Document Type : Original Research Paper

Authors

• Marco Antonio Cotrina-Teatino ¹
• Jairo Jhonatan Marquina-Araujo ¹
• Jose Nestor Mamani-Quispe ²
• Solio Marino Arango-Retamozo ¹
• Johnny Henrry Ccatamayo-Barrios ³
• Joe Alexis Gonzalez-Vasquez ⁴
• Teofilo Donaires-Flores ²
• Maxgabriel Alexis Calla-Huayapa ⁵

¹ Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru

² Faculty of Chemical Engineering, National University of the Altiplano of Puno, Puno, Peru

³ Department of Mining Engineering, Universidad Nacional San Cristobal de Huamanga, Ayacucho, Peru

⁴ Department of Industrial Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru

⁵ Faculty of Industrial Process Engineering, National University of Juliaca, Juliaca, Peru

Abstract

This work aimed to categorize mineral resources in a copper deposit in Peru, using a machine learning model, integrating the K-prototypes clustering algorithm for initial classification and Random Forest (RF) as a spatial smoother. A total of 318,443 blocks were classified using geostatistical and geometric variables derived from Ordinary Kriging (OK) such as kriging variance, sample distance, number of drillholes, and geological confidence. The model was trained and validated using precision, recall, and F1-score metrics. The results indicated an overall accuracy of 97%, with the measured category achieving 98% precision and an F1-score of 0.98. The total estimated tonnage was 5,859.36 Mt, distributed as follows: 1,446.13 Mt (measured), 2,249.22 Mt (Indicated), and 2,164.01 Mt (Inferred), with average copper grades of 0.43%, 0.33%, and 0.31% Cu, respectively. Compared to the traditional geostatistical methods, this hybrid approach improves classification objectivity, spatial continuity, and reproducibility, minimizing abrupt transitions between categories. The RF model proved to be a robust tool, reducing classification inconsistencies and better capturing geological uncertainty. Future studies should explore hybrid models (K-means with RF, ANN with K-Prototypes, gradient boosting, and deep learning) and incorporate economic variables to optimize decision-making in resource estimation.

Keywords

• Random Forest
• mineral resource categorization
• geostatistics
• kriging variance

Main Subjects

• Geostatistics