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

Abstract

Abstract. The subglacial bed topography is critical for modelling 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 uncertainties of up to hundreds of metres, primarily due to large gaps of up to tens of kilometres in airborne ice-penetrating radar flight lines. Deterministic interpolation approaches do not reflect such spatial uncertainty. While traditional geostatistical simulations can model such uncertainty, they become 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 (MPS) 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-quality topographic training images (TIs) of resolution 500 m 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 modelling, we introduce a Bayesian framework that models the posterior distribution of non-stationary TIs assigned to the local line data. Sampling from this distribution then provides candidate training images for local topographic modelling with uncertainty, constrained to radar flight line data. Compared to traditional MPS approaches that do not consider uncertain TI sampling, our approach results in a significant improvement in the topographic modelling 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.

GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation

“…To overcome these challenges we present a recursive implementation of a divisive, densitybased clustering approach we call adaptive partitioning. A similar approach was used in Yin et al (2022). The algorithm begins by treating the data set as a single cluster and quartering this cluster into four rectangles of equal area.…”

Integrating Multi-Point Geostatistics, Machine Learning, and Image Correlation for Characterizing Positional Errors in Remote-Sensing Images of High Spatial Resolution

“…As mentioned in the Introduction, systematic components are usually present and often of major proportion in positional errors, even if they are initially georectified. We needed to de-trend positional errors (for both TD and CD) as a way to handle nonstationarity [54,56] for geostatistical simulation-based error modeling.…”

“…In the future, while it is important to improve computing efficiency of the DS algorithm [60], especially when simulations at finer resolution and/or over larger areas are required, there is a pressing need to advance methods for TD construction [56], especially for simulations at fine resolution and over heterogeneous areas. It is worth exploring how we may benefit from the complementary use of DS and SGSIM (and other variogram-based geostatistical simulators).…”

“…Multi-point geostatistics (MPS) is better suited for simulating regionalized variables with complex patterns and for use in various applications [53][54][55][56][57]. Instead of variogram models, MPS uses training images (TIs) as templates of spatial structures that are deemed representative of the problem domains.…”

Pixel-MPS: Stochastic Embedding and Density-Based Clustering of Image Patterns for Pixel-Based Multiple-Point Geostatistical Simulation

“…Recent developments have also explored TI-free approaches [5,6]. MPS algorithms have found applications across various fields, including reservoir geology [7,8], mineral deposits modeling [9,10], seismic inversion [11], porosity modeling [12], hydrology [13], groundwater modeling [14][15][16], climate modeling [17][18][19], and remote sensing [20][21][22][23].…”