Document Type : Original Research Paper

Authors

• Marco Antonio Cotrina Teatino ¹
• Jairo Jhonatan Marquina-Araujo ¹
• Jose Nestor Mamani-Quispe ²
• Juan Antonio Vega-Gonzalez ³
• Moises Bartolome Guia-Pianto ⁴

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 Metallurgical Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru

⁴ Department of Mining Engineering, National University of the Center of Peru, Huancayo, Peru

Abstract

The Quiulacocha tailings deposit in central Peru, containing 70 Mt of historical mine waste, presents both environmental risks and opportunities for secondary metal recovery. This study applies data-driven machine learning techniques to estimate the remaining silver resources using 927 one-meter composites from 40 vertical drillholes. Three supervised learning models—Random Forest (RF), k-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost)—were trained using spatial coordinates (X, Y, Z) as the sole input features. Model validation was performed using leave-one-out cross-validation (LOOCV), and results were benchmarked against ordinary kriging (OK). Among the models, RF delivered the highest predictive performance (mean error = 0.53 g/t, RMSE = 7.21 g/t, R = 0.82), outperforming OK (R = 0.63, RMSE = 10.47 g/t). Block model predictions indicated higher silver content from machine learning models: 1,532.86 t (RF), 1,542.16 t (XGBoost), and 1,492.09 t (KNN), compared to 1,463.73 t from OK. Additionally, XGBoost maintained superior grade-tonnage relationships under elevated cutoff thresholds, highlighting its potential to delineate high-grade subdomains within the deposit. These findings confirm the value of machine learning in resource estimation under conditions of low spatial continuity, such as tailings, where material mixing and irregular deposition patterns limit correlation across space.

Keywords

• Tailings
• machine learning
• resource estimation
• ordinary kriging

Main Subjects

• Environmental Management