Automatic arrival time detection for earthquakes based on Modified Laplacian of Gaussian filter. (April 2018)

• Record Type: Journal Article
• Authors: Saad, Omar M.; Shalaby, Ahmed; Samy, Lotfy; Sayed, Mohammed S.
• Is Part Of: Computers & geosciences. Volume 113(2018)
• Page Start: 43
• Page End: 53
• Publication Date: 2018-04
• Subjects: Arrival time of earthquake (P-wave) -- Laplacian of Gaussian filter (LoG) -- Akaike Information Criterion (AIC) -- Automatic time picks -- Short and long time average (STA/LTA) algorithm; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2018.01.013
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Precise identification of onset time for an earthquake is imperative in the right figuring of earthquake's location and different parameters that are utilized for building seismic catalogues. P-wave arrival detection of weak events or micro-earthquakes cannot be precisely determined due to background noise. In this paper, we propose a novel approach based on Modified Laplacian of Gaussian (MLoG) filter to detect the onset time even in the presence of very weak signal-to-noise ratios (SNRs). The proposed algorithm utilizes a denoising-filter algorithm to smooth the background noise. In the proposed algorithm, we employ the MLoG mask to filter the seismic data. Afterward, we apply a Dual-threshold comparator to detect the onset time of the event. The results show that the proposed algorithm can detect the onset time for micro-earthquakes accurately, with SNR of −12 dB. The proposed algorithm achieves an onset time picking accuracy of 93% with a standard deviation error of 0.10 s for 407 field seismic waveforms. Also, we compare the results with short and long time average algorithm (STA/LTA) and the Akaike Information Criterion (AIC), and the proposed algorithm outperforms them. Highlights: Automatic picking algorithm for onset time detection is proposed. The algorithm based on Modified Laplacian of Gaussian. The algorithm is denoising filtering algorithm to smooth the background noise. The algorithm is working well with local/micro earthquakes with low SNR. The algorithm is implemented on FPGA to be compatible with real-time EEWS.