Deblending of simultaneous source data using a structure-oriented space-varying median filter

Chen, Yangkang; Zu, Shaohuan; Wang, Yufeng; Chen, Xiaohong

doi:10.1093/gji/ggy487

Cited by 32 publications

(24 citation statements)

References 46 publications

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“…We iteratively calculate the slope and update the input slope for the structure-oriented filtering during the inversion. Note that other options to select the filter length are possible (Chen, 2015;Chen et al, 2020). Figure 2 shows a synthetic test of the enhanced median filter on a synthetic dataset with hyperbolic events.…”

Section: Structure-oriented Space-varying Median Filtermentioning

confidence: 99%

“…Correspondingly, Figure 2(e,f) shows the removed noise. It is clear that the median-filtering method proposed by Chen et al (2020) obtains a successful preservation of seismic events while removing most of the erratic noise. The above-mentioned median filter is referred as the structureoriented space-varying median filter (Chen et al, 2020), which will also be used in the benchmark comparison in the section of examples.…”

Section: Structure-oriented Space-varying Median Filtermentioning

confidence: 99%

“…Most traditional denoising methods designed for Gaussiantype random noise may fail if the seismic data are also corrupted by erratic noise. To resolve this problem, in this paper, we combine the methods of Wong and Lee (2017), Zhao et al (2018) and Chen et al (2020). The general idea of pseudodata transformation was from Wong and Lee (2017).…”

Section: O N C L U S I O N Smentioning

confidence: 99%

“…This general method has great potential for transforming a variety of methods into their robust counterparts. Following Wong and Lee (2017) and Zhao et al (2018), we propose a more effective method to denoise seismic data that contain both erratic and random noise by applying a powerful structure-oriented space-varying median filter (Chen et al, 2020) within the iterative curvelet thresholding framework (Zhao et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…We organize the paper as follows: we first review the robust inversion model for noise suppression that we use to suppress both Gaussian-type random noise and erratic noise. Then, we review a median-filtering technique recently proposed by Chen et al (2020). We propose to use this filter to constrain the pseudodata.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

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et al. 2020

Geophysical Prospecting

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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.

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Section: Structure-oriented Space-varying Median Filtermentioning

confidence: 99%

Section: Structure-oriented Space-varying Median Filtermentioning

confidence: 99%