Data-Driven Decoupling of Metallogenic Patterns: A Case Study of Skarn-Type vs. Hydrothermal Vein-Type Pb-Zn Deposits in the Shanghulin Area, Inner Mongolia, China

The close spatial and genetic coexistence of Skarn-type and Hydrothermal Vein-type Pb-Zn deposits in the Shanghulin area, Inner Mongolia, poses a significant challenge to conventional “ undifferentiated” prediction models. This study aims to decouple these distinct metallogenic patterns using a data-driven, “type-specific modeling” strategy, establishing separate prediction models for Skarn-type and Hydrothermal Vein-type mineralization. Our workflow first employs Lasso–RFECV for rigorous pre-screening of over 60 geoscience features to identify the optimal predictive subset. Subsequently, an XGBoost model is trained on these selected features, and the SHAP framework is applied to interpret the geological significance of its decision logic. The results confirm two distinct indicator systems. (1) The Skarn-type model is controlled by spatial proximity to a heat source, heavily relying on Distance_to_Volcano and high-temperature indicators (CLR_Mo, CLR_W, CLR_Mn). (2) The Hydrothermal Vein-type model is “chemical fingerprint-driven”, prioritizing CLR_Y and identifying a complex “leaching-enrichment” pattern: mineralization requires simultaneous wall-rock leaching (low CLR_Al2O3, low CLR_Y) and specific metal enrichment (high CLR_Co, high CLR_Zn). This study confirms the controlling factors: Skarn-type deposits are governed by magmatic proximity, whereas Hydrothermal Vein-type deposits are defined by specific alteration geochemical signatures. The proposed “Lasso–RFECV → XGBoost → SHAP” workflow successfully decouples these independent, geologically meaningful prospectivity models from complex data, offering a new paradigm for precise exploration.