Applied Mineral Inventory Estimation

Abstract

Applied Mineral Inventory Estimation presents a comprehensive applied approach to the estimation of mineral resources/reserves with particular emphasis on the geological basis of such estimations, the need for and maintenance of a high quality assay data base, the practical use of a comprehensive exploratory data evaluation, and the importance of a comprehensive geostatistical approach to the estimation methodology. Practical problems and real data are used throughout as illustrations: each chapter ends with a summary of practical concerns, a number of practical exercises and a short list of references for supplementary study. This textbook is suitable for any university or mining school that offers senior undergraduate and graduate student courses on mineral resource/reserve estimation. It will also be valuable for professional mining engineers, geological engineers and geologists working with mineral exploration and mining companies.

The effect of accumulation in 2D estimates in phosphatic ore

“…it is a procedure that involves numerical calculation of the weighted means of the grades in larger volumes than the original samples. Typically, such a composition is linear in nature, involving calculation of a weighted mean of adjacent samples uniformly over a greater length than a single sample length (Sinclair and Blackwell, 2002). Some of the common reasons for sample regularization are: to provide an equal basis (support) for geostatistical analysis, and reduce the number of extreme values and the variability that these values may cause.…”

“…There are several mathematical tools to measure the spatial continuity of a mineral deposit, including madograms, covariograms, correlograms, etc. Studies of autocorrelation in geostatistics are often referred to as variography because of traditional emphasis on the variogram or semivariogram (Sinclair and Blackwell, 2002).…”

Computer Aided Ore Body Modelling and Mine Valuation

“…where, e 2 is estimation error (variance), Z * is estimated value, Z is actual value (Sinclair and Blackwell, 2004).…”

“…Underground assets are invisible bodies whose shapes, quality compositions and quantities are unknown. Geological explorations and investigations aim at determining all these unknowns (Sinclair and Blackwell, 2004). At the beginning of process, topographical and lithological data are gathered and a database is generated.…”

Sampling Errors and Control of Assay Data Quality in Exploration and Mining Geology

“…Precision error is mathematically deduced from differences between matching pairs of data, and is usually represented as variance of the assayed values normalised to the means of the corresponding pairs of the data. The different statistical methods are available for estimation precision error from paired data (Garrett, 1969;Thompson & Howarth, 1978;Bumstead, 1984;Shaw, 1997;FrancoisBongarson, 1998;Sinclair & Bentzen, 1998;Sinclair & Blackwell, 2002). All these methods have been reviewed by the author (Abzalov 2008) and compared by applying the same sets of duplicated pairs of samples collected in operating mines and mining projects.…”