Machine learning-based simulation of borehole grade identical twins from geophysical attributes: Comparative study of LR, GB, RF, and SVM in Kahang, Iran | ||
| Journal of Mining and Environment | ||
| مقاله 8، دوره 16، شماره 5، مهر و آبان 2025، صفحه 1637-1652 اصل مقاله (6.73 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2025.15310.2936 | ||
| نویسندگان | ||
| Hassanreza Ghasemi Tabar1؛ Sajjad Talesh Hosseini2؛ Andisheh Alimoradi* 2؛ Mahdi Fathi3؛ Maryam Sahafzadeh4 | ||
| 1Department of Mining, Petroleum and Geophysics, Shahrood University of Technology,Shahrood, Iran | ||
| 2Faculty Member, Department of Mining and Petroleum Engineering, Imam Khomeini International University, Qazvin, Iran | ||
| 3Senior Exploration Engineer, Kavoshgaran Consulting Engineers, Tehran, Iran | ||
| 4Senior Mining Engineer, SRK Consulting, Vancouver, Canada | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Linear Regression؛ Gradient Boosting؛ Random Forest؛ Support Vector Machine؛ Identical Twin | ||
| مراجع | ||
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