Simulation of the 2022 Mw 6.6 Luding, China, earthquake by a stochastic finite-fault model with a nonstationary phase

Dang, Pengfei; Cui, Jie; Ma, Wanjun; Li, Yadong

doi:10.1016/j.soildyn.2023.108035

Cited by 6 publications

(1 citation statement)

References 55 publications

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“…To collect seismic-signal data, it is necessary to install multiple sensors in the ground, and a real ground-truth dataset is essential, involving manual checking for the P-wave FAP in the seismic signals. However, training a deep-learning model requires a large dataset, and it is extremely challenging to secure enough data to accurately detect the P-wave FAP [28]. To overcome this limitation, we used the SMSIM program to generate seismic signals with intensities similar to those of earthquakes in South Korea.…”

Section: Seismic-signal Datamentioning

confidence: 99%

Deep-Learning-Based Seismic-Signal P-Wave First-Arrival Picking Detection Using Spectrogram Images

Choi,

Lee,

Kim

et al. 2024

Electronics

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The accurate detection of P-wave FAP (First-Arrival Picking) in seismic signals is crucial across various industrial domains, including coal and oil exploration, tunnel construction, hydraulic fracturing, and earthquake early warning systems. At present, P-wave FAP detection relies on manual identification by experts and automated methods using Short-Term Average to Long-Term Average algorithms. However, these approaches encounter significant performance challenges, especially in the presence of real-time background noise. To overcome this limitation, this study proposes a novel P-wave FAP detection method that employs the U-Net model and incorporates spectrogram transformation techniques for seismic signals. Seismic signals, similar to those encountered in South Korea, were generated using the stochastic model simulation program. Synthesized WGN (White Gaussian Noise) was added to replicate background noise. The resulting signals were transformed into 2D spectrogram images and used as input data for the U-Net model, ensuring precise P-wave FAP detection. In the experimental result, it demonstrated strong performance metrics, achieving an MSE of 0.0031 and an MAE of 0.0177, and an RMSE of 0.0195. Additionally, it exhibited precise FAP detection capabilities in image prediction. The developed U-Net-based model exhibited exceptional performance in accurately detecting P-wave FAP in seismic signals with varying amplitudes. Through the developed model, we aim to contribute to the advancement of microseismic monitoring technology used in various industrial fields.

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Section: Seismic-signal Datamentioning

confidence: 99%

Deep-Learning-Based Seismic-Signal P-Wave First-Arrival Picking Detection Using Spectrogram Images

Choi,

Lee,

Kim

et al. 2024

Electronics

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Analysis of ground motion characteristics and near-fault effects for the September 5, 2022, MS6.8 Luding earthquake

Lin,

Ma,

Xie

et al. 2024

J Seismol

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Simulation of the 1934 Nepal–Bihar earthquake in Kathmandu

Ghimire,

Parajuli,

Maskey

2024

Nat Hazards

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In this study, utilizing appropriate target spectra and a special envelope function, an artificial time history is generated for the major earthquakes of 1934 and 2015 A.D. The peak ground acceleration (PGA), mean time period, predominant period, significant duration, response spectra, and Fourier spectra of the 2015 earthquake are obtained from the simulated time history These ground motion parameters of simulated earthquake are compared to those of the recorded earthquake of 2015. For the earthquake of 1934 A.D., the maximum PGA derived from the simulation and the PGA derived from the damage-based MMI scale are compared. These earthquakes' significant duration is also compared with other subduction zone earthquakes.Compared to other subduction zone earthquakes, Nepal's earthquakes have a longer significant duration.

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Simulation of the 2022 Mw 6.6 Luding, China, earthquake by a stochastic finite-fault model with a nonstationary phase

Cited by 6 publications

References 55 publications

Deep-Learning-Based Seismic-Signal P-Wave First-Arrival Picking Detection Using Spectrogram Images

Deep-Learning-Based Seismic-Signal P-Wave First-Arrival Picking Detection Using Spectrogram Images

Analysis of ground motion characteristics and near-fault effects for the September 5, 2022, MS6.8 Luding earthquake

Simulation of the 1934 Nepal–Bihar earthquake in Kathmandu

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