A Machine Learning-Based Framework for Mineral Prospectivity Modeling: Predicting Epithermal Gold Mineralization in Northern New Brunswick, Canada

Gold deposits in New Brunswick, part of the Canadian Appalachians, formed during various stages of the Appalachian orogeny. Significant regional-scale transcurrent faults that are locally controlling cogenetic magmatic, include the Restigouche, Rocky Brook-Millstream, McCormack-Ramsay Brook, McKenzie Gulch, and Moose Lake faults, played a crucial role in shaping the geological framework and enabling the focusing of mineralizing fluifds in northern New Brunswick. The mineral systems approach is applied here to link conceptual models of mineralization processes with available exploration data, aiming to achieve effective mineral prospectivity mapping (MPM). This method is designed to streamline exploration efforts, minimizing both time and cost, which are key priorities in the mineral exploration industry. A machine learning-based data-driven approach was utilized to evaluate 18 predictor maps with a pixel size of 200 meters. These MPM maps integrated diverse features, including geochemical indicators for Au, As, Sb, Zn, Pb, Cu, and Mo in till to define geochemical anomalies, airborne radiometric data for K, eU, and eTh, as well as aeromagnetic and LiDAR datasets, to interpret geological characteristics, structural features, faults, intrusive and extrusive units, and lithological contacts. A series of edge enhancement filters, including Reduced to Pole (RTP), first vertical derivative (FVD), tilt derivative (TDR), and analytic signal (AS), were applied to the dataset, followed by a 3D inversion. Our results show that bimodal felsic to mafic intrusive and extrusive igneous systems exhibit a strong magnetic response, a conclusion validated through correlation with drill core assay data. Moreover, this study utilized principal component analysis (PCA) of till data to determine pathfinder and indicator elements associated with gold mineralization. A MPM model was created for epithermal gold mineralization using a Support Vector Machine (SVM), incorporating the known gold occurrences and deposits of the area. The performance of the resulting MPM maps was evaluated using the area under the receiver operating characteristic curves (AUC-ROC). The study concludes that SVM is a robust tool for mineral exploration, providing a data-driven approach to identifying new mineral deposits with greater accuracy and efficiency.