Explainable convolutional neural network for iron ore prospectivity mapping: a case study of the Yemaquan district, Qinghai, China
Shitao Yin
Convolutional neural networks (CNNs) have been widely applied to mineral prospectivity mapping; however, their “black-box” nature limits geological interpretability and practical acceptance. To address this issue, this study proposes an explainable convolutional neural network (Ex-CNN) framework that integrates deep learning with SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP). The Yemaquan iron ore concentration area in Qinghai Province, northwestern China, was selected
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