The use of conditional simulation to characterise mine plan uncertainty is increasing for the assessment of risk in mining projects. While the development of grade uncertainty profiles is relatively straightforward and can be validated using standard geostatistical techniques, the addition of geological uncertainty to evaluate total risk remains problematic. Some of the problems associated with geological uncertainty methods include the clustering of data in favourable geologic units, difficulty in training image definition and the inability to address change of support issues for categorical variables. Despite these obstacles, the importance of geological uncertainty as a contributor to total uncertainty has prompted Newmont to explore and evaluate the use of various techniques (and combinations of techniques) on different deposit types.Two orogenic deposits of different geological complexity were selected for the study: Subika, a shear zone-hosted deposit, and Merian, a deposit containing gold mineralisation associated with quartz vein zones and stockwork within which are found higher-grade quartz breccia zones. Newmont trialled various categorical simulation approaches to determine the applicability of these methods for each deposit type and the effect of parameter choice on the width of the uncertainty interval. Some of the techniques that were trialled include multiple point statistics methods, sequential indicator simulation using local probabilities as well as variations of these methodologies.The goals of this study included: gaining an understanding of which techniques may work best in which deposit types gaining an understanding of the intricacies of each method gaining an understanding of the effect that each method has on total uncertainty analysis._x000D_
This paper presents a comparison of the various techniques and makes recommendations for their use in uncertainty analysis.CITATION:Jewbali, A, Perry, R, Allen, L and Inglis, R, 2014._x000D_
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).