Random noise suppression of seismic data by time–frequency peak filtering with variational mode decomposition

Abstract

Random noise suppression is of great importance in seismic processing and interpretation, and time–frequency peak filtering (TFPF) is a classic denoising approach. In TFPF, pseudo Wigner–Ville distribution (PWVD) is used to linearise the given signal for an unbiased estimation of the instantaneous frequency. However, window length is a trade-off parameter for preserving valid signals and attenuating random noise. A long window length may cause loss of the desired signal, whereas a short window length may be inadequate to suppress noise. To ensure a good trade-off between signal preservation and noise reduction, empirical mode decomposition (EMD) has been introduced into the TFPF method. Although the EMD-TFPF method can achieve good results, the mode mixing problem in EMD is non-negligible. In this article, we introduce variational mode decomposition (VMD) to overcome the mode mixing problem. VMD decomposes a signal into an ensemble of modes that own their respective centre frequencies. Thus, the modes obtained by VMD contain less noise, which simplifies selection of the window width of TFPF. Therefore, we propose the VMD-based TFPF (VMD-TFPF) method to suppress random noise. Synthetic and field seismic data examples are employed to illustrate the superior performance of the proposed method in attenuating random noise and preserving the desired signal.

KEYWORDS:

• Random noise suppression
• time–frequency peak filtering
• empirical mode decomposition
• variational mode decomposition
• desired signal preservation

Acknowledgements

The authors appreciate the editor and reviewers for their valuable comments, which improve the presentation of this work.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors would like to thank the Major Research Plan of the National Natural Science Foundation of China (91730306), the National Key Research and Development Project of the Ministry of Science and Technology of China (2018YFC0603501), and the National Science and Technology Major Project (2016ZX05024-001-007 and 2017ZX05069).