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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.
