Interpretable ore classification using SHAP-enhanced LightGBM: A case study from the Qiaomaishan deposit, China

Accurate ore classification is essential for geological exploration and mineral resource assessment, particularly in geologically complex settings. This study presents an interpretable classification framework that integrates the Light Gradient Boosting Machine (LightGBM) algorithm with post hoc model interpretation using SHapley Additive exPlanations (SHAP). The framework is applied to geochemical and spatial data from the Qiaomaishan Cu-S polymetallic deposit in Anhui Province, China. A dataset comprising 1,588 samples-each containing concentrations of 29 geochemical elements along with 3D spatial coordinates-was used to train and evaluate 5 machine learning models: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Random Forest (RF), LightGBM, and CatBoost. Among these, LightGBM achieved the best performance, with an average F1 score of 0.990 across 10 ore and lithological categories. An ablation experiment further confirmed the critical role of spatial coordinates, as removing them led to a notable drop in classification accuracy, particularly for underrepresented ore types. To interpret the LightGBM model, SHAP analysis was used to quantify feature contributions, identifying key geochemical elements such as W, Sr, Ca, and Fe as significant drivers of classification-consistent with the known mineralogical characteristics of the deposit. Moreover, SHAP beeswarm plots provided insights into the direction and magnitude of each feature’s influence, enhancing the model’s geological interpretability. SHAP-based feature selection further improved classification performance for underrepresented classes, including copper–sulphur–tungsten ore and copper ore. The proposed framework demonstrates strong potential for facilitating automated ore identification and supporting data-driven decision-making in complex geological environments.