Intelligent Metallogenic Evaluation of Podiform Chromitites Based on Multi-Source Data Fusion and Deep Learning of Geological Structures

Chromite is a strategically critical mineral resource in short supply in China, and detecting the spatial distribution of concealed chromitite orebodies in plateau regions remains challenging. The Yarlung Zangbo Suture Zone in Tibet has a surface environment characterized by high elevations, deeply dissected terrain, and thick Quaternary cover. Remote sensing exploration in this setting is constrained by the spatial–spectral resolution trade-off, while Quaternary cover further obscures surface spectral and structural information. Accordingly, taking the Kubinongyue–Mendangga’ermu area in Zhongba County as the study area, we propose a geologically constrained intelligent exploration framework based on high-resolution hyperspectral (HR-HSI) data that integrates super-resolution reconstruction, deep visual interpretation, and ensemble machine learning. The HySure algorithm is introduced to fuse high-resolution multispectral (HR-MSI) and low-resolution hyperspectral (LR-HSI) data, reconstructing a data cube that preserves both fine spatial topological details and continuous hyperspectral signatures. To overcome the severe class imbalance resulting from extremely sparse lineaments and the interference of topographic artifacts, we developed a Trans-CBAM UNet model—integrating the Convolutional Block Attention Module (CBAM) and Transformer architectures—to extract ore-controlling physical boundaries with high connectivity. On this basis, ensemble learning models such as XGBoost and Random Forest were jointly employed to conduct high-dimensional nonlinear classification of subtle metallogenic indicators, including dunite and serpentinization. Quantitative evaluation demonstrates that the reconstructed data significantly enhance the spatial texture representation of hyperspectral imagery while largely preserving overall spectral fidelity, thereby improving the accuracy of lithological unit classification. The Trans-CBAM UNet model achieved an Area Under the Curve (AUC) exceeding 0.90 for fault prediction, providing robust physical constraints for subsequent intelligent extraction. Using the reconstructed HR-HSI data as the primary input and the extracted fault network as a geological constraint, high- and moderate-prospectivity zones for concealed chromitite were delineated. By combining HR-HSI reconstruction, deep-learning-based structural extraction, and ensemble lithological classification, this study establishes a geologically constrained remote sensing workflow for delineating concealed chromitite prospectivity zones.