4,169 research outputs found
Queueing Aspects of Integrated Information and Computing Systems in Geosciences and Natural Sciences
Categorization of Mineral Resources Based on Different Geostatistical Simulation Algorithms: A Case Study from an Iron Ore Deposit
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
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