Attention and Geological Knowledge Guided Spectral‐Spatial Networks for Geochemical Anomalies Recognition

Abstract

Achieving both accuracy and interpretability in deep learning models for geochemical anomaly recognition constitutes a significant challenge. To overcome this challenge, this study developed a novel interpretable dual‐branch network combining a spectral attention bidirectional RNN (BiRNN) branch and a spatial attention CNN branch guided with geological knowledge for geochemical anomaly recognition. The dual‐branch network comprehensively extracts rich spectral and spatial features from geochemical data cubes, which record both the elemental composition within individual pixels and the spatial relationships between neighboring pixels. To mitigate deep learning's black‐box nature and enhance interpretability, geological knowledge (e.g., key ore‐controlling faults) and attention mechanisms are incorporated into the model, boosting both predictive accuracy and interpretability. Geological knowledge integrated during training constrains the model, penalizing predictions that violate geological principles. Concurrently, the attention mechanisms applied to both branches enable visual interpretation of the decision‐making process. The model's efficacy for identifying mineralization‐associated geochemical anomalies was validated in a case study within the southern Tianshan, China. Ablation experiments focusing on pre‐model interpretability via geologically constrained input, in‐model interpretability via incorporating geological knowledge into the loss function of the model, and post‐model interpretability via visualization of attention weights are conducted. Comparative results show that the geologically constrained dual‐branch network improves both accuracy and interpretability in geochemical anomaly detection. Furthermore, spectral and spatial weight attention visualizations further demonstrate the model's alignment with geological principles.