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    Journal of the Southern African Institute of Mining and Metallurgy

    versión impresa ISSN 0038-223X

    Resumen

    MONTOYA, C; EMERY, X.; RUBIO, E.  y  WIERTZ, J.. Multivariate resource modelling for assessing uncertainty in mine design and mine planning. J. S. Afr. Inst. Min. Metall. [online]. 2012, vol.112, n.5, pp. 353-363. ISSN 0038-223X.

    This paper shows, through a case study, the impact of multivariate grade modelling upon mine design and mine planning. A deposit explored by drill holes is considered, in which the grades of five elements (copper, silver, molybdenum, arsenic, and antimony) are of interest. Forty alternative models of the deposit are constructed by fitting the joint correlation structure of the grade variables and using conditional cosimulation. In addition, a reference model, obtained by averaging the alternative models, is also considered. The study shows that the resulting mine design (final pit characteristics and production schedules) is sensitive to the grade model under consideration, and that the design based on the reference model may not be optimal when compared to the alternative models based on cosimulation. However, when assuming a given long-term plan and extraction sequence, the grades and net present value (NPV) calculated on the reference model are unbiased with respect to those calculated on the alternative models with the same extraction sequence. The latter allow assessing the possible dispersion of the actual grades and NPV around their expected values, and are useful for the planner in order to determine the probability of meeting given production targets and of exceeding or falling short of given threshold grades. Additionally, unlike cosimulation, the separate simulation of each grade variable leads to unrealistic resource models and to biased results in mine design and mine planning. This approach should therefore be avoided, unless the grade variables are spatially uncorrelated.

    Palabras llave : coregionalization models; cosimulation; grade uncertainty; conditional bias.

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