A Novel Spatial–Element Transformer with Dual Attention Architecture for Mineral Prospectivity Mapping

In recent years, data-driven mineral prospectivity mapping (MPM) has become a key technical approach for delineating exploration targets. Nevertheless, two major challenges persist. First, sampling is often sparse and irregularly distributed, making it difficult to represent spatial relationships effectively. Second, many existing models implicitly assign equal importance to all geochemical elements. They also fail to jointly model heterogeneous elemental contributions and spatial neighborhood relationships. To address these issues, we propose the Spatial–Element Transformer (SETransformer), which integrates SHapley Additive exPlanations (SHAP)-derived element-importance attention bias with adaptive-bandwidth spatial-decay attention for data-driven MPM. We evaluate the proposed method using data from the Jiama porphyry–skarn Cu-polymetallic deposit in Tibet, China, within two a priori defined metallogenic feature subspaces: Mo-type and Cu-type. SETransformer is benchmarked against conventional machine learning models (Random Forest (RF), eXtreme Gradient Boosting (XGBoost), support vector machine (SVM)), and deep learning baselines (graph convolutional network (GCN) and TabNet). Model performance is assessed under two spatial validation schemes: a south–north hold-out validation and a five-fold spatial block cross-validation. Under both schemes, SETransformer achieves the best overall performance in the two subspaces. The predicted MPMs are consistent with regional geological evidence and exhibit clearer boundaries, providing a useful reference for subsequent target delineation in the study area.