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...
| Published in: | Communications Earth & Environment |
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| Main Authors: | , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Nature Portfolio
2024
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| Subjects: | |
| Online Access: | https://doi.org/10.1038/s43247-023-01166-w https://doaj.org/article/c14ba144e49140b797275c049f5e7ec6 |
| Summary: | 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. |
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