QuickSampling v1.0: a robust and simplified pixel-based multiple-point simulation approach
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...
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Published in | Geoscientific Model Development Vol. 13; no. 6; pp. 2611 - 2630 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Katlenburg-Lindau
Copernicus GmbH
08.06.2020
Copernicus Publications |
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Abstract | 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 (https://github.com/GAIA-UNIL/G2S, last access: 19 May 2020) to promote reuse
and further evolution. The highlights are the following:
A new approach is proposed for pixel-based multiple-point geostatistics
simulation. The method is flexible and straightforward to parametrize. It natively handles continuous and multivariate simulations. It has high computational performance with predictable simulation times. A free and open-source implementation is provided. |
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AbstractList | 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 (https://github.com/GAIA-UNIL/G2S, last access: 19 May 2020) to promote reuse and further evolution.The highlights are the following:A new approach is proposed for pixel-based multiple-point geostatistics simulation.The method is flexible and straightforward to parametrize.It natively handles continuous and multivariate simulations.It has high computational performance with predictable simulation times.A free and open-source implementation is provided. 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 ( https://github.com/GAIA-UNIL/G2S , last access: 19 May 2020) to promote reuse and further evolution. The highlights are the following: A new approach is proposed for pixel-based multiple-point geostatistics simulation. The method is flexible and straightforward to parametrize. It natively handles continuous and multivariate simulations. It has high computational performance with predictable simulation times. A free and open-source implementation is provided. 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 (https://github.com/GAIA-UNIL/G2S, last access: 19 May 2020) to promote reuse and further evolution. The highlights are the following: A new approach is proposed for pixel-based multiple-point geostatistics simulation. The method is flexible and straightforward to parametrize. It natively handles continuous and multivariate simulations. It has high computational performance with predictable simulation times. A free and open-source implementation is provided. |
Author | Gravey, Mathieu Mariethoz, Grégoire |
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Snippet | Multiple-point geostatistics enable the realistic simulation of complex
spatial structures by inferring statistics from a training image. These
methods are... Multiple-point geostatistics enable the realistic simulation of complex spatial structures by inferring statistics from a training image. These methods are... |
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SubjectTerms | Advantages Algorithms Computer applications Computer simulation Continuity (mathematics) Geostatistics Handles Methods Pattern matching Pixels Simulation Statistical methods Training Variables |
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Title | QuickSampling v1.0: a robust and simplified pixel-based multiple-point simulation approach |
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