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Volume 45 Issue 2
Mar.  2023
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Article Navigation >  Acta Seismologica Sinica  > 2023  >  45(2): 258-270
Mao S R，Shi S P，Yu Z J，Su M Y，Li S，He J X，Fu H，Zhang Q. 2023. A seismic data denoising method based on complete ensemble empirical mode decomposition with adaptive noise and Hurst exponent. Acta Seismologica Sinica，45（2）：258−270 doi: 10.11939/jass.20210165
Citation: Mao S R，Shi S P，Yu Z J，Su M Y，Li S，He J X，Fu H，Zhang Q. 2023. A seismic data denoising method based on complete ensemble empirical mode decomposition with adaptive noise and Hurst exponent. Acta Seismologica Sinica，45（2）：258−270 doi: 10.11939/jass.20210165
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A seismic data denoising method based on complete ensemble empirical mode decomposition with adaptive noise and Hurst exponent

doi: 10.11939/jass.20210165

  • Earthquake Agency of Guangxi Zhuang Autonomous Region，Nanning 530022，China

  • Received Date: 2021-10-26
  • Rev Recd Date: 2022-03-25
    • Available Online: 2022-12-13
  • Publish Date: 2023-03-15
    Abstract
  • In seismic observation, seismic data generally contain ambient noise, which reducesthe efficiency of seismic analysis. Traditional denoising methods usually need a priori knowledge of noise, and some effective data will be lost when filtering. To solve this problem, this paper proposes a seismic data denoising method based on complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN） and Hurst exponent. Firstly, the signal is decomposed into a series intrinsic mode functions （IMF） by CEEMDAN method. Secondly, the Hurst exponent is used to identify the filtered IMF component. Finally, the IMF component of seismic data is reconstructed to realize data denoising. Compared with the denoising effect of traditional methods, the filtering ability of this method for low SNR waveforms is improved by 32%, and the filtering ability for high SNR waveforms is 6 times higher. At the same time, as shown in the denoising results of geomagnetic data, this method can completely filter subway noise from geomagnetic signal waveform.

     

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