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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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...
