Seismic signal de-noising using time–frequency peak filtering based on empirical wavelet transform

Liu, Naihao; Yang, Yang; Li, Zhen; Gao, Jinghuai; Jiang, Xiudi; Pan, Shulin

doi:10.1007/s11600-020-00413-4

Cited by 24 publications

(3 citation statements)

References 42 publications

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“…Liu et al proposed an EWT-based denoising method in 2020 and effectively suppressed noise. Synthetic data and 3D feld data examples also prove the validity and efectiveness of the TFPF-EWT for both attenuating random noise and preserving valid seismic amplitude (Liu et al, 2020). In this case, as with reconstruction, MCA and dictionary learning are also well applied to the field of denoising.…”

Section: Introductionmentioning

confidence: 75%

Self-adaptive seismic data reconstruction and denoising using dictionary learning based on morphological component analysis

Wang

Sun

et al. 2023

Front. Earth Sci.

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Data reconstruction and data denoising are two critical preliminary steps in seismic data processing. Compressed Sensing states that a signal can be recovered by a series of solving algorithms if it is sparse in a transform domain, and has been well applied in the field of reconstruction, when, sparse representation of seismic data is the key point. Considering the complexity and diversity of seismic data, a single mathematical transformation will lead to incomplete sparse expression and bad restoration effects. Morphological Component Analysis (MCA) decomposes a signal into several components with outstanding morphological features to approximate the complex internal data structure. However, the representation ability of combined dictionaries is constrained by the number of dictionaries, and cannot be self-adaptively matched with the data features. Dictionary learning overcomes the limitation of fixed base function by training dictionaries that are fully suitable for processed data, but requires huge amount of time and considerable hardware cost. To solve the above problems, a new dictionary library (K-Singluar Value Decomposition learning dictionary and Discrete Cosine Transform dictionary) is hereby proposed based on the efficiency of fixed base dictionary and the high precision of learning dictionary. The self-adaptive sparse representation is achieved under the Morphological Component Analysis framework and is successfully applied to the reconstruction and denoising of seismic data. Real data tests have proved that the proposed method performs better than single mathematical transformation and other combined dictionaries.

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Section: Introductionmentioning

confidence: 75%

Self-adaptive seismic data reconstruction and denoising using dictionary learning based on morphological component analysis

Wang

Sun

et al. 2023

Front. Earth Sci.

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“…And the correlation coefficient and kurtosis of the first ten IMF components are calculated to determine the IMFs that need noise reduction and the window length of TFPF process. The calculation results are shown in table 5.…”

Section: Adaptive Ewt-atfpf Noise Reduction Processingmentioning

confidence: 99%

“…Time-frequency peak filtering (TFPF) proposed by Bouake and Mostefa [4], is an effective method for suppressing Gaussian noise in non-stationary deterministic limited-band signals, which has the advantages of wide applicability and requiring less additional information. Therefore, this method has been successfully applied in the fields of seismic monitoring [5], Electro Encephalo Graphy (EEG) signal enhancement [6], frequency hopping signal detection [7] and mechanical fault diagnosis [8], showing a good suppression effect on strong random noise. It should be noted that the window length of TFPF plays an important role in balancing noise suppression ability and signal fidelity.…”

Section: Introductionmentioning

confidence: 99%

A noise reduction method of rolling bearing based on empirical wavelet transform and adaptive time frequency peak filtering

Lu,

Jia,

et al. 2023

Meas. Sci. Technol.

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The vibration signal of rolling bearing with variable operating conditions contains complex interference components, which will cause low fault diagnosis accuracy, especially in strong noise case. To solve this problem, we proposed a noise reduction method of rolling bearing with variable operating based on empirical wavelet transform and adaptive time-frequency peak filtering (EWT-ATFPF). Firstly, empirical wavelet transform (EWT) is used to obtain different frequency intrinsic mode functions (IMFs). Secondly, a modified adaptive window length formula for TFPF is constructed by combining the sampling ratio index and a fault sensitivity indicator that calculated by kurtosis and correlation coefficients of IMFs, which can better characterize the impact components. Thirdly, to balance noise reduction effect and the fidelity of IMFs, we proposed an improved time-frequency peak filtering (TFPF) method by adaptively adjusting its windows length. The adaptive method could be carried out using the proposed fault sensitivity indicator and window length formula, and the denoising IMFs could be obtained by ATFPF. Finally, the denoising vibration signal is reconstructed by using the denoising IMFs. The performance of fault diagnosis of the proposed method is verified by using simulated signal and bearing fault test data. The results show that the proposed EWT-ATFPF method could effectively achieve noise reduction under variable operating conditions.

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Processing Technology for Bioinspired Polarization Compass Noise

Zhao

2024

Navigation: Science and Technology

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Seismic signal de-noising using time–frequency peak filtering based on empirical wavelet transform

Cited by 24 publications

References 42 publications

Self-adaptive seismic data reconstruction and denoising using dictionary learning based on morphological component analysis

Self-adaptive seismic data reconstruction and denoising using dictionary learning based on morphological component analysis

A noise reduction method of rolling bearing based on empirical wavelet transform and adaptive time frequency peak filtering

Processing Technology for Bioinspired Polarization Compass Noise

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