Detecting changes in long-period site responses after the M w 7.6 Chi-Chi earthquake, Taiwan, using strong motion records

Yu, Teng-To; Wu, Chi Shin; Cheng, Youg-Sin

doi:10.1007/s11803-015-0018-1

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…For the comprehensive models, as shown in Formula (11), the noise e is hidden in the dictionary D, and the sparse coefficient α, according to the BPDN algorithm, transforms the sparse decomposition of the noisy signal into the following optimization problems [22]:…”

Section: Noise Reduction Based Oncompressive Sensingmentioning

confidence: 99%

“…Due to the ubiquitous presence of noise, the conventional signal processing method needs to be used first to reduce the noise of the observed signal [2,[7][8][9][10][11]. Time-domain filtering is the most common noise reduction method for seismic data in earthquake early warning systems, which generally uses low-pass or band-pass filters, such as the FIR or IIR filter with the flattest amplitude-frequency response.…”

Section: Introductionmentioning

confidence: 99%

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Research on Sparse Denoising of Strong Earthquakes Early Warning Based on MEMS Accelerometers

et al. 2022

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In view of the fact that the noise in the same frequency band as the useful signal in the MEMS acceleration sensor observation data cannot be effectively removed by traditional filtering methods, a denoising method for strong earthquake signals based on the theory of sparse representation and compressive sensing is proposed in this paper. This skillfully realized the separation of strong earthquake signals from noise by adopting a fixed dictionary and utilizing sparse characteristics. Furthermore, considering the weakness of the sparse denoising method based on the fixed dictionary in the high signal-to-noise ratio, a spare denoising method based on learning an over-complete dictionary is proposed. Through the initial given seismic data, the ideal over-complete dictionary is trained to achieve seismic data denoising. In addition, for the interference waves of non-seismic events, this paper proposes an idea based on sparse representation classification to remove such non-seismic interference directly. Combining the ideas of noise reduction and non-seismic event elimination, we can obtain a standard sparse anti-interference denoising model for earthquake early warning. It’s innovative that this model implements the sparse theory into the field of earthquake early warning. According to the experimental results, in the case of heavy noise, the denoising model based on sparse representation can reach average SNRof 8.73 and an average MSEof 29.53, and the denoising model based on compression perception can reach average SNRof 7.29 and an average MSE 41.34, and the denoising model based on learning dictionary can reach average SNR 11.07 and average MSE 17.32. The performance of these models is better than the traditional FIR filtering method (average SNR −0.73 and average MSE 260.37) or IIR filtering method (average SNR 4.73 and average MSE 73.95). On the other hand, the anti-interference method of the sparse classification proposed in this paper can accurately distinguish non-seismic interference events from natural earthquakes. The classification accuracy of the method based on the noise category of the selected test data set reaches 100% and achieves good results.

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Section: Noise Reduction Based Oncompressive Sensingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Sparse Denoising of Strong Earthquakes Early Warning Based on MEMS Accelerometers

et al. 2022

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“…Wu and Wu 19 introduced the concept of flatness to determine permanent displacement. During the Wenchuan earthquake, Yu et al 20 employed multisegment linear fitting for baseline correction across multiple ruptures. Akkar and Boore 21 proposed a Monte Carlo-based baseline correction scheme.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the identification methods of permanent displacement derived from strong motion records: Case studies in the 2023 Turkey–Syria earthquake

Zhou,

Guo,

Ren

et al. 2024

Earthquake Engineering and Resilience

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On February 6, 2023, the Republic of Turkey experienced a rare occurrence of two successive earthquakes, each with a magnitude exceeding 7.0. The Disaster and Emergency Management Agency (AFAD) swiftly shared the strong motion records, thereby enriching the global database of near‐fault strong motion records. Identification of permanent displacement is essential for effectively utilizing these records. In this study, we refined the permanent displacement identification method, which combines the Hermit interpolation baseline correction with flatness determination by incorporating a low‐pass filter. Following this, we compared four permanent displacement identification methods, including our improved approach. We applied these to the strong motion record of station 4404 and compared the results with the Global Positioning System coseismic displacement. At the same time, we used field investigation data to verify the effectiveness of our improved method, studying its applicability for both single‐wave packet and multiwave packet records. The conclusions are the following: the improved method provides a more reasonable and effective means of identifying permanent displacement. When the peak ground acceleration (PGA) exceeds 1 g, the permanent displacement identifications from the four methods differ significantly. The discrepancy in permanent displacements identified by the four methods in the horizontal direction is larger than that in the U−D direction. For the record with the largest PGA (station 4614), our improved method yields more reasonable results compared with other techniques. Furthermore, the choice of segmentation time nodes in the velocity time history significantly affects the identification of permanent displacement.

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Dynamic response of a dip slope with multi-slip planes revealed by shaking table tests

Lin

et al. 2018

Landslides

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Detecting changes in long-period site responses after the M w 7.6 Chi-Chi earthquake, Taiwan, using strong motion records

Cited by 4 publications

References 17 publications

Research on Sparse Denoising of Strong Earthquakes Early Warning Based on MEMS Accelerometers

Research on Sparse Denoising of Strong Earthquakes Early Warning Based on MEMS Accelerometers

Evaluating the identification methods of permanent displacement derived from strong motion records: Case studies in the 2023 Turkey–Syria earthquake

Dynamic response of a dip slope with multi-slip planes revealed by shaking table tests

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