Damped multichannel singular spectrum analysis for 3D random noise attenuation

Huang, Weilin; Wang, Runqui; Zhang, Ming; Chen, Yangkang

doi:10.1190/segam2015-5750526.1

Cited by 23 publications

(28 citation statements)

References 19 publications

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“…K equals the rank of the Hankel matrix of the true signal (Huang et al . ). Then the low‐rank approximation of matrix

H^{ω}

is expressed as

{scriptP}_{scriptR} false(H^{ω} false) = {\overset{H}{true}}^{ω} = {boldU}_{1}^{ω} {boldΣ}_{1}^{ω} {({boldV}_{1}^{ω})}^{H},

where

{\bar{boldH}}^{ω}

represents a low‐rank approximation of

H^{ω}

.…”

Section: Basis Of Cadzow Filteringmentioning

confidence: 97%

See 1 more Smart Citation

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

Huang

Feng

Chen

2019

Geophysical Prospecting

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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.

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“…K equals the rank of the Hankel matrix of the true signal (Huang et al . ). Then the low‐rank approximation of matrix

H^{ω}

is expressed as

{scriptP}_{scriptR} false(H^{ω} false) = {\overset{H}{true}}^{ω} = {boldU}_{1}^{ω} {boldΣ}_{1}^{ω} {({boldV}_{1}^{ω})}^{H},

where

{\bar{boldH}}^{ω}

represents a low‐rank approximation of

H^{ω}

.…”

Section: Basis Of Cadzow Filteringmentioning

confidence: 97%

“…Huang et al . () indicate that SVD actually decomposes the data into a noise subspace and a signal‐plus‐noise subspace, and derive a new formula of rank‐reduction, named damped SVD, which can theoretically remove the noise from the signal‐plus‐noise subspace. Chen et al .…”

Section: Introductionmentioning

confidence: 99%

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

Huang

Feng

Chen

2019

Geophysical Prospecting

Self Cite

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“…Here, we further extend the DMSSA algorithm to simultaneous reconstruction and denoising of 3D seismic data. Since the missing data at each frequency slice can be regarded as the random noise, the derivation are similar with Huang et al (2016) except for the extra reconstruction process based on the weighted POCS-like method. Therefore, we conclude the approximation of S as:…”

Section: D Seismic Data Reconstruction Via Dmssamentioning

confidence: 99%

“…The main reasons is that the traditional TSVD can only decompose the data into a noise subspace and a signal-plus-noise subspace. Huang et al (2016) suggest using damped MSSA (DMSSA) algorithm to better decompose the data into the signal subspace and noise subspace for random noise attenuation. In order to overcome the defect mentioned above, we extend the DMSSA algorithm further to simultaneous reconstruction and denoising of 3D seismic data.…”

Section: Introductionmentioning

confidence: 99%

An open-source Matlab code package for improved rank-reduction 3D seismic data denoising and reconstruction

Chen

Huang

Zhang

et al. 2016

Computers & Geosciences

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117

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“…To improve the performance and efficiency of SSA, the fast SSA (Gao, Sacchi and Chen ) and damped multi‐channel singular spectrum analysis (MSSA) (Huang et al . ) have been proposed for random noise attenuation. Huang et al .…”

Section: Introductionmentioning

confidence: 99%

A compound method for random noise attenuation

Gemechu

Yong

2018

Geophysical Prospecting

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Random noise attenuation, preserving the events and weak features by improving signal‐to‐noise ratio and resolution of seismic data are the most important issues in geophysics. To achieve this objective, we proposed a novel seismic random noise attenuation method by building a compound algorithm. The proposed method combines sparsity prior regularization based on shearlet transform and anisotropic variational regularization. The anisotropic variational regularization which is based on the linear combination of weighted anisotropic total variation and anisotropic second‐order total variation attenuates noises while preserving the events of seismic data and it effectively avoids the fine‐scale artefacts due to shearlets from the restored seismic data. The proposed method is formulated as a convex optimization problem and the split Bregman iteration is applied to solve the optimization problem. To verify the effectiveness of the proposed method, we test it on several synthetic seismic datasets and real datasets. Compared with three methods (the linear combination of weighted anisotropic total variation and anisotropic second‐order total variation, shearlets and shearlet‐based weighted anisotropic total variation), the numerical experiments indicate that the proposed method attenuates random noises while alleviating artefact and preserving events and features of seismic data. The obtained result also confirms that the proposed method improves the signal‐to‐noise ratio.

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Damped multichannel singular spectrum analysis for 3D random noise attenuation

Cited by 23 publications

References 19 publications

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

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

An open-source Matlab code package for improved rank-reduction 3D seismic data denoising and reconstruction

A compound method for random noise attenuation

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