Dual-Branch Convolutional Neural Network and Its Post Hoc Interpretability for Mapping Mineral Prospectivity

Abstract

The purpose of mineral prospectivity mapping (MPM) is to discover unknown mineral deposits by means of fusing multisource prospecting information. In recent years, with rapid advancements in artificial intelligence, deep learning algorithms (DLAs) as a groundbreaking technique have exhibited outstanding capabilities in geoscience. However, conventional DLAs for MPM face certain challenges in feature extraction and the fusion of multimodal prospecting data. Moreover, opaque DLAs lead to an insufficient understanding of the predictive results by experts. In this study, a dual-branch convolutional neural network (DBCNN) and its post hoc interpretability were jointly constructed to map gold prospectivity in western Henan Province of China. In particular, channel and spatial attention modules were integrated into two branches to complement the respective advantages of multichannel and high spatial prospecting data for MPM. The Shapley additive explanations (SHAP) framework was then adopted to explain the predictive results by exploring the feature contributions. The comparative experiments illustrated that DBCNN can enhance feature representation and fusion abilities to improve the performance of MPM compared to conventional DLAs. The high-probability areas delineated by the DBCNN model exhibited close spatial relevance with known gold deposits, and the SHAP further confirmed the reliability of the predictive result obtained by the DBCNN model, thereby guiding future gold exploration in this study area.