McFarlane Lake Mining Limited announced the completion of a surface and subsurface Prospectivity Analysis performed by Mercator Geological Services (Mercator) on McFarlane's 100% owned High Lake property with the use of Artificial Intelligence (AI). The Prospectivity Analysis used a combination of Knowledge-driven and Supervised Machine Learning algorithms to generate surface and subsurface exploration targets. These targets are generated using inputs such as geological structure, lithology, mineralization, and geochemistry within the High Lake property.

This is then related to the local geology, which is classed as a greenstone-hosted lode gold deposit. These generated target areas will help guide future exploration of the property. The Supervised Machine Learning algorithm utilized in the prospectivity analysis was Random Forest.

The 3D subsurface prospective analysis used all available geological data from historical and recent drilling, which was compiled and formatted using the geological software Leapfrog Geo. Tables were imported and processed by Mercator's proprietary machine-learning algorithm. Downhole prospectivity results were interpolated within Leapfrog to create 3D contoured surfaces.

These surfaces reflect the predicted prospectivity score. The Supervised Machine Learning model predicted a probability score that reflects similarity to gold mineralization observed within McFarlane's gold resource area - the Purdex zone. Probability scores were compared to gold assay results and correlated well.

The downhole Supervised Machine Learning identified key geological features that are associated with the mineralization present within the Purdex Zone, as well as areas with the most similar features to the Purdex Zone. The High Lake prospects identified as the most similar to the Purdex Zone are the R and W Zones. The surface prospectivity analysis defines a number of target areas that align with the results of the downhole prospectivity model.

It also identified new areas of interest in the north of the property. The results produced by machine learning models are not absolute and should be interpreted with some degree of caution. However, the results do confirm previous results from historical prospecting, geophysics, and diamond drilling and serve as computer-generated predictions that can help guide future exploration of the property.

McFarlane Lake is currently drilling a 10,000-metre drill program at the Purdex Zone as a follow-up to the filing of an NI 43-101 compliant Mineral Resource Estimate in July 2023 that identified 96,200 Inferred ounces and 45,800 Indicated ounces of gold at a grade of 10.43 grams per tonne of gold and 9.38 grams per tonne of gold, respectively.