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ArcPacific Resources Completes Data Compilation and Shows up to 54 g/t Gold, 3.4% Copper, and 2,444 g/t Silver at LMSL Project
Vancouver, British Columbia--(Newsfile Corp. - January 19, 2021) - ArcPacific Resources Corp. (TSXV: ACP) ("ACP" or the "Company") is pleased to announce it has
About this update from First Atlantic Nickel & Cobalt Corp.
[{"type":"text","content":" Vancouver, British Columbia--(Newsfile Corp. - January 19, 2021) - ArcPacific Resources Corp. (TSXV: ACP) (\"ACP\" or the \"Company\") is pleased to announce it has completed an extensive data compilation program resulting in the identification of highly elevated gold, copper and silver occurrences spread across the 8,136 hectare Lucky Mike Silver Lode Project (the \"LMSL\" or the \"Project\"). The work program compiled all available data including 75 separate reports dating back as far as the 1950's into a single merged database. This data includes 11,306 rock, soil, silt and core samples containing 280,716 values for multiple elements, and multiple historic geophysical surveys. ArcPacific is utilizing this information to perform extensive and in-depth detailed analysis which is presently underway. Compilation results show extensive areas of anomalous mineralization. The merged database allows for advanced and modern integration of data and analysis including: Litho-geochemical and geochemical analysis for geological modeling; and, Artificial Intelligence (\"AI\") integration for advanced target generation. Ongoing detailed analysis of the complete merged database will produce a property geological model. Planned 2021 follow up work will leverage the complete dataset and analysis once finalized. ArcPacific also reports that it has engaged two separate independent contractors to interpret and model the new extensive database. This includes a copper gold porphyry expert focused on litho-geochemical and geochemical modeling with the goal to determine the probability and potential locations of possible economic mineralization on the Project. ACP has also contracted an AI company to utilize the extensive database to sus-out \"hidden\" targets within the Project area. AI machine learning programs are able to identify subtle indicators hidden within 200 thousand plus geochemical sample data points and incorporate multiple types of data including the various geophysical surveys completed to draw comparisons with known areas of elevated mineralization. This can lead to AI predictions potentially identifying new mineralised areas, either within the areas covered by existing surveys, or amazingly, predict mineralized areas outside of the coverage areas even potentially predicting mineralization under cover. Early analysis indicates the p...