The Cryosphere
The Cryosphere
TC
Articles | Volume 19, issue 2
Articles | Volume 19, issue 2
https://doi.org/10.5194/tc-19-645-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-645-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 2
Research article
 | 
07 Feb 2025
Research article |  | 07 Feb 2025

Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard

Viola Steidl, Jonathan Louis Bamber, and Xiao Xiang Zhu

Viewed

Total article views: 1,076 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
804 236 36 1,076 22 25
  • HTML: 804
  • PDF: 236
  • XML: 36
  • Total: 1,076
  • BibTeX: 22
  • EndNote: 25
Views and downloads (calculated since 14 Jun 2024)
Cumulative views and downloads (calculated since 14 Jun 2024)

Viewed (geographical distribution)

Total article views: 1,076 (including HTML, PDF, and XML) Thereof 1,052 with geography defined and 24 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 21 Feb 2025
Download
Short summary
Glacier ice thickness is difficult to measure directly but is essential for glacier evolution modelling. In this work, we employ a novel approach combining physical knowledge and data-driven machine learning to estimate the ice thickness of multiple glaciers in Spitsbergen, Barentsøya, and Edgeøya in Svalbard. We identify challenges for the physics-aware machine learning model and opportunities for improving the accuracy and physical consistency that would also apply to other geophysical tasks.
Read more
Share