Mcfarlane Lake Mining Completes Artificial Intelligence Prospectivity Analysis on Its High Lake Property

TORONTO, ON / ACCESSWIRE / March 5, 2024 / McFarlane Lake Mining Limited ("McFarlane" or the "Company") (CBOE formerly) (NEO:MLM)(OTCQB:MLMLF), a Canadian mineral exploration and development company, is pleased to announce 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 information this study provides will be valuable as we continue to work on expanding the resource at High Lake," said Roger Emdin, COO of McFarlane. "We are pleased to see that it validates our view of the high-quality resource available at the Purdex Zone and has identified additional targets in areas where we have recently added new claims."

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, as highlighted in Figure 1. 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 3D contoured surfaces are projected to surface and are presented in Figure 1.

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.