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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Bibliographic Details
Published in:IEEE Geoscience and Remote Sensing Letters Vol. 15; no. 11; pp. 1687 - 1691
Main Authors: Saad, Omar M., Inoue, Koji, Shalaby, Ahmed, Samy, Lotfy, Sayed, Mohammed S.
Format: Journal Article
Language:English
Japanese
Published: Piscataway IEEE 01.11.2018
Institute of Electrical and Electronics Engineers (IEEE)
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2018.2861218