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

Zhenjing Yao1,2,3, Wenzhe Li1,2,3, Jingyi Zhu4

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This study introduces an enhanced variational mode decomposition (VMD) method optimized with the grey wolf algorithm (GWO) for effective seismic noise suppression. The GWO-VMD approach significantly improves seismic data quality by separating random noise while preserving essential signal information.

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Area of Science:

  • Geophysics and Seismic Signal Processing
  • Computational Intelligence in Earth Sciences

Background:

  • Seismic noise significantly degrades the quality of recorded seismic data.
  • Effective seismic noise separation and suppression are crucial for accurate geophysical interpretation.
  • Existing denoising methods often struggle to balance noise reduction with signal preservation.

Purpose of the Study:

  • To develop a novel method for seismic random noise suppression.
  • To enhance seismic data quality by improving signal-to-noise ratio (SNR).
  • To effectively separate random noise while preserving valid seismic signals.

Main Methods:

  • Proposed a seismic noise suppression method based on enhanced Variational Mode Decomposition (VMD).
  • Integrated Grey Wolf Optimization (GWO) algorithm to optimize VMD parameters (K and α).
  • Utilized envelope entropy for GWO fitness evaluation and Kurtosis comparison for IMF selection.

Main Results:

  • The GWO-VMD method achieved a 27.78% increase in SNR compared to other methods.
  • Demonstrated a 78.82% improvement in RMSE while maintaining structural similarity (SSIM).
  • Successfully separated random noise and preserved valid seismic signal components in synthetic and real data.

Conclusions:

  • The proposed GWO-VMD method is effective for seismic random noise separation and suppression.
  • This technique offers superior performance in enhancing seismic data quality.
  • The method proves valid for both separating noise and preserving crucial seismic signal information.