Incoherent dictionary learning for reducing crosstalk noise in least-squares reverse time migration

Wu, Juan

doi:10.1016/j.cageo.2018.01.010

Cited by 40 publications

(8 citation statements)

References 93 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The zero value means that the columns are entirely independent of each other; that is, no correlation or coherence between atoms in the signal, and the value 1 indicates a 100% correlation. According to Wu and Bai (2018), the ability of a redundant dictionary to adequately represent a signal is determined by the degree of correlation between its atoms. A lower degree of correlation between atoms favours a greater ability of the dictionary to represent the data.…”

Section: Methodsmentioning

confidence: 99%

“…One of the main problems that often arise from this inverse solution of the blended data is the generation of correlation noise or crosstalk. Because one column of the blended shot record consisting of responses from multiple sources and the nonlinearity of the imaging condition, the deblending operation introduces some crosstalk noise which is known as blending noise or correlation noise (Berkhout et al ., 2010; Schleicher et al ., 2014; Wu and Bai, 2018).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Simultaneous‐source deblending using adaptive coherence‐constrained dictionary learning and sparse approximation

Evinemi

Mao

2021

Geophysical Prospecting

View full text Add to dashboard Cite

The dictionary learning and sparse approximation method using the K‐singular value decomposition algorithm rely on the knowledge of the sparsity or noise variance as a constraint when it is used for data denoising. However, the determination of the sparsity or noise variance of seismic data can be tricky and sometimes unknown, especially in seismic field data. Thus, where the cardinality or the noise variance is not known, the intrinsic character of the relative coherence between the removed noise from noisy data and its learned dictionary is instead used as a constraint for the sparse approximation of simultaneous‐source seismic data. The dictionary learning is obtained using a modified orthogonal matching pursuit algorithm which uses coherence as a constraint and is referred to as coherence dictionary learning. The coherence dictionary learning is then adapted to handle the simultaneous‐source seismic data deblending. A blending structure with random time dithering of sequential source shooting is used to guarantee adequate randomness of the noise. Two‐dimensional overlap patches of the noisy data were extracted from the common receiver gather domain to train the dictionary and to determine the sparse representation of the signal. The method is tested on both synthetic and field data, and it shows adequate data recovery. Comparing the result of this method to the matching pursuit algorithm constrained by the signal sparsity and the noise variance reveals that our approach performs better at noise attenuation and yields a reasonable data recovery especially for strong seismic signal.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simultaneous‐source deblending using adaptive coherence‐constrained dictionary learning and sparse approximation

Evinemi

Mao

2021

Geophysical Prospecting

View full text Add to dashboard Cite

show abstract

“…Incoherent dictionary learning, as an extension of generic dictionary learning, aims at minimizing the reconstruction error by imposing sparsity on the coefficient and coherence of the dictionary, simultaneously. For this purpose, several incoherent dictionary learning algorithms have been proposed, within two major strategies: either adding a decorrelation step after dictionary updating at each iteration, such as INK-SVD and related algorithms [10,11], or introduced an additional regularization of the coherence in the optimization problem [12,13,14]. While the latter strategy may provide better performance, the former is often recommended because it allows the fix the coherence level beforehand.…”

Section: Introductionmentioning

confidence: 99%

Incoherent dictionary learning via mixed-integer programming and hybrid augmented Lagrangian

Liu

Canu

Honeiné

et al. 2020

Digital Signal Processing

View full text Add to dashboard Cite

During the past decade, the dictionary learning has been a hot topic in sparse representation. With theoretical guarantees, a low-coherence dictionary is demonstrated to optimize the sparsity and improve the accuracy of the performance of signal reconstruction. Two strategies have been investigated to learn incoherent dictionaries: (i) by adding a decorrelation step after the dictionary updating (e.g. INK-SVD), or (ii) by introducing an additive penalty term of the mutual coherence to the general dictionary learning problem. In this paper, we propose a third method, which learns an incoherent dictionary by solving a constrained quadratic programming problem. Therefore, we can learn a dictionary with a prior fixed coherence value, which cannot be realized by the second strategy. Moreover, it updates the dictionary by considering simultaneously the reconstruction error and the incoherence, and thus does not suffer from the performance reduction of the first strategy. The constrained quadratic programming problem is difficult problem due to its non-smoothness and non-convexity. To deal with the problem, a two-step alternating method is used: sparse coding by solving a problem of mixed-integer programming and dictionary updating by the hybrid method of augmented Lagrangian and alternating proximal linearized minimization. Finally, extensive experiments conducted in image denoising demonstrate the relevance of the proposed method, and illustrate the relation between coherence of dictionary and reconstruction quality.

show abstract

“…Unlike general digital images, the spatial changes of the seismic model always have some specific geologic structures such as tilted layers, faults, or edges of a salt body (Chen et al, 2017(Chen et al, , 2018Bai et al, 2018;Wu and Bai, 2018). Bayram and Kamasak (2012) propose a directional TV method and apply it to digital image denoising.…”

Section: Introductionmentioning

confidence: 99%

Full-waveform inversion and joint migration inversion with an automatic directional total variation constraint

Verschuur

Chen

2019

GEOPHYSICS

View full text Add to dashboard Cite

As full-waveform inversion (FWI) is a nonunique and typically ill-posed inversion problem, it needs proper regularization to avoid cycle skipping. To reduce the nonlinearity of FWI, we have developed joint migration inversion (JMI) as an alternative, explaining the reflection data with decoupled velocity and reflectivity parameters. However, the velocity update may also suffer from being trapped in local minima. To optimally include geologic information, we have developed FWI/JMI with directional total variation (TV) as an L1-norm regularization on the velocity. We design the directional TV operator based on the local dip field, instead of ignoring the local structural direction of the subsurface and only using horizontal and vertical gradients in the traditional TV. The local dip field is estimated using plane-wave destruction based on a raw reflectivity model, which is usually calculated from the initial velocity model. With two complex synthetic examples, based on the Marmousi model, we determine that our method is much more effective compared with FWI/JMI without regularization and FWI/JMI with the conventional TV regularization. In the JMI-based example, we also determine that L1 directional TV works better than L2 directional Laplacian smoothing. In addition, by comparing these two examples, it can be seen that the impact of regularization is larger for FWI than for JMI because in JMI the velocity model only explains the propagation effects and, thereby, makes it less sensitive to the details in the velocity model.

show abstract

Incoherent dictionary learning for reducing crosstalk noise in least-squares reverse time migration

Cited by 40 publications

References 93 publications

Simultaneous‐source deblending using adaptive coherence‐constrained dictionary learning and sparse approximation

Simultaneous‐source deblending using adaptive coherence‐constrained dictionary learning and sparse approximation

Incoherent dictionary learning via mixed-integer programming and hybrid augmented Lagrangian

Full-waveform inversion and joint migration inversion with an automatic directional total variation constraint

Contact Info

Product

Resources

About