Abstract
Mineral inventory determination consists of estimating the amount of mineral resources on a block-by-block basis and classifying individual blocks into categories with increasing level of geologic confidence. Such classification is a crucial issue for mining companies, investors, financial institutions, and authorities, but it remains subject to some confusion because of the wide variations in methodologies and the lack of standardized procedures. The first part of this paper considers some of the criteria used to classify resources in practice and their impact through a sensitivity study using data from a Chilean porphyry copper deposit. Five classification criteria are compared and evaluated, namely: Search neighborhoods, absolute and relative kriging variances, absolute and relative conditional simulation variances. It is shown that some classification criteria either favor or penalize the high-grade areas if the grade distribution presents a proportional effect. In the second part of the paper, conditional simulations are used to quantify the uncertainty on the overall mineral resources. This approach is promising for risk analysis and decision-making. Unlike linear kriging, simulations allow inclusion of a cutoff grade in the calculation of the resources and also provide measures of their joint uncertainty over production volumes.
Similar content being viewed by others
References
Annels, A. E., and Dominy, S. C., 2003, Core recovery and quality: Important factors in mineral resource estimation: Trans. Inst. Min. Metall. Sect. B Appl. Earth Sci., v. 112, no. 3, p. 305–312.
Blackwell, G. H., 1998, Relative kriging errors—A basis for mineral resource classification: Explor. Min. Geol., v. 7, no. 1–2, p. 99–106.
Chilés, J. P., and Delfiner, P., 1999, Geostatistics: Modeling spatial uncertainty: Wiley, New York, 695 p.
CIM, 2000, CIM standards on mineral resources and reserves—Definitions and guidelines. Prepared by the CIM Standing Committee on Reserve Definitions: CIM Bull., v. 93, no. 1044, p. 53–61.
CSA, 2001, Standards of disclosure for mineral projects: National Instrument 43–101, Canadian Securities Administration, 22 p.
David, M., 1988, Handbook of applied advanced geostatistical ore reserve estimation: Elsevier, Amsterdam, 216 p.
Diehl, P., and David, M., 1982, Classification of ore reserves/resources based on geostatistical methods: CIM Bull., v. 75, no. 838, p. 127–136.
Dohm, C., 2005, Quantifiable mineral resource classification-A logical approach, in Leuangthong, O., and Deutsch, C. V., eds., Geostatistics Banff/2004: Springer, Dordrecht, v. 1, p. 333–342.
Dominy, S. C., Noppé, M. A., and Annels, A. E., 2002, Errors and uncertainty in mineral resource and ore reserve estimation: The importance of getting it right: Explor. Min. Geol., v. 11, no. 1–4, p. 77–98.
Emery, X., 2002, Conditional simulation of nongaussian random functions: Math. Geol., v. 34, no. 1, p. 79–100.
Emery, X., 2005, Conditional simulation of random fields with bivariate gamma isofactorial distributions: Math. Geol., v. 37, no. 4, p. 419–445.
Emery, X., and Ortiz, J. M., 2005, Histogram and variogram inference in the multigaussian model: Stochastic environmental research and risk assessment, v. 19, no. 1, p. 48–58.
EURO, 2002, Code for reporting of mineral exploration results, mineral resources and mineral reserves (the European code): Report prepared by the Institution of Mining and Metallurgy Working Group on Resources and Reserves in conjunction with the European Federation of Geologists and the Institute of Geologists of Ireland, 34 p.
Froidevaux, R., Roscoe, W. E., and Valiant, R. I., 1986, Estimating and classifying gold reserves at Page-Williams C zone: A case study in nonparametric geostatistics, in David, M., Froidevaux, R., Sinclair, A. J., and Vallée, M., eds., Proceedings of the Symposium on Ore reserve estimation—Methods, models and reality: Canadian Institute of Mining, Metallurgy and Petroleum, Montreal, p. 280–300.
Goovaerts, P., 1997, Geostatistics for natural resources evaluation: Oxford University Press, New York, 480 p.
JORC, 2004, Australasian Code for reporting of exploration results, mineral resources and ore reserves (the JORC Code, 2004 Edition): Report prepared by the Joint Ore Reserve Committee of the Australasian Institute of Mining and Metallurgy, Australian Institute of Geoscientists and Minerals Council of Australia, 21 p.
Journel, A. G., and Huijbregts, C. J., 1978, Mining geostatistics: Academic Press, London, 600 p.
Matheron, G., 1973, The intrinsic random functions and their applications: Adv. Appl. Probab., v. 5, p. 439–468.
Matheron, G., 1984, The selectivity of the distributions and the “second principle” of geostatistics, in Verly, G., David, M., Journel, A. G., and Maréchal, A., eds., Geostatistics for natural resources characterization: Reidel, Dordrecht, v. 1, p. 421–433.
Rendu, J. M., and Miskelly, N., 2001, Mineral resources and mineral reserves: Progress on international definitions and reporting standards: Trans. Inst. Min. Metall. Sect. A Min. Technol., v. 110, p. 133–138.
Royle, A. G., 1977, How to use geostatistics for ore reserve classification: Eng. Min. J., v. 30, p. 52–55.
Sabourin, R., 1984, Application of a geostatistical method to quantitatively define various categories of resources, in Verly, G., David, M., Journel, A. G., and Maréchal, A., eds., Geostatistics for natural resources characterization: Reidel, Dordrecht, v. 1, p. 201–215.
SAMREC, 2000, South African Code for Reporting of Mineral Resources and Mineral Reserves (The SAMREC Code): Report prepared by the South African Mineral Resource Committee SAMREC under the auspices of the South African Institute of Mining and Metallurgy, 38 p.
Serrano, L., Vargas, R., Stambuk, V., Aguilar, C., Galeb, M., Holmgren, C., Contreras, A., Godoy, S., Vela, I., Skewes, M. A., and Stern, C. R., 1996, The late Miocene to early Pliocene Río Blanco-Los Bronces copper deposit, Central Chilean Andes, in Camus, F., Sillitoe, R. H., and Petersen, R., eds., Andean copper deposits: New discoveries, mineralizations, styles and metallogeny: Society of Economic Geologists, Special Publication no. 5, Littleton, Colorado, p. 119–130.
Sinclair, A. J., and Blackwell, G. H., 2000, Resource/reserve classification and the qualified person: CIM Bull., v. 93, no. 1038, p. 29–35.
SME, 1999, A guide for reporting exploration information, resources, and reserves: Report prepared by SME Resources and Reserves Committee, Society for Mining, Metallurgy and Exploration, 17 p.
Snowden, D. V., 2001, Practical interpretation of mineral resource and ore reserve classification guidelines, in Edwards, A. C., ed., Mineral resource and ore reserve estimation—The AusIMM Guide to Good Practice: The Australasian Institute of Mining and Metallurgy, Monograph 23, Melbourne, p. 643–652.
USGS, 1980, Principles of a resource/reserve classification for minerals: U.S. Geological Survey Circular 831, 5 p.
Vallée, M., 1999, Resource/reserve inventories: What are the objectives?: CIM Bull., v. 92, no. 1031, p. 151–155.
Vallée, M., 2000, Mineral resource + engineering, economic and legal feasibility = ore reserve: CIM Bull., v. 93, no. 1039, p. 53–61.
Wober, H. H., and Morgan, P. J., 1993, Classification of ore reserves based on geostatistical and economic parameters: CIM Bull., v. 86, no. 966, p. 73–76.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Emery, X., Ortiz, J.M. & Rodríguez, J.J. Quantifying Uncertainty in Mineral Resources by Use of Classification Schemes and Conditional Simulations. Math Geol 38, 445–464 (2006). https://doi.org/10.1007/s11004-005-9021-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11004-005-9021-9