Data quality control programs used in the mineral industry normally define tolerance limits based on values considered as good practice or those that have previously been applied to similar deposits, although the precision and accuracy of estimates depend on a combination of geological characteristics, estimation parameters, sample spacing and data quality. This study investigates how the sample quality limits affect the estimates results. The proposed methodology is based on a series of metrics used to compare the impact on the estimates using a synthetic database with an increasing amount of error added to the original sample grades or positions, emulating different levels of precision. The proposed approach results lead to tolerance limits for the grades similar to those recommended in literature. The influence of the positional uncertainty on model estimates is at a minimum, because of the accuracy of current surveying methods that have a deviation in the order of millimeters, so its impact can be considered negligible.

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.

Geoscientific datasets can contain individual data for more than 50 different chemical elements. The association between these variables is as important as their individual values. However, it is commonly overlooked that the observed covariance may be overestimated due to correlated errors. Dependent errors arise from many sources, such as the segregation process of minerals associated with these variables during delimitation, extraction, and preparation steps. This study extends a classical model composed of grade-independent (additive) and grade-proportional (multiplicative) errors to a generalised multivariate model that can estimate the real variance, covariance, and correlation from observations affected by shared errors. The use of estimates of the real covariance is recommended when the study objective is to evaluate or estimate the association between processes instead of the association between observations. A numerical example illustrates the bias in statistics and discusses the relevance of considering shared errors in linear regression and kriging.

Large geochemical datasets are commonly composed of several subgroups of measurements with a diverse level of uncertainty. Ignoring data quality and heterogeneity by pooling all data together leads to information loss and suboptimal analysis. Therefore, we propose a novel approach to define the measurement uncertainty attached to each sample by defining subgroups which have the same relationship between concentration and its measurement uncertainty and use this equation to estimate the uncertainty of measurements made on the original samples. The proposed approach is not only based on available information such as rock type or in the used sampling protocols, but also by the extraction of information gained from duplicates in successive steps of graphical analysis to define subgroup candidates and test their statistical significance. The illustrative example defined subgroups very close to the simulated ones and estimates of each original concentration measurement were at least 30% better than other evaluated methods.