Mineral Prospectivity Modeling of Graphite Deposits and Occurrences in Canada

Abstract

Exploration for graphite in Canada is of economic, strategic and governance priority. In this study, we aimed to develop a reliable prospectivity map for graphite in Canada. Our approach mitigated multiple sources of workflow-induced uncertainty by propagating uncertainty due to the selection of negative labels, machine learning algorithms, feature space dimensionality, and hyperparameter tuning metrics. By averaging an ensemble of de-correlated models, we produced a single-merged model that clearly represents propagated uncertainty through a consensus map and an uncertainty map. These maps adhere to the metrological convention of "result plus/minus associated uncertainty" and are intuitive to use. Our ensemble demonstrated robustness, quickly converging to the consensus model, suggesting that new mineral prospectivity mapping (MPM) products using the same data would unlikely perturb our consensus model’s coverage. We conducted a maximally double-blind study, avoiding geoscientific knowledge during model generation to ensure impartial post-hoc analysis and interpretation. Therefore, our MPM products complement geoscientific knowledge-based exploration, because the targeting information provided in our MPM products constitute a maximally independent source. Our MPM products showed excellent spatial variability, aligning with existing knowledge of graphite deposits in Canada, indicating that combining data-driven rigor with independent interpretation enhances the robustness of our MPM products. Consequently, we believe our MPM products could effectively guide regional exploration of natural graphite in Canada.