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Errors and uncertainty in mineral resource and ore reserve estimation: The importance of getting it right / Ошибки и неопределенность при оценке минеральных ресурсов и запасов руды: важность правильной оценки

Автор(ы):Annels A.E., Dominy S.C., Noppe M.A.
Издание:Canadian Institute of Mining Metallurgy and Petroleum, 2004 г., 22 стр.
Язык(и)Английский
Errors and uncertainty in mineral resource and ore reserve estimation: The importance of getting it right / Ошибки и неопределенность при оценке минеральных ресурсов и запасов руды: важность правильной оценки

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. Any company expecting to make sound investment or operational decisions must base this on both relevant and reliable information. An Ore Reserve statement generally contains a single set of grade and tonnage figures without a discussion of the potential inherent errors in these estimates. Some sensitivity studies may be run, but confidence limits are rarely quoted and, if they are, often do not take into account many of the factors that cause uncertainty in the grade and tonnage estimates. Mineral Resource and Ore Reserve estimates thus carry certain errors leading to uncertainty and risk; some of these are unquantifiable for various reasons, for which the operator should be aware. This paper presents a review of the potential sources of error that might occur during a resource estimation program, which are carried through into the reserve estimate. A number of methods are discussed that allow the estimator to be more transparent about the inherent risks in his/her estimate. Emphasis is placed on data quality, and the requirement for strong quality management to be linked to continuous improvement. <...>

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