EGU2020-10104
https://doi.org/10.5194/egusphere-egu2020-10104
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

GSTools: The Python toolbox for your geo-statistical project!

Sebastian Müller1,3, Lennart Schüler1,2, Alraune Zech1,4, Sabine Attinger1,3, and Falk Heße1,3
Sebastian Müller et al.
  • 1Department of Computational Hydrosystems, UFZ – Helmholtz Centre for Environmental Research, Leipzig, Germany
  • 2Department of Environmental Informatics, UFZ – Helmholtz Centre for Environmental Research, Leipzig, Germany
  • 3Institute of Earth and Environmental Sciences, University Potsdam, Potsdam, Germany
  • 4Department of Earth Sciences, University Utrecht, Utrecht, Netherlands

Geo-scientific model development is lacking comprehensive open source tools, that are providing state-of-the art geo-statistic methods. To bridge this gap, we developed a geo-statistical toolbox named GSTools, which is a Python package providing an abundance of methods in a modern object oriented approach. Covered use-cases are:

  • covariance models (many readily provided and even user-defined models with a lot of functionality)

  • random field generation (multigaussian and in-compressible vector fields)

  • field transformations (boxcox, Zinn and Harvey, log-normal, binary)

  • kriging (simple, ordinary, universal, external drift or detrended)

  • variogram estimation (Cressie and Matheron estimators)

  • I/O routines (interfaces to pyvista and meshio for mesh support)

  • plotting routines (inspect your covariance model or random field on the fly)

GSTools is developed openly within a GitHub organization (https://github.com/GeoStat-Framework). On the one hand to be able to respond to the needs of the modeling community and integrate suggested functionalities and contributions, on the other hand to guarantee stability and reliability of the code-base through continuous-integration features provided by the GitHub infrastructure.

We will present several applications of the mentioned routines to demonstrate the interface and capabilities of GSTools.

How to cite: Müller, S., Schüler, L., Zech, A., Attinger, S., and Heße, F.: GSTools: The Python toolbox for your geo-statistical project!, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10104, https://doi.org/10.5194/egusphere-egu2020-10104, 2020

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