Recurrence of Deep Long-Period Earthquakes beneath the Klyuchevskoi Volcano Group, Kamchatka

Galina, Natalia; Shapiro, N. M.; Дрознин, Д. В.; Droznina, S.; Senyukov, S.; Chebrov, Danila

doi:10.1134/s1069351320060026

Cited by 12 publications

(4 citation statements)

References 36 publications

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“…The sustained volcanic activity of the KVG results in nearly constantly occurring seismicity including long periods of seismo‐volcanic tremors (Droznin et al., 2015; Journeau et al., 2022; Soubestre et al., 2018, 2019) and numerous earthquakes (Koulakov et al., 2021; Senyukov, 2013; Senyukov et al., 2009; Thelen et al., 2010). In particular, the deep long‐period earthquakes (DLP) have been observed at the crust‐mantle boundary beneath the Klyuchevskoy volcano (Frank et al., 2018; Galina et al., 2020; Gorelchik et al., 2004; Levin et al., 2014; Melnik et al., 2020; Shapiro, Droznin, et al., 2017). The temporal correlation between the deep and shallow seismic activity has been attributed to the transfer of the fluid pressure from the deep‐seated parts of the magmatic system toward shallow magmatic reservoirs beneath the active volcanoes (Journeau et al., 2022; Shapiro, Droznin, et al., 2017).…”

Section: Klyuchevskoy Volcano Group (Kvg) and Its Seismic Activitymentioning

confidence: 99%

Machine Learning Analysis of Seismograms Reveals a Continuous Plumbing System Evolution Beneath the Klyuchevskoy Volcano in Kamchatka, Russia

Steinmann,

Seydoux,

Journeau

et al. 2024

JGR Solid Earth

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Volcanoes produce a variety of seismic signals and, therefore, continuous seismograms provide crucial information for monitoring the state of a volcano. According to their source mechanism and signal properties, seismo‐volcanic signals can be categorized into distinct classes, which works particularly well for short transients. Applying classification approaches to long‐duration continuous signals containing volcanic tremors, characterized by varying signal characteristics, proves challenging due to the complex nature of these signals. That makes it difficult to attribute them to a single volcanic process and questions the feasibility of classification. In the present study, we consider the whole seismic time series as valuable information about the plumbing system (the combination of plumbing structure and activity distribution). The considered data are year‐long seismograms recorded at individual stations near the Klyuchevskoy Volcanic Group (Kamchatka, Russia). With a scattering network and a Uniform Manifold Approximation and Projection (UMAP), we transform the continuous data into a two‐dimensional representation (a seismogram atlas), which helps us to identify sudden and continuous changes in the signal properties. We observe an ever‐changing seismic wavefield that we relate to a continuously evolving plumbing system. Through additional data, we can relate signal variations to various state changes of the volcano including transitions from deep to shallow activity, deep reactivation, weak signals during quiet times, and eruptive activity. The atlases serve as a visual tool for analyzing extensive seismic time series, allowing us to associate specific atlas areas, indicative of similar signal characteristics, with distinct volcanic activities and variations in the volcanic plumbing system.

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Section: Klyuchevskoy Volcano Group (Kvg) and Its Seismic Activitymentioning

confidence: 99%

Machine Learning Analysis of Seismograms Reveals a Continuous Plumbing System Evolution Beneath the Klyuchevskoy Volcano in Kamchatka, Russia

Steinmann,

Seydoux,

Journeau

et al. 2024

JGR Solid Earth

View full text Add to dashboard Cite

show abstract

“…The sustained volcanic activity of the KVG results in nearly constantly occurring seismicity including long periods of seismo-volcanic tremors (Droznin et al, 2015;Soubestre et al, 2018Soubestre et al, , 2019Journeau et al, 2022) and numerous earthquakes (Senyukov et al, 2009;Thelen et al, 2010;Senyukov, 2013;Koulakov et al, 2021). In particular, the deep longperiod earthquakes (DLP) have been observed at the crust-mantle boundary beneath the Klyuchevskoy volcano (Gorelchik et al, 2004;Levin et al, 2014;Shapiro, Droznin, et al, 2017;Frank et al, 2018;Galina et al, 2020;Melnik et al, 2020). The temporal correlation between the deep and shallow seismic activity has been attributed to the transfer of the fluid pressure from the deep-seated parts of the magmatic system towards shallow magmatic reservoirs beneath the active volcanoes (Shapiro, Droznin, et al, 2017;Journeau et al, 2022).…”

Section: Klyuchevskoy Volcano Group and Its Seismic Activitymentioning

confidence: 99%

Machine learning analysis of seismograms reveals a continuous plumbing system evolution beneath the Klyuchevskoy volcano in Kamchatka, Russia

Steinmann

Seydoux

Journeau

et al. 2023

Preprint

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Seismic time series provide crucial information for monitoring the state of a volcano with discrete event catalogs describing impulsive seismic activity and hand-designed features describing more emergent signals (e.g. real-time seismic amplitude measurement for volcanic tremor signals). However, the emergent and long-term seismo-volcanic activity such as volcanic tremors are a complex and non-stationary phenomena that might contain more information than current methods can retrieve. In the present study, we consider the whole seismic time series as a valuable source of information by retrieving data-driven continuous features with an independent component analysis (ICA) and seismogram atlases with Uniform Manifold Approximation and Projection (UMAP). The data of interest are year-long seismic time series recorded at individual stations near the Klyuchevskoy Volcanic Group (Kamchatka, Russia). The features extracted from data recorded close to the active volcano depict a succession of short-lived patterns in the time series, indicating continuously changing signal characteristics. Additionally, the seismogram atlas reveals that, especially during periods of volcanic activation, the signal evolves continuously with some occasional sudden changes, resulting in new patterns throughout the recording time. The features and seismogram atlases reveal unique characteristics of the continuous seismograms recorded close to the volcano and related to its activity, suggesting that the complete seismic time series contains subtle but interesting information not captured by conventional methods. The seismogram atlases open new avenues to perceive large seismic time series visually and to connect the signal changes to physical processes.

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“…At the same time, there are various types of seismicity with properties different from the “regular earthquake regime” mentioned above. One example are the volcanic earthquakes that often do not follow the Gutenberg Richter distribution with b = 1 (e.g., Galina et al., 2020; Jacobs & McNutt, 2010; Wyss et al., 1998) Another example is provided by the induced seismicity with reported stress drops found to be comparable with tectonic ones (e.g., Huang et al., 2017) as well as being very variable (e.g., Lengliné et al., 2014; Shapiro & Dinske, 2021; Q. Wu et al., 2018). Finally, the scaling laws for recently discovered slow earthquakes are often reported being different from those known for “regular” earthquakes (e.g., Bostock et al., 2015; Farge et al., 2020; Ide & Beroza, 2023; Ide et al., 2007; Peng & Gomberg, 2010).…”

Section: Introductionmentioning

confidence: 99%

Source Parameters of Laboratory Acoustic Emission Events Estimated From the Coda of Waveforms

Kartseva,

Shapiro,

Patonin

et al. 2024

JGR Solid Earth

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We develop a method to estimate relative seismic moments M0 and corner frequencies fc of acoustic emission events recorded in laboratory experiments from amplitude spectra of signal's coda composed of reverberated and scattered waves. This approach has several advantages with respect to estimations from direct waves that are often clipped and also are difficult to separate in experiments performed on small samples. Also, inversion of the coda spectra does not require information about the source locations and mechanisms. We use the developed method to analyze the data of two experiments: (a) on granite from the Voronezh crystal massif and (b) on Berea sandstone. The range of absolute corner frequencies estimated in both experiments is around 70 − −700 kHz. The range of relative seismic moments covers 103.5. The relation between fc and M0 observed on the first stages of both experiments, consisted of increasing isotropic confining pressure, approximately follow scaling and the b‐value of the Gutenberg‐Richter distribution was found close to 1. This can be interpreted as rupturing of preexisting material defects with a nearly constant stress‐drop and has a similarity with observations of “natural” earthquakes. Deviations from this “earthquake‐like” behavior observed after applying axial loading and initiation of sample damaging can be interpreted as changes in stress‐drop. Lower stress‐drops prevail for sandstone and higher for granite sample respectively that can be related to the strength of corresponding material.

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Recurrence of Deep Long-Period Earthquakes beneath the Klyuchevskoi Volcano Group, Kamchatka

Cited by 12 publications

References 36 publications

Machine Learning Analysis of Seismograms Reveals a Continuous Plumbing System Evolution Beneath the Klyuchevskoy Volcano in Kamchatka, Russia

Machine Learning Analysis of Seismograms Reveals a Continuous Plumbing System Evolution Beneath the Klyuchevskoy Volcano in Kamchatka, Russia

Machine learning analysis of seismograms reveals a continuous plumbing system evolution beneath the Klyuchevskoy volcano in Kamchatka, Russia

Source Parameters of Laboratory Acoustic Emission Events Estimated From the Coda of Waveforms

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