Exploring the link between microseism and sea ice in Antarctica by using machine learning
Abstract The most continuous and ubiquitous seismic signal on Earth is the microseism, closely related to ocean wave energy coupling with the solid Earth. A peculiar feature of microseism recorded in Antarctica is the link with the sea ice, making the temporal pattern of microseism amplitudes differ...
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crspringernat:10.1038/s41598-019-49586-z 2023-05-15T14:08:10+02:00 Exploring the link between microseism and sea ice in Antarctica by using machine learning Cannata, Andrea Cannavò, Flavio Moschella, Salvatore Gresta, Stefano Spina, Laura 2019 http://dx.doi.org/10.1038/s41598-019-49586-z http://www.nature.com/articles/s41598-019-49586-z.pdf http://www.nature.com/articles/s41598-019-49586-z en eng Springer Science and Business Media LLC https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 CC-BY Scientific Reports volume 9, issue 1 ISSN 2045-2322 Multidisciplinary journal-article 2019 crspringernat https://doi.org/10.1038/s41598-019-49586-z 2022-01-04T10:05:06Z Abstract The most continuous and ubiquitous seismic signal on Earth is the microseism, closely related to ocean wave energy coupling with the solid Earth. A peculiar feature of microseism recorded in Antarctica is the link with the sea ice, making the temporal pattern of microseism amplitudes different with respect to the microseism recorded in low-middle latitude regions. Indeed, during austral winters, in Antarctica the oceanic waves cannot efficiently excite seismic energy because of the sea ice in the Southern Ocean. Here, we quantitatively investigate the relationship between microseism, recorded along the Antarctic coasts, and sea ice concentration. In particular, we show a decrease in sea ice sensitivity of microseism, due to the increasing distance from the station recording the seismic signal. The influence seems to strongly reduce for distances above 1,000 km. Finally, we present an algorithm, based on machine learning techniques, allowing to spatially and temporally reconstruct the sea ice distribution around Antarctica based on the microseism amplitudes. This technique will allow reconstructing the sea ice concentration in both Arctic and Antarctica in periods when the satellite images, routinely used for sea ice monitoring, are not available, with wide applications in many fields, first of all climate studies. Article in Journal/Newspaper Antarc* Antarctic Antarctica Arctic Sea ice Southern Ocean Springer Nature (via Crossref) Antarctic Arctic Austral Southern Ocean The Antarctic Scientific Reports 9 1 |
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Multidisciplinary Cannata, Andrea Cannavò, Flavio Moschella, Salvatore Gresta, Stefano Spina, Laura Exploring the link between microseism and sea ice in Antarctica by using machine learning |
topic_facet |
Multidisciplinary |
description |
Abstract The most continuous and ubiquitous seismic signal on Earth is the microseism, closely related to ocean wave energy coupling with the solid Earth. A peculiar feature of microseism recorded in Antarctica is the link with the sea ice, making the temporal pattern of microseism amplitudes different with respect to the microseism recorded in low-middle latitude regions. Indeed, during austral winters, in Antarctica the oceanic waves cannot efficiently excite seismic energy because of the sea ice in the Southern Ocean. Here, we quantitatively investigate the relationship between microseism, recorded along the Antarctic coasts, and sea ice concentration. In particular, we show a decrease in sea ice sensitivity of microseism, due to the increasing distance from the station recording the seismic signal. The influence seems to strongly reduce for distances above 1,000 km. Finally, we present an algorithm, based on machine learning techniques, allowing to spatially and temporally reconstruct the sea ice distribution around Antarctica based on the microseism amplitudes. This technique will allow reconstructing the sea ice concentration in both Arctic and Antarctica in periods when the satellite images, routinely used for sea ice monitoring, are not available, with wide applications in many fields, first of all climate studies. |
format |
Article in Journal/Newspaper |
author |
Cannata, Andrea Cannavò, Flavio Moschella, Salvatore Gresta, Stefano Spina, Laura |
author_facet |
Cannata, Andrea Cannavò, Flavio Moschella, Salvatore Gresta, Stefano Spina, Laura |
author_sort |
Cannata, Andrea |
title |
Exploring the link between microseism and sea ice in Antarctica by using machine learning |
title_short |
Exploring the link between microseism and sea ice in Antarctica by using machine learning |
title_full |
Exploring the link between microseism and sea ice in Antarctica by using machine learning |
title_fullStr |
Exploring the link between microseism and sea ice in Antarctica by using machine learning |
title_full_unstemmed |
Exploring the link between microseism and sea ice in Antarctica by using machine learning |
title_sort |
exploring the link between microseism and sea ice in antarctica by using machine learning |
publisher |
Springer Science and Business Media LLC |
publishDate |
2019 |
url |
http://dx.doi.org/10.1038/s41598-019-49586-z http://www.nature.com/articles/s41598-019-49586-z.pdf http://www.nature.com/articles/s41598-019-49586-z |
geographic |
Antarctic Arctic Austral Southern Ocean The Antarctic |
geographic_facet |
Antarctic Arctic Austral Southern Ocean The Antarctic |
genre |
Antarc* Antarctic Antarctica Arctic Sea ice Southern Ocean |
genre_facet |
Antarc* Antarctic Antarctica Arctic Sea ice Southern Ocean |
op_source |
Scientific Reports volume 9, issue 1 ISSN 2045-2322 |
op_rights |
https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1038/s41598-019-49586-z |
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Scientific Reports |
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9 |
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1 |
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1766280204692488192 |