Improvement of Autoregressive Model-Based Algorithms for Picking the Arrival Times of the P-Wave of Rock Acoustic Emission

Acoustic emission (AE) monitoring technology has been widely used in rock engineering. In addition, the accurate picking of P-wave arrival times is the key to in-depth rock mechanics and AE research. Autoregressive (AR) model-based algorithms such as AR-Akaike information criterion (AR-AIC) and AR-B...

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Veröffentlicht in:Geotechnical and geological engineering Jg. 41; H. 2; S. 707 - 719
Hauptverfasser: Wang, Guilin, He, Chenhao, Liang, Feng, Gong, Sheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.03.2023
Springer Nature B.V
Schlagworte:
Accuracy
Acoustic emission
Acoustics
Algorithms
Autoregressive models
Autoregressive processes
Bayesian analysis
Civil Engineering
Criteria
Earth and Environmental Science
Earth Sciences
Efficiency
Errors
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Methods
Original Paper
P waves
Probability theory
Rock
Rock mechanics
Rocks
Terrestrial Pollution
Time series
Waste Management/Waste Technology
Waveforms
Wavelet transforms
Acoustic emission
Autoregressive model
P-wave arrival time picking
Algorithm improvement
ISSN:0960-3182, 1573-1529
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Zusammenfassung:Acoustic emission (AE) monitoring technology has been widely used in rock engineering. In addition, the accurate picking of P-wave arrival times is the key to in-depth rock mechanics and AE research. Autoregressive (AR) model-based algorithms such as AR-Akaike information criterion (AR-AIC) and AR-Bayesian information criterion (AR-BIC) are efficient methods currently adopted for P-wave arrival time picking; however, their picking results sometimes have large errors, owing to the complexity of amplitude distribution in the time series of AE waveform data. To minimize these errors, this study improved the adopted algorithms by leveraging the surge phenomenon in AR model variance. Specifically, a single-segment AR-BIC algorithm was developed by pre-processing the time series of AE waveform data. It was verified that the improved AR-BIC algorithm achieved a 50% higher efficiency than the conventional algorithm, including a picking accuracy above 98.5%.
Bibliographie:ObjectType-Article-1
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ISSN:0960-3182
1573-1529
DOI:10.1007/s10706-022-02296-2