A parsimonious parametrization of the Direct Sampling algorithm for multiple-point statistical simulations
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 inequali...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | Applied Computing and Geosciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590197422000131 |
| _version_ | 1828123623933607936 |
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| author | Przemysław Juda Philippe Renard Julien Straubhaar |
| author_facet | Przemysław Juda Philippe Renard Julien Straubhaar |
| author_sort | Przemysław Juda |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T14:54:32Z |
| format | Article |
| id | doaj.art-02712a31f9e74389b5252ea115c62b84 |
| institution | Directory Open Access Journal |
| issn | 2590-1974 |
| language | English |
| last_indexed | 2024-04-11T14:54:32Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Computing and Geosciences |
| spelling | doaj.art-02712a31f9e74389b5252ea115c62b842022-12-22T04:17:17ZengElsevierApplied Computing and Geosciences2590-19742022-12-0116100091A parsimonious parametrization of the Direct Sampling algorithm for multiple-point statistical simulationsPrzemysław Juda0Philippe Renard1Julien Straubhaar2Stochastic Hydrogeology and Geostatistics Group, Centre for Hydrogeology and Geothermics, University of Neuchâtel, Rue Emile-Argand 11, 2000, Neuchâtel, SwitzerlandStochastic Hydrogeology and Geostatistics Group, Centre for Hydrogeology and Geothermics, University of Neuchâtel, Rue Emile-Argand 11, 2000, Neuchâtel, Switzerland; Department of Geosciences, University of Oslo, Oslo, Norway; Corresponding author. Stochastic Hydrogeology and Geostatistics Group, Centre for Hydrogeology and Geothermics, University of Neuchâtel, Rue Emile-Argand 11, 2000, Neuchâtel, Switzerland.Stochastic Hydrogeology and Geostatistics Group, Centre for Hydrogeology and Geothermics, University of Neuchâtel, Rue Emile-Argand 11, 2000, Neuchâtel, SwitzerlandMultiple-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.http://www.sciencedirect.com/science/article/pii/S2590197422000131GeostatisticsMultiple-point statisticsHydrogeologyStochastic simulationDirect sampling |
| spellingShingle | Przemysław Juda Philippe Renard Julien Straubhaar A parsimonious parametrization of the Direct Sampling algorithm for multiple-point statistical simulations Applied Computing and Geosciences Geostatistics Multiple-point statistics Hydrogeology Stochastic simulation Direct sampling |
| title | A parsimonious parametrization of the Direct Sampling algorithm for multiple-point statistical simulations |
| title_full | A parsimonious parametrization of the Direct Sampling algorithm for multiple-point statistical simulations |
| title_fullStr | A parsimonious parametrization of the Direct Sampling algorithm for multiple-point statistical simulations |
| title_full_unstemmed | A parsimonious parametrization of the Direct Sampling algorithm for multiple-point statistical simulations |
| title_short | A parsimonious parametrization of the Direct Sampling algorithm for multiple-point statistical simulations |
| title_sort | parsimonious parametrization of the direct sampling algorithm for multiple point statistical simulations |
| topic | Geostatistics Multiple-point statistics Hydrogeology Stochastic simulation Direct sampling |
| url | http://www.sciencedirect.com/science/article/pii/S2590197422000131 |
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