Preprints
https://doi.org/10.5194/gmd-2022-229
https://doi.org/10.5194/gmd-2022-229
Submitted as: model description paper
17 Oct 2022
Submitted as: model description paper | 17 Oct 2022
Status: this preprint is currently under review for the journal GMD.

AutoQS v1: Automatic parameterization of QuickSampling based on training images analysis

Mathieu Gravey1,2 and Grégoire Mariethoz1 Mathieu Gravey and Grégoire Mariethoz
  • 1University of Lausanne, Faculty of Geosciences and Environment, Institute of Earth Surface Dynamics, Switzerland
  • 2Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, Netherlands

Abstract. Multiple‐point geostatistics are widely used to simulate complex spatial structures based on a training image. The use of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. While methods for selecting training images are available, parametrization can be cumbersome. Here, we propose finding an optimal set of parameters using only the training image as input. The difference between this and previous work that used parametrization optimization is that it does not require the definition of an objective function. It is based on the analysis of the errors that occur when filling artificially constructed patterns that have been borrowed from the training image. The main advantage of our approach is to remove the risk of overfitting an objective function, which may result in underestimating the variance or in a verbatim copy of the training image. Since it is not based on optimization, our approach finds a set of acceptable parameters in a predictable manner by using the knowledge and understanding of how the algorithms work. The technique is explored in the context of the recently developed QuickSampling algorithm, but it can be easily adapted to other pixel-based multiple-point statistics algorithms using pattern matching, such as Direct Sampling or Single Normal Equation Simulation (SNESIM).

Mathieu Gravey and Grégoire Mariethoz

Status: open (until 12 Dec 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-229', Anonymous Referee #1, 17 Oct 2022 reply

Mathieu Gravey and Grégoire Mariethoz

Mathieu Gravey and Grégoire Mariethoz

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Short summary
Multiple‐point geostatistics are widely used to simulate complex spatial structures based on a training image. The use of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. Here, we propose finding an optimal set of parameters using only the training image as input. The main advantage of our approach is to remove the risk of overfitting an objective function.