Articles | Volume 17, issue 3
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

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Cited articles

Andén, J. and Mallat, S.: Deep Scattering Spectrum, IEEE T. Sign. Process., 62, 4114–4128,, 2014. a, b, c, d, e, f
Anderson, D.: Preliminary results and review of sea ice elasticity and related studies, Transactions of the Engineering Institute of Canada, 2, 2–8, 1958. a
Ardyna, M., Babin, M., Gosselin, M., Devred, E., Rainville, L., and Tremblay, J.-É.: Recent Arctic Ocean sea ice loss triggers novel fall phytoplankton blooms, Geophys. Res. Lett., 41, 6207–6212,, 2014. a
Barruol, G., Cordier, E., Bascou, J., Fontaine, F. R., Legrésy, B., and Lescarmontier, L.: Tide-induced microseismicity in the Mertz glacier grounding area, East Antarctica, Geophys. Res. Lett., 40, 5412–5416,, 2013. a
Caline, F. and Barrault, S.: Measurements of stresses in the coastal ice on both sides of a tidal crack., in: 19th IAHR International Symposium on Ice, 6 to 11 July 2008, Vancouver, British Columbia, Canada, 2008. a
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.