Seismic random noise separation and suppression based on improved variational mode decomposition via grey wolf optimization

Zhenjing Yao, Wenzhe Li, Jingyi Zhu, Lei Hao and Mengtao Xing

PLOS ONE, 2025, vol. 20, issue 9, 1-20

Abstract: Seismic noise separation and suppression is an important topic in seismic signal processing to improve the quality of seismic data recorded at monitoring stations. We propose a novel seismic random noise suppression method based on enhanced variational mode decomposition (VMD) with grey wolf optimization (GWO) algorithm, which applies the envelope entropy to evaluate the wolf individual fitness, determine the grey wolf hierarchy, and obtain the optimized key elements K and α in VMD. Then, the decomposed effective intrinsic mode functions (IMFs) are extracted to separate and suppress random noises. It is worth to be noted that the Kurtosis comparison method can select the IMFs ensuring to preserve valid seismic signal. Finally, the denoised seismic signal is restored by the effective IMFs. The experimental results from synthetic and real field seismic data show that compared with several denoising methods, the proposed method can obtain higher signal-to-noise ratio (SNR) with increasement of 27.78% and lower root mean square error (RMSE) with improvements of 78.82% under the same level of structural similarity (SSIM) which prove the validity and effectiveness of the GWO-VMD method for both separating random noise and preserving valid seismic signal.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0330988

DOI: 10.1371/journal.pone.0330988

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