A hybrid method for noise suppression using variational mode decomposition and singular spectrum analysis

Zhou, Yatong; Zhu, Zhaolin

doi:10.1016/j.jappgeo.2018.10.025

Cited by 16 publications

(8 citation statements)

References 86 publications

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“…Seismic data denoising based on mode decomposition considers that the mode of the effective signal is similar to that of the original signal, while the mode of the noise signal differs widely from the original signal. Therefore, the noise in the signal is separated by mode decomposition, which suppresses the random noise in the seismic data (Chen, 2016;Liu et al, 2017;Yu and Ma, 2018;Zhou and Zhu, 2019). However, model-driven methods are not very versatile, and algorithms are often designed for specific data.…”

Section: Introductionmentioning

confidence: 99%

Seismic random noise suppression using improved CycleGAN

et al. 2023

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Random noise adversely affects the signal-to-noise ratio of complex seismic signals in complex surface conditions and media. The primary challenges related to processing seismic data have always been reducing the random noise and increasing the signal-to-noise ratio. In this study, we propose an improved cycle-consistent generative adversarial network (CycleGAN) seismic random noise suppression method. First, the generator replaces the original cycle-consistent generative adversarial network generator network structure with the Unet structure combined with the Resnet structure in order to increase the diversity of seismic data feature extraction and decrease the loss of seismic data details. Second, in order to improve the network’s stability, the feature extraction effect, the event texture preservation effect, and the signal-to-noise ratio, the Least Square GAN (LSGAN) square difference loss is used in place of the conventional generative adversarial network cross-entropy loss. The feasibility of the proposed method was confirmed using model and real seismic data, both of which demonstrated that the improved cycle-consistent generative adversarial network method effectively suppressed random noise in seismic data. In addition, the denoising effect was superior to both the widely used FX deconvolution denoising method and original cycle-consistent generative adversarial network denoising method.

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Section: Introductionmentioning

confidence: 99%

Seismic random noise suppression using improved CycleGAN

et al. 2023

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“…Huang et al [26] performed a correlation analysis on the IMFs of the VMD to suppress the noise. Zhou et al [27] combined VMD and odd spectrum analysis to remove the remaining low-frequency noise of the VMD. Li et al [28] applied time-frequency peak filtering for the IMFs of VMD and reconstructed the signal to reduce noise.…”

Section: Introductionmentioning

confidence: 99%

Three-Component Microseismic Data Denoising Based on Re-Constrain Variational Mode Decomposition

et al. 2021

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Microseismic monitoring is an important technology used to evaluate hydraulic fracturing, and denoising is a crucial processing step. Analyses of the characteristics of acquired three-component microseismic data have indicated that the vertical component has a higher signal-to-noise ratio (SNR) than the two horizontal components. Therefore, we propose a new denoising method for three-component microseismic data using re-constrain variational mode decomposition (VMD). In this method, it is assumed that there is a linear relationship between the modes with the same center frequency among the VMD results of the three-component data. Then, the decomposition result of the vertical component is used as a constraint to the whole denoising effect of the three-component data. On the basis of VMD, we add a constraint condition to form the re-constrain VMD, and deduce the corresponding solution process. According to the synthesis data analysis, the proposed method can not only improve the SNR level of three-component records, it also improves the accuracy of polarization analysis. The proposed method also achieved a satisfactory effect for field data.

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“…In Li et al (2020), the maximum envelope kurtosis has been used as an index for optimizing and determining the number of VMD modes. In Zhou and Zhu (2019), an algorithm combining VMD and singular spectrum analysis (SSA) has been used to filter seismic noise. In Zhou et al (2020), a multi-feature fusion method has been proposed to extract the characteristics of pipeline signal.…”

Section: Introductionmentioning

confidence: 99%

Application of a noise reduction method combining AVMD and SVD in natural gas pipeline leakage signal

Wang

et al. 2021

Systems Science & Control Engineering

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Due to the large amount of noise in pipeline leakage signal, the accuracy of the leakage detection device's judgment will be reduced by direct leakage detection. Therefore, the noise reduction of pipeline leakage signal is critical for preprocessing technology of pipeline leakage detection. A denoising method combining adaptive variational mode decomposition (AVMD) and singular value decomposition (SVD) is proposed in this paper. First, the mode number and the penalty factor of VMD are searched automatically by AVMD. The AVMD algorithm is coupled to a fitness function based on improved refine composite multiscale dispersion entropy (RCMDE). Subsequently, a time-frequency matrix which obtains time-frequency subspace after SVD is constructed for all mode components decomposed by VMD, and the number of effective time-frequency subspaces is determined by the relative change rate of singular values, thereby the denoised signal is achieved. Finally, the experimental results show that the AVMD-SVD method proposed in this paper has the significant denoising effect and strong robustness.

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A hybrid method for noise suppression using variational mode decomposition and singular spectrum analysis

Cited by 16 publications

References 86 publications

Seismic random noise suppression using improved CycleGAN

Seismic random noise suppression using improved CycleGAN

Three-Component Microseismic Data Denoising Based on Re-Constrain Variational Mode Decomposition

Application of a noise reduction method combining AVMD and SVD in natural gas pipeline leakage signal

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