Separation of simultaneous sources using a structural-oriented median filter in the flattened dimension

Gan, Shuwei; Wang, Shoudong; Chen, Yangkang; Chen, Xiaohong; Xiang, Kai

doi:10.1016/j.cageo.2015.10.001

Cited by 102 publications

(12 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All of these methods can be almost classified into two categories of deblending approaches that are carried out in some sorted gathers as follows: 1) treating it as a noise suppression problem [14]- [16]. According to the assumptions that useful signal is coherent and blending noise is incoherent in some sorted gathers, the blending noise can be removed by the noise filtering method, such as the median filtering and others [17], [18]. Filtering-based deblending method is efficient and easy to implement.…”

Section: Introductionmentioning

confidence: 99%

Iterative Deblending of off-the-Grid Simultaneous Source Data

et al. 2021

View full text Add to dashboard Cite

Simultaneous source acquisition can enhance the seismic data quality or improve the field acquisition efficiency. However, one of the disadvantages is that the simultaneous source data are often obtained on a non-uniform sampled grid in realistic acquisition. Except for the blending noise introduced by the temporal overlap, the non-uniform samplings usually causes serious artifacts which also greatly reduces the data quality and imaging resolution, such that data regularization must be implemented during the deblending. At present, most deblending algorithms are suitable for the uniformly sampled data. Thus, we propose the nonequispaced fast discrete curvelet transform-based deblending approach to deal with the nonuniformly sampled simultaneous source data. In order to enhance the deblending efficiency, we introduce the iterative shrinkage thresholding algorithm to solve the undetermined problem via a soft thresholding operator. Once the coefficients in the curvelet domain are acquired, uniformly sampled deblended data can be obtained via inverse curvelet transform. During the iterations, the threshold is decreased from a large value to a small value according to the new exponential function. Compared with the existing deblending methods that are applied on uniform grids, the key contribution is that the proposed method can directly work on the non-uniformly sampled simultaneous source data. The numerical examples are used to demonstrate the successful performance of the proposed method in attenuating blending noise and correcting the distorted events.

show abstract

Section: Introductionmentioning

confidence: 99%

Iterative Deblending of off-the-Grid Simultaneous Source Data

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Strong noise existing in the seismic data often deteriorates many processing and interpretation tasks in the whole seismic imaging workflow, thus the removal of seismic noise is of great importance (Liu et al, 2009a(Liu et al, , 2012Gan et al, 2016;Wang et al, 2018;Zhao et al, 2018). According to the difference of spatial correlation in the seismic profiles, seismic noise is usually classified as coherent and incoherent.…”

Section: Introductionmentioning

confidence: 99%

Erratic noise suppression using iterative structure‐oriented space‐varying median filtering with sparsity constraint

Huang

Zhao

Chen

et al. 2020

Geophysical Prospecting

Self Cite

View full text Add to dashboard Cite

Erratic noise often has high amplitudes and a non-Gaussian distribution. Leastsquares-based approaches therefore are not optimal. This can be handled better with non-least-squares approaches, for example based on Huber norm which is computationally expensive. An alternative method has been published which involves transforming the data with erratic noise to pseudodata that have Gaussian distributed noise. It can then be attenuated using traditional least-squares approaches. This alternative method has previously been used in combination with a curvelet transform in an iterative scheme. In this paper, we introduce a median-filtering step in this iterative scheme. The median filter is applied following the slope direction of the seismic data to maximally preserve the energy of useful signals. The new method can suppress stronger erratic noise compared with the previous iterative method, and can better deal with random noise compared with the single-step implementation of the median filter. We apply the proposed robust denoising algorithm to a synthetic dataset and two field data examples and demonstrate its advantages over three different noise attenuation algorithms.

show abstract

“…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). Instead of developing a standalone denoising strategy, Chen & Fomel (2015b) proposed a two-step denoising approach that tries to solve a long-existing problem in almost all denoising approaches: the signal leakage problem.…”

Section: Introductionmentioning

confidence: 99%

Seismic noise attenuation using an online subspace tracking algorithm

Zhou

Zhang

et al. 2020

Geophysical Journal International

Self Cite

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

Separation of simultaneous sources using a structural-oriented median filter in the flattened dimension

Cited by 102 publications

References 38 publications

Iterative Deblending of off-the-Grid Simultaneous Source Data

Iterative Deblending of off-the-Grid Simultaneous Source Data

Erratic noise suppression using iterative structure‐oriented space‐varying median filtering with sparsity constraint

Seismic noise attenuation using an online subspace tracking algorithm

Contact Info

Product

Resources

About