Indicator element selection and lithological mapping using deep learning methods in the Dahongliutan area, NW China

Rare metal resources are extensively used in the emerging energy field, making the security and sustainability of rare metal supply chains critical issues. Pegmatite-type rare metal deposits are a significant source of rare metal resources. Geochemistry is one of the most direct and effective methods of mineral exploration. In this study, whole-rock geochemical data from the Akesayi region, located in the Western Kunlun area of China, were used to identify the indicative elements of pegmatite automatically. Based on the stream sediment geochemical data, various deep learning models have been employed to achieve automatic lithological identification of the area. The results indicate that a novel interpretable model using SHapley Additive exPlanations (SHAP) and eXtreme Gradient Boosting (XGBoost) was employed to select indicative elements for the pegmatite in the Akesayi region, identifying Ta and Rb as key elements. The state-of-the-art application of deep-learning algorithms for lithological mapping has proven to be highly effective. Among the four approaches, the ensemble strategy integrating 1D convolutional neural networks, 2D3D convolutional neural networks, and dual-branch neural networks yields the best lithological mapping results. This approach resulted in a total classification accuracy of 90.422 %, an average accuracy of 90.502 %, a Kappa coefficient of 89.643 %, and a user accuracy of 65.530 % for the pegmatite lithological unit. These results demonstrate that the proposed model can provide robust technical support for the exploration of rare metal pegmatites in regions with challenging natural conditions and limited research.