2022
DOI: 10.1155/2022/2132732
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Seismic Random Noise Denoising Using Mini-Batch Multivariate Variational Mode Decomposition

Abstract: Seismic noise attenuation plays an important role in seismic interpretation. The empirical mode decomposition, synchrosqueezing wavelet transform, variational mode decomposition, etc., are often applied trace by trace. Multivariate empirical mode decomposition, multivariate synchrosqueezing wavelet transform, and multivariate variational mode decomposition were proposed for lateral continuity consideration. Due to large input data, mini-batch multivariate variational mode decomposition is proposed in this pape… Show more

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Cited by 5 publications
(3 citation statements)
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“…Variational mode decomposition (VMD) [15] is a signal processing algorithm under a new framework, which uses an iterative approach to minimize an objective function and obtain a set of restricted narrowband signals centered at different frequencies. It is proved that VMD can effectively overcome the mode mixing problem of EMD, as well as exhibit good noise robustness and solid theoretical foundation [28,29], making VMD widely used in various fields [30][31][32][33]. However, in practical applications, the results of VMD are heavily dependent on the parameter settings, including mode number K and balancing parameter α.…”
Section: Introductionmentioning
confidence: 99%
“…Variational mode decomposition (VMD) [15] is a signal processing algorithm under a new framework, which uses an iterative approach to minimize an objective function and obtain a set of restricted narrowband signals centered at different frequencies. It is proved that VMD can effectively overcome the mode mixing problem of EMD, as well as exhibit good noise robustness and solid theoretical foundation [28,29], making VMD widely used in various fields [30][31][32][33]. However, in practical applications, the results of VMD are heavily dependent on the parameter settings, including mode number K and balancing parameter α.…”
Section: Introductionmentioning
confidence: 99%
“…The results show that complete ensemble empirical mode decomposition has high feature recognition ability in complex random desert noise. Wu et al (2022) uses multivariate variational modal decomposition on the segmented seismic data. This method significantly improves the lateral continuity and SNR of the seismic data.…”
Section: Introductionmentioning
confidence: 99%
“…Zhang applied VMD to obtain the noise intensity in intrinsic mode functions (IMFs) [4]. Wu applied the mini-batch multivariate VMD to achieve the de-noising of random seismic noise [5]. Wu and Zhang proposed a new method using VMD and a convolutional neural network to attenuate the white noise of a seismic trace [6].…”
Section: Introductionmentioning
confidence: 99%