Discrimination of doubled acoustic emission events using neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985530%3A_____%2F24%3A00597655" target="_blank" >RIV/67985530:_____/24:00597655 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985831:_____/24:00598246 RIV/67985891:_____/24:00597655

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0041624X24002026?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0041624X24002026?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ultras.2024.107439" target="_blank" >10.1016/j.ultras.2024.107439</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Discrimination of doubled acoustic emission events using neural networks

Popis výsledku v původním jazyce

In observatory seismology, the effective automatic processing of seismograms is a time-consuming task. A contemporary approach for seismogram processing is based on the Deep Neural Network formalism, which has been successfully applied in many fields. Here, we present a 4D network, based on U-net architecture, that simultaneously processes seismograms from an entire network. We also interpret Acoustic Emission data based on a laboratory loading experiment. The obtained data was a very good testing set, similar to real seismograms. Our Neural network is designed to detect multiple events. Input data are created by augmentation from previously interpreted single events. The advantage of the approach is that the positions of (multiple) events are exactly known, thus, the efficiency of detection can be evaluated. Even if the method reaches an average efficiency of only around 30% for the onset of individual tracks, average efficiency for the detection of double events was approximately 97% for a maximum target, with a prediction difference of 20 samples. Such is the main benefit of simultaneous network signal processing.

Název v anglickém jazyce

Discrimination of doubled acoustic emission events using neural networks

Popis výsledku anglicky

In observatory seismology, the effective automatic processing of seismograms is a time-consuming task. A contemporary approach for seismogram processing is based on the Deep Neural Network formalism, which has been successfully applied in many fields. Here, we present a 4D network, based on U-net architecture, that simultaneously processes seismograms from an entire network. We also interpret Acoustic Emission data based on a laboratory loading experiment. The obtained data was a very good testing set, similar to real seismograms. Our Neural network is designed to detect multiple events. Input data are created by augmentation from previously interpreted single events. The advantage of the approach is that the positions of (multiple) events are exactly known, thus, the efficiency of detection can be evaluated. Even if the method reaches an average efficiency of only around 30% for the onset of individual tracks, average efficiency for the detection of double events was approximately 97% for a maximum target, with a prediction difference of 20 samples. Such is the main benefit of simultaneous network signal processing.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20701 - Environmental and geological engineering, geotechnics

Projekt

<a href="/cs/project/GA22-00580S" target="_blank" >GA22-00580S: Vliv anizotropie hornin při hydraulickém štěpení zkoumaný akustickou emisí</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Ultrasonics

ISSN

0041-624X

e-ISSN

1874-9968

Svazek periodika

144

Číslo periodika v rámci svazku

Dec.

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

8

Strana od-do

107439

Kód UT WoS článku

001301301800001

EID výsledku v databázi Scopus

2-s2.0-85201752816