Articles | Volume 15, issue 4
https://doi.org/10.5194/gmd-15-1477-2022
© Author(s) 2022. 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-15-1477-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
18 Feb 2022
Development and technical paper |
| 18 Feb 2022
| 18 Feb 2022
Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1)
Department of Geological Sciences, Stanford University, Stanford, CA
94305, USA
Chen Zuo
CORRESPONDING AUTHOR
Department of Big Data Management and Application, Chang'an
University, Xi'an, China
Emma J. MacKie
Department of Geophysics, Stanford University, Stanford, CA 94305, USA
Department of Geological Sciences, Stanford University, Stanford, CA
94305, USA
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Cited
16 citations as recorded by crossref.
- Multiple-Point Geostatistical Simulation of Nonstationary Sedimentary Facies Models Based on Fuzzy Rough Sets and Spatial-Feature Method D. Zhang et al. 10.2118/215843-PA
- Integrating Multi-Point Geostatistics, Machine Learning, and Image Correlation for Characterizing Positional Errors in Remote-Sensing Images of High Spatial Resolution L. Xin et al. 10.3390/rs15194734
- A nearest neighbor multiple-point statistics method for fast geological modeling C. Zuo et al. 10.1016/j.cageo.2022.105208
- Geological Mapping Using Direct Sampling and a Convolutional Neural Network Based on Geochemical Survey Data Z. Wang et al. 10.1007/s11004-022-10023-z
- Adaptive direct sampling-based approach to ore grade modeling Z. Li et al. 10.1007/s12145-024-01297-4
- Bedmap3 updated ice bed, surface and thickness gridded datasets for Antarctica H. Pritchard et al. 10.1038/s41597-025-04672-y
- Super-resolution of digital elevation models by using multiple-point statistics and training image selection G. Hu et al. 10.1016/j.cageo.2024.105688
- Generative Elevation Inpainting: An Efficient Completion Method for Generating High-Resolution Antarctic Bed Topography Y. Cai et al. 10.1109/TGRS.2023.3303231
- Pixel-MPS: Stochastic Embedding and Density-Based Clustering of Image Patterns for Pixel-Based Multiple-Point Geostatistical Simulation A. Asadi & S. Chatterjee 10.3390/geosciences14060162
- Sedimentary microfacies prediction based on multi-point geostatistics under the constraint of INPEFA curve X. Wang et al. 10.3389/feart.2025.1506709
- A Pattern Classification Distribution Method for Geostatistical Modeling Evaluation and Uncertainty Quantification C. Zuo et al. 10.3390/rs15112708
- Multiple-point geostatistics-based spatial downscaling of heavy rainfall fields W. Zou et al. 10.1016/j.jhydrol.2024.130899
- Geomorphometry and terrain analysis: data, methods, platforms and applications L. Xiong et al. 10.1016/j.earscirev.2022.104191
- GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation E. MacKie et al. 10.5194/gmd-16-3765-2023
- Siamese topographic generation model: A deep learning model for generating Antarctic subglacial topography with fine details Y. Cai et al. 10.1016/j.cageo.2025.105857
- Stochastic Reconstruction of 3D Heterogeneous Microstructure Using a Column-Oriented Multiple-Point Statistics Program C. Zuo et al. 10.2113/2024/lithosphere_2023_233
16 citations as recorded by crossref.
- Multiple-Point Geostatistical Simulation of Nonstationary Sedimentary Facies Models Based on Fuzzy Rough Sets and Spatial-Feature Method D. Zhang et al. 10.2118/215843-PA
- Integrating Multi-Point Geostatistics, Machine Learning, and Image Correlation for Characterizing Positional Errors in Remote-Sensing Images of High Spatial Resolution L. Xin et al. 10.3390/rs15194734
- A nearest neighbor multiple-point statistics method for fast geological modeling C. Zuo et al. 10.1016/j.cageo.2022.105208
- Geological Mapping Using Direct Sampling and a Convolutional Neural Network Based on Geochemical Survey Data Z. Wang et al. 10.1007/s11004-022-10023-z
- Adaptive direct sampling-based approach to ore grade modeling Z. Li et al. 10.1007/s12145-024-01297-4
- Bedmap3 updated ice bed, surface and thickness gridded datasets for Antarctica H. Pritchard et al. 10.1038/s41597-025-04672-y
- Super-resolution of digital elevation models by using multiple-point statistics and training image selection G. Hu et al. 10.1016/j.cageo.2024.105688
- Generative Elevation Inpainting: An Efficient Completion Method for Generating High-Resolution Antarctic Bed Topography Y. Cai et al. 10.1109/TGRS.2023.3303231
- Pixel-MPS: Stochastic Embedding and Density-Based Clustering of Image Patterns for Pixel-Based Multiple-Point Geostatistical Simulation A. Asadi & S. Chatterjee 10.3390/geosciences14060162
- Sedimentary microfacies prediction based on multi-point geostatistics under the constraint of INPEFA curve X. Wang et al. 10.3389/feart.2025.1506709
- A Pattern Classification Distribution Method for Geostatistical Modeling Evaluation and Uncertainty Quantification C. Zuo et al. 10.3390/rs15112708
- Multiple-point geostatistics-based spatial downscaling of heavy rainfall fields W. Zou et al. 10.1016/j.jhydrol.2024.130899
- Geomorphometry and terrain analysis: data, methods, platforms and applications L. Xiong et al. 10.1016/j.earscirev.2022.104191
- GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation E. MacKie et al. 10.5194/gmd-16-3765-2023
- Siamese topographic generation model: A deep learning model for generating Antarctic subglacial topography with fine details Y. Cai et al. 10.1016/j.cageo.2025.105857
- Stochastic Reconstruction of 3D Heterogeneous Microstructure Using a Column-Oriented Multiple-Point Statistics Program C. Zuo et al. 10.2113/2024/lithosphere_2023_233
Latest update: 01 Apr 2025
Short summary
We provide a multiple-point geostatistics approach to probabilistically learn from training images to fill large-scale irregular geophysical data gaps. With a repository of global topographic training images, our approach models high-resolution basal topography and quantifies the geospatial uncertainty. It generated high-resolution topographic realizations to investigate the impact of basal topographic uncertainty on critical subglacial hydrological flow patterns associated with ice velocity.
We provide a multiple-point geostatistics approach to probabilistically learn from training...
