TY - JOUR
T1 - Automatic Arrival Time Detection for Earthquakes Based on Stacked Denoising Autoencoder
AU - Saad, Omar M.
AU - Inoue, Koji
AU - Shalaby, Ahmed
AU - Samy, Lotfy
AU - Sayed, Mohammed S.
N1 - Funding Information:
Manuscript received February 2, 2018; revised June 19, 2018; accepted July 25, 2018. Date of publication August 15, 2018; date of current version November 5, 2018. This work was supported in part by the Egypt-Japan University of Science and Technology and in part by the Egyptian Ministry of Higher Education. (Corresponding author: Omar M. Saad.) O. M. Saad is with the Department of Electronics and Communications Engineering, Egypt-Japan University of Science and Technology, Alexandria 21934, Egypt, and also with the National Research Institute of Astronomy and Geophysics, Helwan 11731, Egypt (e-mail: omar.saad@ejust.edu.eg).
Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018/11
Y1 - 2018/11
N2 - The accurate detection of P-wave arrival time is imperative for determining the hypocenter location of an earthquake. However, precise detection of onset time becomes more difficult when the signal-to-noise ratio (SNR) of the seismic data is low, such as during microearthquakes. In this letter, a stacked denoising autoencoder (SDAE) is proposed to smooth the background noise. The SDAE acts as a denoising filter for the seismic data. In the proposed algorithm, the SDAE is utilized to reduce background noise such that the onset time becomes more clear and sharp. Afterward, a hard decision with one threshold is used to detect the onset time of the event. The proposed algorithm is evaluated on both synthetic and field seismic data. As a result, the proposed algorithm outperforms the short-time average/long-time average and the Akaike information criterion algorithms. The proposed algorithm accurately picks the onset time of 94.1% for 407 field seismic waveforms with a standard deviation error of 0.10 s. In addition, the results indicate that the proposed algorithm can pick arrival times accurately for weak SNR seismic data with SNR higher than -14 dB.
AB - The accurate detection of P-wave arrival time is imperative for determining the hypocenter location of an earthquake. However, precise detection of onset time becomes more difficult when the signal-to-noise ratio (SNR) of the seismic data is low, such as during microearthquakes. In this letter, a stacked denoising autoencoder (SDAE) is proposed to smooth the background noise. The SDAE acts as a denoising filter for the seismic data. In the proposed algorithm, the SDAE is utilized to reduce background noise such that the onset time becomes more clear and sharp. Afterward, a hard decision with one threshold is used to detect the onset time of the event. The proposed algorithm is evaluated on both synthetic and field seismic data. As a result, the proposed algorithm outperforms the short-time average/long-time average and the Akaike information criterion algorithms. The proposed algorithm accurately picks the onset time of 94.1% for 407 field seismic waveforms with a standard deviation error of 0.10 s. In addition, the results indicate that the proposed algorithm can pick arrival times accurately for weak SNR seismic data with SNR higher than -14 dB.
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U2 - 10.1109/LGRS.2018.2861218
DO - 10.1109/LGRS.2018.2861218
M3 - Article
AN - SCOPUS:85051641855
SN - 1545-598X
VL - 15
SP - 1687
EP - 1691
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
IS - 11
M1 - 8437146
ER -