Sparse time-frequency representation for seismic noise reduction using low-rank and sparse decomposition

Siahsar, Mohammad Amir Nazari; Gholtashi, Saman; Kahoo, Amin Roshandel; Marvi, Hossein; Ahmadifard, Alireza

doi:10.1190/geo2015-0341.1

Cited by 63 publications

(5 citation statements)

References 24 publications

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“…The rank‐reduction‐based approach regards the seismic data reconstruction as a low‐rank tensor or a matrix completion problem (Nazari Siahsar et al . ). This category of approaches exploits the fact that the irregular data decimation and incoherent noise will increase the rank of a data matrix or a tensor (Chen et al .…”

Section: Introductionmentioning

confidence: 97%

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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Section: Introductionmentioning

confidence: 97%

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

Huang

Feng

Chen

2019

Geophysical Prospecting

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“…Notably, sparse representation-based techniques have gained significant popularity (Candès et al, 2006;Chen et al, 2017;Wu B Y et al, 2022). While seismic data is not inherently sparse, it can be effectively transformed into a sparse signal by sparse transformation (Siahsar et al, 2016). Random noise cannot be transformed into a sparse signal due to lacking sparsity.…”

Section: Introductionmentioning

confidence: 99%

Compressed sensing with log-sum heuristic recover for seismic denoising

Sun,

Zhang,

Wang

et al. 2024

Front. Earth Sci.

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The compressed sensing (CS) method, commonly utilized for restructuring sparse signals, has been extensively used to attenuate the random noise in seismic data. An important basis of CS-based methods is the sparsity of sparse coefficients. In this method, the sparse coefficient vector is acquired by minimizing the l1 norm as a substitute for the l0 norm. Many efforts have been made to minimize the lp norm (0 < p < 1) to obtain a more desirable sparse coefficient representation. Despite the improved performance that is achieved by minimizing the lp norm with 0 < p < 1, the related sparse coefficient vector is still suboptimal since the parameter p is greater than 0 rather than infinitely approaching 0 p→0+. Therefore, the CS method with the limit p→0+ is proposed to enhance the sparse performance and thus generate better denoised results in this paper. Our proposed method is referred to as the CS-LHR method because the solving process for minimizing p→0+ is the log-sum heuristic recovery (LHR). Furthermore, to improve the computational efficiency, we incorporate the majorization-minimization (MM) algorithm in this CS-LHR method. Experimental results of synthetic and real seismic records demonstrate the remarkable performance of CS-LHR in random noise suppression.

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“…There are also some denoising methods based on the median filtering strategy (Liu et al., 2009; Liu, 2013) and singular spectrum analysis strategy (Oropeza & Sacchi, 2011; Huang et al., 2016; Chen et al., 2019). An important random noise attenuation strategy is to utilize the reduced‐rank or low‐rank features to separate the signal and noise, such as robust reduced‐rank filtering (Chen & Sacchi, 2015), low‐rank and sparse decomposition (Siahsar et al., 2016), the damped rank‐reduction method (Chen et al., 2016), empirical low‐rank approximation (Chen et al., 2017) and synchrosqueezed wavelet transform low‐rank approximation (Anvari et al., 2017). Another frequently used denoising strategy is the sparse transform based on interpolation (Liu et al., 2015; Wang et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

Random noise attenuation of ocean bottom seismometers based on a substep deep denoising autoencoder

Lin

Xing

et al. 2023

Geophysical Prospecting

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Ocean bottom seismometer data usually contain a large amount of random noise, which seriously reduces the signal‐to‐noise ratio of the data and affects subsequent imaging. Hence, random noise attenuation is one of the most essential steps in ocean bottom seismometer data processing. In this paper, a novel approach is proposed to attenuate the marine seismic random noise of ocean bottom seismometers based on a six‐dense‐layer denoising autoencoder. We input the domain data into the denoising autoencoder, the encoder compresses the signal and noise and extracts the main features and the decoder finally reconstructs the denoised data with the same dimension as the input. In this approach, because few raw labelled examples are available, we first constructed the pretraining, training and test data sets by patch processing. Then, we pretrained the encoder based on clean synthetic seismic data through unsupervised learning and pretrained the decoder based on noisy synthetic seismic data through supervised learning. Next, the pretrained model was fine‐tuned with the encoder–decoder on a raw seismic data set in an unsupervised manner. Finally, we used the model to attenuate the random noise in raw ocean bottom seismometer data for testing. Synthetic and raw examples are used to compare the deconvolution, multichannel singular spectrum analysis, deep denoising autoencoder and substep deep denoising autoencoder approaches. Experimental tests demonstrate that the proposed method has higher processing efficiency and precision.

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Sparse time-frequency representation for seismic noise reduction using low-rank and sparse decomposition

Cited by 63 publications

References 24 publications

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

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

Compressed sensing with log-sum heuristic recover for seismic denoising

Random noise attenuation of ocean bottom seismometers based on a substep deep denoising autoencoder

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