This document gives guidelines for taking duplicate measurements by in-situ analytical instruments, or collecting duplicate samples for analysis in the mobile field laboratory, to assess random errors originating from sampling and analytical procedures, and to estimate the uncertainty of measurements. Since, NORISC is dealing with the assessment of contamination of small-size areas within cities, and relies mainly on in-situ analytical methods, a cost-effective technique, using robust analysis of variance for the estimation of necessary quality control parameters, and measurement uncertainty, is explained with examples <...>

Mineral Resources and their subsequent conversion to Ore Reserves are of key importance to mining companies. Their reliable estimation is critical to both the confidence in a feasibility study, and also to the day-to-day operation of a mine. Together with sampling, assaying, geological and other errors introduced during interpretation and estimation, additional errors are likely to be introduced during the application of technical and economic parameters used for conversion of resources to reserves. There is thus a requirement for high-quality interpretation and estimation to be supported by high-quality data.

There are two main issues stemming from Vallée’s (2002) comments on Dominy et al. (2001b). First, he raises the importance of quality assurance/quality control (QA/QC) during resource estimation programs, and second, he indicates that the resolution and understanding of continuity (grade and geological) issues are paramount in the classification of resources. In particular, continuity is critical at the boundary between the Inferred and Indicated Mineral Resource categories.

The often complex, erratic, and localized nature of gold is a common feature of many vein-style gold deposits. This style of mineralization is often referred to as being nuggety or possessing a high-nugget effect. As a result of these complexities resource estimation is difficult and in general, only Exploration Results can be provided or an Inferred Mineral Resource estimated from surface drilling data alone. Underground development, further drilling, and probably bulk sampling will be required to delineate Indicated and Measured Resources.

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

Control of analytical data quality is usually referred to in the mining industry as Quality Assurance and Quality Control (QAQC), and involves the monitoring of sample quality and quantification of analytical accuracy and precision. QAQC procedures normally involve using sample duplicates and specially prepared standards whose grade is known. Numerous case studies indicate that reliable control of sample precision is achieved by using approximately 5% to 10% of field duplicates and 3% to 5% of pulp duplicates. These duplicate samples should be prepared and analyzed in the primary laboratory.

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.