Multivariate Block-Support Simulation of the Yandi Iron Ore Deposit, Western Australia
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Multivariate Block-Support Simulation of the Yandi Iron Ore Deposit, Western Australia Alexandre Boucher · Roussos Dimitrakopoulos
Received: 13 April 2012 / Accepted: 17 April 2012 / Published online: 5 May 2012 © International Association for Mathematical Geosciences 2012
Abstract Mineral deposits frequently contain several elements of interest that are spatially correlated and require the use of joint geostatistical simulation techniques in order to generate models preserving their spatial relationships. Although jointsimulation methods have long been available, they are impractical when it comes to more than three variables and mid to large size deposits. This paper presents the application of block-support simulation of a multi-element mineral deposit using minimum/maximum autocorrelation factors to facilitate the computationally efficient joint simulation of large, multivariable deposits. The algorithm utilized, termed dbmafsim, transforms point-scale spatial attributes of a mineral deposit into uncorrelated service variables leading to the generation of simulated realizations of block-scale models of the attributes of interest of a deposit. The dbmafsim algorithm is utilized at the Yandi iron ore deposit in Western Australia to simulate five cross-correlated elements, namely Fe, SiO2 , Al2 O3 , P and LOI, that are all critical in defining the quality of iron ore being produced. The block-scale simulations reproduce the direct- and cross-variograms of the elements even though only the direct variograms of the service variables have to be modeled. The application shows the efficiency, excellent performance and practical contribution of the dbmafsim algorithm in simulating large multi-element deposits. Keywords Multi-element mineral deposits · Block-support simulation · Joint simulation · Minimum/maximum autocorrelation factors · Iron ore deposits A. Boucher () Advanced Resources and Risk Technology, LLC, Sunnyvale, CA, 94086, USA e-mail: [email protected] R. Dimitrakopoulos COSMO Stochastic Mine Planning Lab, McGill University, Montreal, QC, H3A 2A7, Canada e-mail: [email protected]
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Math Geosci (2012) 44:449–468
1 Introduction Geostatistical simulation is the most commonly used approach to quantify spatial geological uncertainty in mineral deposits, with the usual goal being better informed engineering and business decisions (Dimitrakopoulos 2011). Such a framework requires transferring the spatial uncertainty provided by the set of simulations to engineering or economic uncertainty; a typical example would be to exploit the characterization of the spatial uncertainty for drilling scheme selection. For instance, determining efficient drilling density can be achieved with stochastic simulations (Boucher et al. 2005) that may require a relatively large number of realizations to assess the expected performance of drilling schemes. The applicability of such a study depends on the availability of efficient simulation algorithms. Although methods for simulating individual attributes are gen
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