Abstract
Geological and grade models in the mining industry provide information that is used from the project phase until the short-term planning phase. Since model precision and accuracy depends strongly on data quality, data generation is subject to quality assurance and quality control (QA/QC) protocols that typically adopt error tolerance limits based on reference values. This paper proposes tolerance limits based on the relation between data acquisition costs and block misclassification rate sensitivity to data uncertainty. Using several scenarios generated by adding errors to sampled grades, approach applied to the Miraí bauxite deposit (Brazil) defined the optimum sampling error (measured by its CV) ranging within 6 and 15%. Moreover, sequential Gaussian simulation block classification performed better and was less sensitive to sampling uncertainty than ordinary kriging. The proposed approach results showed that sampling error optimisation is an opportunity to improve mine’s profitability that must be carefully investigated even in benchmark mines.
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