Mineral Processing
Khadijeh farokhi nasab; Ali Imamalipour; Amin Hossein-Morshedy
Abstract
Accurate mineral resource estimation is the cornerstone of effective mine planning, especially within geologically intricate deposits like the Janja copper-gold system in southeastern Iran. This study introduces a comprehensive approach that synergizes high-resolution 3D geological modeling with robust ...
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Accurate mineral resource estimation is the cornerstone of effective mine planning, especially within geologically intricate deposits like the Janja copper-gold system in southeastern Iran. This study introduces a comprehensive approach that synergizes high-resolution 3D geological modeling with robust geostatistical techniques, Ordinary Kriging and Inverse Distance Weighting, while leveraging advanced computational methods to enhance grade estimation precision and resource confidence. An extensive dataset from 108 drill holes informs a detailed 3D model integrating lithological diversity, alteration patterns, and assay results. Employing dynamic anisotropic variogram modeling, the framework adeptly captures spatial continuity and complex structural heterogeneity, surpassing conventional stationary models. Rigorous validation through variogram analysis and swath plotting confirms consistent spatial patterns and dependable copper and gold grade predictions, addressing inherent spatial heterogeneity and uneven sampling issues through optimized mineralized boundary delineation and ore-waste differentiation. Conforming to JORC reporting standards, resource classification into Measured, Indicated, and Inferred categories is anchored on spatial variance metrics, culminating in a robust estimate of approximately 482 million tonnes at cutoff grades of 0.1% Cu and 0.2 ppm Au. Geological insights underline the pivotal roles of structural controls and alteration zones in governing mineralization, providing strategic guidance for exploration and mine development. While the dataset is comprehensive, peripheral zones with sparse drilling highlight areas for future investigation to reduce uncertainty. This integrated, replicable methodology offers a scalable blueprint for resource estimation in complex porphyry deposits worldwide, advancing predictive accuracy and fostering sustainable mining practices aligned with responsible resource stewardship.
