Mineral exploration is inherently challenging because geological formations are complex and geochemical relationships are often nonlinear and spatially variable. Although artificial intelligence has recently shown strong potential in improving mineral potential mapping, many existing approaches struggle to fully capture spatial relationships within geochemical data. In this study, an integrated framework that combines Graph Neural Networks (GNNs), ensemble learning classifiers, and unsupervised K-means clustering was developed to analyze geochemical data from Saudi Arabia. The geochemical samples were modeled as a spatial graph, where each node represents a sampling location, and the connections between nodes reflect their geographic proximity. This structure allows the GNN to better capture spatial relationships within the data, while ensemble models serve as baseline methods for performance comparison. K-means clustering was further used to examine spatial patterns and highlight potential mineralization zones. The proposed approach achieved strong predictive results, with classification accuracies reaching 85.08% for lithium and 90.62% for tungsten, alongside comparable performance for other elements. Overall, these results demonstrate the value of incorporating spatially-aware artificial intelligence techniques to support more accurate mineral exploration and more informed resource management.
Alzubidi et al. (Thu,) studied this question.