Package: gstatsim
GStatSimGStatSim is a Python package specifically designed for geostatistical interpolation and simulation. It is inspired by open source geostatistical resources such as GeostatsPy and SciKit-GStat. The functions are intended to address the challenges of working with large data sets, non-linear trends, variability in measurement density, and non-stationarity. These tools are part of our ongoing effort to develop and adapt open-access geostatistical functions.We have created Jupyter Book tutorials here:https://gatorglaciology.github.io/gstatsimbook/intro.htmlSee our paper here:https://doi.org/10.5194/gmd-16-3765-2023In its current state, the demos focus on the geostatistical simulation of subglacial topography. However, these protocols could be applied to a number geoscientific topics.We will continuously develop new tools and tutorials to address specific technical challenges in geostatistics. Do you have feedback or suggestions? Specific things that we should account for? Feel free to contact me atemackie@ufl.edu. Our goal is to create tools that are useful and accessible, so we welcome your thoughts and insight.FeaturesFunctionsSome of the tools in GStatSim:skrige- Simple krigingokrige- Ordinary krigingskrige_sgs- Sequential Gaussian simulation using simple krigingokrige_sgs- Sequential Gaussian simulation using ordinary krigingcluster_sgs- Sequential Gaussian simulation where different variograms are used in different areascokrige_mm1- Cokriging (kriging with a secondary constraint) under Markov assumptionscosim_mm1- Cosimulation under Markov assumptionsDemosWe have created tutorials that are designed to provide an intuitive understanding of geostatistical methods and to demonstrate how these methods are used. The current demos are:1_Experimental_Variogram.ipynb- Demonstration of experimental variogram calculation to quantify spatial relationships.2_Variogram_model.ipynb- A tutorial on fitting a variogram model to an experimental variogram.3_Simple_kriging_and_ordinary_kriging.ipynb- Demonstration of simple kriging and ordinary kriging interpolation.4_Sequential_Gaussian_Simulation.ipynb- An introduction to stochastic simulation.5_Variogram_interpolation_comparison.ipynb- A demonstration of kriging and SGS with different variogram models.6_interpolation_with_anisotropy.ipynb- A demonstration of kriging and SGS with anisotropy.7_non-stationary_SGS_example1.ipynb- A tutorial on SGS with multiple variograms. This demo uses k-means clustering to divide the conditioning data into groups that are each assigned their own variogram.8_non-stationary_SGS_example2.ipynb- SGS using multiple variograms where the clusters are determined automatically.9_interpolation_with_a_trend.ipynb- Kriging and SGS in the presence of a large-scale trend.10_cokriging_and_cosimulation_MM1.ipynb- Kriging and SGS using secondary constraints.Contributors(Emma) Mickey MacKie, University of FloridaMichael Field, University of FloridaLijing Wang, Lawrence Berkeley National Laboratory(Zhen) David Yin, Stanford UniversityNathan Schoedl, University of FloridaMatthew Hibbs, University of FloridaMirko Malicke, Karlsruhe Institute of TechnologyUsageInstall GStatSim withpip install gstatsimorgit clonehttps://github.com/GatorGlaciology/GStatSimPackage dependenciesNumpyPandasScipytqdmSklearnRequirements for visualization and variogram analysisMatplotlibSciKit-GStatThese can all be installed using the commandpip install (package name).Educational useGStatSim is well-suited for educational use. Please contact us if you plan on using GStatSim material in a course so we can track the impact of our work.Cite asMacKie, E. J., Field, M., Wang, L., Yin, Z., Schoedl, N., Hibbs, M., & Zhang, A. (2023). GStatSim V1. 0: a Python package for geostatistical interpolation and conditional simulation. Geoscientific Model Development, 16(13), 3765-3783.or@article{mackie2023gstatsim,
title={GStatSim V1. 0: a Python package for geostatistical interpolation and conditional simulation},
author={MacKie, Emma J and Field, Michael and Wang, Lijing and Yin, Zhen and Schoedl, Nathan and Hibbs, Matthew and Zhang, Allan},
journal={Geoscientific Model Development},
volume={16},
number={13},
pages={3765--3783},
year={2023},
publisher={Copernicus Publications G{"o}ttingen, Germany}
}DatasetsThe demos use radar bed measurements from the Center for the Remote Sensing of Ice Sheets (CReSIS, 2020).CReSIS. 2020. Radar depth sounder, Lawrence, Kansas, USA. Digital Media.http://data.cresis.ku.edu/.