Preprints
https://doi.org/10.5194/gmd-2021-297
https://doi.org/10.5194/gmd-2021-297

Submitted as: development and technical paper 14 Sep 2021

Submitted as: development and technical paper | 14 Sep 2021

Review status: this preprint is currently under review for the journal GMD.

Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1)

Zhen Yin1, Chen Zuo2, Emma J. MacKie3, and Jef Caers1 Zhen Yin et al.
  • 1Department of Geological Sciences, Stanford University, California 94305, USA
  • 2Department of Big Data Management and Application, Chang'an University, Xi'an, China
  • 3Department of Geophysics, Stanford University, California 94305, USA

Abstract. The subglacial bed topography is critical for modeling the evolution of Thwaites Glacier in the Amundsen Sea Embayment (ASE), where rapid ice loss threatens the stability of the West Antarctic Ice Sheet. However, mapping of subglacial topography is subject to high uncertainty. This is mainly because the bed topography is measured by airborne ice-penetrating radar along flight lines with large gaps up to tens of kilometers. Deterministic interpolation approaches do not reflect such spatial uncertainty. While traditional geostatistical simulation can model such uncertainty, it may be difficult to apply because of the significant non-stationary spatial variation of topography over such large surface area. In this study, we develop a non-stationary multiple-point geostatistical approach to interpolate large areas with irregular geophysical data and apply it to model the spatial uncertainty of entire ASE basal topography. We collect 166 high-resolution topographic training images (TIs) to train the gap-filling of radar data gaps, thereby simulating realistic topography maps. The TIs are extensively sampled from deglaciated regions in the Arctic as well as Antarctica. To address the non-stationarity in topographic modeling, we introduce a Bayesian framework that models the posterior distribution of non-stationary training images to the local modeling domain. Sampling from this distribution then provide candidate training images for local topographic modeling with uncertainty, constrained to radar flight line data. Compared to traditional MPS approaches without considering TI sampling, our approach demonstrates significant improvement in the topographic modeling quality and efficiency of the simulation algorithm. Finally, we simulate multiple realizations of high-resolution ASE topographic maps. We use the multiple realizations to investigate the impact of basal topography uncertainty on subglacial hydrological flow patterns.

Zhen Yin et al.

Status: open (until 09 Nov 2021)

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Zhen Yin et al.

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Short summary
We provide a geospatial modeling approach to sample training images to fill large scale irregular geophysical data gaps in West Antarctica. Using a repository of global topographic training images, our approach models high-resolution basal topography and quantifies the geospatial uncertainty . It generated multiple high-resolution topographic realizations to investigate the impact of basal topographic uncertainty on critical subglacial hydrological flow patterns associated with ice velocity.