LUNAR MINING: CHINA PROBES MOON’S PROSPECTS FOR RESOURCE DEVELOPMENT / 3 Geotech_Earlug_2016_Alt2.pdf 1 2016-06-24 4:27:20 PM
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Exciting projects targeting the yellow metal / 9-14
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MARCH 30–APRIL 12, 2020 / VOL. 106 ISSUE 07 / GLOBAL MINING NEWS ¡ SINCE 1915 / $5.25 / WWW.NORTHERNMINER.COM
AI, machine learning to deliver ‘wave of discoveries’ EXPLORATION  
| New technologies set to tackle massive volumes of data
BY CARL A. WILLIAMS cwilliams@northernminer.com
T
Drillers at SilverCrest Metals’ Las Chispas gold-silver property in Sonora, Mexico.   SILVERCREST METALS
SilverCrest weighs options after underwriter bails PRECIOUS METALS  
| Funds would have advanced Las Chispas
BY STEVE STAKIW Special to The Northern Miner
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s global financial markets began to melt down in response to the coronavirus pandemic, SilverCrest Metals (TSX: SIL; NYSE-A: SILV) inked a $75 million bought-deal financing on Mar. 11 that was underwritten by a banking syndicate led by National Bank Financial (and including Eight Capital and Scotia Capital). The deal was terminated seven days later.
National Bank Financial said it was terminating its obligations under the deal citing a “disaster out� clause in its agreement due to the coronavirus pandemic, SilverCrest said in a Mar. 18 press release. SilverCrest is digging in its heels, however, asserting that the signed agreement “created a binding legal obligation on the part of National Bank Financial to complete the transaction as is customary in Canada for bought-deal financings.� The company disputes t he
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bank’s pandemic justification as the situation “was fully evident when the bought-deal financing was agreed upon with expectations that the precious metals market would respond positively to this known risk.� SilverCrest says it intends to pursue legal options against National Bank Financial for its breach of the agreement. Bought deal financings are generally the most favoured equity financing method for issuers as the underwriting syndicate of bankers assures a price and commits to purchase the entire amount of the offer, then reselling it to its investor clients. The financing agreement to sell 9.1 million SilverCrest shares at $8.25 apiece, a 5% discount to the $8.69 share price prior to the announcement, would have been lucrative for the bankers. They would have grossed 5% of the $75 million, or about $3.75 million, plus further potential commission under a 15% over-allotment option to place up to an additional 1.37 million shares if there was investor appetite. The underwriters in the now See SILVERCREST / 2
he past 20 years have seen remarkable advances in the mining industry, particularly in mineral exploration technologies with vast volumes of data generated from geologic, geophysical, geochemical, satellite and other surveying techniques. However, the abundance of data has not necessarily translated into the discovery of new deposits, according to Colin Barnett, co-founder of BW Mining, a Boulder, Colorado-based data mining and mineral exploration company. “One of the problems we’re facing in exploration is the huge increase in the amounts of data we have to look at,� said Barnett, in his presentation at PDAC 2020, during a session on managing and exploring big data through artificial intelligence and machine learning. “And although it’s high-quality data, the sheer volume is becoming almost overwhelming for human interpreters, and so we need help in getting to the bottom of it.� By integrating hundreds or even thousands of interdependent layers of data, with each layer making its own statistically determined contribution, machine learning offers a solution to the problem of tackling the massive amounts of data generated, and a powerful new tool in the search for mineral deposits. But, in an interview with The Northern Miner, he cautioned that to fully exploit the potential of machine learning in mineral exploration, “prospectors will still need to devote considerable time and effort to the preparation of data before machine learning techniques can add value for companies.� To illustrate his point, Barnett demonstrated how he and his partner at BW Mining, Peter Williams, are using machine learning to analyze data from geological, geochemical and geophysical surveys of the Yukon in northwestern Canada to locate new deposits. The Yukon became famous for the Klondike gold rush during the late 1890s, which petered out after a few years as prospectors moved onto Alaska. Today the area is experiencing a renewed interest in what has become known as the Tintina Gold
Belt, with significant lode deposits being found over the past two decades and, according to Barnett, “more waiting to be discovered.� “We used the Yukon bedrock geology map published by the Yukon Geological Survey, which is very detailed and shows over 200 different geological formations,� explained Barnett. “However, you can’t simply put 200 formations into a machine learning process. First, the data requires special treatment.� By representing each of the formations with a separate grid and by continuing the grids upward, they were able to see overlaps between formations, allowing them to consolidate the data by grouping the formations by rock type and age, and thereby reducing the data set down to around 50 discrete and different formations. They then used the same process to represent structural data provided by the map. “The structural data is important because it represents the pathways that the mineralization generally took to reach the surface,� explained Barnett. “We then used geophysical See AI / 2 PM40069240
EURO MANGANESE: ADVANCING ASSET IN THE CZECH REPUBLIC / 7
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