Articles | Volume 16, issue 13
https://doi.org/10.5194/gmd-16-3765-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-3765-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
06 Jul 2023
Development and technical paper |
| 06 Jul 2023
| 06 Jul 2023
GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation
Department of Geological Sciences, University of Florida, Gainesville, FL 32611, USA
Michael Field
Department of Geological Sciences, University of Florida, Gainesville, FL 32611, USA
Lijing Wang
Department of Earth and Planetary Sciences, Stanford University, Stanford, CA 94305, USA
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Department of Earth and Planetary Sciences, Stanford University, Stanford, CA 94305, USA
Nathan Schoedl
Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA
Department of Mathematics, University of Florida, Gainesville, FL 32611, USA
Department of Statistics, University of Florida, Gainesville, FL 32611, USA
Matthew Hibbs
Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA
Allan Zhang
Department of Statistics, University of Florida, Gainesville, FL 32611, USA
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Cited
13 citations as recorded by crossref.
- Stochastic Simulations of Bed Topography Constrain Geothermal Heat Flow and Subglacial Drainage Near Dome Fuji, East Antarctica C. Shackleton et al. https://doi.org/10.1029/2023JF007269
- Next-generation radar bed measurements should be optimized for assimilation or repeat-pass profiling D. Schroeder et al. https://doi.org/10.1098/rsta.2024.0548
- Enhancing Urban Air Quality Resilience Through Nature-Based Solutions: Evidence from Green Spaces in Bangkok A. Aung et al. https://doi.org/10.3390/architecture6010016
- Improved bathymetry estimates beneath Amundsen Sea ice shelves using a Markov Chain Monte Carlo gravity inversion (GravMCMC, version 1) M. Field et al. https://doi.org/10.5194/gmd-19-1749-2026
- A Markov chain Monte Carlo approach for geostatistically simulating mass-conserving subglacial topography N. Shao et al. https://doi.org/10.1017/jog.2026.10164
- A Python Multiprocessing Approach for Fast Geostatistical Simulations of Subglacial Topography N. Schoedl et al. https://doi.org/10.1109/MCSE.2023.3317773
- Evidence of active subglacial lakes under a slowly moving coastal region of the Antarctic Ice Sheet J. Arthur et al. https://doi.org/10.5194/tc-19-375-2025
- High-resolution national mapping of natural gas composition substantially updates methane leakage impacts P. Burdeau et al. https://doi.org/10.1038/s41467-025-66465-6
- Geostatistical modeling of subsurface heterogeneity in the Al-Haouz-Mejjate aquifer system, morocco: A T-PROGS approach for enhanced hydrogeological characterization L. El Mezouary et al. https://doi.org/10.1016/j.sciaf.2025.e03163
- A revised and expanded deep radiostratigraphy of the Greenland Ice Sheet from airborne radar sounding surveys between 1993 and 2019 J. MacGregor et al. https://doi.org/10.5194/essd-17-2911-2025
- Gravity topography modeling of the Denman Glacier region using a geostatistical approach M. Lösing et al. https://doi.org/10.5194/tc-20-5629-2026
- Interpolation of large-scale airborne geophysical data with uncertainty quantification J. Rines et al. https://doi.org/10.1016/j.cageo.2026.106201
- Synthetic bed topographies for Antarctica and their utility in ice sheet modelling F. McCormack et al. https://doi.org/10.1098/rsta.2024.0537
13 citations as recorded by crossref.
- Stochastic Simulations of Bed Topography Constrain Geothermal Heat Flow and Subglacial Drainage Near Dome Fuji, East Antarctica C. Shackleton et al. https://doi.org/10.1029/2023JF007269
- Next-generation radar bed measurements should be optimized for assimilation or repeat-pass profiling D. Schroeder et al. https://doi.org/10.1098/rsta.2024.0548
- Enhancing Urban Air Quality Resilience Through Nature-Based Solutions: Evidence from Green Spaces in Bangkok A. Aung et al. https://doi.org/10.3390/architecture6010016
- Improved bathymetry estimates beneath Amundsen Sea ice shelves using a Markov Chain Monte Carlo gravity inversion (GravMCMC, version 1) M. Field et al. https://doi.org/10.5194/gmd-19-1749-2026
- A Markov chain Monte Carlo approach for geostatistically simulating mass-conserving subglacial topography N. Shao et al. https://doi.org/10.1017/jog.2026.10164
- A Python Multiprocessing Approach for Fast Geostatistical Simulations of Subglacial Topography N. Schoedl et al. https://doi.org/10.1109/MCSE.2023.3317773
- Evidence of active subglacial lakes under a slowly moving coastal region of the Antarctic Ice Sheet J. Arthur et al. https://doi.org/10.5194/tc-19-375-2025
- High-resolution national mapping of natural gas composition substantially updates methane leakage impacts P. Burdeau et al. https://doi.org/10.1038/s41467-025-66465-6
- Geostatistical modeling of subsurface heterogeneity in the Al-Haouz-Mejjate aquifer system, morocco: A T-PROGS approach for enhanced hydrogeological characterization L. El Mezouary et al. https://doi.org/10.1016/j.sciaf.2025.e03163
- A revised and expanded deep radiostratigraphy of the Greenland Ice Sheet from airborne radar sounding surveys between 1993 and 2019 J. MacGregor et al. https://doi.org/10.5194/essd-17-2911-2025
- Gravity topography modeling of the Denman Glacier region using a geostatistical approach M. Lösing et al. https://doi.org/10.5194/tc-20-5629-2026
- Interpolation of large-scale airborne geophysical data with uncertainty quantification J. Rines et al. https://doi.org/10.1016/j.cageo.2026.106201
- Synthetic bed topographies for Antarctica and their utility in ice sheet modelling F. McCormack et al. https://doi.org/10.1098/rsta.2024.0537
Saved (final revised paper)
Latest update: 11 Oct 2026
Short summary
Earth scientists often have to fill in spatial gaps in measurements. This gap-filling or interpolation can be accomplished with geostatistical methods, where the statistical relationships between measurements are used to inform how these gaps should be filled. Despite the broad utility of these methods, there are few freely available geostatistical software applications. We present GStatSim, a Python package for performing different geostatistical interpolation methods.
Earth scientists often have to fill in spatial gaps in measurements. This gap-filling or...
