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
• Joe Alexis Gonzalez-Vasquez ³
• Kevin Daniel Rondo-Jalca ¹

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

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

³ Department of Industrial Engineering, Faculty of Engineering, National University of Trujillo

Abstract

The classification of mineral resources significantly impacts mine planning, economic feasibility, and regulatory compliance. Despite its importance, such classification frequently depends on the subjective judgment of the Qualified Person (QP), owing to the absence of internationally standardized technical criteria for delineating resource categories. To mitigate this limitation, an innovative methodology integrating clustering based on Riemannian geometry with machine learning techniques was developed for mineral resource classification. A database of 5,654 composited samples from 185 diamond drill holes in a copper deposit in central Peru was utilized to classify 318,443 blocks. Copper grades were estimated through Ordinary Kriging (RMSE = 0.102; MAE = 0.069), generating geostatistical variables kriging variance, average distance to samples, and number of samples that served as input features for the classification. Clustering was performed using both classical KMeans and Riemannian KMeans, followed by spatial smoothing via XGBoost and Random Forest algorithms. Absolute coordinates were incorporated to address spatial discontinuities in classification outputs. The combination of the Riemannian model with Random Forest produced the highest classification performance, with a Silhouette index of 0.26 and a Davies-Bouldin index of 0.72. The resulting metal content was estimated at 4.24 Mt of copper at 0.44% grade (measured), 6.49 Mt at 0.34% Cu (indicated), and 7.68 Mt at 0.32% Cu (inferred), demonstrating close alignment with QP estimates while exhibiting improved spatial coherence. In summary, the Riemannian-based approach outperformed classical KMeans and conventional classification methods, providing a more robust, objective, and globally consistent alternative.

Keywords

• Mineral resource classification
• Riemannian geometry
• Machine Learning
• Geostatistics

Main Subjects

• Geostatistics