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  4. A parsimonious parametrization of the Direct Sampling algorithm for multiple-point statistical simulations
 
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A parsimonious parametrization of the Direct Sampling algorithm for multiple-point statistical simulations

Auteur(s)
Juda, Przemyslaw 
Collaborateurs de la Faculté des sciences 
Renard, Philippe 
Centre d'hydrogéologie et de géothermie 
Straubhaar, Julien 
Centre d'hydrogéologie et de géothermie 
Date de parution
2022
In
Applied Computing and Geosciences
Vol.
16
De la page
100091
Mots-clés
  • Geostatistics
  • Multiple-point statistics
  • Hydrogeology
  • Stochastic simulation
  • Direct sampling
  • Geostatistics

  • Multiple-point statis...

  • Hydrogeology

  • Stochastic simulation...

  • Direct sampling

Résumé
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.
Identifiants
https://libra.unine.ch/handle/123456789/32957
_
10.1016/j.acags.2022.100091
Type de publication
journal article
Dossier(s) à télécharger
 main article: 1-s2.0-S2590197422000131-main.pdf (12.03 MB)
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