Articles | Volume 17, issue 3
https://doi.org/10.5194/tc-17-1327-2023
https://doi.org/10.5194/tc-17-1327-2023
Research article
 | 
22 Mar 2023
Research article |  | 22 Mar 2023

Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring

Ludovic Moreau, Léonard Seydoux, Jérôme Weiss, and Michel Campillo

Viewed

Total article views: 1,683 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,113 492 78 1,683 58 65
  • HTML: 1,113
  • PDF: 492
  • XML: 78
  • Total: 1,683
  • BibTeX: 58
  • EndNote: 65
Views and downloads (calculated since 17 Nov 2022)
Cumulative views and downloads (calculated since 17 Nov 2022)

Viewed (geographical distribution)

Total article views: 1,683 (including HTML, PDF, and XML) Thereof 1,632 with geography defined and 51 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
In the perspective of an upcoming seasonally ice-free Arctic, understanding the dynamics of sea ice in the changing climate is a major challenge in oceanography and climatology. It is therefore essential to monitor sea ice properties with fine temporal and spatial resolution. In this paper, we show that icequakes recorded on sea ice can be processed with artificial intelligence to produce accurate maps of sea ice thickness with high temporal and spatial resolutions.