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<p>Denoising of seismic signals is a critical preprocessing step in seismic data analysis, directly impacting the accuracy of subsequent data processing. To enhance the effectiveness of seismic signal denoising, this study proposes a method that combines Variational Mode Decomposition (VMD) optimized by Particle Swarm Optimization (PSO) with an improved Wavelet Transform (WT). First, the key parameters (number of modal functions and penalty factor) of VMD are adaptively selected using an enhanced PSO algorithm to ensure effective decomposition and mitigate mode mixing. Then, for each Intrinsic Mode Function (IMF) obtained from VMD, an improved wavelet denoising technique incorporating a modified thresholding function is applied to further suppress high-frequency noise while preserving signal details. Comparative experiments on both synthetic and field seismic data demonstrate that the proposed method outperforms traditional VMD in terms of signal-to-noise ratio (SNR) improvement and waveform preservation. These results highlight the method’s practicality and robustness in complex signal environments, offering strong support for seismic data preprocessing and interpretation.</p>

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Improved PSO-VMD seismic signal denoising method combined with modified wavelet transform

  • 期刊论文

  • 发表日期：

    2025年12月3日

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  • Hui Sun,
  • Hong-yong Ren,
  • Rui Chen,
  • Xing-guo Huang,
  • Wei Zhang,
  • Yan-song Li,
  • Jian Zhang,
  • Yu-bo Yue

摘要

Denoising of seismic signals is a critical preprocessing step in seismic data analysis, directly impacting the accuracy of subsequent data processing. To enhance the effectiveness of seismic signal denoising, this study proposes a method that combines Variational Mode Decomposition (VMD) optimized by Particle Swarm Optimization (PSO) with an improved Wavelet Transform (WT). First, the key parameters (number of modal functions and penalty factor) of VMD are adaptively selected using an enhanced PSO algorithm to ensure effective decomposition and mitigate mode mixing. Then, for each Intrinsic Mode Function (IMF) obtained from VMD, an improved wavelet denoising technique incorporating a modified thresholding function is applied to further suppress high-frequency noise while preserving signal details. Comparative experiments on both synthetic and field seismic data demonstrate that the proposed method outperforms traditional VMD in terms of signal-to-noise ratio (SNR) improvement and waveform preservation. These results highlight the method’s practicality and robustness in complex signal environments, offering strong support for seismic data preprocessing and interpretation.