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  • 04.06. Seismology  (1)
  • Klyuchevskoy Volcanic Group  (1)
  • 1
    Publication Date: 2021-07-03
    Description: The Klyuchevskoy Volcanic Group is a cluster of the world's most active subduction volcanoes, situated on the Kamchatka Peninsula, Russia. The volcanoes lie in an unusual off‐arc position within the Central Kamchatka Depression (CKD), a large sedimentary basin whose origin is not fully understood. Many gaps also remain in the knowledge of the crustal magmatic plumbing system of these volcanoes. We conducted an ambient noise surface wave tomography, to image the 3‐D shear wave velocity structure of the Klyuchevskoy Volcanic Group and CKD within the surrounding region. Vertical component cross correlations of the continuous seismic noise are used to measure interstation Rayleigh wave group and phase traveltimes. We perform a two‐step surface wave tomography to model the 3‐D Vsv velocity structure. For each inversion stage we use a transdimensional Bayesian Monte Carlo approach, with coupled uncertainty propagation. This ensures that our model provides a reliable 3‐D velocity image of the upper 15 km of the crust, as well as a robust assessment of the uncertainty in the observed structure. Beneath the active volcanoes, we image small slow velocity anomalies at depths of 2–5 km but find no evidence for magma storage regions deeper than 5 km—noting the 15 km depth limit of the model. We also map two clearly defined sedimentary layers within the CKD, revealing an extensive 8 km deep sedimentary accumulation. This volume of sediments is consistent with the possibility that the CKD was formed as an Eocene‐Pliocene fore‐arc regime, rather than by recent (〈2 Ma) back‐arc extension.
    Description: Plain Language Summary: The Klyuchevskoy Volcanic Group is a cluster of 13 volcanoes on the Kamchatkan corner of the Pacific ring of fire. The volcanoes regularly produce large eruptions, but good knowledge of the magma plumbing system beneath the surface is still lacking. Why the Klyuchevskoy Volcanic Group volcanoes lie in the location they do, in a large low‐lying depression, is also unexplained. We undertook a seismic experiment and used the data to produce a 3‐D velocity image of the subsurface beneath the volcanoes and the depression. We found that small regions of slow seismic velocity are located beneath the active volcanoes, at 2–5 km depth below sea level. This slower velocity is probably caused by magma lying within the porous fracture spaces in this rock. The seismic velocities are much faster beneath the dormant volcanoes, suggesting they have no magma beneath them. With our velocity image, we also find that the Central Kamchatka Depression is very deep, filled with over 8 km of sediments. This supports an idea that the sediments accumulated as a fore‐arc basin over many millions of years, since 40 Ma, when the active line of volcanoes was found 100 km to the west.
    Description: Key Points: Three‐dimensional shear velocity structure of the Klyuchevskoy area was determined using coupled transdimensional Monte Carlo inversions. Slow velocity anomalies suggest magma storage beneath active volcanoes at 2–5 km depth (below sea level) but not in the midcrust. Sediments filling the Central Kamchatka Depression are 8 km deep, consistent with an origin of the depression as a fore‐arc basin.
    Description: European Union Horizon 2020 Research and Innovation Programme http://dx.doi.org/10.13039/501100007601
    Description: Russian Ministry of Education and Science http://dx.doi.org/10.13039/501100003443
    Description: Alexander von Humboldt Foundation http://dx.doi.org/10.13039/100005156
    Keywords: 551.1 ; tomography ; Central Kamchatka Depression ; transdimensional ; Bayesian ; ambient noise ; Klyuchevskoy Volcanic Group
    Type: article
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  • 2
    Publication Date: 2022-02-11
    Description: Seismic event detection and phase picking are the base of many seismological workflows. In recent years, several publications demonstrated that deep learning approaches significantly outperform classical approaches, achieving human-like performance under certain circumstances. However, as studies differ in the datasets and evaluation tasks, it is unclear how the different approaches compare to each other. Furthermore, there are no systematic studies about model performance in cross-domain scenarios, that is, when applied to data with different characteristics. Here, we address these questions by conducting a large-scale benchmark. We compare six previously published deep learning models on eight data sets covering local to teleseismic distances and on three tasks: event detection, phase identification and onset time picking. Furthermore, we compare the results to a classical Baer-Kradolfer picker. Overall, we observe the best performance for EQTransformer, GPD and PhaseNet, with a small advantage for EQTransformer on teleseismic data. Furthermore, we conduct a cross-domain study, analyzing model performance on data sets they were not trained on. We show that trained models can be transferred between regions with only mild performance degradation, but models trained on regional data do not transfer well to teleseismic data. As deep learning for detection and picking is a rapidly evolving field, we ensured extensibility of our benchmark by building our code on standardized frameworks and making it openly accessible. This allows model developers to easily evaluate new models or performance on new data sets. Furthermore, we make all trained models available through the SeisBench framework, giving end-users an easy way to apply these models.
    Description: This work was supported by the Helmholtz Association Initiative and Networking Fund on the HAICORE@KIT partition. J. Münchmeyer acknowledges the support of the Helmholtz Einstein International Berlin Research School in Data Science (HEIBRiDS). The authors thank the Impuls-und Vernetzungsfonds of the HGF to support the REPORT-DL project under the grant agreement ZT-I-PF-5-53. This work was also partially supported by the project INGV Pianeta Dinamico 2021 Tema 8 SOME (CUP D53J1900017001) funded by Italian Ministry of University and Research “Fondo finalizzato al rilancio degli investimenti delle amministrazioni centrali dello Stato e allo sviluppo del Paese, legge 145/2018.” Open access funding enabled and organized by Projekt DEAL.
    Description: Published
    Description: e2021JB023499
    Description: 3T. Fisica dei terremoti e Sorgente Sismica
    Description: JCR Journal
    Keywords: seismic phase recognition ; deep learnig ; 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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