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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-211
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-211
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 22 Nov 2019

Submitted as: model description paper | 22 Nov 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Quantile Sampling: a robust and simplified pixel-based multiple-point simulation approach

Mathieu Gravey and Grégoire Mariethoz Mathieu Gravey and Grégoire Mariethoz
  • University of Lausanne, Faculty of Geosciences and Environment, Institute of Earth Surface Dynamics, Switzerland

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 or/and 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), to promote reuse and further evolution.

Mathieu Gravey and Grégoire Mariethoz
Interactive discussion
Status: open (until 17 Jan 2020)
Status: open (until 17 Jan 2020)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Mathieu Gravey and Grégoire Mariethoz
Model code and software

GAIA-UNIL/G2S: G2S_v0.95 M. Gravey and G. Mariethoz https://doi.org/10.5281/zenodo.3546338

Mathieu Gravey and Grégoire Mariethoz
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Latest update: 15 Dec 2019
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Short summary
Stochastic simulations are key tools to generate complex spatial structures uses as input in geoscientific models. In this paper, we present a new open-source tool that enables to simulate complex structures in a straightforward and efficient manner, based on analogues. The method is tested on a variety of use cases to demonstrate the generality of the framework.
Stochastic simulations are key tools to generate complex spatial structures uses as input in...
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