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Exploring the link between microseism and sea ice in Antarctica by using machine learning
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6736882/ https://www.ncbi.nlm.nih.gov/pubmed/31506539 http://dx.doi.org/10.1038/s41598-019-49586-z |
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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 |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-6736882 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-67368822019-09-20 Exploring the link between microseism and sea ice in Antarctica by using machine learning Cannata, Andrea Cannavò, Flavio Moschella, Salvatore Gresta, Stefano Spina, Laura Sci Rep Article 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. Nature Publishing Group UK 2019-09-10 /pmc/articles/PMC6736882/ /pubmed/31506539 http://dx.doi.org/10.1038/s41598-019-49586-z Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Cannata, Andrea Cannavò, Flavio Moschella, Salvatore Gresta, Stefano Spina, Laura Exploring the link between microseism and sea ice in Antarctica by using machine learning |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6736882/ https://www.ncbi.nlm.nih.gov/pubmed/31506539 http://dx.doi.org/10.1038/s41598-019-49586-z |
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