Mineral Prospectivity Prediction in the Mayoumu Area, Tibet, Based on Multi-Source Exploration Information and Ensemble Learning Models

Plateau–orogenic belts host a substantial share of global gold resources, yet quantitative prospectivity mapping is challenged by complex mineralization and strongly heterogeneous, multi-scale datasets. Using the Mayoumu area (Tibet) as a representative orogenic gold district, we develop an integrated multi-source workflow that fuses remote-sensing alteration information with regional geochemical and structural constraints within an ensemble-learning framework. Alteration anomalies were mapped from GF-5 hyperspectral imagery using mixture-tuned matched filtering (MTMF) and from Sentinel-2 multispectral imagery using the iCrosta method to extend alteration signals across scales. Geochemical anomalies were extracted from 1:200,000 stream-sediment data through isometric log-ratio (ILR) transformation and robust principal component analysis (RPCA). At the same time, ore-controlling structures were quantified using Euclidean-distance-to-fault layers. Three Boosting-based ensemble models—gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)—were trained to predict mineral prospectivity. Performance was evaluated using confusion matrix metrics and ROC–AUC, and key predictors were interpreted using SHAP. All three models achieved AUC values > 0.90, with LightGBM performing best (AUC = 0.94) and delineating high-prospectivity zones that coincide with known occurrences and highlight additional targets. The proposed workflow provides a practical, transferable reference for gold prospectivity mapping in complex orogenic belts worldwide.