Tremor clustering reveals pre-eruptive signals and evolution of the 2021 Geldingadalir eruption of the Fagradalsfjall Fires, Iceland
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 Geld...
Published in: | Communications Earth & Environment |
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Language: | English |
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2024
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Online Access: | https://gfzpublic.gfz-potsdam.de/pubman/item/item_5024410 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5024410_1/component/file_5024511/5024410.pdf |
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ftgfzpotsdam:oai:gfzpublic.gfz-potsdam.de:item_5024410 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 Zali, Z. Mousavi, S. Ohrnberger, M. Eibl, E. Cotton, F. 2024 application/pdf https://gfzpublic.gfz-potsdam.de/pubman/item/item_5024410 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5024410_1/component/file_5024511/5024410.pdf eng eng info:eu-repo/semantics/altIdentifier/doi/10.1038/s43247-023-01166-w https://gfzpublic.gfz-potsdam.de/pubman/item/item_5024410 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5024410_1/component/file_5024511/5024410.pdf info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ Communications Earth and Environment info:eu-repo/semantics/article 2024 ftgfzpotsdam https://doi.org/10.1038/s43247-023-01166-w 2024-01-15T00:44:23Z 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 GFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam) Communications Earth & Environment 5 1 |
institution |
Open Polar |
collection |
GFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam) |
op_collection_id |
ftgfzpotsdam |
language |
English |
description |
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 |
Zali, Z. Mousavi, S. Ohrnberger, M. Eibl, E. Cotton, F. |
spellingShingle |
Zali, Z. Mousavi, S. Ohrnberger, M. Eibl, E. Cotton, F. Tremor clustering reveals pre-eruptive signals and evolution of the 2021 Geldingadalir eruption of the Fagradalsfjall Fires, Iceland |
author_facet |
Zali, Z. Mousavi, S. Ohrnberger, M. Eibl, E. Cotton, F. |
author_sort |
Zali, Z. |
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 |
publishDate |
2024 |
url |
https://gfzpublic.gfz-potsdam.de/pubman/item/item_5024410 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5024410_1/component/file_5024511/5024410.pdf |
genre |
Iceland |
genre_facet |
Iceland |
op_source |
Communications Earth and Environment |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1038/s43247-023-01166-w https://gfzpublic.gfz-potsdam.de/pubman/item/item_5024410 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5024410_1/component/file_5024511/5024410.pdf |
op_rights |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1038/s43247-023-01166-w |
container_title |
Communications Earth & Environment |
container_volume |
5 |
container_issue |
1 |
_version_ |
1790602014345920512 |