Enabling Scalable Mineral Exploration: Self-Supervision and Explainability
This work introduces a unified framework for Mineral Prospectivity Mapping that combines self-supervised learning, explainable AI, and uncertainty modeling to overcome challenges in deep learning caused by limited labeled data and interpretability. The approach improves prediction robustness and provides geologically meaningful insights, demonstrated through evaluations on Zn–Pb deposits in North America and Australia.
Jan 1, 2024
