Multidimensional Seismic Data Reconstruction Using Frequency-Domain Adaptive Prediction-Error Filter

Li, Chao; Liu, Guochang; Hao, Zhenjiang; Zu, Shaohuan; Mi, Fang; Chen, Xiaohong

doi:10.1109/tgrs.2017.2778196

Cited by 37 publications

(4 citation statements)

References 26 publications

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“…This strategy includes methods based on the Fourier transform (Mousavi and Langston, 2016a), wavelet transform (Mousavi and Langston, 2016b; Mousavi et al ., 2016), seislet transform (Fomel and Liu, 2010; Chen et al ., 2014), curvelet transform (Naghizadeh and Sacchi, 2010), shearlet transform (Liu et al ., 2016), Radon transform (Beylkin, 1987), wavelet transform (Sweldens, 1995), dictionary learning (Siahsar et al ., 2017a, 2017b; Chen, 2020) and deep‐learning techniques (Li et al ., 2018b; Zhu et al ., 2019). Another type of approach for random‐noise attenuation focuses on enhancing useful signals and suppressing random noise by utilizing a predictable property, such as

t - x

predictive filtering (Abma and Claerbout, 1995),

f - x

predictive filtering (Canales, 1984; Gulunay, 1986), the complex‐trace analysis method (Karsli et al ., 2006), non‐stationary predictive filtering (Liu et al ., 2012; Liu and Chen, 2013; Liu et al ., 2018; Li et al ., 2018a) and the polynomial fitting–based approach (Lu and Lu, 2009).…”

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

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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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t - x

predictive filtering (Abma and Claerbout, 1995),

f - x

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

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“…Seismic data denoising and interpolation play a vital role in seismic processing and have an influence on seismic interpretation particularly. Because of the presence of the complex surface geologic conditions and the irregular acquisition field area [1,2], random noise and missing data are common cases in seismic exploration. In general, acquired data from field should be preprocessed before data processing [3,4].…”

Section: Introductionmentioning

confidence: 99%

An Alternative Adaptive Method for Seismic Data Denoising and Interpolation

Zhang

Nuan

Sun

et al. 2020

Mathematical Problems in Engineering

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Seismic data denoising and interpolation are generally essential steps for reflection processing and imaging workflow especially for the complex surface geologic conditions and the irregular acquisition field area. The rank-reduction method is a valid way for the attenuation of random noise and data interpolation by selecting the suitable threshold, i.e., the rank of the useful signals. However, it is difficult for the traditional rank-reduction method to select an appropriate threshold. In this paper, we propose an adaptive rank-reduction method based on the energy entropy to automatically estimate the rank as the threshold for seismic data processing and interpolation. This method considers the energy entropy into the traditional rank-reduction method. The energy entropy of signals can be used to indicate the energy intensity of a signal component in the total energy. The difference of the energy entropy between the useful signals and random noise is perceived as a measurement for selecting the appropriate threshold. Synthetic and field examples indicate that the proposed method can well achieve the attenuation of random noise and interpolation automatically without the estimation of the ranks and demonstrate the feasibility of the new adaptive method in seismic data denoising and interpolation.

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“…The decomposition‐based denoising methods consider the separability of seismic signal and random noise and attempt to extract useful information from the principal components of the noisy data. Typical methods include the empirical‐mode decomposition (EMD) related methods (Huang et al ., 1998), for example, the ensemble EMD (Wu and Huang, 2009), complete ensemble EMD (Colominas et al ., 2012), improved complete ensemble EMD (Colominas et al ., 2012), singular value decomposition‐related methods (Bekara and van der Baan, 2007) and non‐stationary decomposition with regularization (Li et al ., 2018).…”

Section: Introductionmentioning

confidence: 99%

Seismic signal enhancement based on the low‐rank methods

Huang

Wang

Chen

2020

Geophysical Prospecting

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Based on the fact that the Hankel matrix constructed by noise-free seismic data is low-rank, low-rank approximation (or rank-reduction) methods have been widely used for removing noise from seismic data. Due to the linear-event assumption of the traditional low-rank approximation method, it is difficult to define a rank that optimally separates the data subspace into signal and noise subspaces. For preserving the most useful signal energy, a relatively large rank threshold is often chosen, which inevitably leaves residual noise. To reduce the energy of residual noise, we propose an optimally damped rank-reduction method. The optimal damping is applied via two steps. In the first step, a set of optimal damping weights is derived. In the second step, we derive an optimal singular value damping operator. We review several traditional low-rank methods and compare their performance with the new one. We also compare these low-rank methods with two sparsity-promoting transform methods. Examples demonstrate that the proposed optimally damped rank-reduction method could get significantly cleaner denoised images compared with the state-of-the-art methods.

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Multidimensional Seismic Data Reconstruction Using Frequency-Domain Adaptive Prediction-Error Filter

Cited by 37 publications

References 26 publications

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

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

An Alternative Adaptive Method for Seismic Data Denoising and Interpolation

Seismic signal enhancement based on the low‐rank methods

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