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. <...>

Quality Control in Mineral Exploration

There many goals associated with a QA/QC program for the mineral industry:

• Prevent the entry of large errors into the geological Database.

• Demonstrate that sampling and analytical variances are small relative to geological variance.

• Provide assurance that the accuracy of the data can be confirmed.

• Save management time by automating sample names and performing automatic checks on sample names.

• Allow problems to be realized rapidly to minimize their effects.

• Keep an archive for a complete audit trail.

• Comply meticulously with government regulations and standards with a minimal amount of employee time and effort.

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.