COEFFICIENT OF VARIATION FRAMEWORK FOR MINERAL RESOURCE ESTIMATION METHOD SELECTION: REVIEW ACROSS DEPOSIT TYPES
DOI:
https://doi.org/10.23960/jge.v12i2.530Keywords:
Coefficient of Variation, Cross-validation, Indicator Kriging, Mineral Resource Estimation, Ordinary KrigingAbstract
Selecting an accurate resource estimation method is a critical challenge in the mining industry, which is often based solely on a posteriori evaluation using validation metrics such as Root Mean Square Error (RMSE). This study aims to address this methodological gap by proposing a framework for selecting estimation methods based on the Coefficient of Variation (COV) as an a priori decision parameter. Through a Systematic Literature Review (SLR) approach of case studies from 2015 to 2026, this study synthesizes data from various deposit genotypes, including lateritic nickel, epithermal gold, bauxite, and copper. The analysis results show a strong aligment between the COV threshold and the reliability of the estimator. For deposits with low variability (COV < 1.5), deterministic methods such as Inverse Distance Weighting (IDW) provide accuracy comparable to Ordinary Kriging (OK), making them more efficient for the initial exploration phase. Conversely, in high-variability deposits (COV > 2.5), characterized by highly skewed distributions and extreme nugget effects, the use of indicator based methods (Indicator Kriging and Multiple Indicator Kriging) proved to be more reliable in mitigating bias compared to linear methods. This study outlines a systematic five step workflow that integrates COV classification with quantitative validation. The primary contribution of this study is the provision of guidelines for the selection of objective, transparent, and reproducible methods to support technical justifications in Indonesian Mineral Reserve Committee (KCMI) and Joint Ore Reserve Committee (JORC) standard mineral resource reporting.
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