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Harnessing machine learning to improve ice sheet bed mapping.

Steven Palmer1, Charlie Kirkwood2

  • 1Department of Geography, University of Exeter Faculty of Environment Science and Economy, Exeter, UK.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|April 23, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) can significantly speed up the processing of ice sheet radio-echo sounding (RES) data. This enhances the value of existing RES surveys and improves subglacial topography mapping for sea-level rise projections.

Keywords:
glaciologyice sheetsmachine learningremote sensing

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Area of Science:

  • * Geophysics
  • * Glaciology
  • * Data Science

Background:

  • * Airborne radio-echo sounding (RES) is crucial for measuring ice sheet thickness and subglacial topography.
  • * Current processing of RES data is time-consuming, limiting scientific impact.
  • * Machine learning (ML) offers potential solutions to accelerate RES data analysis.

Purpose of the Study:

  • * To provide an overview of ML advancements in ice sheet RES research.
  • * To demonstrate how ML can enhance the value of past and future RES campaigns.
  • * To highlight ML applications in denoising, automated data picking, and spatial interpolation.

Main Methods:

  • * Review of recent ML techniques applied to ice sheet RES data.
  • * Application of ML for denoising radar returns and automated picking of reflections.
  • * Utilization of ML for spatial interpolation of flightline data and uncertainty quantification.
  • * Comparison of ML-driven approaches with traditional methods for basal topography mapping.

Main Results:

  • * ML-based methods outperform traditional approaches in interpolating basal topography.
  • * Significant advances in ML for automated extraction of reflecting horizons from radargrams.
  • * ML enhances denoising and automated picking of radar returns, improving data quality.
  • * ML-driven spatial interpolation provides improved uncertainty quantification.

Conclusions:

  • * Integrating ML into RES data processing workflows maximizes observational value.
  • * ML can guide strategic efforts for future ice sheet surveys in Polar Regions.
  • * ML accelerates analysis, enabling more sophisticated ice sheet modeling and sea-level rise projections.