Geo-U-Mamba: A Mamba-Based Framework for Mineral Prospectivity Mapping of Gold Exploration Using Multi-Source Geoscientific Data

Modern mineral exploration faces the pivotal challenge of detecting concealed mineral deposits in complex geology, as depleting outcropping ores have driven global exploration to depths where 1000 m deep mining is now commonplace. To address this, this study proposes Geo-U-Mamba, an unsupervised deep learning framework for gold mineral prospectivity mapping. The model integrates multi-source geoscientific data, encompassing geochemistry, remote sensing alteration indicators, topography, and structural distance fields. By incorporating a Mamba-driven four-directional cross-scan mechanism into a U-Net architecture, the framework effectively models the complex nonlinear mapping relationships between metallogenic elements and the geological environment. This approach recognizes gold geochemical anomalies with an 86.11% deposit capture rate, decoupling environmental noise by reconstructing the geochemical background field and extracting anomalies in combination with C-A fractal theory. When applied to China’s Hatu gold belt in Xinjiang, Geo-U-Mamba achieved an AUC of 0.83, consistently outperforming classical baselines such as CAE, U-Net, and ViT. Ultimately, the findings indicate that this framework provides a reliable and high-precision tool for modern mineral exploration, successfully separating mineralization signals from geological backgrounds in complex metallogenic belts to facilitate exploration targeting.