The enthusiastic response to our initial suggestion for this volume, and willingness of colleagues to contribute manuscripts, are clear indicators that qualitative methods are gaining in acceptance and popularity within the geoscience education and geocognition community. What we currently lack, however, is an extensive, formal literature base for qualitative inquiry into how novice and expert geoscientists think and learn.

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. <...>

Resource modelling is a complex process involving different specialists with relevant experience using a multi-disciplinary approach and the best available technology and reviews by independent auditors. The reliability of the final resource estimate is highly dependent on the quality control exercised at each stage of the process. At each step in the resource modelling process it is necessary to define the specific objectives, the methodology proposed to achieve those objectives and to establish a set of checks and validation tools to assess the effectiveness of the proposed methodology. Designation of responsibility and authority for meeting these objectives must also be clearly identified. External audits must also be incorporated to review and validate the implementation of new procedures.

Resource modelling is the basis for any economic appraisal of a mining project and includes a number of steps from data acquisition and validation to resource reporting, classification, and risk analysis

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.