Analysis of Weak Links in the Mechanized Mining of Underground Metal Mines: Insights from Machine Learning and SHAP Explainability Models

Abstract

In the mechanized mining of metal mines, identifying and optimizing vulnerabilities within the production system is essential for enhancing operational efficiency and ensuring sustainable development. By leveraging data from 88 stopes at Guangxi Tongkeng Mine over a decade, we constructed a comprehensive dataset encompassing drilling, charging, blasting, ventilation, support, ore drawing, and maintenance. The XGBoost algorithm was employed to model factors influencing stope production capacity (PC), with its parameters optimized using the Marine Predator Algorithm (MPA). The MPA–XGBoost model demonstrates a high predictive accuracy for PC (R2 = 0.958, VAF = 95.981%, MAE = 4.844, RMSE = 7.033). A Shapley Additive Explanations (SHAP) analysis reveals that drilling efficiency (DE) contributes most positively (35.6%), while ventilation time (VT) and equipment maintenance time (EMT) negatively impact PC. SHAP dependence plots indicate that increasing DE significantly enhances PC, whereas excessive VT or EMT leads to a substantial decline in PC. These findings offer valuable insights and a robust foundation for optimizing design and improving production management in mechanized mining operations.

Keywords

• underground metal mines
• mechanized mining
• machine learning
• SHAP analysis
• production capacity optimization