Improving the copper grade estimation at the Chehel Kureh copper mine using machine learning methods

Norouzi, Hamed; Daya, Aliakbar

doi:10.22044/jme.2025.16326.3177

Document Type : Original Research Paper

Authors

Hamed Norouzi ¹
Aliakbar Daya ²

¹ Department of Mining Engineering, Isfahan University of Technology, Iran

² University of Sistan and Baluchestan

10.22044/jme.2025.16326.3177

Abstract

Estimating mineral reserves in exploration or extraction projects is a critical and challenging process. It must be conducted precisely, regardless of the mining scale and mineral type. With the growing significance of mineral resources in economic and industrial development, the importance of adopting advanced technologies in mineral assessment has also surged. Modern spatial grade modeling techniques can play a pivotal role in decision-making processes. This study aims to compare the performance and capabilities of two popular machine learning methods, including Gaussian Process Regression (GPR) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) in spatial grade modeling of copper at the Chehel Kureh Copper deposit. The dataset comprises 42 drill holes with an average copper grade of 0.18%. Each core sample data point includes seven variables: three spatial coordinates (X, Y, and Depth), lead grade, zinc grade, lithology and copper grade, which serves as the target variable. The Gaussian Process Regression (GPR) and Multilayer Perceptron (MLP-ANN) neural network were employed for copper grade estimation. To make a better assessment, the hyperparameters of both models were optimized using the Bayesian Optimization algorithm. The results showed that the Gaussian Process Regression outperformed MLP-ANN, achieving an RMSE of 0.04 and a coefficient of determination (R²) of 0.89 compared to an RMSE of 0.05 and a coefficient of determination (R²) of for MLP-ANN, suggest the superiority of the Gaussian Process Regression method in estimating copper grade spatial variability.

Keywords

Main Subjects

Geostatistics

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Improving the copper grade estimation at the Chehel Kureh copper mine using machine learning methods

References

References

Volume 17, Issue 4July and August 2026Pages 1429-1443

Volume 17, Issue 4
July and August 2026
Pages 1429-1443