English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

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

Steinmann, R., Seydoux, L., Journeau, C., Shapiro, N. M., Campillo, M. (2024): Machine Learning Analysis of Seismograms Reveals a Continuous Plumbing System Evolution Beneath the Klyuchevskoy Volcano in Kamchatka, Russia. - Journal of Geophysical Research: Solid Earth, 129, e2023JB027167.
https://doi.org/10.1029/2023JB027167

Item is

Files

show Files
hide Files
:
5026270.pdf (Publisher version), 24MB
Name:
5026270.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-

Locators

show

Creators

show
hide
 Creators:
Steinmann, René1, Author              
Seydoux, Léonard2, Author
Journeau, Cyril2, Author
Shapiro, Nikolai M.2, Author
Campillo, Michel2, Author
Affiliations:
12.6 Seismic Hazard and Risk Dynamics, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146032              
2External Organizations, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: 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.

Details

show
hide
Language(s): eng - English
 Dates: 2024-03-252024
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1029/2023JB027167
GFZPOF: p4 T3 Restless Earth
OATYPE: Hybrid Open Access
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Journal of Geophysical Research: Solid Earth
Source Genre: Journal, SCI, Scopus
 Creator(s):
Affiliations:
Publ. Info: -
Pages: 3 Volume / Issue: 129 Sequence Number: e2023JB027167 Start / End Page: - Identifier: ISSN: 2169-9313
ISSN: 2169-9356
CoNE: https://gfzpublic.gfz-potsdam.de/cone/journals/resource/jgr_solid_earth
Publisher: American Geophysical Union (AGU)
Publisher: Wiley