Improving the 3D Estimation of Copper Grade in Sarcheshme Copper Deposit using Deep Learning Methods

Document Type : Research - Paper

Authors

1 M.Sc, Dept. of Mining Engineering, Isfahan University of Technology, Isfahan, Iran

2 Associate Professor, Dept. of Mining Engineering, Isfahan University of Technology, Isfahan, Iran

Abstract

Minimizing grade estimation errors through modern methods is crucial for optimizing mining operations and mitigating the risk of economic failure in mining projects. The current research is an attempt to enhance the three-dimensional estimation of copper grades in the Sarcheshmeh copper deposit by employing advanced deep-learning techniques and comparing their outcomes with those of traditional geometric methods. The Sarcheshmeh copper deposit is among the largest porphyry copper deposits located within the Urmia-Dokhtar volcanic belt, south of Kerman in central Iran. In this study, data from a number of 526 boreholes containing 38,006 core samples assayed for total copper were deployed. The average copper grade in the samples is 0.54 percent, with a maximum value of 3.43. Prior to grade estimation, preprocessing steps were performed, including outlier correction and data compositing. The anisotropy ellipsoid of the copper grade was determined using the covariance matrix. Two methods were applied for grade estimation: a deep learning algorithm based on the ResNet-50 architecture and the inverse distance weighting (IDW) method, a conventional geometric approach. Validation results indicated that the ResNet-50 algorithm, with an RMSE of 0.45, outperformed the IDW method, which had an RMSE of 0.6.

Keywords

Main Subjects

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Journal of Mineral Resources Engineering
Volume 10, Issue 2 - Serial Number 36
September 2025
Pages 71-89

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History

  • Receive Date: 16 August 2024
  • Revise Date: 20 August 2025
  • Accept Date: 22 December 2024

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