xing and Yin, 2009), and the Wenjiagou landslide (Xing et al., 2017), the FVV, FFV, and FFV modeling schemes were well suited to analyze the respective landslide motion processes. In summary, (1) the frictional model is most suitable for the simulation of the source area when the sliding speed is low.

(2) The Voellmy model is more suitable than the frictional model for the simulation of the scraping area.

CONCLUSION We conducted a numerical simulation of the catastrophic 2009 Jiweishan landslide in Wulong, Chongqing, China, which was characterized by its high velocity and long-runout distance. The analysis was based on simulations of the displacement and accumulation processes of the debris flow, employing DANW runout analysis software in conjunction with field surveys of the geological conditions prevailing at the Jiweishan Hill site. The best match to the actual characteristics of the debris-flow process was obtained by applying the friction rheological model in the source and accumulation areas, and the Voellmy fluid-friction model in the scraping area. The primary characteristics of the debris flow, including the initial source volume, total accumulation volume, runout distance, and kinematic velocity, obtained from an analysis of the DANW numerical simulation results were in good agreement with the corresponding characteristics of the actual event. However, owing to the limitations associated with the extension of a twodimensional model into three dimensions, the influence of the ravine cross-sectional shape on the overall movement process was not reflected in the simulation results. This suggests that the DAN-3D model should be employed for future simulation efforts. The results of this study are expected to provide assistance for the simulation and prediction of the debris-flow velocity, runout distance, and accumulation thickness of potential landslide sites. As such, these efforts should greatly contribute to the future assessment of related geohazards.

ACKNOWLEDGMENTS

Figure 13. Debris-flow thicknesses based on the results presented in Figure 12.

After the 2009 Jiwieshan landslide, the authors participated in on-site geohazard field investigations lasting about 2 months. This research was supported and organized by Science Foundation of Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Chinese Academy of Sciences (No. KLSG201705), the National Natural Science Fund Project (No. 41472295 and No.41702342). This paper

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was completed based on these efforts and the abundant data collected thereby. The authors would like to sincerely thank Mr. Guozhang Wang, Shanghai Jiao Tong University, Dr. Kai He and Mr. Yufeng Yan, Chang’an University, and Dr. Lei Wang and Dr. Ruixin Zhao, Chinese Academy of Geological Sciences, for their support and assistance.

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Application of Hydraulic Flushing in Coal Seams to Reduce Hazardous Outbursts in the Mengjin Mine, China JINGYU JIANG* Key Laboratory of Coal Methane and Fire Control, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China, and National Engineering Research Center for Coal and Gas Control, Faculty of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, China

WEIHUA YANG Key Laboratory of Coal Methane and Fire Control, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China, and National Engineering Research Center for Coal and Gas Control, Faculty of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, China

YUANPING CHENG* Key Laboratory of Coal Methane and Fire Control, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China, and National Engineering Research Center for Coal and Gas Control, Faculty of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, China

BAOMIN LV Research Institution for Gas Control of Technology Center, Henan Dayou Energy Corporation Limited, Sanmenxia 472300, China

KAI ZHANG State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221116 China

KE ZHAO Key Laboratory of Coal Methane and Fire Control, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China, and National Engineering Research Center for Coal and Gas Control, Faculty of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, China

Key Terms: Coal and Gas Outburst, Hydraulic Flushing, ECBM Drainage, Effective Influence Radius, Gas Pressure ABSTRACT Hydraulic fracturing and waterjet slotting fracturing have been demonstrated to be effective in creating artificial fractures and stimulating gas production in hard coal seams. However, these methods are inefficient for soft-outburst coal seams because these created fractures are short and easy to close. To eliminate the outburst risk of soft coals, a novel enhanced coalbed

*Corresponding authors’ email: Jiangjingyu@cumt.edu.cn, chengypcumt@163.com

methane under-panel cross-strata drainage technique via hydraulic flushing was proposed in this work. The hydraulic flushing effects of boreholes of different sizes in the coal seam were also pre-evaluated by a simulation approach. The modeling results indicate that as the radius of the borehole increases, the plastic and stressdecreasing zone expands. A field test was also conducted in the Minjin mine, China, that investigated the gas pressure variation between three monitoring boreholes at different distances from a hydraulic flushing borehole. Test results indicate that the effective influence radius of gas extraction is approximately 5.5 m. Based on the results of the field test and borehole camera observation, the unloaded coal quantity and the average diameter of the boreholes were estimated to be 8.0 t and 942 mm, respectively. The borehole diameter expanded up to 10 times larger than its original size. The average gas

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extraction concentration and gas flow rate increased by approximately 2 and 3.5 times, respectively, demonstrating the effectiveness of the proposed hydraulic flushing in improving the gas extraction efficiency. The hydraulic flushing technique therefore is proved to be efficient in eliminating the outburst risk of coal and reducing greenhouse gas emissions.

INTRODUCTION The serious safety situation of China’s coal mines has caused great concern, as the Chinese coal industry bears the worst safety record in the world (State Administration of Coal Mine Safety [SACMS], 2011; Wang et al., 2014; and Zhang et al., 2015c). Coal mine methane (CMM)–induced accidents account for a significant share of these fatalities in Chinese coal mining (Wang et al., 2014). In 2017, there were 15 gas accidents in China’s coal mines, resulting in the deaths of 72 miners (SACMS, 2018). CMM has always been a serious threat to underground coal mining safety because of its outburst risk (Jiang et al., 2011; Karacan et al., 2011; and Zhang et al., 2016a). The greenhouse effect of methane is 22 times higher than carbon dioxide. The impact on global climate change from CMM emissions in China has been drawing attention in recent years (Cheng et al., 2011).

To reduce hazardous outbursts, mining the protective coal seam and the other pre-drainage gas technologies were historically applied in outburst-prone coal seams before mining (SACMS, 2009; Lu et al., 2011; Wang et al., 2013; Jiang et al., 2015a; Zhang et al. 2015a and Zhang et al., 2015b). However, for a single outburst-prone coal seam, the under-panel cross-strata drilling and pre-drainage technique has become one of the most frequently used regional treatments in the world (Baimukhametov et al., 2012; Zhang et al., 2016b).

As a large number of drilled boreholes lie in the rock strata, the main disadvantages of the cross-strata drainage method are the high costs and poor gas drainage efficiency (Wang et al., 2013; Lu et al., 2015a).

To improve the efficiency of gas drainage and to prevent the occurrence of outbursts, the under-panel cross-strata drainage technique has been widely used in conjunction with various methods of hydraulics (Hubbert and Willis, 1957; Morita et al., 1996a, 1996b; Wang et al., 2008; Al Rbeawi et al., 2013; Hou et al., 2013; and Xu et al., 2013). These hydraulic methods (hydraulic fracturing, explosive fracturing, waterjet slotting fracturing, and combinations thereof) are proven to be effective in creating artificial fractures and stimulating gas production (Lu et al., 2009, 2015a, 2015b, 2016; Huang et al., 2011, 2012; Li et al., 2015; and Yan et al., 2015). However, in soft-outburst coal 426

seams, these methods are inefficient because the created fractures are short and easy to close. Hydraulic flushing technology has the capacity to solve these problems by flushing out a large amount of coal, eliminating the outburst risk of soft outburst-prone coal (Liu et al., 2005, 2016; Kong et al., 2016). Conducted from under-panel roadways, hydraulic flushing uses high-pressure water jets to shock coal and wash out part of the coal from boreholes; this treatment thus reduces the stress around boreholes and increases the permeability of the soft coal (Li et al., 2011).

During CMM drainage via drilling borehole, the coal seam is transformed from an outburst coal seam to a safe coal seam when the gas pressure of the coal seam drops down to 0.74 MPa or lower according to the Chinese Coal Mining Safety Regulations (SACMS, 2016). Therefore, the effective influence radius is defined as the region starting from the wall of the borehole to the location in coal where the coal seam gas pressure does not exceed 0.74 MPa (Yu and Cheng, 2012; Kong et al., 2016).

The method of drilling large-diameter crossmeasure boreholes by using the waterjet technique is proposed in Pingdingshan coalfield, and a field test shows that when the borehole diameter is 1.0 m, the effective influence radius reaches 4 m, which is 2.67 times larger than that of a conventional borehole (diameter is 94 mm) (Gao et al., 2015). The coal deformation and gas flow after hydraulic flushing have been modeled by COMSOL Multiphysics, indicating that hydraulic flushing not only improves the effective influence radius but also enhances the coal permeability (Kong et al., 2016). Comparisons among these methods, such as hydraulic flushing, hydraulic slotting, and hydraulic fracturing, are listed in Table 1.

The hydraulic mining of the thin sub-layer (with drills along the coal seams and hydraulic flushing) of a self-protective coal seam has been conducted successfully in eliminating the risk of outburst in underground mines (Li, 2014a, 2014b). However, the hydraulics techniques must not be used in the extraction face of the outburst-prone coal seam according to State Coal Mining Safety Regulations (SACMS, 2016).

Thus, the effective influence radius, the plastic zone, and the gas extraction effect of coal after hydraulic flushing in under-panel rock roadway still require further research by a combination of numerical simulation and field tests.

GEOLOGICAL BACKGROUND The Mengjin mine is part of the Yima coalfield in northwest Luoyang in northern Henan province. The Mengjin mine is approximately 5.7–10 km wide and 7.0 km long and covers 57.5 km2 (Figure 1). The main

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Hydraulic Flushing Mengjin Mine, China Table 1. Comparison between hydraulic flushing, hydraulic slotting, hydraulic fracturing, and so on.

Technology

Principle

Used in Hard/ Soft Coal

Cost

Sites Field simulation test (Huang et al., 2011) Pingdignshan tenth mine (Huang et al., 2012) Yuwu coal mine (Li et al., 2015) Jinjiachong coal mine (Lu et al., 2011) Coal mine in Chongqing (Ge et al., 2014) Guhanshan coal mine (Yan et al., 2015) Pingdingshan coalfield (Yang et al., 2016) Yangliu coal mine (Zou et al., 2015) Numerical simulation (Gao et al., 2016) Xiashijie cole mine (Wang et al., 2008) Field simulation test (Huang et al., 2011) No. 13 mine (Lu et al., 2015a) Yian coal mine (Li et al., 2011) Yian coal mine (Li et al., 2014a) Yian coal mine (Li et al., 2014b) Jiulishan coal mine (Liu et al., 2005) Pingdingshan coalfield (Gao et al., 2015) Xinan coalfield (Liu et al., 2016) Numerical simulation (Kong et al., 2016)

Hydraulic fracturing

Fracturing coal using plenty of water mixed with chemicals

Hard

High

Waterjet slotting

Creating artificial fractures by cutting coal with high-pressure water

Hard, soft

Medium

Explosive fracturing

Creating artificial fractures by explosive blasting

Hard

Low

Hydraulic flushing

Flushing out the coal around the borehole using a high-pressure waterjet

Soft

Low

coal-bearing strata are the Shanxi formation of the Lower Permian system. The No. 2 coal seam is the main mining layer (Figure 1) and is soft and prone to outburst. Gas outburst accidents have occurred in the coal seam during the mining process of gate road development. The low permeability of the coal seam and the variation of the geological settings significantly influence the rate of gate road development and mining safety. The rate of gate road development is approximately 50 m per month in the mine.

The No. 2 coal seam is soft and composed of broken tectonic coal, with low values of the Protodyakonov coefficient of the coal seam (Li, 2014a). Both the roof and the floor of the coal seam are sandy mudstone. This means that the No. 2 coal seam is a threesoft (soft roof, soft coal seam, and soft floor) coal seam. The permeability coefficient of the coal seam is low with a value of 0.4–0.04 mD. The measured coal seam gas pressure and content are 3.1 MPa and 16.37 m3 /t, respectively, indicating that the No. 2 seam is an outburst-prone coal seam.

To study the gas desorption characteristic, fresh coal samples were obtained from the coal face 11011, No. 2 coal seam. A gas desorption test was conducted in the laboratory. First, crushed coal samples of 50 g and 1–3 mm in particle size were placed in a can for vacuum degassing and placed in a 60°C water bath. Then the can was connected to a high-pressure gas cylinder, and the samples were exposed to gas (CH4 ) in a 30°C water bath until reaching an adsorption equilibrium (Jiang et al., 2015b, 2016). The adsorption equilibrium pressures of the experiment were set to 6.00, 4.50, 3.00, 2.00, and 1.00 MPa. The borehole camera observation

was used for the quantitative analysis of the hydraulic flushing coal cavities.

UNDER-PANEL CROSSING BOREHOLE HYDRAULIC FLUSHING TECHNOLOGY Technology Principle Rapid outbursts and violent releases of coal and gas into the mining area result from a complex effect of the gas pressure, stress regime, and mechanical properties of coal (Hyman, 1987). The power of outbursts comes from the sudden release and elastic emergence of stress in the coal seam and surrounding rock and the expanding energy of gas as the mine face advances (Beamish and Crosdale, 1998). Thus, pre-releasing the energy and in situ stress in the coal seam can prevent the occurrence of gas outbursts.

The under-panel crossing borehole hydraulic flushing gas extraction technology is a novel technique, which is able to eliminate the outburst risk of soft and outburst-prone coal seams. A brief description of the technique is as follows: (1) take the rock strata as a natural safety barrier between the coal seam and the floor roadway (Figure 2), (2) use the high-pressure waterjet to shock the coal seam, and (3) flush a large amount of broken coal and gas out through the borehole several times, and the volume of the borehole will gradually expand (Liu et al., 2005, 2016). Since the coal around the borehole moves toward the borehole, new artificial fractures may develop around the borehole and connect with the original ones (Lu et al., 2009, 2011; Li et al., 2014a, 2014b). This technique can effectively

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Figure 1. Map showing the location of the study area and generalized stratigraphy of the Mengjin mine.

reduce the emergence of the elasticity and stress in the coal surrounding the borehole. It not only eliminates the risk of outburst but also improves the efficiency of gas extraction in the coal mine.

Figure 2. Under-panel cross-strata drilling and flushing. Modified from Lu et al. (2015a).

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Experimental Device The hydraulic flushing experiment equipment is composed mainly of a high-pressure water injection pump station, a drill machine, drill pipes, a prevention hole spray device, and pipes for the extraction gas (Figure 3). A high-pressure water injection pump, which has a maximum output pressure of 31.5 MPa and a flow rate of 400 L/min, utilizes the BRW400/31.5 from the Nanjing Liuhe coal machine company. The extraction pipe is a high-pressure hose with an inner diameter of 32 mm. The drill machine, which is produced by the Chongqing Coal Research Institute, is ZYW-1200; the hydraulic flushing drill and the prevention spray hole device were developed by the Institution of Gas Control of Henan Dayou Energy Corporation Limited.

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Figure 3. Schematic of the hydraulic flushing equipment (1: highpressure pump; 2: pressure regulator; 3: drill machine; 4: prevention spray hole device; 5: extraction pipeline; 6: coal-gas-water separator; 7: drill pipe; 8: under-panel rock roadway; 9: safety rock pillars; 10: hydraulic jet; 11: coal seam; 12: roof; 13: floor).

NUMERICAL SIMULATIONS Setup of the Model To study the hydraulic flushing effect, a simulation study was conducted. The simulation was carried out using the finite difference code FLAC3D, which is a 3-D code marketed by Itasca Consulting Group Inc. (2006). In the numerical model, the boundary is set at 20 m away from the center of the borehole. For a borehole with a radius of 0.1 or 0.2 m, the boundary is set at 12 m from the center of the borehole according to the criterion in FLAC3D. With such a small borehole, when the boundary is too far, the model will be in equilibrium immediately after borehole excavation.

The numerical model for one borehole is shown in Figure 4, taking symmetry into consideration.

According to the geological conditions of the Mengjin mine, the coal is soft, with an unconfined compressive strength below 1 MPa. Compared with the coal, the rock mass in the roof and floor is considered elastic in the simulation. The thicknesses of the rock layers in the roof and floor are set at 10 m. The three principal in situ stresses are determined following Cai et al. (2014) and are 25, 17.5, and 15 MPa in the x, y, and z directions (Figure 5), respectively. In Figure 5a, the top boundary was applied with an average stress of 17.5 MPa, and the other boundaries were constrained in the standard direction. According to the experiments in the laboratory, the coal mass exhibited brittle fractures in the conventional compression tests.

Therefore, a strain softening constitutive model with

Figure 4. Simulation model for a borehole. (a) Geometry of the model. (b) A-A section.

a non-associated flow rule was adopted here to model the coal (Zhang et al., 2013). Based on the geological report and the literature (Lu et al., 2011; Yang et al., 2016), the mechanical parameters of the coal were determined as listed in Table 2.

Effect of Hydraulic Flushing The excavation of hydraulic flushing boreholes with different radii were simulated. In the simulation, the boreholes were excavated in one step. Figure 6 shows the plastic zones around the borehole after equilibrium. It is clear that, as the radius of the borehole increases, the plastic zone expands. As for the asymmetry of the initial stress, the range of the plastic zone is a little larger in the direction of the minor principal stress.

The deformation and failure of the coal mass induce the redistribution of stress around the borehole. In practice, the stress-reducing zone around the

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Figure 5. Plastic zones around the borehole. (a) Stress condition. (b) Radius of 0.1 m. (c) Radius of 0.2 m. (d) Radius of 0.4 m. (e) Radius of 0.6 m. (f) Radius of 0.8 m.

borehole is more important, as the seam gas is more easily drained in this area (Yang et al., 2016). For comparison, the zones decreasing in the minor principal stress of over 20% were obtained under different borehole radii, as shown in Figure 7. It is interesting to see that the extent of the stress-decreasing zone is al-

most the same in the directions of the major and minor principal stresses. Therefore, the value of stress, rather than the direction, is the main factor in evaluating the stress-reducing zone.

Figure 6 demonstrates that the borehole radius has a great influence on the stress-decreasing range, which

Table 2. Parameters of the stratum.

Mechanical Parameters Stratum

c0 (MPa)

cr (MPa)

ϕ (Degree)

(Degree)

G (GPa)

K (GPa)

Coal seam Roof Floor

0.05 / /

0.02 / /

18 / /

10 / /

0.2 4 4

0.25 6 6

c0 = initial cohesion; cr = residual cohesion; ϕ = friction angle; = dilatancy angle; G = shear modulus; K = bulk modulus.

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Figure 6. Distribution of the stress-decreasing zones (dropping 20%) around the borehole. (a) Radius of 0.1 m. (b) Radius of 0.2 m. (c) Radius of 0.4 m. (d) Radius of 0.6 m. (e) Radius of 0.8 m.

is beneficial for methane drainage. The extents of the stress-decreasing zones in the direction of the minor principal stress are achieved for different conditions, as illustrated in Figure 7. When the borehole is not treated by hydraulic flushing (borehole radius less than 0.1 m), the extent of the stress-decreasing zone is approximately 1.0 m around the borehole. After hydraulic flushing is applied, the extent could be enlarged up to 10 times in some stress-decreasing zones.

Figure 7 also shows that the effect of the borehole radius on the stress reduction increases quickly when the radius is small (less than 0.5 m). However, when the radius is over 0.5 m, the range of the stress-reducing zone gradually increases at a lower rate. Considering the stress-relief effect and the hydraulic flushing cost, 0.5 m appears to be an optimal value for the radius of the hydraulic flushing borehole in the Mengjin mine.

Regardless of the rheological behavior of coal after the drilling disturbance, the relationship between the radius of the borehole and the unloading coal quantity is represented by Eq. (1): πR2 × H × U = W,

(1)

where R is the radius of the hydraulic flushing borehole (m); H is the thickness of the coal seam (m); U is the volume weight (t/m3 ), which is 1.3–1.4 for the bituminous coal; and W is the total weight of the discharged coal (t). Based on the details of the No. 2 coal seam in the Mengjin mine, (R = 0.5 m, H = 4.5 m, U = 1.4), the unloading coal quantity, W, should be 4.95 t.

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Figure 7. Range of the stress-decreasing zones (20%) under different borehole sizes.

FIELD EXPERIMENT Field Test Location The study area is the No. 11011 coal face of the Mengjin mine, which is 1,000 m long and 120 m wide, with a buried depth of 691–732 m (Figure 8). The average thickness and angle of the No. 2 coal seam in the No. 11011 coal face are 4.5 m and 4°, respectively. The coal seam is soft and is coal and gas outburst prone; thus, the under-panel rock roadways were excavated before drilling and hydraulic flushing (Figure 8).

Field Test Procedure First, the drill bit (diameter of 133 mm) was installed on the ZYW-1200 drill machine (Figure 3). The crossing borehole was drilled toward the No. 2 coal seam in the under-panel rock roadway in the Mengjin mine.

When the drilling depth was 2 m, the drilling pipes were pulled out. The bolts were set in near the orifices of the drilling hole. Then the prevention spray hole de-

Figure 8. Plan of coal face 11011 in the Minjin mine.

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vice was installed and fixed using the anchor and steel strand (Figure 3). Second, the drill bit (diameter of 94 mm) was installed on the 133-mm drill pipe. After drilling to the roof of the coal seam, the drill bit was removed. The 50-mm-diameter hydraulic flushing drill bit was installed and sent to the bottom of the borehole. The high-pressure water pump station was turned on, and the pressure was continuously increased until it was maintained at approximately 6.00 MPa. The pressure was kept stable for 5 minutes, and the pipeline connection was assessed to determine whether it was in good condition. Once the connection was confirmed to be intact, the slow uniform rotation drill bit was turned on, and the high-pressure water jet and hydraulic flushing on the coal began. During the operations, the drilling was confirmed to be conducted in the open position with the water coming out to prevent the borehole from suppressing. Hydraulic flushing continued until the coal seam roof was reached, the high-pressure water was shut off, the drill pipe was removed, and the drilling operation was complete. In the process of hydraulic flushing, safety protection measures, such as monitoring probes and warning lines, are in place to prevent injury from the high-pressure water.

RESULTS AND DISCUSSION Effect of Hydraulic Flushing on the Effective Influence Radius The effective influence radius of the borehole was determined experimentally in coal face 11011 (Figure 8). First, three gas pressure monitoring boreholes were drilled in the under-panel rock roadway of coal face 11011 toward the No. 2 coal seam. The normal distances between the three pressure monitoring boreholes (nos. 1, 2, and 3) and the hydraulic flushing boreholes were 4.0, 5.5, and 7.0 m, respectively.

The borehole diameter was 94 mm and approximately perpendicular to the coal seam with the bottom located 0.5 m in the coal seam roof (Figure 9a). The boreholes were sealed using mixing materials, such as polyurethane and expansion cement (Zou et al., 2015).

A schematic of the pressure measuring device is shown in Figure 9b.

After the pressure gauge was stable, the measured borehole pressures for nos. 1, 2, and 3 were 1.50, 1.60, and 1.40 MPa, respectively. Next, the no. 0 hydraulic flushing borehole was performed according to the field test procedure mentioned above, and the gas was drained from the hydraulic flushing borehole. The measured flushed coal cavity diameter and the unloading coal quantity of the no. 0 hydraulic flushing borehole were 1,095 mm and 7.9 t, respectively. The

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Figure 9. (a) Profile sketch of the effective extraction radius investigation. (b) Profile of the gas pressure determination device.

pressure gauge readings in the monitoring boreholes were recorded daily as illustrated in Figure 10. When the extraction time was 120 days, the gas pressure of borehole no. 3 decreased from 1.40–0.65 MPa (less than the critical value of 0.74 MPa), which was a drop of more than 53% (Figures 9a and 10). Thus, when the extraction time was 120 days, the effective influence radius of borehole no. 3 was more than 4.0 m.

When the extraction time was 120 days, the gas pres-

sure of borehole no. 2 decreased from 1.60–0.70 MPa (less than 0.74 MPa), which was a drop of about 56% (Figures 9a and 10). When the extraction time was 120 days, the effective pressure relief extraction radius of borehole no. 2 was a little more than 5.5 m. However, when the extraction time was 120 days, the gas pressure of borehole no. 1 decreased from 1.50–1.00 MPa (higher than 0.74 MPa), which was a drop of only about 33%, indicating that the effective influence radius of borehole no. 1 was less than 7.0 m within extraction time of 120 days (Figures 9a and 10). The experimental result demonstrates that when the extraction time was 120 days, the effective influence radius of the no. 0 hydraulic flushing borehole was about 5.5 m. For comparison, the effective influence radius of a borehole without hydraulic flushing (with a diameter of 94 mm) was measured in the 11011 ventilation roadway. The result indicates that, after drainage for 120 days, the effective extraction radius of the borehole was no more than 2.0 m. It can be concluded that the hydraulic flushing can significantly increase the effective extraction radius.

Stress Analysis around Coal Cavities

Figure 10. Changes in the gas pressure of borehole nos. 1–3 with the extraction time.

In soft rock roadway, the stress concentration caused by excavation often exceeds the ultimate strength of the surrounding rock, which forms a nonelastic deformation region. Because of the stronger

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coal seam around the flushed borehole; (2) producing artificial fractures within the coal seam in the plastic zone (Figure 11a), which is better for the gas flow; and (3) expanding the scope of the plastic zone around the borehole compared to traditional drilling (Figure 11b).

Unloading the Coal Quantity and the Diameter of the Coal Cavities

Figure 11. (a) Photo inside the flushing coal cavity was obtained using the borehole camera. (b) Stress division around the hydraulic flushing coal cavity. Modified from Yan et al. (2015).

plastic creep properties of coal softening, when the axial stress suffered by coal is close to the peak, plastic strain gradually appears in coal, and the coal enters the plastic softening stage (Yuan and Chen, 1986).

The compressive strength gradually decays to a residual strength with increasing strain. Based on this, coal around the punching grotto room can be divided into the elastic zone, plastic zone, and broken areas (Hao et al., 2014). Figure 11(a) shows the photo inside the flushing coal cavities obtained using the borescope.

The stress distribution around the flushing coal cavity can be divided into four zones (I–IV) (Figure 11b). In Figure 11b, area I is the stress-reducing area, and we define it as the broken zone. Areas II and III are the stages before and after the peak stress, both of which are called the plastic zone. Area IV is the original rock stress zone belonging to the elastic zone. Elastic plastic deformation occurs in zones I–III, producing a large number of artificial cracks, subsequently increasing the gas permeability of the coal (Yan et al., 2015).

On the basis of the above results, the boreholeincreasing mechanism of the effective extraction radius under the condition of hydraulic flushing can be summarized as follows: (1) reducing the stress within the 434

To study the effect of hydraulic flushing on the borehole, the unloading coal quantity and variation of the borehole diameter have been quantitatively analyzed.

The plan and profile of the boreholes used for hydraulic flushing and the conventional boreholes are shown in Figure 12.

To eliminate the risk of outburst around a longwall heading and its two sides as soon as possible, the row spacing was designed to be 5.0 m, and the pitches of holes for the hydraulic flushing were designed to be 3.0, 4.0, 5.0, and 6.0 m. Nine boreholes (nos. 4–12) were drilled for hydraulic flushing (Figure 12a). Three boreholes (nos. 13–15) without hydraulic flushing were drilled and are shown in Figure 12b. The unloading coal quantity, borehole diameter, and expanding multiples of boreholes after hydraulic flushing are listed in Table 3. The unloaded coal quantity of borehole no. 4 (the first hydraulic flushing borehole) reached 18.2 t. After hydraulic flushing, the diameter of the borehole was estimated to be 1,285 mm. The drilling diameter (94 mm) expanded to 13.7 times its original size (Figure 13). The quantities of the unloaded coal of borehole nos. 9–11 were less than that of borehole no. 4. The average unloaded coal quantity of the boreholes was approximately 8.0 t, and the average diameter of the borehole was 942 mm after hydraulic flushing, which was approximately 10 times its original size. The in situ practice shows that the intensity of hydraulic flushing works well in the Pingmei coalfield. Too much or too little unloading coal is not optimal for gas drainage and stress relief, which is demonstrated by the numerical simulation both in the present research and in the work of Yang et al. (2016).

Before the boreholes were drilled, the gas pressure and stress in the coal seam were in balance. When the first borehole (no. 4) was drilled and treated using hydraulic flushing, the high-pressure gas and highly stressed coal created by the high-pressure waterjet quickly spurted from the borehole and formed a cavity in the coal hole. The high gas pressure gradient led the coal around the borehole to migrate to the cavity and to discharge with the help of high-pressure water. Therefore, the quantity of unloading coal for the first borehole (no. 4) was higher. Because the spacing between borehole nos. 4–12 was small, their extraction radii may have overlapped, effectively releas-

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Figure 12. (a) Profile of the hydraulic flushing boreholes on the No. 2 coal seam. (b) Plan of the hydraulic flushing boreholes (nos. 5–7) and conventional boreholes (nos. 13–15) on coal face 11011.

ing the stress in the coal. A large coal cavity formed after the coal was unloaded, providing space for gas enrichment and a good channel for gas extraction (Liu et al., 2005, 2016). An X-ray computed tomography

scan indicated that the damage and fractures developed during the coal sample unloading stress (Chen et al., 2013), and this might provide good conditions for the gas flow around coal cavities.

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Jiang, Yang, Cheng, Lv, Zhang, and Zhao Table 3. Unloading coal quantity, flushed coal cavity diameter and expanding multiples of the flushed boreholes.

Borehole No.

Borehole diameter (mm) Length of the coal borehole (m) Unloading coal quantity (t) Unloading coal quantity per meter (t/m) Slotted coal cavity diameter (mm) Borehole expanding multiples

4

5

6

7

8

9

94.0 9.8 18.2 1.86 1285 13.7

94.0 8.4 9.3 1.11 990 10.5

94.0 7.3 8.4 1.15 1007 10.7

94.0 6.6 6.2 0.95 914 9.7

94.0 6.1 7.4 1.22 1039 11.1

94.0 5.6 5.3 0.94 913 9.7

10 94.0 5.2 5.2 1.00 942 10.0

11

12

94.0 5.0 5.8 1.16 1013 10.8

94.0 5.4 6.7 1.24 1048 11.1

The value of flushed coal cavity diameter was estimated using the volume of unloading coal and the length of the coal borehole.

Influence of Hydraulic Flushing on the Gas Extraction Efficiency To study the effects of hydraulic flushing technology on gas extraction, the hydraulic flushing boreholes and the conventional boreholes were compared. The relationship between the gas extraction concentration and the extraction time of the tested boreholes is shown in Figure 14. The gas extraction concentration of the boreholes that used the hydraulic flushing was clearly higher than those of conventional drilling. The gas extraction concentration of the no. 5 borehole (hydraulic flushing) decreased gradually from 89% to 29.8%, with an extraction time of 90 days. However, the gas concentration of the conventional drilling no. 15 borehole decreased rapidly from 65% to 8.2% during the first 30 days. After that, its gas concentration attenuated slowly from 8.2% to 6.0% (Figure 14).

The efficiency of the gas extraction is obviously improved with hydraulic flushing technology. The minimum gas concentration of the hydraulic flushing borehole no. 7 was 19.8% after 90 days of extraction, and the average flow rate was 0.34 m3 /min. Moreover, the maximum gas concentration of the conventional bore-

Figure 13. Unloading coal quantity and the slotted coal cavity diameter after hydraulic flushing.

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hole no. 13 was less than 14.0%, and the average flow rate was 0.1 m3 /min. Therefore, after using the underpanel cross-strata drilling and hydraulic flushing technology, the average gas extraction concentration and gas flow rate increased approximately 2 and 3.5 times, respectively. After 90 days of hydraulic flushing and gas drainage, the residual gas content of the No. 2 seam was measured at 4.83–6.56 m3 /t, which was much less than the original gas content of 16.37 m3 /t.

The risk of gas outburst for a coal face was essentially eliminated. The gas extraction efficiency was also enhanced, allowing the rate of gate road development to increase from 50 to 100 m per month.

The schematics of gas migration in the coal seams at a variety of scales are shown in Figure 15a (Remner et al., 1986). Gas flow in a coal seam generally follows Darcy’s law (Wu et al., 1998; Gilman and Beckie, 2000), and the basic parameters affecting the gas flow behaviors are the gas pressure, gas content, and seam permeability. The gas flow surrounding a borehole is stable radial flow (Figure 15b). The pressure distribution surrounding a borehole gas can be expressed by Eq. (2) (Zhou and Sun, 1965; Shen

Figure 14. Relationship between the gas extraction concentration and extraction time.

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Figure 15. (a) Schematic summary of the gas migration in the coal seams at a variety of scales. (b) Photo obtained from the borehole camera after hydraulic flushing (r1 is radius of the conventional borehole; r2 is radius of the flushed borehole). (c) Gas desorption test results for the Mengjin coal sample under the conditions of five different pressures and a desorption period of 60 minutes.

et al., 2015): P = P0

In

r r0 /In , r1 r1

(2)

where r0 is the radius of the coal seam and r is distance from one place to the drilling center. The gas pressure around drilling shows a logarithmic curve distribution.

The gas pressure in the stress concentration area will improve rapidly with increasing borehole radius, which formed a gas pressure gradient force (Fg ), expressed by Eq. (3):

dP r r0 = P0 2r In In .

Fg = (3) dr r1 r1 A gas desorption test was conducted in the laboratory. The desorption volumes of the coal samples under the five different equilibrium pressures and desorp-

tion period of 60 minutes are shown in Figure 15c.

The desorption quantity increases with increasing gas pressure. However, the mechanism of improving of gas extraction concentration and extraction efficiency by the hydraulic flushing technology has not been deeply studied. According to the aforementioned experimental result, the following statements can be made: (1) improving the radius of the borehole, which has been proven by a peep photo underground (Figure 15b); (2) increasing the gas pressure around the borehole since the borehole radius has been enlarged; and (3) improving the gas adsorption/desorption volume after the gas pressure has been enhanced (Figure 15c).

CONCLUSIONS (1) To eliminate the outburst risk of soft and outburstprone coal seams in the Mengjin mine, the

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technique of under-panel cross-strata drainage in conjunction with hydraulic flushing was introduced. The hydraulic flushing effects of the different size radii of the boreholes in the coal seam were pre-evaluated by a numerical simulation. As the radius of the borehole increased, the plastic and stress-decreasing zone expanded.

(2) The field test of the effective influence (extraction) radius showed that after 120 days of gas extraction, the gas pressure of monitoring borehole no. 1 (5.5 m from the hydraulic flushing hole) decreased from 1.60 to 0.70 MPa. This indicates that the gas effective influence radius was approximately 5.5 m.

(3) During the test of hydraulic flushing, the unloaded coal quantity and diameter of the boreholes were estimated. The average unloaded coal quantity of the boreholes was approximately 8.0 t. The average diameter of the hydraulic flushing coal hole was 942 mm, which is 10 times higher than its original size.

(4) The hydraulic flushing technology improved the CBM extraction efficiency. After 90 days of extraction, the average gas extraction concentration and gas flow rate increased approximately 2 and 3.5 times, respectively. The residual gas content was measured to be 4.83–6.56 m3 /t, which was much less than the original gas content of 16.37 m3 /t. The gas outburst risk of the coal face was eliminated. The hydraulic flushing– based ECBM drainage technique can reduce greenhouse gas emissions, which is good for the environment.

ACKNOWLEDGMENTS Financial support was provided by the Fundamental Research Funds for the Central Universities (no. 2015XKMS004), the China Postdoctoral Science Foundation (no. 2016T90670), and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

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Phytoremediation Ability of the New Heavy Metal Accumulator Plants FARIBA MOHSENZADEH* ROGHAYEH MOHAMMADZADEH Department of Biology, Bu-Ali Sina University, Hamedan, Iran

Key Terms: Pollution, Heavy Metals, Phytoremediation, Conium maculatum, Stachys inflata, Reseda lutea ABSTRACT Environmental pollution with heavy metals is a global disaster. This study investigated metal-accumulating ability of plants growing in a lead and zinc mine area located in Hamedan, Iran. Three dominant plants, including Conium maculatum, Stachys inflata, and Reseda lutea, were collected, and the concentrations of Cd, Cu, Pb, Ni, and Zn in the aerial parts of the plants and in the soils, collected from the mine area and out of the mine, were measured via atomic absorption spectrometry. The concentrations of all the metals in the soil of the mine were greater than the control area (1 km out of mine area); Pb, Zn, Cu, Ni, and Cd were 120, 17, 17, 2.6, and 40 times higher than in the control area, respectively. In the studied plants, Pb and Zn were the highest in C. maculatum (1,200 and 820 mg kg−1 , respectively). The highest concentrations of Cu, Ni, and Cd were in S. inflata (140, 96, and 20 mg kg−1 , respectively). Phytoremediation tests were done using experimental pots, and results indicate that the plant species are effective accumulator plants for the phytoremediation of heavy metal–polluted soils. Specifically, C. maculatum was effective in removing Pb and Zn, S. inflata was effective in reducing Ni, and R. lutea was effective in reducing Cu. INTRODUCTION Industrial activities contribute to the fast and considerable degradation of soil and vegetation, one of the gravest being heavy metal pollution (Keller et al., 1998). These elements have recently received the attention of researchers all over the world, mainly due to their harmful effects on plants, animals, and humans (Astolfi et al., 2005; Yousefi et al., 2011). The toxic effects of these metals have been intensively studied at the biochemical, physiological, and histological processes levels, such as photosynthesis (Küpper et al., 2002), transpiration (Pandey and Sharma, 2002), *Corresponding author email: fmohsnzade@gmail.com

enzyme activity (Astolfi et al., 2005), metal accumulation in tissue (Palmieri et al., 2005), and organ development in plants (Malayeri et al., 2005; Sawidis, 2008; Yousefi et al., 2011).

In heavy metal–polluted areas, vegetation plays an increasingly important ecological and sanitary role (Malayeri et al., 2013). The proper management of plants in such areas may contribute to the restoration of the natural environment. Numerous efforts have been undertaken in the past 30 years to find methods for removing metals and other pollutants from the soil via phytoremediation (Mohsenzadeh et al., 2010; Malayeri et al., 2013). Phytoremediation was initially proposed as an effective and environmentally friendly cleanup technology for the remediation of heavy metal–contaminated soils (Baker et al., 1996; Keller et al., 1998). The identification of plants that are capable of accumulating metals to extraordinarily high levels demonstrates their potential to clean up contaminated soils (Malayeri et al., 2013).

Prior researchers found that some plant species are endemic to metalliferous soils and can tolerate greaterthan-usual amounts of heavy metals or other toxic compounds (Dahmani-Muller et al., 2000; Raskin and Ensley, 2000). Less than 0.2 percent of angiosperms have the ability to accumulate heavy metals considerably and act as hyperaccumulators (Sarma, 2011); the ones that do use certain mechanisms to absorb and accumulate high levels of metals without affecting their growth (Küpper et al., 2004; Gosh and Singh, 2005; and Callahan et al., 2006). Chehregani and Malayeri (2007) reported that certain plants, including Euphorbia cheiradenia, Scariola orientalis, Centaurea virgata, Gundelia tournefortii, and Eleagnum angustifolia, could accumulate Fe, Mn, Cu, and Zn. The species that do not appear to be affected by excessive metal contents may possess metal-resistant capabilities or a higher tolerance than more sensitive species; therefore, their potential for use in remediation is promising (Keller et al., 1998).

The aims of this research were (1) to find heavy metal accumulator plants growing in a metal-polluted mine area with phytoremediation potency and (2) to evaluate the phytoremediation potency of the selected plant species.

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442

76 ± 8* 32 ± 4*

P

124 ± 160 48 ± 52* 75 50 49 ± 5 17 ± 3

NP

21 ± 4 13 ± 2

*

P NP

356 ± 28 174 ± 23* 140 100

P

*

NP

*

1.4 ± 1 0.5 ± 0.3

3 3

Cd Ni

Data represent the mean ± SE of three to five samples.

Significant differences between non-polluted and polluted soil samples (P 0.001).

149 ± 3 22.2 ± 3

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*

3,320 ± 295 1,582 ± 71* 300 300 137 ± 22 63 ± 14

NP

*

P

17,850 ± 3,320 6,500 ± 1,640* 300 100

P

*

1

The aerial parts of the studied plants were washed with tap water and then distilled water to remove dust, then they were dried and ground up. One gram of the ground plant material was weighed and mixed with 10 mL of a 65% nitric acid solution. The samples were placed in a hot-water bath at 65°C for 2 hours, then 2.6 mL of hydrogen peroxide were added to each sample. The samples were filtered and standardized to a volume of 50 mL. The amounts of Pb, Zn, Cu, Ni,

Total amount Available amount European Union Standard Germany Standard

Amount of Metals in the Studied Plants

NP

Two grams of the dry soil collected from the polluted area (the pool in the mine) and the non-polluted area (away from the mine) were added to 15 mL of nitric acid (4 M). The samples were placed in a water bath at 80°C for 12 hours. The samples were filtered, and the total amounts of Pb, Zn, Cu, Ni, and Cd were analyzed using an atomic absorption spectrometer (Varian 220) (Sposito et al., 1982; Palmieri et al., 2005). The amounts of the metals found in the soils from the polluted and non-polluted areas were measured and compared with a standard amount of each metal (Table 1).

To determine the available metal levels in the soils, 10 g of soil were weighed and mixed with 20 mL of diethylenetriaminepentaacetic acid (pH = 7.3). Then the samples were shaken for 2 hours and centrifuged for 10 minutes. The samples were filtered, and the amounts of Pb, Zn, Cu, Ni, and Cd were analyzed with an atomic absorption spectrometer (Lindsay and Norvell, 1978).

At least three samples were analyzed for each studied area.

Soil sample

Determination of Metals in Soils

Cu

This part of our study is a field study. A lead and zinc mine, Ahangaran, located in Hamedan province (lat 24°11� 8�� N., long 48°59� 25�� E.), was chosen as the polluted area of study (Figure 1a and b). A vast, dried pool in the mine area that was no longer used served as the polluted area in this research (Figure 1c). The three plant species that dominate the area—Conium maculatum L. (Apiaceae), Stachys inflata Benth. (Laminaceae), and Reseda lutea L. (Resedaceae) (Figure 1e and f)—and polluted soil samples were collected and compared with non-polluted control samples. We chose a location 1 km away from the active mining area as the non-polluted area. The collected plants from both polluted and non-polluted areas have the same age and were in the same phenological status.

Zn

Study Area

Pb

MATERIAL AND METHODS

Table 1. Comparison of metal concentrations (mg kg−1 ) in soils of non-polluted (NP) (out of mine) and polluted (P) (mine) areas and the standard amounts of heavy metals in different countries.1

Mohsenzadeh and Mohammadzadeh

Phytoremediation Ability of the New Heavy Metal Accumulator Plants

Figure 1. The map of studied mine area. (a) Map of Iran; location of Hamedan province indicated by a circle. (b) Map of Hamedan province; the mine area indicated at south of the province by a circle. (c) The mine area with geographical characteristics (lat 48°59 25 E., long 24°11 8 N.). (d) The waste pool that is out of use in the mine area. (e and f) The studied plants that grow in the unused sate pool. Arrow shows north direction. Scale bars: (b) 20 km. (c) 1 km. (d) 20 m. (e) 20 cm. (f) 40 cm.

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and Cd were analyzed with an atomic absorption spectrometer (Varian 220) (Palmieri et al., 2005); the results were compared to the plants growing in the polluted and non-polluted areas and analyzed statistically.

Calculation of Bio-Concentration Factor and Translocation Factor The bio-concentration factor (BCF) for the aerial parts of the plants was also calculated with the following formula (Elekes et al., 2010): BCF =

Cp × 100 Cs

where Cp is the metal concentration in the aerial part of plants and Cs is the metal concentration in the soil.

While the BCF provides information on the transition of metals from soil to plant, the translocation factor (TF) is defined as the ratio of metal concentration in shoots (mg kg−1 ) to that in roots (mg kg−1 ). It shows the ability of a plant to translocate metals from the roots to the shoots. TF and BCF parameters were used to evaluate the phytoremediation potential of the studied plants. BCF and TF were determined using three replicas including three soil samples and three different individual plant for each species. They were presented as the mean of the three replicas.

Evaluation of Metal Removal The studied plants were used as accumulator plants for the phytoremediation of metal-polluted soil in the pot experiments. The species C. maculatum, S. inflata, and R. lutea occurred more than other plant species in the polluted studied area, especially at the center of the polluted pool, in visual examination. Ten pots were selected for each plant species and filled with 5 kg of originally polluted sediment soil that had been collected from the waste pool. The plants were then placed in the pots. Each pot contained three seedlings, the plant growing from seeds, with about 20 days of age. The plants were removed after an 8-month growth period. Heavy metals were measured in the soil of the pots before starting the study and also after the 8-month experiment period. Each pot received about 200 mL of running water twice each week. Leaching water was drained. Decreases in the studied metals in all of the experimental pots were determined by comparing the amount of each metal in the beginning of experiment with the end of the experimental period. The heavy metals were analyzed using an atomic absorption spectrometer. Five pots without plants were used as controls for the all three plant species; they were kept in the same conditions as the experimental pots. Amounts of the above-mentioned 444

metals were also determined in the soil of the control pots. The decreased amount of the metals in the control pots is considered to be the result of leaching. The control pots were the same for all the studied plant species.

Statistical Analysis To detect significant differences between the experimental and control groups, analysis of variance, followed by the least significant difference test, was performed (Chehregani and Malayer, 2007). Each datum was represented as the mean ± SE of five samples for the experimental groups and five samples for the control groups. The Duncan multiple range test was used for comparing the amount of heavy metals between non-polluted and polluted areas.

RESULTS Soil Analysis Soil samples analysis showed that the amounts of Pb, Zn, Cu, Ni, and Cd were 149, 137, 21, 49, and 1.4 mg kg−1 , respectively, in the non-polluted soils (Table 1). The obtained data, in comparison with the European standard (Table 1), indicated no metal pollution in the out-of-mine area. Soil samples were also collected from the mine area, and their metal analysis showed that the amounts of Pb, Zn, Cu, Ni, and Cd were higher than the out-of-mine area: Pb (17,850), Zn (3,320), Cu (356), Ni (124), and Cd (76) (all measured in mg kg−1 ). The metal analysis results showed that the total amount of the studied metals in the soil from the mine area was several times greater than the European standard. Further, Pb, Zn, Cu, Ni, and Cd were about 120, 24, 17, 2.6, and 40 times higher than in those control area, respectively.

Metal Analysis of the Plants Metal analysis in the plants (C. maculatum, S. inflata, and R. lutea) showed that the amount of Pb in the plants growing in the polluted area was significantly greater if compared to those recorded in the plants growing in the non-polluted area (Table 2). The highest amount of Pb (1,200 mg kg−1 ) was in the C. maculatum collected from the polluted mine area.

Pb is high also in S. inflata (1,172 mg kg−1 ), and Zn is high also in R. lutea (760 mg kg−1 ). Conium maculatum had also accumulated the highest amount of Zn (820 mg kg−1 ). The accumulation ability of the studied plants for Cd is also presented in Table 2. All of the plants collected from the polluted area exhibited higher amounts of Cd in comparison to non-polluted

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Phytoremediation Ability of the New Heavy Metal Accumulator Plants Table 2. Comparison of heavy metal accumulation (mg kg−1 ) in the aerial parts of the plants growing in the polluted (P) and non-polluted (NP) areas.1 Plant Metals

Conium maculatum (NP)

Pb Zn Cu Ni Cd 1 *

64 56 40 38 9

± ± ± ± ±

C. maculatum (P) 1,200 820 118 44 17

8 4 2 3 1

± ± ± ± ±

Stachys inflata (NP)

110* 90* 18* 3 2*

74 62 56 65 7

± ± ± ± ±

5 9 4 8 1

S. inflata (P) 1,172 384 140 96 20

± ± ± ± ±

Reseda lutea (NP)

98* 40* 20* 12* 3*

43 48 90 43 8

± ± ± ± ±

R. lutea (P)

6 3 7 2 1

940 760 126 40 18

± ± ± ± ±

80* 45* 20* 3 2*

Data represent the mean ± SE at least three to five samples.

Data are statistically significant different (P 0.05).

plants. The highest amount of Cd was found in the S. inflata (20 mg kg−1 ) collected from the mine area. In the same area, the lowest amounts of Pb (940 mg kg−1 ) and Zn (384 mg kg−1 ) were in R. lutea and S. inflata, respectively. In comparison to the polluted area, the plants growing in the non-polluted area accumulated lower amounts of Pb and Zn (Table 2).

The highest accumulation of Cu was in S. inflata (140 mg kg−1 ) taken from the polluted areas (Table 2).

The highest amount of Ni was also found in S. inflata (Table 2).

In the all studied plants, TF for the subjected metals was more than 1 (Table 3). This means that the location of the heavy metal accumulation is aerial part of the studied plants. In C. maculatum, TF for Pb is 1.12, while BCF is 0.412, meaning that the most absorbed metal translocated to aerial parts of the plant.

The highest TF for Pb was evaluated in S. inflata. The highest TF for Zn, Cu, Ni, and Cd were measured in R. lutea, R. lutea, C. maculatum, and R. lutea, respectively (Table 3).

Phytoremediation Results

Evaluation of BCF and TF

After the 8-month phytoremediation experiments using C. maculatum, S. inflata, and R. lutea, concentrations of heavy metals in the soil were measured, and results showed that the concentrations of all of the studied heavy metals decreased considerably after the experiment. The decreases in Pb, Zn, Cu, Ni, and Cd are illustrated in Figures 2–4. There was a decrease of Pb in the experimental pots containing C. maculatum greater than the other metals (92% decrease) (Figure 2). This species also showed the highest Zn removal ability (82%). Stachys inflata plants were the most effective in removing Ni (90%) (Figure 3).

Stachys inflata is also effective in removing a considerable amount of Pb (88%). For Cd, the greatest decrease (65%) was in the pot containing S. inflata (Figure 3). The greatest decrease of Cu was found in

BCF was measured in all of the studied plants from both the non-polluted and polluted areas (Table 3).

The highest BCF for Pb was found in the S. inflata collected from the non-polluted area. In the polluted area, the BCF was lower due to the high concentration of Pb in the soil that exceeded the absorption capacity of the plant. All of the studied plants had accumulated a moderate amount of Zn, with BCFs between 0.4 and 0.99. The highest BCFs for Cu were in the R. lutea and S. inflata growing in the non-polluted area. Reseda lutea and S. inflata had the highest and lowest BCF for Ni, respectively. The highest BCF for Cu was found in the C. maculatum collected from the non-polluted area, while its BCF for Cu was lower in the polluted area.

Table 3. Bio-concentration factor (BCF) and translocation factor (TF) in the plants collected from non-polluted (NP) and polluted (P) areas.

Pb

Zn

Cu

Ni

Cd

Plant Species

BCF

TF

BCF

TF

BCF

TF

BCF

TF

BCF

TF

Conium maculatum (NP) C. maculatum (P) Stachys inflata (NP) S. inflata (P) Reseda lutea (NP) R. lutea (P)

0.410 0.690 0.754 0.963 0.482 0.520

1.12 1.23 1.18 1.62 0.82 1.05

0.865 0.645 0.956 0.529 0.995 0.442

1.76 1.43 1.03 1.22 1.82 0.94

2.30 0.632 4.23 0.804 5.384 0.689

1.94 1.25 1.49 1.23 2.85 1.41

2.235 0.747 0.160 0.153 2.352 0.692

1.76 1.04 1.27 1.06 1.73 1.26

0.702 0.206 0.486 0.253 0.612 0.244

1.16 1.12 1.25 1.05 1.21 1.32

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Figure 2. Decrease of some heavy metals (percentage) in the polluted soils during phytoremediation by Conium maculatum. Data indicate that amounts of all studied heavy metals decreased during phytoremediation. Decrease of metals in control pots is the result of leaching. Decreases of metal concentration in experimental groups are significant (P 0.01). Data represent the means ± SE of 10 samples in experimental groups and five samples in control group.

the pot containing R. lutea (88%). This species was able to remove Pb (84%) and Zn (75%). Results indicated that all of the studied plants were able to remove considerable amounts of the studied metals (Figures 2–4).

DISCUSSION The metal analysis results showed that the amounts of Pb, Zn, Cu, Ni, and Cd were 149, 137, 21, 49, and 1.4 mg kg−1 , respectively, in the non-polluted soils, indicating that there was no metal pollution in the out-of-mine (control) area. The amounts of the metals in the mine area were higher than they were in the out-of-mine area. Concentrations of the heavy metals in the soil from the mine area were also more than the 446

European standard. Overall, Pb, Zn, Cu, Ni, and Cd were about 120, 24, 17, 2.6, and 40 times higher than they were in the control area, respectively. This indicates critical levels of pollution in the mine area that must be included in the area’s remediation programs (Chehregani et al., 2009b).

Three dominant plant species, including C. maculatum, S. inflata, and R. lutea, were found in the mine area, indicating that metal pollution did not inhibit the growth of the plant species as some reports claim (Baker et al., 1996). Metal analysis of the plants showed that the highest concentrations of Pb were found in C. maculatum (1,200 mg kg−1 ) and S. inflata (1,172 mg kg−1 ) collected from the polluted area; this is close to the accumulation ability of a best

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Phytoremediation Ability of the New Heavy Metal Accumulator Plants

Figure 3. Decrease of some heavy metals (percentage) in the polluted soils during phytoremediation by Stachys inflata. Data indicate that amounts of all studied heavy metals decreased due to phytoremediation. Decrease of metals in control pots is the result of leaching. Decreases of metal concentration in experimental groups are significant (P 0.01). Data represent the means ± SE of 10 samples in experimental groups and five samples in control group.

Pb accumulator plant (Noea mucronata, with 1,484 mg kg−1 Pb) reported previously (Chehregani et al., 2009b). Conium maculatum had also accumulated the highest amount of Zn; it seems that C. maculatum is a hyperaccumulator plant for Pb and Zn. The highest accumulation ability for Cu was found in S. inflata (140 mg kg−1 ); this is higher than the accumulation ability of N. mucronata (84 mg kg−1 ) as reported by Chehregani et al. (2009b). Stachys inflata accumulated the highest amounts of Cu in both the non-polluted and the polluted areas, and it should be considered as a Cu accumulator plant. The highest amount of Ni was found in S. inflata; thus, the species can be considered as a Ni accumulator. The present study represents the first report about the metal

accumulation ability of the three aforementioned plants.

Due to the similar ecological conditions for the studied plants, it is possible that the species’ different properties might explain the different metal accumulation levels in the aerial parts of the plants (Gosh and Singh, 2005; Chehregani et al., 2009a; and Mohsenzadeh and Shirkhani, 2016). Tables 1 and 2 illustrate that the amounts of the above-mentioned metals decreased between the soil measurements and the aerial measurements. This indicates that the elements were suppressed in their pathway from the soil to the aerial part of the plants, which is accordance with some prior reports (Baker et al., 1996; Dahmani-Muller et al., 2000; and Sarma, 2011). Table 2 shows that the plants

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Figure 4. Decrease of some heavy metals (percentage) in the polluted soils during phytoremediation by Reseda lutea. Data indicate that amounts of all studied heavy metals decreased due to phytoremediation. Decrease of metals in control pots is the result of leaching. Decreases of metal concentration in experimental groups are significant (P 0.01). Data represent the means ± SE of 10 samples in experimental groups and five samples in control group.

have metal accumulation ability that are species specific for each metal. Conium maculatum acts as a Pb and Zn accumulator, while S. inflata accumulates Cu, Ni, and Cd metals in high amounts. Reseda lutea acts also as a common heavy meal accumulator. Plants with high and moderate metal accumulation can be used in the decontamination of polluted soils (Chehregani et al., 2009b).

Based on the results showing the accumulation ability of C. maculatum, this study proposes that it can be used as a Pb and Zn accumulator; S. inflata can be regarded as a Cu, Ni, and Cd accumulator. Using such plants in phytoremediation to clean metalpolluted sites is a more cost-effective method than others, such excavation and stabilizing (Chehregani 448

et al., 2009a). Overall, the results confirm that the studied species—C. maculatumfor, R. lutea, and S. inflata—have phytoremediation potency in decontaminating heavy metal–polluted soils.

The ability of plants to up take chemical elements from growth media was evaluated using BCF. Species with a BCF from 1 to 10 are known as hyperaccumulator plants, those with a BCF from 0.1 to 1 are moderate accumulator plants, those with a BCF from 0.1 to 0.01 are low accumulator plants, and those with a BCF <0.01 are non-accumulator plants (Baker et al., 1996). The present results indicated that all three studied plants transferred Pb to their aerial parts at a moderate level. The BCF was lower in the plants growing in the polluted area due to the high concentration of Pb

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Phytoremediation Ability of the New Heavy Metal Accumulator Plants

metal in the soil (Table 3). All three studied plants also accumulated moderate levels of Zn, with BCFs ranging from 0.1 to 1. High accumulation of Cu was seen in R. lutea from the non-polluted area. Conium maculatum and S. inflata had low and moderate accumulations of Ni, respectively. All of the studied plants had a moderate accumulation of Cd. BCF is not a good criterion for determining accumulator plants because in the soils with high metal pollution, the ratio of metal concentration in the aerial part of plant to that in the soil is not high, even though the metal concentration was several times more than metal accumulation in the non-accumulator plants (Sposito et al., 1982). TF of the studied plants was higher than 1 (Table 3), meaning that they accumulated the metals in their aerial parts more that roots. TF is a good criterion for a description of real heavy metal accumulator plants (Elekes et al., 2010).

Experimental phytoremediation data showed that the concentrations of all of the subjected metals were decreased considerably (Figures 2–4). Phytoremediation tests showed that all of the studied plants were able to remove considerable amounts of the studied metals, but there was a species-specific relationship between the plants and their ability for metal removal.

The results showed that the phytoremediation ability of the studied plants was near to other previously reported accumulator plants, such as N. mucronata (Chehregani et al., 2009b), and even higher than that of others, such as soybean and Polygonum hydropiperoides (Gosh and Singh, 2005; Sarma, 2011).

Phytoremediation has become the subject of intense public and scientific interest and the topic of much recent research (Malayeri et al., 2013; Mohsenzadeh and Shirkhani, 2016; and Shihab Ahmed et al., 2016). The phytoremediation of heavy metals is a cost-effective and green technology, and there are many advantages to using native and naturally growing plants such as the plants studied here. These plants can grow in non-sufficient and poor soils, which are major advantages when using them in non-sufficient conditions because they decrease remediation costs. This is the first report on the phytoremediation ability of C. maculatum, S. inflata, and R. lutea for soil cleaning.

CONCLUSION The results showed that the amounts of the studied metals (Cd, Cu, Pb, Ni, and Zn) in the mine area exceeded the European standard; thus, the area should be regarded as polluted. The plants showed different accumulation abilities for the metals, and this study concludes that C. maculatum is a Pb and Zn accumulator; further, S. inflata is a Cu, Ni, and Cd accumulator.

The highest BCF for Pb was found in the S. inflata col-

lected from the non-polluted area. In the polluted area, the BCF was lower due to the high concentration of Pb in the soil that exceeded the absorption capacity of the plant. TF for the subjected metals was more than 1, meaning that the location of the heavy metal accumulation is the aerial part of the studied plants. The phytoremediation tests showed that the heavy metal amounts decreased in the polluted soils under the effect of C. maculatum, S. inflata, and R. lutea, suggesting that the species are effective accumulators and can be used in the phytoremediation of heavy metal– polluted soils. Conium maculatum plants are effective for Pb (92%) and Zn (82%), S. inflata plants for Cu (90%), and R. lutea plants for Cd (65%). We are suggesting more phytoremediation test in the field and real metal-polluted sites using the above-mentioned plant species.

ACKNOWLEDGMENTS This research was done using the research facility provided by the research council of Bu-Ali Sina University. We are thankful for the appropriate and useful comments and corrections suggested by this journal’s reviewers.

Conflict of Interest The authors declare that they have no conflict of interest.

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Book Review Geology Applied to Engineering

(Terry R. West and Abdul Shakoor) Review by: John Rockaway, Retired Northern Kentucky University, Nunn Drive, Highland Heights, KY 41099

Geology Applied to Engineering is a unique text that applies the principles of geologic science to the solution of problems in engineering practice. The book emphasizes the practical applications of geology and although the fundamental concepts of geology are covered, they are presented in a manner to support the applied concepts discussed. Geology Applied to Engineering is an advanced level text but written to appeal to a wide range of upper division and graduate students who have an interest in geology, environmental science or civil engineering.

Because the text is written to appeal to students with diverse interests, backgrounds and experience, the basic concepts of geology are presented but the materials and processes that are an integral part of engineering design and construction are emphasized. For example, the applications of geology to the location and design of dams, highways and tunnels, the development of groundwater resources, and procedures for landslide correction and prevention are presented. This emphasis also includes a thorough selection of example problems that illustrate the application of geologic principals to a wide range of engineering design in the areas of soil mechanics, rock mechanics, surface and groundwater ďŹ&#x201A;ow, earthquakes and geophysics. These example problems are then supported with a thorough selection of student exercises to reinforce the concepts discussed. In addition, a very useful component of this text that is that there is often a discussion of how to

collect the geologic data that is required for the analysis so that the student knows how to collect the data as well as analyze the data.

In addition to covering the core concepts of geology as it is applied to engineering however, the text also includes a wide range of supporting material necessary to understand these applied concepts. This includes discussions of geologic materials, such as soils, sedimentary, metamorphic and igneous rock. It also includes discussion of geologic processes such as running water, glaciers, weathering, wind and coastal processes.

As such, it provides the student with an excellent background not only in applying geology to engineering design and construction but also provides the student with the background necessary for understanding the geologic conditions that have contributed to this application. Both undergraduate students and graduate students will ďŹ nd the book an excellent text. It is easy to read and well-illustrated, there is an excellent selection of example problems to follow and a thorough bibliography at the end of each chapter to refer to for further information about the concepts discussed.

REFERENCE West, T.R., and Shakoor, A., 2018, Geology Applied to Engineering, 2nd Edition, Waveland Press, Indiana, 576 p., ISBN 9781-4786-3500-0.

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