Knowledge–Data Collaboration-Driven Mineral Prospectivity Prediction with Graph Attention Networks

Predicting mineral deposits accurately requires capturing the complex interactions among geological structures, geochemical anomalies, and alteration patterns. To address this challenge, this study develops a Knowledge–Data Collaboration Graph Attention Network (KDCGAT) to improve copper mineralization prediction by integrating multi-source geological data. The model combines Graph Attention Network (GAT) with multimodal geoscience data, including fracture structures, remote sensing alteration maps, and geochemical anomalies. Spatial correlations are captured through a self-attention mechanism, aligning deep learning predictions with geological and geochemical knowledge. Using the eastern Tien Shan copper belt in Xinjiang as a case study, KDCGAT achieves a copper deposit identification accuracy of 85.9%, outperforming Weight of Evidence (WoE) by 7%, Graph Convolutional Network (GCN) by 11.3%, and Convolutional Neural Network (CNN) by 19.7%. Ablation experiments show a 21.1% improvement over the baseline GAT model. Finally, five Class A and three Class B mineralization prediction zones are delineated. This study demonstrates the effectiveness of graph neural networks for copper prospectivity prediction and highlights knowledge–data collaboration as a practical tool for mineral exploration.