QuickSampling v1.0: a robust and simplified pixel-based multiple-point simulation approach
<p>Multiple-point geostatistics enable the realistic simulation of complex spatial structures by inferring statistics from a training image. These methods are typically computationally expensive and require complex algorithmic parametrizations. The approach that is presented in this paper is e...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-06-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://www.geosci-model-dev.net/13/2611/2020/gmd-13-2611-2020.pdf |
| _version_ | 1818849399259791360 |
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| author | M. Gravey G. Mariethoz |
| author_facet | M. Gravey G. Mariethoz |
| author_sort | M. Gravey |
| collection | DOAJ |
| description | <p>Multiple-point geostatistics enable the realistic simulation of complex
spatial structures by inferring statistics from a training image. These
methods are typically computationally expensive and require complex
algorithmic parametrizations. The approach that is presented in this paper
is easier to use than existing algorithms, as it requires few independent
algorithmic parameters. It is natively designed for handling continuous
variables and quickly implemented by capitalizing on standard libraries.
The algorithm can handle incomplete training images of any dimensionality,
with categorical and/or continuous variables, and stationarity is not
explicitly required. It is possible to perform unconditional or conditional
simulations, even with exhaustively informed covariates. The method provides
new degrees of freedom by allowing kernel weighting for pattern matching.
Computationally, it is adapted to modern architectures and runs in constant
time. The approach is benchmarked against a state-of-the-art method. An
efficient open-source implementation of the algorithm is released and can be
found here (<span class="uri">https://github.com/GAIA-UNIL/G2S</span>, last access: 19 May 2020) to promote reuse
and further evolution.</p>
<p>The highlights are the following:
</p><ol><li>
<p id="d1e99">A new approach is proposed for pixel-based multiple-point geostatistics
simulation.</p></li><li>
<p id="d1e103">The method is flexible and straightforward to parametrize.</p></li><li>
<p id="d1e107">It natively handles continuous and multivariate simulations.</p></li><li>
<p id="d1e111">It has high computational performance with predictable simulation times.</p></li><li>
<p id="d1e115">A free and open-source implementation is provided.</p></li></ol> |
| first_indexed | 2024-12-19T06:32:37Z |
| format | Article |
| id | doaj.art-17ea2eea37b846f3b7c570f437d66a0a |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-12-19T06:32:37Z |
| publishDate | 2020-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-17ea2eea37b846f3b7c570f437d66a0a2022-12-21T20:32:19ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032020-06-01132611263010.5194/gmd-13-2611-2020QuickSampling v1.0: a robust and simplified pixel-based multiple-point simulation approachM. GraveyG. Mariethoz<p>Multiple-point geostatistics enable the realistic simulation of complex spatial structures by inferring statistics from a training image. These methods are typically computationally expensive and require complex algorithmic parametrizations. The approach that is presented in this paper is easier to use than existing algorithms, as it requires few independent algorithmic parameters. It is natively designed for handling continuous variables and quickly implemented by capitalizing on standard libraries. The algorithm can handle incomplete training images of any dimensionality, with categorical and/or continuous variables, and stationarity is not explicitly required. It is possible to perform unconditional or conditional simulations, even with exhaustively informed covariates. The method provides new degrees of freedom by allowing kernel weighting for pattern matching. Computationally, it is adapted to modern architectures and runs in constant time. The approach is benchmarked against a state-of-the-art method. An efficient open-source implementation of the algorithm is released and can be found here (<span class="uri">https://github.com/GAIA-UNIL/G2S</span>, last access: 19 May 2020) to promote reuse and further evolution.</p> <p>The highlights are the following: </p><ol><li> <p id="d1e99">A new approach is proposed for pixel-based multiple-point geostatistics simulation.</p></li><li> <p id="d1e103">The method is flexible and straightforward to parametrize.</p></li><li> <p id="d1e107">It natively handles continuous and multivariate simulations.</p></li><li> <p id="d1e111">It has high computational performance with predictable simulation times.</p></li><li> <p id="d1e115">A free and open-source implementation is provided.</p></li></ol>https://www.geosci-model-dev.net/13/2611/2020/gmd-13-2611-2020.pdf |
| spellingShingle | M. Gravey G. Mariethoz QuickSampling v1.0: a robust and simplified pixel-based multiple-point simulation approach Geoscientific Model Development |
| title | QuickSampling v1.0: a robust and simplified pixel-based multiple-point simulation approach |
| title_full | QuickSampling v1.0: a robust and simplified pixel-based multiple-point simulation approach |
| title_fullStr | QuickSampling v1.0: a robust and simplified pixel-based multiple-point simulation approach |
| title_full_unstemmed | QuickSampling v1.0: a robust and simplified pixel-based multiple-point simulation approach |
| title_short | QuickSampling v1.0: a robust and simplified pixel-based multiple-point simulation approach |
| title_sort | quicksampling v1 0 a robust and simplified pixel based multiple point simulation approach |
| url | https://www.geosci-model-dev.net/13/2611/2020/gmd-13-2611-2020.pdf |
| work_keys_str_mv | AT mgravey quicksamplingv10arobustandsimplifiedpixelbasedmultiplepointsimulationapproach AT gmariethoz quicksamplingv10arobustandsimplifiedpixelbasedmultiplepointsimulationapproach |