A parsimonious parametrization of the Direct Sampling algorithm for multiple-point statistical simulations. (December 2022) - Juda, Przemysław; Renard, Philippe; Straubhaar, Julien
Abstract: Multiple-point statistics algorithms allow modeling spatial variability from training images. Among these techniques, the Direct Sampling (DS) algorithm has advanced capabilities, such as multivariate simulations, treatment of non-stationarity, multi-resolution capabilities, conditioning by inequality or connectivity data. However, finding the right trade-off between computing time and simulation quality requires tuning three main parameters, which can be complicated since simulation time and quality are affected by these parameters in a complex manner. To facilitate the parameter selection, we propose the Direct Sampling Best Candidate (DSBC) parametrization approach. It consists in setting the distance threshold to 0. The two other parameters are kept (the number of neighbors and the scan fraction) as well as all the advantages of DS. We present three test cases that prove that the DSBC approach allows to identify efficiently parameters leading to comparable or better quality and computational time than the standard DS parametrization. We conclude that the DSBC approach could be used as a default mode when using DS, and that the standard parametrization should only be used when the DSBC approach is not sufficient. Highlights: A new parametrization, Direct Sampling Best Candidate (DSBC), of the Direct Sampling (DS) algorithm is presented. It consists of setting the distance threshold to zero and reducing the number of parameters from three to two. Three test cases are studied and show that DSBC can help identifying efficiently optimal parameters.