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Seismic Random Noise Denoising Using Mini-Batch Multivariate Variational Mode Decomposition.

Guoning Wu1, Guochang Liu2, Junxian Wang1

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This study introduces mini-batch multivariate variational mode decomposition for seismic noise attenuation. The method improves signal-to-noise ratio by processing data in smaller batches, enhancing seismic interpretation.

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Area of Science:

  • Geophysics
  • Signal Processing

Background:

  • Seismic noise attenuation is crucial for accurate seismic interpretation.
  • Traditional methods like empirical mode decomposition are often applied trace-by-trace.
  • Multivariate methods consider lateral continuity but struggle with large datasets.

Purpose of the Study:

  • To propose a novel seismic noise attenuation method for large datasets.
  • To enhance seismic interpretation by improving signal-to-noise ratio.
  • To address limitations of existing multivariate decomposition techniques.

Main Methods:

  • Mini-batch multivariate variational mode decomposition (MB-MVMD) is introduced.
  • Data is segmented into smaller batches for processing.
  • MB-MVMD is applied to batches, followed by concatenation of denoised components.

Main Results:

  • The proposed MB-MVMD method effectively filters high-frequency noise.
  • Experiments on synthetic and field data demonstrate improved signal-to-noise ratio compared to standard VMD.
  • Validation across different batch sizes confirms method robustness.

Conclusions:

  • Mini-batch multivariate variational mode decomposition is an effective technique for seismic noise attenuation.
  • The method offers a scalable solution for large seismic datasets.
  • This approach enhances seismic data quality for interpretation.