Awesome
GeoStatsImages.jl
Training images for geostastical simulation in Julia.
This package converts famous training images from the geostatistcs literature to a standard format for quick experimentation in Julia. It is part of the GeoStats.jl framework and can be used in conjunction with multiple-point simulation solvers.
The author does not hold any copyright on the data. Please give credit to the sources in the table.
Usage
TI = geostatsimage(identifier)
where identifier can be any of the strings listed with the command GeoStatsImages.available()
Preview
| Identifier | Preview | Type | Data source |
|---|---|---|---|
| WalkerLake |  | Continuous | Mariethoz & Caers, 2014 |
| WalkerLakeTruth |  | Continuous | Mariethoz & Caers, 2014 |
| StoneWall |  | Continuous | Mariethoz & Caers 2014 |
| Herten |  | Continuous | Mariethoz & Caers 2014 |
| Lena |  | Continuous | Mariethoz & Caers 2014 |
| StanfordV |  | Continuous | Mao & Journel 2014 |
| Gaussian30x10 |  | Continuous | Hoffimann 2020 |
| Strebelle |  | Categorical | Strebelle 2002 |
| Ellipsoids |  | Categorical | Mariethoz & Caers 2014 |
| WestCoastAfrica |  | Categorical | Mariethoz & Caers 2014 |
| Flumy |  | Categorical | Hoffimann et al 2017 |
| Fluvsim |  | Categorical | Mariethoz & Caers, 2014 |
| Ketton |  | Categorical | Imperial College Pore-Scale Modelling Group |
Collections
FlumeContinuous
FlumeBinary
Contributing
Contributions are very welcome, as are feature requests and suggestions.
If you have any questions, please contact our community on the gitter channel.