Journal
GEOSCIENTIFIC MODEL DEVELOPMENT
Volume 13, Issue 6, Pages 2611-2630Publisher
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/gmd-13-2611-2020
Keywords
-
Categories
Funding
- Swiss National Science Foundation [200021_162882]
- Swiss National Science Foundation (SNF) [200021_162882] Funding Source: Swiss National Science Foundation (SNF)
Ask authors/readers for more resources
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: 1. A new approach is proposed for pixel-based multiple-point geostatistics simulation. 2. The method is flexible and straightforward to parametrize. 3. It natively handles continuous and multivariate simulations. 4. It has high computational performance with predictable simulation times. 5. A free and open-source implementation is provided.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available