Abstract
The prediction of the tonnages and grade of ore recoverable with a particular mining plan is a central problem in mineral resource estimation. The conventional approach to this problem is to estimate the mineral grade for volumes relevant to the mining plan and base the recoverable resource calculations on those estimates. Details of that approach are presented in this Chapter.
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References
Aitchison J (1986) The statistical analysis of compositional data: monographs on statistics and applied probability. Chapman and Hall, London, p 416
Baafi EY, Kim YC (1982) Comparison of different ore reserve estimation methods using conditional simulation. AIME annual meeting, Preprint, pp 82–94
Boyle C (2010) Kriging neighbourhood analysis by slope of regression and weight of mean—evaluation with the Jura data set. Min Technol 119(2):49–58
Chilès JP Delfiner P (2011) Geostatistics: Modeling spatial uncertainty, 2nd ed. Wiley Series in Probability and Statistics, New York, p 695
Delfiner P (1976) Linear estimation of non-stationary spatial phenomena. In: Guarascio M, David M, Huijbregts CJ (eds) Advanced geostatistics in the mining industry. Reidel, Dordrecht, pp 49–68
Deutsch CV (1994) Kriging with strings of data. Math Geol 26(5):623–638 (November)
Deutsch CV (1996) Correcting for negative weights in ordinary kriging. Comp Geosci 22(7):765–773
Deutsch CV (2002) Geostatistical reservoir modeling. Oxford University Press, New York, p 376
Deustch CV, Journel AG (1997) GSLIB: Geostatistical software library and user’s guide, 2nd edn. Oxford University Press, New York, p 369
Dominy SC, Noppé MA, Annels AE (2002) Errors and uncertainty in mineral resource and ore reserve estimation: the importance of getting it right. Explor Min Geol 11:77–98
Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York, p 483
Guertin K (1984) Correcting conditional bias in ore reserve estimation. PhD Thesis, Stanford University
Isaaks EH (2004) The kriging oxymoron: A conditionally unbiased and accurate predictor, 2nd edn. In: Proceedings of geostatistics banff 2004, 1:363–374, Springer 2005
Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, New York, p 561
Journel AG, Huijbregts ChJ (1978) Mining geostatistics. Academic Press, New York
Journel AG, Rossi ME (1989) When do we need a trend model? Math Geol 22(8):715–738
Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans ASME, Ser D: J Bas Eng 82, 35.45
Knudsen HP (1990) Computerized conventional ore reserve methods. In: Kennedy BA (ed) Surface mining, 2nd ed. SME, Littleton, pp 293–300
Knudsen HP, Kim YC, Mueller E (1978) A comparative study of the geostatistical ore reserve estimation method over the conventional methods. Mining Eng 30:54–58
Krige DG (1994) A basic perspective on the roles of classical statistics, data search routines, conditional biases and information and smoothing effects in ore block valuations. Conference on mining geostatistics, Kruger National Park
Krige DG (1996) A Practical analysis of the effects of spatial structure and data available and used. In: Conditional biases in ordinary kriging. Fifth international geostatistics congress, Wollongong
Krige DG (1999) Conditional bias and uncertainty of estimation in geostatistics. Keynote address for APCOM’99 international symposium, Colorado School of Mines, Golden, October 1999
Luenberger DL (1969) Optimization by vector space methods. Wiley, New York, p 326
Martin-Fernandez JA, Olea-Meneses RA, Pawlowsky-Glahn V (2001) Criteria to compare estimation methods of regionalized compositions. Math Geol 33(8):889–909
Matheron G (1971) The theory of regionalized variables and its applications. Fasc. 5, Paris School of Mines, Paris, p 212
Matheron G (1973) The intrinsic random functions and their applications. Adv Appl Probab 5:439–468
McLennan JA, Deutsch CV (2004) Conditional non-bias of geostatistical simulation for estimation of recoverable reserves. CIM bulletin, 97
Pan G (1998) Smoothing effect, conditional bias, and recoverable reserves; Can Inst Min Metall Bull 91(1019):81–86
Pawlowsky V (1989) Cokriging of regionalized compositions. Math Geol 21(5):513–521
Pawlowsky V, Olea RA, Davis JC (1995) Estimation of regionalized compositions: A comparison of three methods. Math Geol 27(1):105–127
Pawlowsky-Glahn V, Olea RA (2004) Geostatistical analysis of compositional data. Oxford University Press, New York, p 304
Popoff CC (1966) Computing reserves of mineral deposits: Principles and conventional methods. Information Circular 8283, US Bureau of Mines
Readdy LA, Bolin DS, Mathieson GA (1982) Ore reserve calculation. In: Hustrulid WA (ed) Underground mining methods handbook. AIME, New York, pp 17–38
Rivoirard J (1987) Two key parameters when choosing the kriging neighborhood. Math Geol 19(8):851–856
Sinclair AJ, Blackwell GH (2002) Applied mineral inventory estimation. Cambridge University Press, New York, p 381
Srivastava RM (1987) Minimum variance or maximum profitability. CIMM 80(901):63–68
Stone JG, Dunn PG (1996) Ore reserve estimates in the real world. Society of Economic Geologists, Special publication 3:150
Vann J, Jackson S, Bertoli O (2003) Quantitative kriging neighbourhood analysis for the mining geologist—a description of the method with worked case examples. In: 5th international mining geology conference, Bedigo, 17–19 November, 2003(8):215–223
Walvoort DJJ, de Gruijter JJ (2001) Compositional kriging: a spatial interpolation method for compositional data. Math Geol 33(8):951–966.
Zhu H (1991) Modeling mixtures of spatial distributions with integration of soft data. Ph.D. Thesis, Stanford University, Stanford
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Rossi, M., Deutsch, C. (2014). Recoverable Resources: Estimation. In: Mineral Resource Estimation. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-5717-5_8
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DOI: https://doi.org/10.1007/978-1-4020-5717-5_8
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