QuickSampling v1.0: a robust and simplified pixel-based multiple-point simulation approach

Gravey, Mathieu; Mariethoz, Grégoire

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 (urihttps://github.com/GAIA-UNIL/G2S, last access: 19 May 2020) to promote reuse and further evolution.

The highlights are the following:

  1. d1e99A new approach is proposed for pixel-based multiple-point geostatistics simulation.

  2. d1e103The method is flexible and straightforward to parametrize.

  3. d1e107It natively handles continuous and multivariate simulations.

  4. d1e111It has high computational performance with predictable simulation times.

  5. d1e115A free and open-source implementation is provided.



Gravey, Mathieu / Mariethoz, Grégoire: QuickSampling v1.0: a robust and simplified pixel-based multiple-point simulation approach. 2020. Copernicus Publications.


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Rechteinhaber: Mathieu Gravey

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