Preprints
https://doi.org/10.5194/tc-2022-212
https://doi.org/10.5194/tc-2022-212
 
17 Nov 2022
17 Nov 2022
Status: this preprint is currently under review for the journal TC.

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

Ludovic Moreau, Léonard Seydoux, Jérôme Weiss, and Michel Campillo Ludovic Moreau et al.
  • Institut des Sciences de la Terre, Université Grenoble Alpes, Grenoble, France

Abstract. In the perspective of upcoming seasonally ice-free Arctic, understanding the dynamics of sea ice in the changing climate is a major challenge in oceanography and climatology. In particular, the new generation of sea ice models will require fine parameterization of sea ice thickness and rheology. With the rapidly evolving state of sea ice, achieving better accuracy, as well as finer temporal and spatial resolutions of its thickness will set new monitoring standards, with major scientific and geopolitical implications. Recent studies have shown the potential of passive seismology to monitor the thickness, density and elastic properties of sea ice with significantly reduced logistical constraints. For example, human intervention is no longer required, except to install and uninstall the geophones. Building up on this approach, we introduce a methodology for estimating sea ice thickness with high spatial and temporal resolutions from the analysis of icequakes waveforms. This methodology is based on a deep convolutional neural network for automatic clustering of the ambient seismicity recorded on sea ice, combined with a Bayesian inversion of the clustered waveforms. By applying this approach to seismic data recorded in March 2019 on fast ice in the Van Mijen fjord (Svalbard), we observe the spatial clustering of icequakes sources along the shore line of the fjord. The ice thickness is shown to follow an increasing trend that is consistent with the evolution of temperatures during the four weeks of data recording. Comparing the energy of the icequakes with that of calibrated seismic sources, we were able to derive a power law of icequake energy, and to relate this energy to the size of the cracks that generate the icequakes.

Ludovic Moreau et al.

Status: open (until 12 Jan 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-212', Rowan Romeyn, 01 Dec 2022 reply

Ludovic Moreau et al.

Ludovic Moreau et al.

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Short summary
In the perspective of 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.