Recognition of multiple geochemical anomalies by dual-branch convolutional neural network with adaptive feature fusion

Abstract

Geochemical anomalies are critical indicators for mineral exploration and resource evaluation. However, due to the diversity and complexity of geological processes, identifying geochemical anomalies remains challenging. This study proposes a dual-branch convolutional neural network based on adaptive feature fusion (1-2D AFFCNN) to simultaneously extract the spectral compositional relationships and spatial structural features of geochemical elements. The model incorporates an Adaptive Feature Fusion Module (AFFM) to effectively integrate features from different branches, significantly improving predictive performance and robustness. Experimental results demonstrate that the 1-2D AFFCNN outperforms traditional single models in terms of accuracy (92.3 %), recall (92.0 %), and AUC value (0.98). The three-stage training strategy effectively mitigates the vanishing gradient problem, enhancing training efficiency and stability. In the application to the Changba ore-concentrated area in Gansu Province, the high-probability anomaly zones generated by the model are highly consistent with the spatial distribution of known lead-zinc deposits, and several high-potential mineralization areas were identified. This study not only provides a novel approach for the comprehensive analysis of multidimensional geochemical data but also opens new avenues for mineral resource prediction and target area localization.