Abstract
The block estimates made by conventional inverse distance or kriging techniques have no reliable measure of uncertainty attached to them. The approach presented in this chapter consists of directly predicting the variability/uncertainty in the mining block grades based on a probability distribution model. The limitations and assumptions supporting these models are summarized, as well as some of the most important issues regarding the estimation of point and block distributions.
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References
Armstrong M, Matheron G (1986) Disjunctive kriging revisited (Parts I and II). Math Geol 18(8):711–742
Chilès JP, Delfiner P (2011) Geostatistics: Modeling spatial uncertainty, 2nd edn. Wiley, New York, p 695
Deustch CV, Journel AG (1997) GSLIB: Geostatistical software library and user’s guide. 2nd edn. Oxford University Press, New York, p 369
Deustch CV, Lewis RE (1992) Advances in the practical implementation of indicator geostatistics. In: Kim YC (ed) Proceeding of the 23rd international APCOM symposium, Society of Mining, Metallurgy, and Exploration. 7–11 April, Tucson, p 169–179
Deutsch CV (2002) Geostatistical reservoir modeling. Oxford University Press, New York, p 376
Goovaerts P (1994) Comparative performance of indicator algorithms for modeling conditional probability distribution functions. Math Geol 26(3):389–411
Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York, p 483
Guibal D (1987) Recoverable reserves estimation at an Australian gold project, Geostatistical case studies, vol 1. In: Matheron G, Armstrong M (eds) Reidel, Dordrecht, p 149–168
Guibal D, Remacre A (1984). Local estimation of the recoverable reserves: Comparing Various methods with the reality on a porphyry copper deposit. In: Verly G et al. (eds) Geostatistics for natural resources characterization, vol 1. Reidel, Dordrecht, p 435–448
Hoerger S (1992) Implementation of indicator kriging at Newmont Gold Company. In: Kim YC (ed) Proceeding of the 23rd international APCOM symposium. Society of Mining, Metallurgy, and Exploration. 7–11 April, Tucson, p 205–213
Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, New York, p 561
Journel AG (1980) The lognormal approach to predicting local distributions of selective mining unit grades. Math Geol 12(4):285–303
Journel AG (1983) Non–parametric estimation of spatial distributions. Math Geol 15(3):445–468
Journel AG (1987) Geostatistics for the environmental sciences: An introduction. U.S. Environmental Protection Agency, Environmental Monitoring Systems Laboratory
Journel AG, Huijbregts ChJ (1978) Mining geostatistics. Academic, New York
Journel AG, Posa D (1990) Characteristic behavior and order relations for indicator variograms. Math Geol 22(8):1011–1028
Krige DG (1951) A statistical approach to some basic mine valuation problems on the Witwatersrand. J Chem Metall Min Soc S Afr 52:119–139
Kumar A (2010) Guide to recoverable reserves with disjunctive kriging. Guidebook Series, Vol 9. Centre for Computational Geostatistics, University of Alberta, Edmonton
Marcotte D, David M (1985) The bi-Gaussian approach: A simple method for recovery estimation. Math Geol 17(6):625–644
Matheron G (1971) The theory of regionalized variables and its applications. Fasc. 5, Paris School of Mines, p 212
Matheron G (1973) The intrinsic random functions and their applications. Adv Appl Prob 5:439–468
Matheron G (1974) Les Fonctions de transfert des petits panneaux. Centre de Géostatistique, Fontainebleau, France, No. N–395
Matheron G (1976) A simple substitute for conditional expectation: the disjunctive kriging. In: Guarascio M., David M, Huijbregts C (eds) Advanced geostatistics in the mining industry. Reidel, Dordrecht, p 221–236
Neufeld C (2005). Guide to recoverable reserves with uniform conditioning. Guidebook series, Vol 4. Centre for Computational Geostatistics, Edmonton
Parker H, Journel A, Dixon W (1979) The use of conditional lognormal probability distribution for the estimation of open-pit ore reserves in stratabound uranium deposits, a case study. In: Proceedings of the 16th APCOM, p 133–148
Remacre AZ (1987) Conditioning by The panel grade for recovery estimation of non-homogeneous ore bodies. In: G. Matheron G, Armstrong M (eds) Geostatistical case studies. Reidel, Dordrecht, p 135–148
Rivoirard J (1994) Introduction to disjunctive kriging and non-linear geostatistics. Claredon Press, Oxford, p 190
Roth C, Deraisme J (2000) The information effect and estimating recoverable reserves. In: Kleingeld WJ, Krige DG (eds) Proceedings of the sixth international geostatistics congress. Cape Town, April, p 776–787
Sichel HS (1952) New methods in the statistical evaluation of mine sampling data. Trans Inst Min Metall Lond 61:261
Sichel HS (1966) The estimation of means and associated confidence limits for small samples from lognormal populations. In: Proceedings of the 1966 symposium South African Institute of Mining and Metallurgy
Sullivan JA (1984) Non parametric estimation of spatial distributions. PhD Thesis, Stanford University, p 367
Suro-Pérez V, Journel AG (1991) Indicator principal component kriging. Math Geol 23(5):759–788
Vann J, Guibal D (1998) Beyond ordinary kriging—An overview of non-linear estimation. In: Beyond ordinary kriging: Non-linear geostatistical methods in practice. The Geostatistical Association of Australasia, Perth, 30 October, p 6–25
Verly G (1984) Estimation of spatial point and block distributions: The multigaussian model. PhD Dissertation, Department of Applied Earth Sciences, Stanford University
Zhang S (1994) Multimetal recoverable reserve estimation and its impact on the Cove ultimate pit design Min Eng July 1998:73–79
Zhu H (1991) Modeling mixtures of spatial distributions with integration of soft data. PhD Thesis, Stanford University, Stanford
Zhu H, Journel A (1992) Formatting and integrating soft data: Stochastic imagining via the Markov-Bayes algorithm. In: Soares A (ed) Geostatistics-Troia. Kluwer, Dordrecht, p 1–12
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Rossi, M., Deutsch, C. (2014). Recoverable Resources: Probabilistic Estimation. In: Mineral Resource Estimation. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-5717-5_9
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