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Uncertainty and Risk

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Abstract

This chapter shows how multiple realizations can be used to support the assessment of uncertainty and risk.

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References

  • Arik A (1999) An alternative approach to resource classification. In: APCOM proceedings of the 1999 Computer Applications in the Mineral Industries (APCOM) symposium, Colorado School of Mines, Colorado, pp 45–53

    Google Scholar 

  • Blackwell GH (1998) Relative kriging errors—a basis for mineral resource classification. Explor Min Geol 7(1,2):99–106

    Google Scholar 

  • Davis BM (1997) Some methods for producing interval estimates for global and local resources. SME Annual Meeting, SME 97(5), Denver

    Google Scholar 

  • Deutsch CV, Leuangthong O, Ortiz J (2006). A case for geometric criteria in resources and reserves classification. Centre for Computational Geostatistics, Report 7, University of Alberta, Edmonton

    Google Scholar 

  • Diehl P, David M (1982) Classification of ore reserves/resources based on geostatistical methods. CIM Bull 75(838):127–136

    Google Scholar 

  • Dohm C (2005) Quantifiable mineral resource classification—a logical approach. In: Leuangthong O, Deutsch CV (eds) Geostatistics Banff 2004, 1:333–342. Kluwer Academic, Dordrecht, p

    Google Scholar 

  • Froidevaux R (1982) Geostatistics and ore reserve classification. CIM Bull 75(843):77–83

    Google Scholar 

  • Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York, p 483

    Google Scholar 

  • Isaaks EH (1990) The application of Monte Carlo methods to the analysis of spatially correlated data. PhD Thesis, Stanford University, p 213

    Google Scholar 

  • Jewbali A, Dimitrakopoulos R (2009) Stochastic mine planning: example and value from integrating long- and short-term mine planning through simulated grade control. In: Orebody modelling and strategic mine planning 2009, Perth, pp 327–334

    Google Scholar 

  • Journel AG (1988) Fundamentals of geostatistics in five lessons. Stanford Center for Reservoir Forecasting, Stanford University, Stanford

    Google Scholar 

  • Journel AG, Kyriakidis P (2004) Evaluation of mineral reserves: a simulation approach, Oxford University Press, New York

    Google Scholar 

  • Matheron G (1976) Forecasting block grade distributions: the transfer function. In: Guarascio M, David M, Huijbregts C (eds) Advanced geostatistics in the mining industry. Reidel, Dordrecht, pp. 237–251

    Google Scholar 

  • Miskelly N (2003) Progress on international standards for reporting of mineral resources and reserves. In: Conference on resource reporting standards, Reston, 3 October

    Google Scholar 

  • Rossi ME (1999) Optimizing grade control: a detailed case study. In: Proceedings of the 101st annual meeting of the Canadian Institute of Mining, Metallurgy, and Petroleum (CIM), Calgary, 2–5 May

    Google Scholar 

  • Rossi ME (2003) Practical aspects of large-ccale conditional simulations. In: Proceedings of the 31st international symposium on applications of Computers and Operations Research in the Mineral Industries (APCOM), Cape Town, 14–16 May

    Google Scholar 

  • Royle AG (1977) How to use geostatistics for ore reserve classification. World Min 30:52–56

    Google Scholar 

  • Van Brunt BH, Rossi ME, (1999) Mine planning under uncertainty constraints. In: Proceedings of the optimizing with Whittle 1999 conference, Perth, 22–25 March

    Google Scholar 

Download references

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Correspondence to Mario E. Rossi .

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Rossi, M., Deutsch, C. (2014). Uncertainty and Risk. In: Mineral Resource Estimation. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-5717-5_12

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