Categorization of Mineral Resources Based on Different Geostatistical Simulation Algorithms: A Case Study from an Iron Ore Deposit
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Mineral resource classification plays an important role in the downstream activities of a mining project. Spatial modeling of the grade variability in a deposit directly impacts the evaluation of recovery functions, such as the tonnage, metal quantity and mean grade above cutoffs. The use of geostatistical simulations for this purpose is becoming popular among practitioners because they produce statistical parameters of the sample dataset in cases of global distribution (e.g., histograms) and local distribution (e.g., variograms). Conditional simulations can also be assessed to quantify the uncertainty within the blocks. In this sense, mineral resource classification based on obtained realizations leads to the likely computation of reliable recovery functions, showing the worst and best scenarios. However, applying the proper geostatistical (co)-simulation algorithms is critical in the case of modeling variables with strong cross-correlation structures. In this context, enhanced approaches such as projection pursuit multivariate transforms (PPMTs) are highly desirable. In this paper, the mineral resources in an iron ore deposit are computed and categorized employing the PPMT method, and then, the outputs are compared with conventional (co)-simulation methods for the reproduction of statistical parameters and for the calculation of tonnage at different levels of cutoff grades. The results show that the PPMT outperforms conventional (co)-simulation approaches not only in terms of local and global cross-correlation reproductions between two underlying grades (Fe and Al2O3) in this iron deposit but also in terms of mineral resource categories according to the Joint Ore Reserves Committee standard.
KeywordsMineral resource classification Projection pursuit multivariate transform Joint simulation Iron deposit JORC code
The authors are grateful to Nazarbayev University for funding this work via “Faculty Development Competitive Research Grants for 2018–2020 under Contract No. 090118FD5336.” The second author acknowledges the Social Policy Grant (SPG) supported by Nazarbayev University. The authors also thank the Geovariances Company for providing the dataset. We are also grateful to Dr. John Carranza and the reviewers for their valuable comments, which substantially helped improving the final version of the manuscript.
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