Publication Date:
2023-06-21
Description:
Volcanic inflation and deflation often precede eruptions and can lead to seismic velocity changes (dv/v $dv/v$) in the subsurface. Recently, interferometry on the coda of ambient noise‐cross‐correlation functions yielded encouraging results in detecting these changes at active volcanoes. Here, we analyze seismic data recorded at the Klyuchevskoy Volcanic Group in Kamchatka, Russia, between summer of 2015 and summer of 2016 to study signals related to volcanic activity. However, ubiquitous volcanic tremors introduce distortions in the noise wavefield that cause artifacts in the dv/v $dv/v$ estimates masking the impact of physical mechanisms. To avoid such instabilities, we propose a new technique called time‐segmented passive image interferometry. In this technique, we employ a hierarchical clustering algorithm to find periods in which the wavefield can be considered stationary. For these periods, we perform separate noise interferometry studies. To further increase the temporal resolution of our results, we use an AI‐driven approach to find stations with similar dv/v $dv/v$ responses and apply a spatial stack. The impacts of snow load and precipitation dominate the resulting dv/v $dv/v$ time series, as we demonstrate with the help of a simple model. In February 2016, we observe an abrupt velocity drop due to the M7.2 Zhupanov earthquake. Shortly after, we register a gradual velocity increase of about 0.3% at Bezymianny Volcano coinciding with surface deformation observed using remote sensing techniques. We suggest that the inflation of a shallow reservoir related to the beginning of Bezymianny's 2016/2017 eruptive cycle could have caused this local velocity increase and a decorrelation of the correlation function coda.
Description:
Plain Language Summary:
Before eruptions, volcanoes inflate due to the rising magma from below. Previous studies have found that these deformations can lead to small changes in the properties of the surrounding rock. We use passive image interferometry, a method that relies on the omnipresent background vibration of the Earth—mostly induced by the oceans, to measure these changes at the Klyuchevskoy Volcanic Group in Kamchatka, Russia. However, in Kamchatka, this background noise is masked and distorted by small earthquakes and tremors originating from the volcanoes themselves. We combine machine learning techniques with established monitoring methods to find times when these tremors remain similar. Afterward, we use data from these time periods in the conventional way to observe changes in the soil and the rock. Our results show that rain‐ and snowfall and the thickness of the snow cover exert the strongest influence on the properties of the rocks. Additionally, we found that a large magnitude 7.2 earthquake, which struck Kamchatka during our study, caused a slight weakening of the rocks due to microstructural damage. We register changes shortly before an eruption and suggest a connection to the beginning of an eruptive cycle in 2016.
Description:
Key Points:
Fluctuating noise conditions lead to distortions in noise interferometry studies, which we avoid with the help of machine learning.
The seismic velocity on Kamchatka is affected by numerous mechanisms, amongst them environmental, tectonic, and volcanic events.
We observe a velocity increase at Bezymianny during February 2016 and link it to the beginning of the eruptive cycle.
Description:
German Research Foundation
Description:
https://doi.org/10.14470/K47560642124
Description:
https://doi.org/10.24381/cds.e2161bac
Description:
https://doi.org/10.5880/GFZ.2.4.2022.002
Description:
https://doi.org/10.5281/zenodo.7481934
Keywords:
ddc:551
;
seismology
;
volcano monitoring
;
machine learning
;
ambient noise
;
seismic velocity change
;
time varying earth structure
Language:
English
Type:
doc-type:article
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