Abstract
Uncertainty in geological models and grade uncertainty are two major contributors to the total resource uncertainty of a mining project. Previous attempts at determining uncertainty in geological models using methods such as MPS (multiple-point statistics), SIS (sequential indicator simulation), and multiple applications of RBF (radial basis functions) with different parameters have shown that it is nontrivial; the uncertainty profiles are dependent on the method and the parameters selected. Most of the methods tested require additional information in the form of either local probabilities or proportions derived from the existing geological interpretation or a conceptual geological model in the form of a training image. This makes some methods amenable to use in the early stages of a project because the method allows for a more complete testing of different geological concepts. In later stage projects where there is an increased level of confidence (due to the amount of data collected) in the geologic interpretation, methods that achieve ranges of uncertainty around the interpretation likely provide a more realistic assessment of uncertainty. This paper details the continuation of research into geostatistical tools suitable for the evaluation of geological uncertainty in order to further understand the intricacies of the methods and the impact of the technique on the resulting uncertainty profile. Suggestions of which methods to use based on the amount of geological information available are provided.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Bibliography
Cáceres A, Emery X, Aedo L, Gálvez O (2011) Stochastic geologic modelling using implicit boundary simulation. In Beniscelli J, Kuyvenhoven R, Hoal K (eds) Proceedings of the 2nd international seminar on geology for the mining industry
Deutsch C (2006) A sequential indicator simulation program for categorical variables with point and block data: BlockSIS. Comput Geosci 32:1669–1681
Guardiano F, Srivastava M (1993) Multivariate geostatistics: beyond bivariate moments. In: Soares A (ed) Geostatistics Troia 1992, vol 1. Kluwer Academic, Dordrecht, pp 133–144
Inglis R (2013) How to select a grade domain-A gold mine case study in exploration, resource and mining geology conference, Cardiff, UK,.21–22 October
Jewbali A, Perry R, Allen L, Inglis R (2014) Applicability of categorical simulation methods for assessment of mine plan risk. In: Proceedings orebody modelling and strategic mine planning symposium 2014, pp 85–98 (The Australasian Institute of Mining and Metallurgy: Melbourne). Reprinted with the permission of The Australasian Institute of Mining and Metallurgy
Lui Y (2006) Using the Snesim program for multiple point statistical simulation. Comput Geosci 32:1544–1563
Munroe MJ, Deutsch C (2008a) Full calibration of C and beta in the framework of vein type deposit tonnage uncertainty, Center for Computational Geostatistics Annual Report 10. University of Alberta, Edmonton
Munroe MJ, Deutsch C (2008b) A methodology for modeling vein-type deposit tonnage uncertainty, Centre for Computational Geostatistics Annual report 10. University of Alberta, Edmonton
Remy N, Boucher A, Wu J (2009) Applied geostatistics with SGeMS: a user’s guide. Cambridge University Press, Cambridge, UK, 263p
Stewart M, de Lacey J, Hodkiewicz PF, Lane R (2014) Grade estimation from radial basis functions – how does it compare with conventional geostatistical estimation. In ninth international mining geology conference 2014, pp 129–139 (The Australian Institute of Mining and Metallurgy)
Strebelle S (2002) Conditional simulation of complex geological structures using multiple-point statistics. Math Geol 34:1–21
Wilde JB, Deutsch C (2012) Kriging and simulation in presence of stationary domains: developments in boundary modelling. In: Abrahamsen P, Hauge R, Kolbjørnsen O (eds) Geostatistics oslo 2012. Springer, Dordrech, pp 289–300
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Jewbali, A., Perry, B., Allen, L., Inglis, R. (2017). Implications of Algorithm and Parameter Choice: Impacts of Geological Uncertainty Simulation Methods on Project Decision Making. In: Gómez-Hernández, J., Rodrigo-Ilarri, J., Rodrigo-Clavero, M., Cassiraga, E., Vargas-Guzmán, J. (eds) Geostatistics Valencia 2016. Quantitative Geology and Geostatistics, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-46819-8_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-46819-8_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46818-1
Online ISBN: 978-3-319-46819-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)