High-resolution 3D modeling for enhanced mineral resource estimation through improved ore-waste separation: a case study of the Janja Cu-Au deposit

Abstract Accurate mineral resource estimation is essential for effective mine planning and unlocking the full potential of a deposit particularly in geologically complex deposits like the Janja copper-gold system in southeastern Iran. This study presents an integrated methodology combining high-resolution 3D geological modeling with conventional geostatistical methods, Ordinary Kriging and Inverse Distance Weighting (IDW), and advanced computational techniques to improve grade estimation accuracy and resource confidence. Utilizing data from 108 drill holes, we constructed a detailed 3D model that incorporates lithological variations, alteration zones, and assay data. Dynamic anisotropic variogram modeling was employed to effectively capture the spatial continuity and structural complexity of the deposit, outperforming traditional stationary models. Validation through variogram analysis and swath plots confirmed consistent spatial patterns and reliable copper and gold grade estimates across multiple orientations. This integrated approach addresses challenges such as spatial heterogeneity and uneven sampling by refining mineralized zone boundaries and optimizing ore-waste classification. Following JORC guidelines, resources were classified into Measured, Indicated, and Inferred categories based on spatial variance, providing a clear assessment of estimation confidence. With cutoff grades of 0.1% Cu and 0.2 ppm Au, the total estimated resource is approximately 482 million tonnes measured, indicated, inferred classes, demonstrating high model reliability. Geological interpretations emphasize the significant influence of structural features and alteration on mineralization, directing exploration and mine development strategies. Although the dataset is comprehensive, limited drilling in peripheral areas suggests that further exploration is necessary to enhance resource confidence in those zones. This study demonstrates that combining detailed geological characterization with advanced geostatistical and computational modeling provides a scalable and replicable framework for resource estimation in complex ore deposits, enhancing predictive accuracy and supporting optimized, sustainable mine planning.