Copper Ore Grade Prediction using Machine Learning Techniques in a Copper Deposit

Marquina-Araujo J.; Cotrina-Teatino M.; Mamani-Quispe J.; Noriega-Vidal E.; Vega-Gonzalez J.; Cruz-Galvez J. · Journal of Mining and Environment (2024) · DOI: 10.22044/jme.2024.14032.2617

Resumen

The objective of this research work to employ machine learning techniques including Multilayer Perceptron Artificial Neural Networks (ANN-MLP), Random Forests (RFs), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR) to predict copper ore grades in a copper deposit located in Peru. The models were developed using 5654 composites containing available geological information (rock type), as well as the locations of the samples (east, north, and altitude) and secondary ore grade (Mo) obtained from drilling wells. The data was divided into 10% (565 composites) for testing, 10% (565 composites) for validation, and 80% (4523 composites) for training. The evaluation metrics included SSE (Sum of Squared Errors), RMSE (Root Mean Squared Error), NMSE (Normalized Mean Squared Error), and R2 (Coefficient of Determination). The XGBoost model could predict the ore grade with an SSE of 15.67, RMSE = 0.17, NMSE = 0.34, and R2 = 0.66, the RFs model with an SSE of 16.40, RMSE = 0.17, NMSE = 0.36, and R2 = 0.65, the SVR model with an SSE of 19.94, RMSE = 0.19, NMSE = 0.43, and R2 = 0.57, and the ANN-MLP model with an SSE = 21.00, RMSE = 0.19, NMSE = 0.46, and R2 = 0.55. In conclusion, the XGBoost model was the most effective in predicting copper ore grades.

Instituciones: Universidad Nacional de Trujillo; Universidad Nacional del Altiplano