A seismic data denoising method based on complete ensemble empirical mode decomposition with adaptive noise and Hurst exponent

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.