As mineral exploration increasingly targets deeper and more geologically complex terrains, the need for reliable predictive models becomes critical to mitigating exploration risk and improving cost efficiency. Correspondingly, the effectiveness of deep mineral exploration strategies depends substantially on the effectiveness and precision of three-dimensional mineral prospectivity mapping (3D MPM) models. However, the inherent spatial non-stationarity—where ore grade variability changes across geological domains—and the strongly skewed distribution of high-grade samples present a dual challenge. Conventional methods, which primarily rely on mean-based regression, often struggle to adequately address this dual challenge, limiting their predictive performance in complex geological settings. To address these issues, this paper proposes a pinball-loss-guided, global–local fusion Transformer model within a unified framework for 3D MPM. It leverages a multi-head self-attention mechanism with global–local fusion to capture long-range dependencies and global geological contexts, while incorporating local feature extraction modules to adaptively model spatially varying mineralization controls, jointly optimized through a pinball loss function to address mineralization distribution skewness. The proposed framework was first rigorously evaluated using the Xiadian gold deposit as a case study. Bootstrap analysis of the ablation experiments confirmed its predictive performance in terms of quantile-specific accuracy and prediction interval (PI) calibration. Ten rounds of random data splits provided further confirmation of the model’s stability. Subsequently, the validated model was applied to prospectivity mapping in unexplored regions, leading to the delineation of several high-potential exploration targets. Finally, comparative analyses with state-of-the-art machine learning methods were conducted, which further validated the competitive fitting capability of the proposed framework.
Huang et al. (Fri,) studied this question.