Three-Dimensional Mineral Prospectivity Modeling of Skarn-Type Copper Deposits in the Anqing Area Based on Causal Inference and Graph Attention Networks

This study proposes a three-dimensional mineral prospectivity modeling method that integrates causal inference with Graph Attention Networks (GAT) to improve the accuracy and efficiency of deep concealed skarn-type copper deposits prediction in complex geological settings. Using the Anqing area of the Middle-Lower Yangtze Metallogenic Belt as a case, a high-precision 3D geological model involving strata, intrusions, faults, and ore bodies was constructed based on geological maps, borehole data, and geophysical information through a hybrid explicit-implicit modeling approach. On this basis, the RESIT causal inference algorithm, which is built upon non-Gaussian assumptions, was employed to analyze 62 ore-controlling factors. A causal graph was established, and 14 key controlling variables were identified. Subsequently, a 3D prediction dataset incorporating spatial adjacency relationships was developed, and the causal structure was introduced into the GAT model for mineralization probability prediction. Comparative experiments demonstrate that the proposed method outperforms commonly used approaches-including Random Forest, Support Vector Machine, Graph Convolutional Networks, and 3D Convolutional Neural Networks-in terms of accuracy, AUC, and success rate curves. Based on the predictions, four deep high-potential target zones were delineated, which are closely associated with diorite intrusions and Triassic carbonate contact zones. The results indicate that integrating causal inference with deep graph learning not only enhances prediction performance but also improves the geological interpretability of the model, providing a promising technical pathway for deep mineral exploration.