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Vallée (1998) indicated that few exploration and mining companies have explicit and systematic quality-assurance policies, and identified three main approaches: laissez-fair, catch-as-catch-can, and systematic quality control, the latter being very uncommon. In the author’s experience, this situation has not significantly improved in the intervening twelve years.
A thorough program of quality assurance/quality control (QA/QC) will enable collection of meaningful and scientifically credible samples. Quality assurance (QA) includes a range of management and technical practices designed to guarantee that the delivered product is commensurate with the intended use. For environmental- or discharge-related studies, QA ensures that the data are of adequate scientific credibility to permit statistical interpretations that lead to resource-use management decisions.
Geochemistry is a constantly expanding science. More and more, scientists are employing geochemical tools to help answer questions about the Earth and earth system processes. Scientists may assume that the responsibility of examining and assessing the quality of the geochemical data they generate is not theirs but rather that of the analytical laboratories to which their samples have been submitted. This assumption may be partially based on knowledge about internal and external quality assurance and quality control (QA/QC) programs in which analytical laboratories typically participate. Or there may be a perceived lack of time or resources to adequately examine data quality. Regardless of the reason, the lack of QA/QC protocols can lead to the generation and publication of erroneous data. Because the interpretations drawn from the data are primary products to U.S. Geological Survey (USGS) stakeholders, the consequences of publishing erroneous results can be significant. The principal investigator of a scientific study ultimately is responsible for the quality and interpretation of the project’s findings, and thus must also play a role in the understanding, implementation, and presentation of QA/QC information about the data. <...>
After several decades of research, papers, conferences and undoubtedly many millions of analyses of rock and soil samples globally, we know that the success of mining projects is largely dependent on the quality of the data on which they are modelled. The importance of proper sampling, data management, quality assurance and quality control have all been extensively addressed in the literature. However, as global standards change and mining companies have to be more and more innovative to extract value out of marginal projects, it is interesting to note that basic quality control concepts are still poorly understood, misapplied and lead to constant confusion in the exploration and mining sectors.
The fundamental cause of the errors of samples of rocks and minerals collected by geologists for evaluation of mining projects is heterogeneity of the sampled materials (Gy, 1982; Francois-Bongarcon, 1993; Pitard, 1993). Constitution heterogeneity and distribution heterogeneity (Pitard, 1993) both are important and cause geological sampling errors. The more heterogeneous the sampled material the more difficult it is to obtain a representative sample and infer characteristics of the geological object from samples. The current chapter overviews sampling theory explaining sampling error types and their likely causes, and also describes the practical approaches used in the mining industry for estimating sampling errors and monitoring them at an acceptably low level. It is based on numerous case studies by the author (Abzalov & Both, 1997; Abzalov, 1999, 2007, 2008; Abzalov & Humphreys, 2002; Abzalov & Mazzoni, 2004; Abzalov & Pickers, 2005; Abzalov et al., 2007; Abzalov & Bower, 2009) and also reviews of the recently published QAQC procedures used in the mining industry (Taylor, 1987; Vallee et al., 1992; Leaver et al., 1997; Long, 1998; Sketchley, 1998).
