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Seismic noise attenuation using an improved variational mode decomposition method

YATONG ZHOU,  YUE CHI
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School of Electronic and Information Engineering, Hebei University of Technology, Xiping Road No. 5340, Beichen District, Tianjin 300401, P.R. China,
JSE 2020, 29(1), 29–47;
Submitted: 9 June 2025 | Revised: 9 June 2025 | Accepted: 9 June 2025 | Published: 9 June 2025
© 2025 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Zhou, Y.T. and Chi, Y., 2020. Seismic noise attenuation using an improved variational mode decomposition method. Journal of Seismic Exploration, 29: 29-47. Seismic noise suppression is an important step in the seismic imaging community. We propose a dip-separated denoising method to attenuate spatially incoherent random noise. The variational mode decomposition (VMD) method is used to decompose the seismic data into different dip bands. It has a solid theoretical foundation of mathematics and high calculation efficiency. Besides, compared with the recursive mode decomposition algorithms, e.g., the EMD and EEMD methods, it has advantages in solving the mode mixing problem and more powerful anti-noise performance. The VMD method can adaptively decompose a seismic signal into several intrinsic mode functions (IMF). Decomposing the seismic data into oscillating IMF components is equivalent of decomposing the seismic data into different dipping components. To automatically define the optimal number of most oscillating components, we design the Kurtosis method. To eliminate the errors caused by end effect, we use a waveform matching extension algorithm to improve the VMD. The singular spectrum analysis (SSA) method is used to approximate the low-rank components in each separated dip band. In this paper, a simulated seismic dataset and a real seismic dataset are analyzed by the proposed algorithm. The results indicate that the proposed algorithm is robust to noise and has high de-noising precision.

Keywords
random noise suppression
variational mode decomposition
singular spectrum analysis
intrinsic mode functions
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