Automatic P-Wave Arrival Picking Based on Inaction Method

被引:4
|
作者
Yao, Yanji [1 ,2 ]
Liu, Lintao [1 ]
机构
[1] Chinese Acad Sci, Innovat Acad Precis Measurement Sci & Technol, State Key Lab Geodesy & Earths Dynam, Wuhan 430077, Peoples R China
[2] Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing 100049, Peoples R China
基金
中国国家自然科学基金;
关键词
Automatic picking; normal time-frequency transform (NTFT); P-wave arrival; seismic signal analysis; PHASE-PICKER; TIME; TRANSFORM; IDENTIFICATION; PERFORMANCE; HAAR;
D O I
10.1109/TGRS.2022.3230411
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Accurately picking the arrival of the P-wave is of great significance for earthquake relief and emergency rescue. The picking accuracy of some previous methods was easily disturbed by noise. We propose an automatic picking algorithm for seismic P-wave first arrivals based on the inaction method (IM) and the Akaike information criterion (AIC), called the IMAIC method, to alleviate the above problems. We use the IMAIC method to directly extract the seismic phase characteristics from the normal time-frequency transform (NTFT) spectrum to pick the P-wave arrival, which does not require inverse transformation of the NTFT. The proposed method essentially implements bandpass filtering to avoid noise interference to the greatest extent possible while optimally preserving the important features of the signal, such as the sharp direct P-wave arrival. We applied the IMAIC method to 9909 aftershock records from Wenchuan in 2008 to verify its reliability and robustness, taking the manually picked P-wave arrival as a reference. The average deviation is -0.010 +/- 0.170 s when using the proposed IMAIC method. Furthermore, a comparative study of P-wave arrivals that are automatically derived using multiscale wavelet analysis and singular value decomposition (SVD)-like picking algorithms with the same dataset demonstrates the effectiveness of the IMAIC method. The proposed method offers much promise for picking seismic phase onsets, even when using datasets with a low signal-to-noise ratio (SNR).
引用
收藏
页数:11
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