Abstract
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%.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.
References
Akaike H (1974) Markovian representation of stochastic process and its application to the analysis of autotregressive moving average process. Annal Ins Stat Math 26(1):363–387
Allen R (1982) Automatic phase pickers: their present use and future prospects. Bull Seismol Soc Am 72(6):225–242
Bai T, Wu S, Wang J (2016) Methods of P-onset picking of acoustic emission compression waves and optimized improvement. Chin J Rock Mech Eng 35(09):1754–1766
Gan Y, Wu S, Ren Y et al (2020) Evaluation indexes of granite splitting failure based on RA and AF of AE parame-ters. Rock Soil Mech 41(07):2324–2332
Hardy JHR (1994) Geotechnical field applications of AE/MS techniques at the Pennsylvania State University: a historical review. Indep Nondestruct Test Eval Int 27(4):191–200
Huang W (2013) Research on acoustic emission source mechanisms of rock failure based on moment tensor theory. Northeast Uni 8:592483
Küperkoch L, Meier T, Lee J (2010) Automated determination of P-phase arrival times at regional and local distances using higher order statistics. Geophys J Int 181(2):1159–1170
Leonard M, Kennett BLN (1999) Multi-component autoregressive techniques for the analysis of seismograms. Phys Earth Planet Interiors 113(1–4):247–264
Li J, Wu S, GAO Y (2014) Review of slope micro-seismic monitoring in open pit mine. Chinese J Rock Mech Eng 33(Supp. 2):3998–4013
Liu X, Zhou H (2002) Gauss linear frequency modulation wavelet transform and its application to seismic phases identification. Acta Seismolog Sinica 2002(06):607–616 ((in Chinese))
Liu X, Zhou H (2000) Wavelet transformation used for recording three-component phase identification. Acta Seismol Sin 22(2):125–131 ((in Chinese))
Liu F, Yang T, Deng W (2018) An improved automatic algorithm of acoustic emission P-wave arrival time picking. J Northeast Univ (nat Sci) 39(12):1774–1778
Maeda N (1985) A method for reading and checking phase times in auto-processing system of seismic wave data. Seismol Soc Jpn 38(3):365–379
Ross ZE, Meier M-A, Hauksson E (2018) P wave arrival picking and first-motion polarity determination with deep learning. J Geophys Res 123(6):5120–5129
Saragiotis CD, Hadjileontiadis LJ, Panas SM (2002) PAI-S/K: a robust automatic seismic P phase arrival identification scheme. IEEE Trans Geosci Remote Sens 40(6):1395–1404
Schwarze (1978) Estimating the dimension of a model. Ann Stat 6(2):461–464
Sleeman R, Van Eck T (1999) Robust automatic P-phase picking: an on-line implementation in the analysis of broadband seismogram recordings. Phys Earth Planet Inter 113(1):265–275
Stevenson R (1976) Micro earthquakes at Flathead Lake, Montana: a study using automatic earthquake processing. Bull Seismol Soc Am 66(1):61–80
Takanami T, Kitagawa G (1988) A new efficient procedure for the estimation of onset times of seismic waves. J Phys Earth 36(6):267–290
Wang G, Chen J, Zhang F (2015) Wavelet analysis combined with amplitude ratio method to pick up P wave first time. Sci Technol Eng 15(36):73–77
Yang L, Qiu Z, Lin G (2017) Research on double-steps pickup of STA/LTA and bayes BIC rule applied to travel time of P-wave in strong earthquake early warning. J Seismol Res 40(4):619–628
Zhang H, Thurber C, Rowe C (2003) Automatic P-wave arrival detection and picking with multiscale wavelet analysis for single component recordings. Bull Seismol Soc Am 93(5):1904–1912
Zhang L, Chen Z, Qin F (2019) Hybrid moment tensor inversion and numerical analysis of rock fracture. J Min Sci Technol 4(5):394–402
Zhang Q, Tang X, Zhou Y, Ma D (2021a) Failure and acoustic emission characteristics of jointed rock masses with different joint inclination angles and combination mode. Geotech Geolo Eng 39(8):5971–5981
Zhang X, Cui X, Tang Q, Sun Y (2021b) Acoustic emission investigation on crack propagation mode of sandstone during brazil splitting process. Geotech Geolo Eng 39(4):2863–2870
Funding
This work was supported by the National Natural Science Foundation of China (No. 51978106)
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflicts of interest to report regarding the present study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wang, G., He, C., Liang, F. et al. Improvement of Autoregressive Model-Based Algorithms for Picking the Arrival Times of the P-Wave of Rock Acoustic Emission. Geotech Geol Eng 41, 707–719 (2023). https://doi.org/10.1007/s10706-022-02296-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10706-022-02296-2