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Automatic Parameter Tuning of Multiple-Point Statistical Simulations for Lateritic Bauxite Deposits
The application of multiple-point statistics (MPS) in the mining industry is not yet widespread and there are very few applications so far. In this paper, we focus on the problem of algorithmic input parameter selection, which is required to perform MPS simulations. The usual approach for selecting the parameters is to conduct a manual sensitivity analysis by testing a set of parameters and evaluating the resulting simulation qualities. However, carrying out such a sensitivity analysis may require significant time and effort. The purpose of this paper is to propose a novel approach to automate the parameter tuning process. The primary criterion used to select the parameters is the reproduction of the conditioning data patterns in the simulated image. The parameters of the MPS algorithm are obtained by iteratively optimising an objective function with simulated annealing. The objective function quantifies the dissimilarity between the pattern statistics of the conditioning data and the simulation image in two steps: the pattern statistics are first obtained using a smooth histogram method; then, the difference between the histograms is evaluated by computing the Jensen–Shanon divergence. The proposed approach is applied for the simulation of the geological interface (footwall contact) within a laterite-type bauxite mine deposit using the Direct Sampling MPS algorithm. The results point out two main advantages: (1) a faster parameter tuning process and (2) more objective determination of the parameters.