The Place of Geostatistical Simulation through the Life Cycle of a Mineral Deposit

Author:

Deutsch Clayton V.1

Affiliation:

1. Centre for Computational Geostatistics, University of Alberta, Edmonton, AB T6G 1H9, Canada

Abstract

Geostatistical techniques are applied to examine the life cycle of a mineral deposit. There are two main classes of geostatistical techniques: (1) deterministic techniques that include kriging and cokriging for a single best estimate, and (2) probabilistic techniques that include simulation, which infer probability distributions and simulate realizations to transfer multivariable and multilocation uncertainty through to larger-scale resource and reserve uncertainty. Probabilistic techniques are newer and more powerful in that they provide access to quantitative measures of uncertainty and models with correct spatial variability; however, they have not seen widespread application in all aspects of the life cycle of mines. Workflows and methodologies for the appropriate use of deterministic and probabilistic techniques have been discussed. Software, engineering practices and management expectations limit some applications. Applications have been reviewed, and enhancements are required to realize the full potential of geostatistical techniques, which have been discussed with examples.

Publisher

MDPI AG

Subject

Geology,Geotechnical Engineering and Engineering Geology

Reference21 articles.

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2. Principles of geostatistics;Matheron;Econ. Geol.,1963

3. Matheron, G. (1971). Les Cahiers du Centre de Morphogie Mathématiqu de Fontainebleau, 5th ed, Ecole Nationale Superieure des Mines de Paris.

4. David, M. (1977). Geostatistical Ore Reserve Estimation, Elsevier.

5. Journel, A.G., and Huijbregts, C. (1978). Mining Geostatistics, Academic Press.

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