ISSN 1004-4140
CN 11-3017/P
| Citation: |
WANG T Z, ZHANG W J, QI S B, et al. Experimental Study on Long Short-term Memory Networks for Identifying P-wave Primary Phase[J]. CT Theory and Applications, 2025, 34(2): 205-215. DOI: 10.15953/j.ctta.2024.279. (in Chinese).
|
Identifying primary phases of seismic waveforms is a routine task in seismic data processing. Owing to the low efficiency of manual identification and the influence of human subjective factors, many methods for the automatic identification of the primary phase have been developed in recent years. Most of these methods determine the arrival time based on the ratio between ambient noise and seismic signals. However, they typically require a threshold value, making their implementation in complex seismic regions and handling massive seismic data challenging. In this study, a seven-layer convolutional recurrent neural network based on long short-term memory (LSTM) network was constructed, and an experimental study was conducted to identify the P-wave primary phase. The network was trained and tested using a data set from Southern California. Compared with the traditional convolutional neural network, automatic identification algorithm, Pick-Net, and EQtransformer network, the recognition accuracy of our new convolutional recurrent neural network is relatively higher; therefore, the seismic waveform data can be directly used as a time series for training. Additionally, while the new convolutional recurrent neural network has only seven network layers, it achieves an accurate phase identification of complex network models, showcasing the strengths of convolutional neural networks. In summary, our study presents a convolutional recurrent neural network based on the LSTM, offers a new idea for the automatic identification of the primary phase, and provides technical support for the rapid and accurate automatic identification of the seismic phase.
| [1] |
VanDECAR J C, CROSSON R S. Determination of teleseismic relative phase arrival times using multi-channel cross-correlation and leastsquares[J]. Bulletin of the Seismological Society of America, 1990, 80(1): 150-169. DOI: https://doi.org/10.1785/BSSA0800010150.
|
| [2] |
CANSI Y. An automatic seismic event processing for detection and location: The P. M. C. C. method[J]. Geophysical Research Letters, 1995, 22(9): 1021-1024. DOI: 10.1029/95gl00468.
|
| [3] |
VANDECAR J C, CROSSON R S. Determination of teleseismic relative phase arrival times using multi-channel cross-correlation and least squares[J]. Bulletin of the Seismological Society of America, 1990, 80(1): 150-169.
|
| [4] |
VANDECAR J C, CROSSON R S. Determination of teleseismic rela-tive phase arrival times using multi-channel cross-correlation and leastsquares[J]. Bulletin of the Seismological Society of America, 1990, 80: 150-169.
|
| [5] |
BAER M, KRADOLFER U. An automatic phase picker for local and teleseismic events[J]. Bulletin of the Seismological Society of America, 1987, 77(4): 1437-1445. DOI: 10.1785/BSSA0770041437.
|
| [6] |
SARAGIOTIS C D, HADJILEONTIADIS L J, PANAS S M. A higher-order statistics-based phase identification of three-component seismograms in a redundant wavelet transform domain[C]//proceedings of the Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics SPW-HOS'99, F, IEEE. 1999.
|
| [7] |
ZHANG H, THURBER C, ROWE C. Automatic P-wave arrival detection and picking with multiscale wavelet analysis for single-component recordings[J]. Bulletin of the Seismological Society of America, 2003, 93(5): 1904-1912. DOI: 10.1785/0120020241.
|
| [8] |
CHEN Z, STEWART R. Multi-window algorithm for detecting seismic first arrivals[C]//Proceedings of the Abstracts, CSEG National Convention, F. 2005.
|
| [9] |
WONG J, HAN L, BANCROFT J, et al. Automatic time-picking of first arrivals on noisy microseismic data[J]. CSEG Recorder, 2009: 1-4.
|
| [10] |
SONG F, KULELI H S, TOKSöZ M N, et al. An improved method for hydrofracture-induced microseismic event detection and phase picking[J]. Geophysics, 2010, 75(6): A47-A52. DOI: 10.1190/1.3484716.
|
| [11] |
LI F, RICH J, MARFURT K J, et al. Automatic event detection on noisy microseismograms[C]//SEG Technical Program Expanded Abstracts. Society of Exploration Geophysicists: 2014: 2363-2367.
|
| [12] |
MA Y, CAO S, RECTOR J W, et al. Automated arrival-time picking using a pixel-level network Arrival-time picking with U-Net[J]. Geophysics, 2020, 85(5): V415-V423. DOI: 10.1190/geo2019-0792.1.
|
| [13] |
FERNHOUT C, ZWARTJES P, YOO J. Automatic first break picking with deep learning[J]. IOSR Journal of Applied Geology and Geophysics, 2020, 8(5): 24-36.
|
| [14] |
YUAN P, WANG S, HU W, et al. A robust first-arrival picking workflow using convolutional and recurrent neural networks[J]. Geophysics, 2020, 85(5): U109-U119. DOI: 10.1190/geo2019-0437.1.
|
| [15] |
ZHENG J, HARRIS J M, LI D, et al. SC-PSNET: A deep neural network for automatic P-and S-phase detection and arrival-time picker using 1C recordings deep learning for phase auto-pickers[J]. Geophysics, 2020, 85(4): U87-U98. DOI: 10.1190/geo2019-0597.1.
|
| [16] |
ROSS Z E, MEIER M A, HAUKSSON E. P wave arrival picking and first-motion polarity determination with deep learning[J]. Journal of Geophysical Research: Solid Earth, 2018, 123(6): 5120-5129. DOI: 10.1029/2017JB015251.
|
| [17] |
HOLLANDER Y, MEROUANE A, YILMAZ O. Using a deep convolutional neural network to enhance the accuracy of first-break picking[C]//Proceedings of the 2018 SEG International Exposition and Annual Meeting, F. OnePetro. 2018.
|
| [18] |
GAO L, JIANG Z Y, MIN F. First-arrival travel times picking through sliding windows and fuzzy c-means[J]. Mathematics, 2019, 7(3): 221. DOI: 10.3390/math7030221.
|
| [19] |
TSAI K C, HU W, WU X, et al. Automatic first arrival picking via deep learning with human interactive learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 58(2): 1380-1391.
|
| [20] |
DUAN X, ZHANG J. 2019 Multi-trace and multi-attribute analysis for first-break picking with the support vector machine[C]//SEG Technical Program Expanded Abstracts 2019. Society of Exploration Geophysicists: 2559-2563.
|
| [21] |
MA Y, CAO S, RECTOR J W, et al. Automatic first arrival picking for borehole seismic data using a pixellevel network[C]//SEG Technical Program Expanded Abstracts 2019. Society of Exploration Geophysicists, 2019: 2463-2467.
|
| [22] |
LIAO X, CAO J, HU J, et al. First arrival time identification using transfer learning with continuous wavelet transform feature images[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 17(11): 2002-2006.
|
| [23] |
ZHU W, BEROZA G C. PhaseNet: A deep-neural-network-based seismicarrival-time picking method[J]. Geophys Int, 2018, 216: 261-273.
|
| [24] |
WANG J, XIAO Z, LIU C, et al. Deep learning for picking seismic arrival times[J]. Journal of Geophysical Research: Solid Earth, 2019, 124(7): 6612-6624. DOI: 10.1029/2019JB017536.
|
| [25] |
CHAI C, MACEIRA M, SANTOS-VILLALOBOS H J, et al. Using a deep neural network and transfer learning to bridge scales for seismic phase picking[J]. Geophysical Research Letters, 2020, 47(16): e2020GL088651. DOI: 10.1029/2020GL088651.
|
| [26] |
HE Z, PENG P, WANG L, et al. Enhancing seismic P-wave arrival picking by target-oriented detection of the local windows using faster-rcnn[J]. IEEE Access, 2020, 8: 141733-141747. DOI: 10.1109/ACCESS.2020.3013262.
|
| [27] |
MOUSAVI S M, ZHU W Q, et al. Earthquake transformer: An attentive deep-learning model for simultaneous earthquake detection and phase picking[J]. Nature Communications, 2020, 11: 3952. DOI: 10.1038/s41467-020-17591-w.
|
| [28] |
LECUN Y A, BOTTOU L, ORR G B, et al. Efficient backprop[M]. Neural Networks: Tricks of the Trade. Springer, 2012: 9-48.
|
| [29] |
AKAZAWA T. T echnique for automatic detection of onset time of P- and S-phases in strong motion recordsin 13th World Conference onEarthquake Engineering[J]. Vancouver, 2004, 786: 786.
|
Supported by: Beijing Renhe Information Technology Co. Ltd 
DownLoad: