A Heterogeneous Graph Construction Method for Mineral Prospectivity Mapping

Abstract

Graph-based models have been utilized for mineral prospectivity mapping (MPM), and they have demonstrated excellent performance owing to their adaptable graph structure, which is conducive to comprehensively considering the spatial anisotropy of mineralization compared with pixel- or image-based models. However, widely used graph-based models cannot fully consider the relationship between geological entities and mineralization. A heterogeneous graph is a type of graph structure containing rich heterogeneous information, allowing the consideration of various relationships and the assignment of suitable attributes to various types of nodes. Nodes in heterogeneous graphs can fully integrate heterogeneous information based on specific relations (i.e., edges). This study introduced a novel method for constructing heterogeneous graphs for MPM. The nodes in the graph consist of different types of geological entities, and the edges (relations) represent the links between the geological entities. The constructed heterogeneous graph cannot only effectively express the spatial anisotropy of mineralization but also consider the shape of geological entities and the relationships among geological entities, which is beneficial for modeling complex ore-forming geological processes. This heterogeneous graph was then trained using graph neural networks to obtain a mineral prospectivity map for southwestern Fujian Province, China. In addition, the proposed graph construction method demonstrated higher feasibility and accuracy in MPM compared to conventional graph construction method and convolutional neural networks.