  Machine Learning (ML) for Mineral Prospectivity Mapping (MPM) remains a
challenging problem as it requires the analysis of associations between
large-scale multi-modal geospatial data and few historical mineral commodity
observations (positive labels). Recent MPM works have explored Deep Learning
(DL) as a modeling tool with more representation capacity. However, these
overparameterized methods may be more prone to overfitting due to their
reliance on scarce labeled data. While a large quantity of unlabeled geospatial
data exists, no prior MPM works have considered using such information in a
self-supervised manner. Our MPM approach uses a masked image modeling framework
to pretrain a backbone neural network in a self-supervised manner using
unlabeled geospatial data alone. After pretraining, the backbone network
provides feature extraction for downstream MPM tasks. We evaluated our approach
alongside existing methods to assess mineral prospectivity of Mississippi
Valley Type (MVT) and Clastic-Dominated (CD) Lead-Zinc deposits in North
America and Australia. Our results demonstrate that self-supervision promotes
robustness in learned features, improving prospectivity predictions.
Additionally, we leverage explainable artificial intelligence techniques to
demonstrate that individual predictions can be interpreted from a geological
perspective.
