GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation
The interpolation of geospatial phenomena is a common problem in Earth science applications that can be addressed with geostatistics, where spatial correlations are used to constrain interpolations. In certain applications, it can be particularly useful to a perform geostatistical simulation, which...
| Published in: | Geoscientific Model Development |
|---|---|
| Main Authors: | , , , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Copernicus Publications
2023
|
| Subjects: | |
| Online Access: | https://doi.org/10.5194/gmd-16-3765-2023 https://noa.gwlb.de/receive/cop_mods_00067598 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00066048/gmd-16-3765-2023.pdf https://gmd.copernicus.org/articles/16/3765/2023/gmd-16-3765-2023.pdf |
| Summary: | The interpolation of geospatial phenomena is a common problem in Earth science applications that can be addressed with geostatistics, where spatial correlations are used to constrain interpolations. In certain applications, it can be particularly useful to a perform geostatistical simulation, which is used to generate multiple non-unique realizations that reproduce the variability in measurements and are constrained by observations. Despite the broad utility of this approach, there are few open-access geostatistical simulation software applications. To address this accessibility issue, we present GStatSim, a Python package for performing geostatistical interpolation and simulation. GStatSim is distinct from previous geostatistical tools in that it emphasizes accessibility for non-experts, geostatistical simulation, and applicability to remote sensing data sets. It includes tools for performing non-stationary simulations and interpolations with secondary constraints. This package is accompanied by a Jupyter Book with user tutorials and background information on different interpolation methods. These resources are intended to significantly lower the technological barrier to using geostatistics and encourage the use of geostatistics in a wider range of applications. We demonstrate the different functionalities of this tool for the interpolation of subglacial topography measurements in Greenland. |
|---|