Uncertainty quantification of deep learning algorithms for mineral prospectivity mapping

Deep learning algorithms have significantly advanced mineral prospectivity mapping (MPM) by facilitating automated feature extraction and capturing nonlinear relationships among multi-source geological datasets. However, deep learning algorithms for MPM frequently neglect the intrinsic uncertainties arising from incomplete geological knowledge, limited sampling, and model variability, leading to overconfident and potentially unreliable predictions. To address this limitation, this study proposes a comprehensive uncertainty quantification framework that jointly evaluates the input data, model, and predictive uncertainties in deep learning-based MPM. Data uncertainty, originating from limited spatial resolution of geochemical/geophysical features and subjective interpretations of geological information, is characterized through stochastic simulation of evidential layers. Model uncertainty, arising from variability in network architecture and parameters estimation, is captured through a joint Bayesian convolutional neural network (CNN) and Monte Carlo Dropout. Predictive uncertainty, representing the overall uncertainty of predictions, is quantified by integrating the contributions of both data and model uncertainties. The proposed framework is demonstrated through a case study of gold prospectivity mapping in western Henan Province of China. These uncertainties are quantified using statistical measures including mean, variance, and entropy. The obtained results indicate that areas exhibiting high prospectivity and low uncertainty represent robust exploration targets, whereas those with high uncertainty highlight regions requiring improved metallogenic interpretation or model refinement. Furthermore, uncertainty contribution analysis reveals that data uncertainty contributes more to total predictive uncertainty than model uncertainty, suggesting that enhancing the quality and representativeness of evidence layers is more effective for reducing uncertainty than merely optimizing models' architecture or parameters. Overall, by modeling and visualizing both data and model uncertainties, the proposed framework transforms deep learning-based MPM from deterministic prediction to probabilistic decision-making, thereby enabling more reliable and trustworthy mineral exploration.