Exploration
Khadijeh farokhi nasab; Ali Imamalipour; Amin Hossein-Morshedy
Abstract
Mineral resource estimation is a fundamental component of exploration and mine planning, forming the basis for economic evaluation and long-term production scheduling. This study presents an integrated, domain-driven geostatistical workflow for estimating mineral resources in the Janja Cu–Au deposit ...
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Mineral resource estimation is a fundamental component of exploration and mine planning, forming the basis for economic evaluation and long-term production scheduling. This study presents an integrated, domain-driven geostatistical workflow for estimating mineral resources in the Janja Cu–Au deposit in southeastern Iran. The approach combines geological domaining, statistical analysis, and dynamic anisotropic variogram modeling to more accurately capture spatial grade continuity within a structurally complex porphyry system. Drillhole assay data for copper (Cu) and gold (Au) were first evaluated through statistical and exploratory data analysis within lithological and alteration-based domains. A three-dimensional geological model was then constructed to delineate mineralized zones and define estimation boundaries. To address spatial heterogeneity and the non-stationary geometry of mineralization, locally oriented variogram models and dynamic search ellipsoids were developed and aligned with structural trends derived from the 3D wireframe model. Block grade estimation was performed using ordinary kriging within each domain. Resource classification into Measured, Indicated, and Inferred categories was guided by the kriging variance ratio and drillhole spacing criteria. Grade–tonnage relationships were assessed across multiple cutoff grades to evaluate model sensitivity. At a representative cutoff grade, the Janja deposit contains approximately 484.7 Mt of Measured plus Indicated resources, averaging 0.26% Cu and 0.23 g/t Au, and a total of about 502.2 Mt when Inferred resources are included. Well-drilled central zones exhibit higher confidence, whereas peripheral areas—characterized by sparse sampling- are classified predominantly as Inferred. Overall, the results demonstrate that integrating geological constraints with dynamic anisotropic variogram modeling substantially improves the representation of spatial grade continuity and enhances the reliability of mineral resource estimation in structurally complex porphyry deposits.
