Deciphering the Whisper of Volcanoes: Monitoring Velocity Changes at Kamchatka's Klyuchevskoy Group With Fluctuating Noise Fields

Medientyp: E-Artikel

Titel: Deciphering the Whisper of Volcanoes: Monitoring Velocity Changes at Kamchatka's Klyuchevskoy Group With Fluctuating Noise Fields

Beteiligte: Makus, Peter [Verfasser:in]; Sens‐Schönfelder, Christoph [Verfasser:in]; Illien, Luc [Verfasser:in]; Walter, Thomas R. [Verfasser:in]; Yates, Alexander [Verfasser:in]; Tilmann, Frederik [Verfasser:in]; 1 Helmholtz Center German Research Center for Geosciences GFZ Potsdam Germany [Verfasser:in]; 4 University Grenoble Alpes University Savoie Mont Blanc CNRS IRD University Gustave Eiffel Grenoble France [Verfasser:in]

Erschienen: GEO-LEOe-docs (FID GEO), 2023-03-30

Sprache: Englisch

DOI: https://doi.org/10.1029/2022JB025738

Schlagwörter: seismic velocity change ; ambient noise ; volcano monitoring ; seismology ; time varying earth structure ; machine learning

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Beschreibung: 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. ; 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 ...

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