Seismic denoising using curvelet analysis

Oliveira, Marilene Silva; Henriques, Marcos Vinícius Cândido; Leite, Fabíola Pessôa Pereira; Corso, Gilberto; Lucena, L. S.

doi:10.1016/j.physa.2011.04.009

Cited by 30 publications

(7 citation statements)

References 10 publications

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“…In [25], the authors discussed the case of correlated noise, e.g., ground roll noise, however, they intuitively find the curvelet coefficients to be eliminated. To the best of our knowledge, this issue of threshold under the correlated noise case has not been addressed so far and previous works mainly used traditional methods.…”

Section: Correlated Noise and Whitening Methodsmentioning

confidence: 99%

“…Consequently, the vertical resolution of the seismic data is lowered. To alleviate this, Oliveira et al [25] proposed a curvelet based denoising method using hard thresholding. In this method, the authors identify the angular sections that contain the ground roll noise and erase the corresponding curvelet coefficients before reconstructing the seismic signal.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Blind Curvelet based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments

Iqbal¹,

Deriche²,

AlRegib³

et al. 2018

Preprint

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The localized nature of curvelet functions, together with their frequency and dip characteristics, makes the curvelet transform an excellent choice for processing seismic data. In this work, a denoising method is proposed based on a combination of the curvelet transform and a whitening filter along with procedure for noise variance estimation. The whitening filter is added to get the best performance of the curvelet transform under coherent and incoherent correlated noise cases, and furthermore, it simplifies the noise estimation method and makes it easy to use the standard threshold methodology without digging into the curvelet domain. The proposed method is tested on pseudo-synthetic data by adding noise to real noise-less data set of the Netherlands offshore F3 block and on the field data set from east Texas, USA, containing ground roll noise. Our experimental results show that the proposed algorithm can achieve the best * N. Iqbal,and M. Deriche are with the Center of Energy and Geo-processing, King

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Section: Correlated Noise and Whitening Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Blind Curvelet based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments

Iqbal¹,

Deriche²,

AlRegib³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…A variety of methods have been proposed for attenuating or removing random noise in order to enhance the signal-to-noise ratio (SNR) [ 4 – 8 ]. The transform-based methods, such as Fourier transform [ 9 ], wavelet transform [ 10 ], curvelet transform [ 11 ], and seislet transform [ 12 ], assume that the input signal has sparse representation with predetermined base, and under the predetermined base, noise and clean signal can be separated in the transform domain [ 7 , 13 – 15 ].…”

Section: Introductionmentioning

confidence: 99%

Seismic Random Noise Denoising Using Mini-Batch Multivariate Variational Mode Decomposition

Liu

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Seismic noise attenuation plays an important role in seismic interpretation. The empirical mode decomposition, synchrosqueezing wavelet transform, variational mode decomposition, etc., are often applied trace by trace. Multivariate empirical mode decomposition, multivariate synchrosqueezing wavelet transform, and multivariate variational mode decomposition were proposed for lateral continuity consideration. Due to large input data, mini-batch multivariate variational mode decomposition is proposed in this paper. The proposed method takes advantages both of variational mode decomposition and multivariate variational mode decomposition. This proposed method firstly segments the input data into a series of smaller ones with no overlapping and then applies multivariate variational mode decomposition to these smaller ones. High frequency-domain noise is filtered through sifting. Finally, the denoised smaller ones are concatenated to form components (or intrinsic mode functions) of the input signal. Synthetic and field data experiments validate the proposed method with different batch sizes and achieve higher signal-to-noise ratio than the variational mode decomposition method.

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“…It combined the correlation between the frequencies and the number of anisotropic wedges (Herrmann et al ). With such unique advantage, curvelets have been used to ground‐roll removal (Zhang et al ; Zheng et al ; Oliveira et al ), coherent noise removal (Neelamani et al ; Zegadi and Zegadi ), and multiple separation (Wang et al ; Lopez and Verschuur ). It is now extensively applied in seismic data denoising in the last decade (Yang, Gao and Chen ; Górszczyk, Adamczyk and Malinowski ; Li et al ).…”

Section: Introductionmentioning

confidence: 99%

Higher‐order correlative stacking for seismic data denoising based on the multiple‐domain combination

Zhang

et al. 2017

Near Surface Geophysics

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To improve the denoising performance of seismic data contaminated with random and coherent noises, a hybrid denoising scheme is proposed in this paper. It aims to whiten the random noise and identify the coherent noise for the preserved or prominent seismic features. Using the wavelet and curvelet basis functions in curvelets alternately, the hybrid denoising scheme utilises the representation of edges and singularities along curves. Then it adapts the wavelet-based higherorder correlative stacking denoising method from seismic exploration sequentially. With regard to seismic records for bedrock surface detection after the artificial backfill, the noisy data are significantly improved both in terms of denoising and improving fidelity. To illustrate the advantage of the hybrid denoising scheme, a comparison of the performances between the different individual denoising methods, including the higher-order correlative stacking method and curvelets with wavelet and curvelet basis functions, has been presented for the complex seismic records contaminated with different noises. Numerical case studies and a field data analysis have been used to show that the proposed hybrid denoising scheme is more effective for seismic data containing complex features than the individual denoising methods. deal with the complicated events of curved and conflicting dips in a single scale (Shan et al. 2009; Warden et al. 2012; Chen and Ma 2014).Correlative statistics in seismic records is introduced early in the theory of second-order stacking (Robinson 1970) for incoherent noise denoising. However, the seismic data contaminated with coherent noises cannot be improved much under the second-order stacking scheme. Therefore, higherorder correlative statistics in spectral domain are designed for seismic data processing (Zhang 1996;Zhang and Xu 2006). One kind of the spectral transform uses the wavelets. It allows the so-called "sparse representation" of the original information, in which the significance is only attached if a high correlation between the target event and the chosen wavelet is found in some characteristic frequency content (Saracco et al. 2007;Ma and Plonka 2010;Mauri et al. 2012). In seismic exploration, a known fact is is that each trace in the records can be formulated using a dilated and translated basic wavelet in one dimension (1D). The selection of such a wavelet leads to representing most energy of the signal using a limited number of chosen coefficients. Therefore, 1D wavelet is only suited for singularity detection and individual trace processing (Roueff, Chanussot and Mars 2006;Shan et al. 2009), and the 2D wavelet representation is only significant in horizontal,

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Seismic denoising using curvelet analysis

Cited by 30 publications

References 10 publications

Blind Curvelet based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments

Blind Curvelet based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments

Seismic Random Noise Denoising Using Mini-Batch Multivariate Variational Mode Decomposition

Higher‐order correlative stacking for seismic data denoising based on the multiple‐domain combination

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