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

Abstract

Abstract. Geostatistics as a subfield of statistics accounts for the spatial correlations encountered in many applications of e.g. Earth Sciences. Valuable information can be extracted from these correlations, also helping to address the often encountered burden of data scarcity. Despite the value of additional data, the use of geostatistics still falls short of its potential. This problem is often connected to the lack of user-friendly software hampering the use and application of geostatistics. We therefore present GSTools, a Python-based software suite for solving a wide range of geostatistical problems. We chose Python due to its unique balance between usability, flexibility, and efficiency and due to its adoption in the scientific community. GSTools provides methods for generating random fields, it can perform kriging and variogram estimation and much more. We demonstrate its abilities by virtue of a series of example application detailing their use.

Geophysics‐Informed Hydrologic Modeling of a Mountain Headwater Catchment for Studying Hydrological Partitioning in the Critical Zone

“…These nine 2D velocity models (Figure 3a) are used to estimate the 3D velocity distribution in the subsurface of the catchment with the ordinary Kriging (e.g., Bourges et al, 2012). We used the GSTools toolbox (Müller et al, 2021) and followed the procedures proposed by Flinchum et al (2018) to construct the 3D velocity model. One advantage of using GSTools is that the topography can be kept during the Kriging such that estimated velocities near the ground surface have low uncertainties.…”

Diffusive‐Dispersive Isotope Fractionation of Chlorinated Ethenes in Groundwater: The Key Role of Incomplete Mixing and Its Multi‐Scale Effects

“…Contaminant transport at field scale is strongly impacted by the heterogeneity of the subsurface. In this work we considered a cross section of a heterogeneous aquifer with a spatially variable distribution of the hydraulic conductivity created with the open-source software package GSTools (Müller et al, 2022;Müller & Schüler, 2021) using the values and statistics reported for the well-characterized Borden field site. An exponential covariance model was adopted, with average hydraulic conductivity K = 7.17 × 10 −5 m s −1 , variance ln K = 0.29, and correlation length in x and z direction I x and I z of 2.8 and 0.12 m, respectively (Sudicky, 1986).…”

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

“…For the specific case of kriging, a third interface exports the variogram directly into a gstools kriging class instance. At the time of writing, available kriging algorithms were simple kriging, ordinary kriging, universal or regression kriging, kriging with external drift and kriging the mean (Müller et al, 2021).…”

“…A multitude of Python packages that were related to geostatistics could be found. A popular geostatistics-related Python package is pykrige (Murphy et al, 2021). As the name already implies, it is mainly intended for kriging interpolation.…”

“…Thus, I decided to develop an open-source geostatistics package for the Python programming language called SciKit-GStat. In the course of the following years, another Python package with similar objectives was developed called gstools (Müller et al, 2021). Both packages emerged at similar times: SciKit-GStat was first published on GitHub in July 2017 and gstools in January 2018.…”