A prior-guided self-supervised contrastive transformer network for sparse geochemical anomaly identification

Geochemical anomaly identification plays a critical role in mineral exploration because mineralization-related geochemical patterns provide important evidence for delineating prospective ore-forming zones. However, geochemical survey data are commonly affected by heterogeneous background and complex inter-element relationships. Mineralization-related anomalies are typically sparse and weak, making them difficult to distinguish from surrounding background variations. These characteristics limit the effectiveness of conventional statistical methods and supervised learning models, particularly when reliable mineralization labels are scarce. To address these challenges, this study proposes a prior-guided self-supervised contrastive Transformer network for sparse geochemical anomaly identification (CTGA). The framework learns discriminative inter-element representations directly from unlabeled geochemical data through geochemically constrained contrastive representation learning, while a known mineral occurrence is used only during inference as a prototype to guide similarity-based anomaly retrieval in the learned feature space. Spatial refinement is further applied to improve anomaly continuity and suppress isolated false-positive responses. The proposed framework was evaluated in the Zhaishang–Liba gold ore cluster area in the Western Qinling Mountains, China. Experimental results demonstrate that CTGA produces geologically meaningful anomaly patterns with high spatial consistency to known mineralization and outperforms representative comparative methods, achieving an AUC of 95.21% under the adopted evaluation protocol. These results indicate that the proposed framework provides an effective solution for geochemical anomaly identification in mineral exploration scenarios where labeled mineralization information is extremely limited.