Subduction, terrane accretion, and continental rifting are fundamental plate tectonic processes. Geologic features such as igneous rocks produced during arc magmatism, terrane boundaries separating regions with different origins, and rift basins filled with sedimentary units reflect such tectonic processes. It is likely

<p>Kamchatka, a remote peninsula in eastern Russia, is home to the Kluchevskoy volcano group, one of the largest and most active clusters of subduction zone volcanoes worldwide. Regular eruptions, volcanic and tectonic earthquakes, but also strong meteorological variations leave an imprint on the regional seismic velocity structure. We quantify the temporal velocity variations by applying the method of ambient seismic noise interferometry to waveform data recorded by the temporary KISS deployment and the permanent Kamchatka network. Due to its ubiquitous nature, ambient seismic noise allows for far denser temporal sampling than, e.g., active source or earthquake coda interferometry. However, source variability related, for example, to volcanic tremor activity affects the results retrieved by this method and can lead to decreased reliability. Here, we investigate the impact of the aforementioned environmental factors on the Green’s function of the medium using SeisMIC (Seismological Monitoring using Interferometric Concepts) – a new Python software to conduct noise interferometry surveys. In addition, we discuss the impact of the frequent volcanic tremors and other local seismic events on the stability of the computed Green’s function estimations (i.e., cross-correlations).</p>

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.

<p>Mt. St. Helens is an explosively erupting volcano located in close vicinity to major metropolitan centres on the US Westcoast. In recent history, Mt. St. Helens erupted twice, in 2004 and 1980, causing more than 50 fatalities and over one billion USD of damage. Mt. St. Helens is also home to the only advancing glacier in the US, making it a unique site for geophysical measurements. Here, we present a seismic velocity change time-series (<em>dv/v</em>) of an unprecedented length covering the years 1998-2021. We quantify<em> </em><em>dv/</em><em>v </em>by applying the method of ambient seismic noise interferometry to waveform data recorded from a combination of various permanent and temporary seismic stations of the Pacific Northwest Seismic Network (PNSN). Due to its ubiquitous nature, ambient seismic noise allows for far denser temporal sampling than, e.g., active source or earthquake coda interferometry. However, source variability related, for example, to volcanic tremor activity affects the results retrieved by this method and can lead to decreased reliability. In this study, we focus on the impact of the complex dynamics at Mt. St. Helens on<em> </em><em>dv/v</em><em> </em>specifically by setting it into context with ground deformation, meteorological changes, and volcanic activity with the ultimate goal of unravelling the complex physical relationship between different forcings and the seismic velocity.</p>