What is the quality of the sample and assay data? Am I confident enough in my data to be able to make a potentially costly decision? These are the types of questions which should be asked when dealing with assay data.

Отражены теоретические основы опробования полезных ископаемых. Представлены типы, виды и способы опробования, а также способы оценки представительности и достоверности опробования. Приведены основные требования к мероприятиям контроля отбора, обработки проб и проведения последующих лабораторных исследований.

Для студентов специальности 21.00.00 «Прикладная геология», может быть использовано при подготовке студентов других специальностей геологического профиля.

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. 

1. Введение в QA/QC (Предыстория и основные принципы)
2. Планирование и реализация программы QA/QC
3. Контрольные пробы

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. <...>