GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation
-
Published:2023-07-06
Issue:13
Volume:16
Page:3765-3783
-
ISSN:1991-9603
-
Container-title:Geoscientific Model Development
-
language:en
-
Short-container-title:Geosci. Model Dev.
Author:
MacKie Emma J., Field Michael, Wang Lijing, Yin ZhenORCID, Schoedl Nathan, Hibbs Matthew, Zhang Allan
Abstract
Abstract. 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.
Publisher
Copernicus GmbH
Reference71 articles.
1. Almeida, A. S. and Journel, A. G.: Joint simulation of multiple variables with a Markov-type coregionalization model, Math. Geol., 26, 565–588, 1994. a, b, c 2. Broomhead, D. S. and Lowe, D.: Radial basis functions, multi-variable functional interpolation and adaptive networks, Tech. rep., Royal Signals and Radar Establishment Malvern, United Kingdom, 1988. a 3. Carle, S. F.: T-PROGS: Transition probability geostatistical software, Version 2.1, Department of Land, Air and Water Resources, University of California, Davis, 1999. a 4. Chiles, J.-P. and Delfiner, P.: Geostatistics: modeling spatial uncertainty, Vol. 497, John Wiley & Sons, https://doi.org/10.1007/s11004-012-9429-y, 2009. a 5. Chu, W., Hilger, A. M., Culberg, R., Schroeder, D. M., Jordan, T. M., Seroussi, H., Young, D. A., Blankenship, D. D., and Vaughan, D. G.: Multisystem synthesis of radar sounding observations of the Amundsen Sea sector from the 2004–2005 field season, J. Geophys. Res.-Earth, 126, e2021JF006296, https://doi.org/10.1029/2021JF006296, 2021. a
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|