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New Targets Identified through Machine Learning at Auryn's Committee Bay High-Grade Gold Project
VANCOUVER, BC / ACCESSWIRE / February 19, 2019 / Auryn Resources Inc. (TSX: AUG, NYSE AMERICA...
About this update from Fury Gold Mines Limited
[{"type":"text","content":"New Targets Identified through Machine Learning at Auryn's Committee Bay High-Grade Gold ProjectVANCOUVER, BC / ACCESSWIRE / February 19, 2019 / Auryn Resources Inc. (TSX: AUG, NYSE AMERICAN: AUG, \"Auryn\" or the \"Company\") is pleased to announce that it has received results from the machine learning targeting exercise for the Committee Bay high-grade gold project in Nunavut. The machine learning technology is provided by Computational Geosciences Inc. (CGI) and their proprietary VNet segmentation deep learning algorithm.Highlights: A total of twelve new targets were generated, including: Two targets overlapping with Auryn's geologist derived targets, adjacent to the Aiviq and Kalulik discoveries. Two targets creating east and west extensions of the Three Bluffs deposit. Multiple targets hidden beneath shallow lakes and glacial-fluvial cover. A third structure has been identified (in addition to the Three Bluffs structure and Aiviq and Kalulik structure) with 15 kilometers of strike length.A Message from Michael Henrichsen, C.O.O. and Chief Geologist:\"The machine learning process is valuable because it removes bias and its in-depth analysis of our extensive, high-quality data sets outreaches the capabilities of the human brain. The resulting targets have brought our exploration plans into focus and have given us confidence in our emerging discoveries at Aiviq and Kalulik. In addition, the machine learning identified new targets under shallow lakes and glacial-fluvial cover, where surface geochemical sampling has not been possible.\"\"As a technical group we remain committed to the substantial opportunities we believe exist at Committee Bay, and we will continue to use innovative methods to make those potential discoveries.\"The Machine Learning Technology:The machine learning targeting was trained using data from gold in drill holes that was primarily taken from the Three Bluffs deposit. The machine randomly selects a percentage (33 - 66%) of the drill holes that contain significant gold mineralization and then analyzes the entire dataset to look for patterns that can then predict the remainder of the mineralized drill holes. After each iteration of this process another random percentage of the drill holes is selected, and this is repeated until the machine is able to predict 99% of the drill holes that contain gold minerali...