Document Type : Original Research Paper

Authors

• Hassanreza Ghasemi Tabar ¹
• Sajjad Talesh Hosseini ²
• Andisheh Alimoradi ²
• mahdi fathi ³
• Maryam Sahafzadeh ⁴

¹ Department of Mining, Petroleum and Geophysics, Shahrood University of Technology

² Faculty Member, Department of Mining and Petroleum Engineering, Imam Khomeini International University, Qazvin, Iran

³ Senior Exploration Engineer, Kavoshgaran Consulting Engineers, Tehran, Iran

⁴ Senior Mining Engineer, SRK Consulting, , Vancouver, Canada

Abstract

Estimating ore grades during the exploration phase is often time-consuming and costly due to the need for extensive drilling. Geophysical surveys, as the last indirect exploration method before drilling, offer valuable insights into subsurface mineralization. This study introduces a novel approach for simulating “identical twins” of borehole copper grade values using geophysical attributes derived from the geoelectrical method in the Kahang porphyry copper deposit, central Iran. By treating the simulated values as digital twins of actual borehole grades, we employed four machine learning algorithms—Linear Regression (LR), Gradient Boosting (GB), Random Forest (RF), and Support Vector Machine (SVM)—to model the complex relationships between geophysical inputs and copper grades. After data preprocessing with Principal Component Analysis (PCA), a refined dataset was used to train, test, and validate each model. The results demonstrate that GB yielded the highest predictive accuracy, generating grade estimates closely aligned with actual values. This identical twin modeling approach highlights the potential of machine learning to enhance early-stage mineral exploration by reducing dependence on costly drilling.

Keywords

• Linear Regression
• Gradient Boosting
• Random Forest
• Support Vector Machine
• Identical Twin

Main Subjects

• Data Mining