Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring
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. In particular, the new generation of sea ice models will require fine parameterization of sea ice thickness and rheology....
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Online Access: | https://doi.org/10.5194/tc-17-1327-2023 https://tc.copernicus.org/articles/17/1327/2023/ |
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ftcopernicus:oai:publications.copernicus.org:tc107422 2023-05-15T15:14:00+02:00 Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring Moreau, Ludovic Seydoux, Léonard Weiss, Jérôme Campillo, Michel 2023-03-22 application/pdf https://doi.org/10.5194/tc-17-1327-2023 https://tc.copernicus.org/articles/17/1327/2023/ eng eng doi:10.5194/tc-17-1327-2023 https://tc.copernicus.org/articles/17/1327/2023/ eISSN: 1994-0424 Text 2023 ftcopernicus https://doi.org/10.5194/tc-17-1327-2023 2023-03-27T16:23:11Z 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. 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 on this approach, we introduce a methodology for estimating sea ice thickness with high spatial and temporal resolutions from the analysis of icequake 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 icequake sources along the shoreline of the fjord. The ice thickness is shown to follow an increasing trend that is consistent with the evolution of temperatures during the 4 weeks of data recording. Comparing the energy of the icequakes with that of artificial 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. Text Arctic Sea ice Svalbard Copernicus Publications: E-Journals Arctic Svalbard The Cryosphere 17 3 1327 1341 |
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Open Polar |
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Copernicus Publications: E-Journals |
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ftcopernicus |
language |
English |
description |
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. 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 on this approach, we introduce a methodology for estimating sea ice thickness with high spatial and temporal resolutions from the analysis of icequake 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 icequake sources along the shoreline of the fjord. The ice thickness is shown to follow an increasing trend that is consistent with the evolution of temperatures during the 4 weeks of data recording. Comparing the energy of the icequakes with that of artificial 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. |
format |
Text |
author |
Moreau, Ludovic Seydoux, Léonard Weiss, Jérôme Campillo, Michel |
spellingShingle |
Moreau, Ludovic Seydoux, Léonard Weiss, Jérôme Campillo, Michel Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring |
author_facet |
Moreau, Ludovic Seydoux, Léonard Weiss, Jérôme Campillo, Michel |
author_sort |
Moreau, Ludovic |
title |
Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring |
title_short |
Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring |
title_full |
Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring |
title_fullStr |
Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring |
title_full_unstemmed |
Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring |
title_sort |
analysis of microseismicity in sea ice with deep learning and bayesian inference: application to high-resolution thickness monitoring |
publishDate |
2023 |
url |
https://doi.org/10.5194/tc-17-1327-2023 https://tc.copernicus.org/articles/17/1327/2023/ |
geographic |
Arctic Svalbard |
geographic_facet |
Arctic Svalbard |
genre |
Arctic Sea ice Svalbard |
genre_facet |
Arctic Sea ice Svalbard |
op_source |
eISSN: 1994-0424 |
op_relation |
doi:10.5194/tc-17-1327-2023 https://tc.copernicus.org/articles/17/1327/2023/ |
op_doi |
https://doi.org/10.5194/tc-17-1327-2023 |
container_title |
The Cryosphere |
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17 |
container_issue |
3 |
container_start_page |
1327 |
op_container_end_page |
1341 |
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1766344505893584896 |