Tremor clustering reveals pre-eruptive signals and evolution of the 2021 Geldingadalir eruption of the Fagradalsfjall Fires, Iceland
Abstract Analyzing seismic data in a timely manner is essential for potential eruption forecasting and early warning in volcanology. Here, we demonstrate that unsupervised machine learning methods can automatically uncover hidden details from the continuous seismic signals recorded during Iceland’s...
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ftdoajarticles:oai:doaj.org/article:c14ba144e49140b797275c049f5e7ec6 2024-02-11T10:05:08+01:00 Tremor clustering reveals pre-eruptive signals and evolution of the 2021 Geldingadalir eruption of the Fagradalsfjall Fires, Iceland Zahra Zali S. Mostafa Mousavi Matthias Ohrnberger Eva P. S. Eibl Fabrice Cotton 2024-01-01T00:00:00Z https://doi.org/10.1038/s43247-023-01166-w https://doaj.org/article/c14ba144e49140b797275c049f5e7ec6 EN eng Nature Portfolio https://doi.org/10.1038/s43247-023-01166-w https://doaj.org/toc/2662-4435 doi:10.1038/s43247-023-01166-w 2662-4435 https://doaj.org/article/c14ba144e49140b797275c049f5e7ec6 Communications Earth & Environment, Vol 5, Iss 1, Pp 1-11 (2024) Geology QE1-996.5 Environmental sciences GE1-350 article 2024 ftdoajarticles https://doi.org/10.1038/s43247-023-01166-w 2024-01-14T01:51:49Z Abstract Analyzing seismic data in a timely manner is essential for potential eruption forecasting and early warning in volcanology. Here, we demonstrate that unsupervised machine learning methods can automatically uncover hidden details from the continuous seismic signals recorded during Iceland’s 2021 Geldingadalir eruption. By pinpointing the eruption’s primary phases, including periods of unrest, ongoing lava extrusion, and varying lava fountaining intensities, we can effectively chart its temporal progress. We detect a volcanic tremor sequence three days before the eruption, which may signify impending eruptive activities. Moreover, the discerned seismicity patterns and their temporal changes offer insights into the shift from vigorous outflows to lava fountaining. Based on the extracted patterns of seismicity and their temporal variations we propose an explanation for this transition. We hypothesize that the emergence of episodic tremors in the seismic data in early May could be related to an increase in the discharge rate in late April. Article in Journal/Newspaper Iceland Directory of Open Access Journals: DOAJ Articles Communications Earth & Environment 5 1 |
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Directory of Open Access Journals: DOAJ Articles |
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language |
English |
topic |
Geology QE1-996.5 Environmental sciences GE1-350 |
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Geology QE1-996.5 Environmental sciences GE1-350 Zahra Zali S. Mostafa Mousavi Matthias Ohrnberger Eva P. S. Eibl Fabrice Cotton Tremor clustering reveals pre-eruptive signals and evolution of the 2021 Geldingadalir eruption of the Fagradalsfjall Fires, Iceland |
topic_facet |
Geology QE1-996.5 Environmental sciences GE1-350 |
description |
Abstract Analyzing seismic data in a timely manner is essential for potential eruption forecasting and early warning in volcanology. Here, we demonstrate that unsupervised machine learning methods can automatically uncover hidden details from the continuous seismic signals recorded during Iceland’s 2021 Geldingadalir eruption. By pinpointing the eruption’s primary phases, including periods of unrest, ongoing lava extrusion, and varying lava fountaining intensities, we can effectively chart its temporal progress. We detect a volcanic tremor sequence three days before the eruption, which may signify impending eruptive activities. Moreover, the discerned seismicity patterns and their temporal changes offer insights into the shift from vigorous outflows to lava fountaining. Based on the extracted patterns of seismicity and their temporal variations we propose an explanation for this transition. We hypothesize that the emergence of episodic tremors in the seismic data in early May could be related to an increase in the discharge rate in late April. |
format |
Article in Journal/Newspaper |
author |
Zahra Zali S. Mostafa Mousavi Matthias Ohrnberger Eva P. S. Eibl Fabrice Cotton |
author_facet |
Zahra Zali S. Mostafa Mousavi Matthias Ohrnberger Eva P. S. Eibl Fabrice Cotton |
author_sort |
Zahra Zali |
title |
Tremor clustering reveals pre-eruptive signals and evolution of the 2021 Geldingadalir eruption of the Fagradalsfjall Fires, Iceland |
title_short |
Tremor clustering reveals pre-eruptive signals and evolution of the 2021 Geldingadalir eruption of the Fagradalsfjall Fires, Iceland |
title_full |
Tremor clustering reveals pre-eruptive signals and evolution of the 2021 Geldingadalir eruption of the Fagradalsfjall Fires, Iceland |
title_fullStr |
Tremor clustering reveals pre-eruptive signals and evolution of the 2021 Geldingadalir eruption of the Fagradalsfjall Fires, Iceland |
title_full_unstemmed |
Tremor clustering reveals pre-eruptive signals and evolution of the 2021 Geldingadalir eruption of the Fagradalsfjall Fires, Iceland |
title_sort |
tremor clustering reveals pre-eruptive signals and evolution of the 2021 geldingadalir eruption of the fagradalsfjall fires, iceland |
publisher |
Nature Portfolio |
publishDate |
2024 |
url |
https://doi.org/10.1038/s43247-023-01166-w https://doaj.org/article/c14ba144e49140b797275c049f5e7ec6 |
genre |
Iceland |
genre_facet |
Iceland |
op_source |
Communications Earth & Environment, Vol 5, Iss 1, Pp 1-11 (2024) |
op_relation |
https://doi.org/10.1038/s43247-023-01166-w https://doaj.org/toc/2662-4435 doi:10.1038/s43247-023-01166-w 2662-4435 https://doaj.org/article/c14ba144e49140b797275c049f5e7ec6 |
op_doi |
https://doi.org/10.1038/s43247-023-01166-w |
container_title |
Communications Earth & Environment |
container_volume |
5 |
container_issue |
1 |
_version_ |
1790602013582557184 |