Automatic Arrival Time Detection for Earthquakes Based on Stacked Denoising Autoencoder

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 st...

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Vydané v:	IEEE Geoscience and Remote Sensing Letters Ročník 15; číslo 11; s. 1687 - 1691
Hlavní autori:	Saad, Omar M., Inoue, Koji, Shalaby, Ahmed, Samy, Lotfy, Sayed, Mohammed S.
Médium:	Journal Article
Jazyk:	English Japanese
Vydavateľské údaje:	Piscataway IEEE 01.11.2018 Institute of Electrical and Electronics Engineers (IEEE) The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Ambient noise Animal behavior Background noise Decoding Deep learning Detection Earthquakes Feature extraction Machine learning Microearthquakes Noise Noise measurement Noise reduction P-wave arrival time of earthquakes Seismic activity Seismic data Seismic waves Seismological data Signal to noise ratio stacked denoising autoencoder (SDAE) Waveforms
ISSN:	1545-598X, 1558-0571
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Abstract	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.
AbstractList	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. 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.
Author	Inoue, Koji Shalaby, Ahmed Samy, Lotfy Sayed, Mohammed S. Saad, Omar M.
Author_xml	– sequence: 1 givenname: Omar M. orcidid: 0000-0002-9989-8070 surname: Saad fullname: Saad, Omar M. email: omar.saad@ejust.edu.eg organization: Department of Electronics and Communications Engineering, Egypt-Japan University of Science and Technology, Alexandria, Egypt – sequence: 2 givenname: Koji surname: Inoue fullname: Inoue, Koji organization: Department of I&E Visionaries, Kyushu University, Fukuoka, Japan – sequence: 3 givenname: Ahmed orcidid: 0000-0002-7326-2701 surname: Shalaby fullname: Shalaby, Ahmed organization: Department of Computer Science, Faculty of Computers and Informatics, Benha University, Benha, Egypt – sequence: 4 givenname: Lotfy orcidid: 0000-0003-2764-3236 surname: Samy fullname: Samy, Lotfy organization: National Research Institute of Astronomy and Geophysics, Helwan, Egypt – sequence: 5 givenname: Mohammed S. orcidid: 0000-0002-5514-6790 surname: Sayed fullname: Sayed, Mohammed S. organization: Department of Electronics and Communications Engineering, Egypt-Japan University of Science and Technology, Alexandria, Egypt
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SubjectTerms	Algorithms Ambient noise Animal behavior Background noise Decoding Deep learning Detection Earthquakes Feature extraction Machine learning Microearthquakes Noise Noise measurement Noise reduction P-wave arrival time of earthquakes Seismic activity Seismic data Seismic waves Seismological data Signal to noise ratio stacked denoising autoencoder (SDAE) Waveforms
Title	Automatic Arrival Time Detection for Earthquakes Based on Stacked Denoising Autoencoder
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