TPEC Algorithm and Its Application in Lau Basin Multi-Beam Echo-Sounder Data Processing

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Studies in Surveying and Mapping Science (SSMS) Volume 3, 2015

TPEC Algorithm and Its Application in Lau Basin Multi-Beam Echo-Sounder Data Processing Li-Shoujun1,2, Wu-Ziyin*1, Zhou-Mengjia1 Second Institute of Oceanography, Key Lab of Submarine Geosciences, SOA, Hangzhou, 310012, China

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Graduate Department, China University of Geosciences, Wuhan, 430074, China

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0911guang@163.com Abstract Currently, multibeam automatic filtering algorithm in processing gross errors has some defects. For example, in trend surface model, we can easily mark obvious gross errors, but it’s hard to identify small gross errors. Median filter model can filter abnormal data well, but it also will lead to the loss of the terrain details. The biggest problem is that the existing filtering algorithms are mostly based on statistical characteristics of the data, without considering the size of the error in the data itself. In this paper, we introduce Total Propagated Error Computation (TPEC) algorithm to improve the efficiency of Multi-Beam Echo-Sounder data (MBES) processing and to avoid losing terrain details in trend surface model and median filter model. The workflow of TPEC algorithm can be summarized as follows: Firstly, compute the Total Propagated Error of MBES data. Next, according to the IHO standard, eliminate noises automatically. Further, generate a plurality of depth and associated uncertainty estimation of grid nodes. Finally, construct “the best” Digital Terrain Model (DTM) based on the principles of density and neighborhood. To evaluate the efficiency and quality of the TPEC algorithm, TPEC algorithm and conventional method are used respectively to process the same MBES data in Lau Basin. The conventional method uses man-machine editor to reject gross errors and construct DTM by beam inverse weighting algorithm. TPEC algorithm removes gross errors automatically by filter based on the computation of Total Propagated Error, and builds DTM by the combined estimation of the depth and related uncertainty.The data processing speed of TPEC algorithm is 5 times of the conventional method. Furthmore, the TPEC model shows much more terrain details than the man-machine model. TPEC model has a better robustness, and is suitable for the research of complex seabed terrain environment, such as Lau Basin. TPEC algorithm provides a possible technological method for the study of terrain features in submarine hydrothermal area. Keywords Total Propagated Error Computation Algorithm; Multi-Beam Echo-Sounder; Density Principle; Neighborhood Principle; Lau Basin

Introduction Due to the influence of the instrument’s noise, waves, sound velocity and other factors, Multi-Beam Echo-Sounder data exists gross errors inevitably. These gross errors make it difficult to reflect the topography accurately, and even leads to the whole data invalid. The traditional method of eliminating the gross error of Multi-Beam EchoSounder data is based on man-machine interaction editing for each line processing, which is less efficient and easily affected by the subjective judgment [1]. Under the assumption of seabed topography continuous change, many researchers proposed some automatic filtering algorithms to eliminate abnormal soundings. The simplest method is setting the maximum and minimum depth threshold; Varma et al. presented the median and standard deviation estimation to exclude abnormal soundings [2]; Eeg used multivariate hypothesis testing to process multibeam data [3]; Du, Wells et al. wrote a program simulating artificial filtering data editing [4]; Lirakis, Bongirvanni et al. rejected the abnormal data by using PFM systems [5]; He Yibin put forward a trend surface model and robust filter based on M-estimation to detect abnormal data [6]; Yang Fanlin et al. combined median filter with local variance estimation and wavelet analysis to remove outliers and noises [7]. Multibeam data automatic filtering algorithms are mostly based on statistical characteristics of the data, or a priori regional topography, without the consideration of data errors. Strict filter settings will cause the loss of terrain detail, while loose settings are eliminating the gross error inefficiently. University of New Hampshire Calder put forward a combined estimation of the depth and related uncertainty by MBES to process multibeam data [8, 9]. According to

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