Mr. Ivan Bebek reports
NEW TARGETS IDENTIFIED THROUGH MACHINE LEARNING AT AURYN'S COMMITTEE BAY HIGH-GRADE GOLD PROJECT
Auryn Resources Inc. 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 its proprietary VNet segmentation deep-learning algorithm.
Highlights:
A total of 12 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 the Aiviq and Kalulik structures) with 15 kilometres of strike length.
Michael Henrichsen, chief operating officerand chief geologist, stated: "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 (33per cent to 66 per cent) of the drill holes that contain significant gold mineralization and then analyzes the entire data set 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 per centof the drill holes that contain gold mineralization. The patterns within the data that accurately predict 99 per centof the drill holes are then applied across the area of study to derive the machine learning targets.
Detailed findings from machine learning targets
Auryn's technical team has concluded the following after comparing the machine learning targets with the geologist-derived targets:
Multiple targets derived from machine learning and geologists overlap. The overlapping of targets gives Auryn's technical team confidence in the machine learning targets and the company's previous targeting efforts. In particular, the targets in immediate proximity to the emerging discoveries at Aiviq and Kalulik have now been confirmed as future drill targets. Machine learning targets indicate the Three Bluffs deposit may extend farther east and west. These targets are largely hosted in glacial-fluvial cover, which masks gold responses in geochemistry, therefore previously limiting Auryn's ability to complete a thorough evaluation. The technical team will now initiate further work on these targets to advance them to drill stage and create the potential to significantly expand Three Bluffs. Machine learning targets have highlighted a 15-kilometre-long structural domain break between greenschist supracrustal rocks and amphibolite intrusive and gneissic rocks. This environment is a common place for gold mineralization to occur in orogenic settings around the world. This domain break provides Auryn with a third structural corridor to focus the company's exploration, besides the trends along the Aiviq-Kalulik shear zone and the Three Bluffs shear zone. Machine learning has identified a number of additional targets under shallow lakes and areas of glacial fluvial sediments where it is not possible to obtain a surficial gold geochemical response. This provides the technical team with targets that may have otherwise been overlooked and opens up a number of areas for further evaluation and to potentially advance to drill stage.
Michael Henrichsen, PGeo, chief operating officer of Auryn, is the qualified person who assumes responsibility for the technical disclosures in this press release.
About Auryn Resources Inc.
Auryn Resources is a technically driven junior mining exploration company focused on delivering shareholder value through project acquisition and development. The company's management team is highly experienced with an impressive record of success and has assembled an extensive technical team as well as a premier gold exploration portfolio. Auryn is focused on scalable high-grade gold deposits in established mining jurisdictions, which include the Committee Bay and Gibson MacQuoid gold projects located in Nunavut, the Homestake Ridge gold project in British Columbia, and a portfolio of copper-gold projects in southern Peru, through Corisur Peru SAC and Sombrero Minerales SAC.
About Committee Bay
The Committee Bay gold project is located in Nunavut, Canada. It includes over 390,000 hectares situated along the Committee Bay greenstone belt (CBGB). High-grade gold occurrences are found throughout the 300-kilometre strike length of the Committee Bay gold belt, with the most significant being the Three Bluffs deposit. The project benefits from existing infrastructure, including bulk-storage fuel facilities, five high-efficiency drill rigs and a 100-person camp. The Committee Bay project is held 100 per centby Auryn subject to a 1-per-cent net smelter royalty on the entire project and an additional 1.5-per-cent net smelter royaltyon a small portion of the project.
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