Seismic Random Noise Attenuation Using Synchrosqueezed Wavelet Transform and Low-Rank Signal Matrix Approximation

Anvari, Rasoul; Siahsar, Mohammad Amir Nazari; Gholtashi, Saman; Kahoo, Amin Roshandel; Mohammadi, Mokhtar

doi:10.1109/tgrs.2017.2730228

Cited by 161 publications

(24 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At this point, the new noise new n composed of weak signal and original noise is no longer Gaussian white noise, and when the number of weak signals is large and the power is strong, the noise will interfere with the strong signal. Synchronous extrusion wavelet transform is proposed by Daubechies [10] et al The algorithm is applied to seismic wave signal processing in [11] [12]. In this paper, there are obvious frequency differences between strong and weak signals or between signal and noise.…”

Section: Reducing the Bit Error Rate Of Strong Signal By Synchrosqueementioning

confidence: 99%

Blind separation of asymmetric signals based on Synchrosqueezing Wavelet Transform

2018

View full text Add to dashboard Cite

A blind separation algorithm based on synchronous squeezing wavelet transform is proposed to solve the blind separation problem of single channel asymmetric signals in satellite communications. First, the algorithm is used to synchronize the strong signal. Then, the signal time-frequency curve is extracted by synchronous extrusion wavelet transform. Finally, the weak signal interference is filtered out from the mixed signal except the noise and the main frequency of the strong signal. Therefore, the ber of the strong signal demodulation is reduced. The algorithm has the characteristics of blind separation of single channel asymmetric signals without prior information sequence sent by cooperative communicators. The simulation results show that the performance of strong signal demodulation error is better than that of mixed signal direct hard decision.

show abstract

Section: Reducing the Bit Error Rate Of Strong Signal By Synchrosqueementioning

confidence: 99%

Blind separation of asymmetric signals based on Synchrosqueezing Wavelet Transform

2018

View full text Add to dashboard Cite

show abstract

“…Rank-reduction based approaches assume the seismic data to be low-rank after some data rearrangement steps. Such methods include the Cadzow filtering (Trickett 2008), singular spectrum analysis (SSA; Vautard et al 1992;Oropeza & Sacchi 2011;Gao et al 2013;Cheng & Sacchi 2015), damped SSA (Huang et al 2015(Huang et al , 2016aChen et al 2016d,e;Siahsar et al 2017c), multistep SSA (Zhang et al 2016c(Zhang et al ,2016d, sparsity-regularized SSA (Siahsar et al 2016;Zhang et al 2016a,b;Anvari et al 2017;Zhang et al 2017), randomized-order SSA (Huang et al 2017d), structural low-rank approximation (Zhou et al 2017), empirical low-rank approximation (Chen et al 2017f). Mean (or stacking) and median filters utilize the statistical difference between signal and noise to reject the Gaussian white noise or impulsive noise (Liu et al 2009b;Liu 2013;Xie et al 2015b;Yang et al 2015;Gan et al 2016d;Chen et al 2017c;Xie et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Seismic noise attenuation using an online subspace tracking algorithm

Zhou

Zhang

et al. 2020

Geophysical Journal International

View full text Add to dashboard Cite

SUMMARY We propose a new low-rank based noise attenuation method using an efficient algorithm for tracking subspaces from highly corrupted seismic observations. The subspace tracking algorithm requires only basic linear algebraic manipulations. The algorithm is derived by analysing incremental gradient descent on the Grassmannian manifold of subspaces. When the multidimensional seismic data are mapped to a low-rank space, the subspace tracking algorithm can be directly applied to the input low-rank matrix to estimate the useful signals. Since the subspace tracking algorithm is an online algorithm, it is more robust to random noise than traditional truncated singular value decomposition (TSVD) based subspace tracking algorithm. Compared with the state-of-the-art algorithms, the proposed denoising method can obtain better performance. More specifically, the proposed method outperforms the TSVD-based singular spectrum analysis method in causing less residual noise and also in saving half of the computational cost. Several synthetic and field data examples with different levels of complexities demonstrate the effectiveness and robustness of the presented algorithm in rejecting different types of noise including random noise, spiky noise, blending noise, and coherent noise.

show abstract

“…There are a number of mathematical transforms studied in the seismological community such as Fourier transform (Zwartjes and Gisolf ), wavelet transform (Mousavi, Langston and Horton ; Anvari et al . ), curvelet transform (Herrmann and Hennenfent ; Cao, Wang and Wang ; Cao and Zhao ), Radon transform (Trad, Ulrych and Sacchi ; Latif and Mousa ; Gong, Wang and Du ), seislet transform (Fomel and Liu ; Gan et al . ) and dreamlet transform (Wu, Geng and Wu ; Huang, Wu and Wang ).…”

Section: Introductionmentioning

confidence: 99%

“…It first transforms the observed seismic data into certain sparsepromoting domain (in which the signal can be represented by a few basis) and then apply a thresholding or masking operator (Wang, Cao and Yang 2011;Chen et al 2018b). There are a number of mathematical transforms studied in the seismological community such as Fourier transform (Zwartjes and Gisolf 2007), wavelet transform (Mousavi, Langston and Horton 2016;Anvari et al 2017), curvelet transform (Herrmann and Hennenfent 2008;Cao, Wang and Wang 2014;Cao and Zhao 2017), Radon transform (Trad, Ulrych and Sacchi 2002;Latif and Mousa 2017;Gong, Wang and Du 2018), seislet transform (Fomel and Liu 2010;Gan et al 2016) and dreamlet transform (Wu, Geng and Wu 2011;Huang, Wu and Wang 2018).…”

Section: Introductionmentioning

confidence: 99%

De‐aliased and de‐noise Cadzow filtering for seismic data reconstruction

Huang

Feng

Chen

2019

Geophysical Prospecting

View full text Add to dashboard Cite

Cadzow filtering is currently considered as one of the most effective approaches for seismic data reconstruction. The basic version of Cadzow filtering first reorders each frequency slice of the seismic data (to be reconstructed) to a block Hankel/Toeplitz matrix, and then implements a rank‐reduction operator, that is truncated singular value decomposition, to the Hankel/Toeplitz matrix. However, basic Cadzow filtering cannot deal with the problem of recovering regularly missing data (up‐sampling) in the case of strongly aliased energy, because the regularly missing data will mix with signals in the singular spectrum. To solve this problem, it has been proposed to precondition the reconstruction of high‐frequency components using information from the low‐frequency components which are less aliased. In this paper, we further extend the de‐aliased Cadzow filtering approach to reconstruct regularly sampled seismic traces from the noisy observed data by modifying the reinserting operation, in which the high‐frequency components are projected onto the sub‐space spanned by several singular vectors of the low‐frequency components. At each iteration, the filtered data are weighted to the original data as a feedback. The weighting factor is related to the background noise level and changes with iteration. The feasibility of the proposed technique is validated via two‐dimensional, three‐dimensional and five‐dimensional synthetic data examples, as well as two‐dimensional post‐stack and three‐dimensional pre‐stack field data examples. The results demonstrate that the proposed technique can effectively interpolate regularly sampled data and is robust in noisy environments.

show abstract

Seismic Random Noise Attenuation Using Synchrosqueezed Wavelet Transform and Low-Rank Signal Matrix Approximation

Cited by 161 publications

References 53 publications

Blind separation of asymmetric signals based on Synchrosqueezing Wavelet Transform

Blind separation of asymmetric signals based on Synchrosqueezing Wavelet Transform

Seismic noise attenuation using an online subspace tracking algorithm

De‐aliased and de‐noise Cadzow filtering for seismic data reconstruction

Contact Info

Product

Resources

About