Using Hilbert–Huang Transform (HHT) to Extract Infrasound Generated by the 2013 Lushan Earthquake in China

Zhu, Xing; Xu, Qiang; Liu, H. X.

doi:10.1007/s00024-016-1438-1

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…Centro NS is used as the mother wave. The non-stationary power spectrum of the wave is extracted using the Hilbert-Huang transformation (HHT) method (Fan et al 2017(Fan et al , 2020Zhu et al 2017;Garcia et al 2019). The generated seismic waves are adjusted to the same acceleration peak value.…”

Section: Generation Of Seismic Wavesmentioning

confidence: 99%

Seismic behavior of MSCSS based on story drift and failure path

Fan

Zhang

Abdulhadi

et al. 2021

Lat. Am. j. solids struct.

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Section: Generation Of Seismic Wavesmentioning

confidence: 99%

Seismic behavior of MSCSS based on story drift and failure path

Fan

Zhang

Abdulhadi

et al. 2021

Lat. Am. j. solids struct.

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“…Currently, this method has found extensive applications in the areas of bridge structure damage identification and detection 21 , marine engineering 22 , and earthquake landslide damage identification 23 , 24 . Zhu et al 25 , Wu et al 26 , and Gong et al 27 employed the HHT method to process seismic signals, enabling the extraction of the energy time–frequency distribution of various seismic signals. This quantified the significant role of the HHT method in structural analysis and structural control.…”

Section: Introductionmentioning

confidence: 99%

Experimental analysis of rotating bridge structural responses to existing railway train loads via time–frequency and Hilbert–Huang transform energy spectral analysis

Liu,

Wu,

Zhao

et al. 2024

Sci Rep

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With the rapid development of national infrastructure projects, there has been a significant increase in intersecting lines in transportation construction. As a result, rotating bridges are increasingly used in engineering projects that span existing railway lines. In order to study the spatial response characteristics and vibration wave transmission mechanisms of the rotating bridge structure under the loading of existing railway trains, field experiments and numerical analyses were conducted. The response characteristics of these bridges were investigated under different types and speeds of adjacent existing lines. A comprehensive methodology has been proposed, integrating the time domain spectrum and the Hilbert–Huang Transform (HHT) energy spectrum for signal processing and vibration analysis. The analysis was carried out using MATLAB 2018a software. This methodology was applied to analyze the test data. The results show that significant resonance phenomenon occurs in the girders of the rotating bridge under the loading of trains on the existing line. The low-frequency component f1 (2–5 Hz) is the primary factor contributing to the amplification of the acceleration response in the rotating bridge, while f3 (10–13 Hz) plays a secondary role. The frequency distribution characteristics of vibration waves caused by train loads on the existing line have a significant influence on the acceleration response of the rotating bridge's girders. The predominant frequency of vibration waves at each measuring point along the transmission path shows a trend of decreasing → increasing → decreasing. The impact on the rotating bridge structure of vibration waves generated by low-speed freight trains on existing railways is greater. The research findings are of great importance for studying the dynamic response of rotating bridges adjacent to existing railway lines.

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“…As a result, the HHT methodology outperforms other spectrum decomposition methods to detect the signal's space-frequency characteristics. HHT method has been implemented in several studies, such as distinguishing variations in geophysical well-log signals (Gairola and Chandrasekhar 2017;Subhakar and Chandrasekhar 2016), identifying surface and subsurface expressions of gas seepage (Schroot et al 2005), performing fault diagnosis in a rotor system (Ji and Wang 2018), extracting infrasound generated by an earthquake (Zhu et al 2017), diagnosing bearing fault (Kabla and Mokrani 2016), and processing ultrasonic echo signal of composites (Gao et al 2019). Therefore, this work aims to improve the total annual rainfall trend visualization using combined ITM and their inspired approaches with time frequency-based methods, i.e., DWT versus HHT.…”

Section: Introductionmentioning

confidence: 99%

Improving the visualization of rainfall trends using various innovative trend methodologies with time–frequency-based methods

et al. 2022

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In this paper, the Innovative Trend Methodology (ITM) and their inspired approaches, i.e., Double (D-ITM) and Triple (T-ITM), were combined with Hilbert Huang transform (HHT) time frequency-based method. The new hybrid methods (i.e., ITM-HHT, D-ITM-HHT, and T-ITM-HHT) were proposed and compared to the DWT-based methods in order to recommend the best method. Three total annual rainfall time series from 1920 to 2011 were selected from three hydrological basins in Northern Algeria. The new combined models (ITM-HHT, D-ITM-HHT, and T-ITM-HHT) revealed that the 1950–1975 period has significant wet episodes followed by a long-term drought observed in the western region of Northern Algeria, while Northeastern Algeria presented a wet period since 2001. The proposed approaches successfully detected, in a visible manner, hidden trends presented in the signals, which proves that the removal of some modes of variability from the original rainfall signals can increase the accuracy of the used approaches.

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Using Hilbert–Huang Transform (HHT) to Extract Infrasound Generated by the 2013 Lushan Earthquake in China

Cited by 4 publications

References 29 publications

Seismic behavior of MSCSS based on story drift and failure path

Seismic behavior of MSCSS based on story drift and failure path

Experimental analysis of rotating bridge structural responses to existing railway train loads via time–frequency and Hilbert–Huang transform energy spectral analysis

Improving the visualization of rainfall trends using various innovative trend methodologies with time–frequency-based methods

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