A two-step algorithm for acoustic emission event discrimination based on recurrent neural networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985530%3A_____%2F22%3A00557153" target="_blank" >RIV/67985530:_____/22:00557153 - isvavai.cz</a>

Alternative codes found

RIV/67985831:_____/22:00557153

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0098300422000772" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0098300422000772</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cageo.2022.105119" target="_blank" >10.1016/j.cageo.2022.105119</a>

Result language

angličtina

Original language name

A two-step algorithm for acoustic emission event discrimination based on recurrent neural networks

Original language description

We present an algorithm for seismic event discrimination and event approximate location based on multi-station seismograms. A deep learning approach was applied using a two-step algorithm: (i) signal onsets were identified in individual tracks based on the use of long-short-term memory neural network layers, (ii) if a sufficient number of onsets were reliably identified, a preliminary location was determined. We adopted a “reverse location approach” where the time sense of a seismogram is reverted and the origin time is predicted using a neural network approach based on previously determined onsets. Successful location or origin time prediction also served as a feedback for confirming previous onset identification. The method was tested using a data set of Acoustic Emission generated from the uniaxial loading of a Westerly Granite specimen. Accuracy of the method was better than 97%. Discriminated events were automatically located and their seismic moment tensor was determined. Both types of results were in good agreement with the baseline data set. With respect to the particular nature of processed data, we provide a demo code which shows examples presented in the article. In addition, a detailed description of the algorithm, including the control parameter values, is provided in the text. Based on this information the method can be applied on any data.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10507 - Volcanology

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computers and Geosciences

ISSN

0098-3004

e-ISSN

1873-7803

Volume of the periodical

163

Issue of the periodical within the volume

June

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

105119

UT code for WoS article

000798193200005

EID of the result in the Scopus database

2-s2.0-85129059539