Prediction of Lithium Mineralization Potential in the Jiulong Area, Western Sichuan (China), Using Spectral Residual Attention Convolutional Neural Network

This study aimed to predict the lithium resource potential in the Jiulong region of western Sichuan using a spectral residual attention convolutional neural network (SRACN) model, which integrates hyperspectral imagery from the GF-5B satellite with spectral measurement data from field rock core samples. By incorporating residual connections and a spectral attention mechanism, the SRACN model efficiently extracts critical spectral features, thereby enhancing mineral identification accuracy and predictive performance. The experimental results demonstrated that: (1) The SRACN model achieved a classification accuracy of 96.46% and an F1 score of 0.9645 for muscovite classification and mineral mapping, indicating superior performance; (2) utilizing hierarchical density-based spatial clustering of applications with noise (HDBSCAN), lithium and rare metal mineralization zones in the Jiulong region were delineated, with results closely aligned with field validation, revealing significant exploration potential in the northern Daqianggou mining area and the Baitaizi region. This study presents a novel scientific and technical approach to regional geological prospecting and demonstrates the effectiveness of integrating SRACN with density clustering analysis for evaluating regional mineral resource potential.