Type analysis of laboratory seismic events by convolutional neural networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985831%3A_____%2F21%3A00542182" target="_blank" >RIV/67985831:_____/21:00542182 - isvavai.cz</a>
Alternative codes found
RIV/67985530:_____/21:00542182
Result on the web
<a href="https://www.irsm.cas.cz/index_en.php?page=acta_detail_doi&id=398" target="_blank" >https://www.irsm.cas.cz/index_en.php?page=acta_detail_doi&id=398</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.13168/AGG.2021.0019" target="_blank" >10.13168/AGG.2021.0019</a>
Alternative languages
Result language
angličtina
Original language name
Type analysis of laboratory seismic events by convolutional neural networks
Original language description
In this work, we successfully identified seismic events (observations of earthquakes) in seismograms using a Convolutional Neural Network (CNN). In accordance with past (analogue) seismogram interpretations, we did not treat digital seismograms as a time series, as per the general method, but, rather, converted them into time snaps of continuous data flow. Multichannel seismograms were represented with a time-frequency domain in the form of multilayer images, with each signal channel forming one image layer. Images were then exposed to CNN (composed of three convolutional blocks whose architecture design was justified using Bayesian optimization). To improve reliability, we evaluated the posterior type function (PTP) as a combination of the probabilities of all of the considered classes of signal types (five in our case) which increased robustness of the identification. For data, we used records of acoustic emission (AE) events. The events were generated during laboratory loading experiments originally performed to study material/rock properties. As known, AE events may be studied in the same manner as natural earthquakes and treated in other ways as laboratory earthquake models. AE events are less complex compared to natural earthquakes where many of the physical parameters are known or may be controlled. Based on our results, we concluded that the successful identification of AE events is a necessary step prior to applying the proposed methodology for identifying natural earthquakes in seismograms.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10507 - Volcanology
Result continuities
Project
<a href="/en/project/GA21-26542S" target="_blank" >GA21-26542S: Influence of postgenetic alterations of granites on their resistance to weathering processes in cultural heritage structures</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Acta geodynamica et geomaterialia
ISSN
1214-9705
e-ISSN
2336-4351
Volume of the periodical
18
Issue of the periodical within the volume
2
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
11
Pages from-to
267-277
UT code for WoS article
000661266800011
EID of the result in the Scopus database
2-s2.0-85109105310