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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/67985831:_____/22:00557153

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10507 - Volcanology

Projekt

<a href="/cs/project/GA21-26542S" target="_blank" >GA21-26542S: Vliv postgenetických přeměn žul na jejich odolnost vůči zvětrávacím procesům v historických stavbách</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Computers and Geosciences

ISSN

0098-3004

e-ISSN

1873-7803

Svazek periodika

163

Číslo periodika v rámci svazku

June

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

13

Strana od-do

105119

Kód UT WoS článku

000798193200005

EID výsledku v databázi Scopus

2-s2.0-85129059539