SciKit-GStat 1.0: a SciPy-flavored geostatistical variogram estimation toolbox written in Python

Abstract

Abstract. Geostatistical methods are widely used in almost all geoscientific disciplines, i.e., for interpolation, rescaling, data assimilation or modeling. At its core, geostatistics aims to detect, quantify, describe, analyze and model spatial covariance of observations. The variogram, a tool to describe this spatial covariance in a formalized way, is at the heart of every such method. Unfortunately, many applications of geostatistics focus on the interpolation method or the result rather than the quality of the estimated variogram. Not least because estimating a variogram is commonly left as a task for computers, and some software implementations do not even show a variogram to the user. This is a miss, because the quality of the variogram largely determines whether the application of geostatistics makes sense at all. Furthermore, the Python programming language was missing a mature, well-established and tested package for variogram estimation a couple of years ago. Here I present SciKit-GStat, an open-source Python package for variogram estimation that fits well into established frameworks for scientific computing and puts the focus on the variogram before more sophisticated methods are about to be applied. SciKit-GStat is written in a mutable, object-oriented way that mimics the typical geostatistical analysis workflow. Its main strength is the ease of use and interactivity, and it is therefore usable with only a little or even no knowledge of Python. During the last few years, other libraries covering geostatistics for Python developed along with SciKit-GStat. Today, the most important ones can be interfaced by SciKit-GStat. Additionally, established data structures for scientific computing are reused internally, to keep the user from learning complex data models, just for using SciKit-GStat. Common data structures along with powerful interfaces enable the user to use SciKit-GStat along with other packages in established workflows rather than forcing the user to stick to the author's programming paradigms. SciKit-GStat ships with a large number of predefined procedures, algorithms and models, such as variogram estimators, theoretical spatial models or binning algorithms. Common approaches to estimate variograms are covered and can be used out of the box. At the same time, the base class is very flexible and can be adjusted to less common problems, as well. Last but not least, it was made sure that a user is aided in implementing new procedures or even extending the core functionality as much as possible, to extend SciKit-GStat to uncovered use cases. With broad documentation, a user guide, tutorials and good unit-test coverage, SciKit-GStat enables the user to focus on variogram estimation rather than implementation details.

GSTools v1.3: a toolbox for geostatistical modelling in Python

“…Salient features of GSTools are its random field generation and its versatile covariance model. It is furthermore integrated with other Python packages, like PyKrige (Murphy et al, 2021), ogs5py (Müller et al, 2020) or scikit-gstat (Mälicke, 2022), and provides interfaces to meshio (Schlömer et al, 2021) and PyVista (Sullivan and Kaszynski, 2019). GeoStat Examples (https: //github.com/GeoStat-Examples, last access: 31 March 2022 ) provides a number of applications, including the four presented workflows.…”

“…Other packages for geostatistics are also supported, such as PyKrige (Sect. 3.3) and scikit-gstat (Mälicke, 2022), the latter having a focus on variography and can be used for more detailed variogram estimation. For both packages, interfaces are provided to convert covariance models of GSTools to or from their counterparts in the respective package.…”

Large-Pore Network Simulations Coupled with Innovative Wettability Anchoring Experiment to Predict Relative Permeability of a Mixed-Wet Rock

“…Fig 13 shows that water has imbibed during spontaneous imbibition and the comparison between the S wi image and the image at the end of spontaneous imbibition clearly shows some of the water-wet pores. Similarly, Fig 14 shows that oil has imbibed during spontaneous drainage and the comparison shows some of the oil-wet pores.…”

Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model

“…To evaluate the spatial correlation of snow depth differences, we can look at semivariograms, which have been generated using the Python SciKit-GStat library (Mälicke, 2022). To estimate the semi-variance, we used a Matheron estimator function (Matheron, 1963):…”