JOURNAL ARTICLE

Application of 2D Variational Mode Decomposition Method in Seismic Signal Denoising

Chao LiuZiang WangYaping HuangAiping ZengFan Hong-Ming

Year: 2024 Journal:   Elektronika ir Elektrotechnika Vol: 30 (2)Pages: 46-53   Publisher: Kaunas University of Technology

Abstract

Seismic data are typical nonlinear and nonstationary data. In the acquisition and processing of seismic data, many factors interfere with it. Seismic data contain both effective waves and random noises, seriously affecting the quality of seismic data and not conducive to the goal of fine interpretation of subsequent seismic data. Therefore, studying new seismic data denoising methods is beneficial for improving the quality of seismic data and plays a very important role in subsequent seismic data interpretation. In this paper, the principle of variational mode decomposition (VMD) and 2D-VMD is introduced in detail, and the seismic profile with a simple signal and fault model is denoised. Compared to traditional empirical mode decomposition (EMD), the 2D-VMD method has the best seismic data denoising effect. The test results of the synthesised signal show that the 2D-VMD method has a signal-to-noise ratio of 47.14 dB after denoising, which is higher than the signal-to-noise ratio after EMD and VMD denoising, indicating that it has a better denoising effect. The VMD and 2D-VMD methods are applied to the denoising of actual seismic data. The application results show that the 2D-VMD method can effectively improve the quality of the seismic data, enhance the continuity and reliability of the seismic data, and is conducive to the fine interpretation of subsequent seismic data.

Keywords:
Noise reduction Mode (computer interface) Decomposition SIGNAL (programming language) Hilbert–Huang transform Computer science Acoustics Algorithm Physics Telecommunications White noise Chemistry

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Topics

Seismic Imaging and Inversion Techniques
Physical Sciences →  Earth and Planetary Sciences →  Geophysics
Machine Fault Diagnosis Techniques
Physical Sciences →  Engineering →  Control and Systems Engineering
Image and Signal Denoising Methods
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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