On the challenge of using sequential indicator simulation for the estimation of recoverable reserves

Abstract

Despite its flexibility when compared with parametric methods, there are significant problems with sequential indicator simulation (SIS) for continuous variables. These come from the successive construction of the local conditional distributions by indicator kriging and the use of Monte–Carlo simulation for sampling the simulated values from such distributions. Some problems of SIS have been addressed satisfactorily; order relation deviations and the extrapolation of the distribution tails are manageable. The most important outstanding challenges are: (1) there is no resolution within a grade range, and (2) there is no correlation between different grade ranges. These problems lead to an over-smoothing of grades and a reduced, but misleading, uncertainty when point scale simulations are average up at the selective mining unit size. Attempting to account for the interclass correlation by cokriging fails because there is no adequate model for fitting the complete matrix of indicator variograms. Also, attempting to improve the within class resolution by using the class edge distance function does not provide a complete solution. In regard of these unresolved challenges, the practitioner is cautioned about the use of SIS of continuous variables for Resource Evaluation. Instead, a combination of categorical variables simulation methods (including SIS) for modeling the mineralisation domains, and Gaussian simulation of grades within these domains is recommended.

Keywords:

• sequential indicator simulation
• indicator kriging
• ore reserves estimation
• geostatistics
• selective mining unit