Abstract:

Since the early 21st century, growing demand for mineral resources and increasing extraction complexity have made deep mineral exploration a major trend. Concurrently, processing and analyzing massive geological data has become a critical challenge for deep mineral exploration. The Huoqiu area in Anhui Province—a strategically important region rich in minerals such as iron and copper—has seen growing emphasis on deep exploration.In this study, we used the Huoqiu area as a test site and adopted a three-dimensional mineral prospectivity modeling method based on the second-generation Graph Attention Network (GATv2). By constructing a three-dimensional geological model of the deposit, we discretized the subsurface space into a voxel graph structure. Leveraging GATv2 for dynamic attention modeling of nonlinear spatial relationships between nodes, the method effectively captures complex topological relationships and nonlinear interactions among geological units, enabling high-precision prediction of deep mineral resources.Comparison with traditional machine learning models demonstrated the superiority of GATv2 in handling complex geological data and revealing mineralization patterns. Based on the model predictions, a significant mineralization target was delineated within the deposit area, providing a scientific basis for exploration. The results indicate that GATv2 exhibits exceptional performance in 3D mineral prospectivity modeling, improving both prediction accuracy and efficiency while offering novel ideas and methods for deep mineral exploration. This study provides valuable insights for mineral exploration in analogous regions.

Key words: three-dimensional geological modeling, deep prospecting prediction, deep learning, machine learning, mineralization potential