Application of curvelet denoising to 2D and 3D seismic data — Practical considerations

Górszczyk, Andrzej; Adamczyk, Agata; Malinowski, M.

doi:10.1016/j.jappgeo.2014.03.009

Cited by 69 publications

(27 citation statements)

References 18 publications

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“…Although it might be more efficient to directly filter monochromatic data in the shot-receiver plane for frequency-domain FWI [e.g., Adamczyk et al, 2015], the 1 km spacing between OBSs in our case prevents the use of this approach. Instead, we apply the DCT in the time domain following the strategy presented by Górszczyk et al [2014]. In the final stage, we apply spiking deconvolution to whiten the amplitude spectrum and eliminate bubble effects.…”

Section: Figures 2a and 2cmentioning

confidence: 99%

Toward a robust workflow for deep crustal imaging by FWI of OBS data: The eastern Nankai Trough revisited

2017

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Crustal‐scale imaging by the full‐waveform inversion (FWI) of long‐offset seismic data is inherently difficult because the large number of wavelengths propagating through the crust makes the inversion prone to cycle skipping. Therefore, efficient crustal‐scale FWI requires an accurate starting model and a stable workflow minimizing the nonlinearity of the inversion. Here we attempt to reprocess a challenging 2‐D ocean‐bottom seismometer (OBS) data set from the eastern Nankai Trough. The starting model is built by first‐arrival traveltime tomography (FAT), which is FWI assisted for tracking cycle skipping. We iteratively refine the picked traveltimes and then reiterate the FAT until the traveltime residuals remain below the cycle‐skipping limit. Subsequently, we apply Laplace‐Fourier FWI, in which progressive relaxation of time damping is nested within frequency continuation to hierarchically inject more data into the inversion. These two multiscale levels are complemented by a layer‐stripping approach implemented through offset continuation. The reliability of the FWI velocity model is assessed by means of source wavelet estimation, synthetic seismogram modeling, ray tracing modeling, dynamic warping, and checkerboard tests. Although the viscoacoustic approximation is used for wave modeling, the synthetic seismograms reproduce most of the complexity of the data with a high traveltime accuracy. The revised FWI scheme produces a high‐resolution velocity model of the entire crust that can be jointly interpreted with migrated images derived from multichannel seismic data. This study opens a new perspective on the design of OBS crustal‐scale experiments amenable to FWI; however, a further assessment of the optimal OBS spacing is required for reliable FWI.

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Section: Figures 2a and 2cmentioning

confidence: 99%

Toward a robust workflow for deep crustal imaging by FWI of OBS data: The eastern Nankai Trough revisited

2017

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“…Curvelet (Hennenfent and Herrmann ; Neelamani et al . ; Ma and Plonka ; Górszczyk, Adamczyk and Malinowski ; Cao, Zhao and Hu ), wavelet (Du and Lines ), shearlet (Merouane Yilmaz and Baysal ), bandelet transform (Wang et al . ) and Radon transform (Sacchi and Porsani ; Sabbione, Sacchi and Velis ) are helpful in seismic data representation and random noise attenuation.…”

Section: Introductionmentioning

confidence: 99%

“…The effectiveness of this method is based on the type of transform we select. Curvelet (Hennenfent and Herrmann 2006;Neelamani et al 2008;Ma and Plonka 2010;Górszczyk, Adamczyk and Malinowski 2014;Cao, Zhao and Hu 2015), wavelet (Du and Lines 2000), shearlet (Merouane Yilmaz and Baysal 2015), bandelet transform and Radon transform (Sacchi and Porsani 1999;Sabbione, Sacchi and Velis 2015) are helpful in seismic data representation and random noise attenuation. Curvelet and shearlet are the two multi-scale geometric transforms that can transfer seismic data to an enhanced sparse domain where the data can be easily manipulated mathematically (Lari and Gholami 2014).…”

Section: Introductionmentioning

confidence: 99%

A compound method for random noise attenuation

Gemechu

Yong

2018

Geophysical Prospecting

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Random noise attenuation, preserving the events and weak features by improving signal‐to‐noise ratio and resolution of seismic data are the most important issues in geophysics. To achieve this objective, we proposed a novel seismic random noise attenuation method by building a compound algorithm. The proposed method combines sparsity prior regularization based on shearlet transform and anisotropic variational regularization. The anisotropic variational regularization which is based on the linear combination of weighted anisotropic total variation and anisotropic second‐order total variation attenuates noises while preserving the events of seismic data and it effectively avoids the fine‐scale artefacts due to shearlets from the restored seismic data. The proposed method is formulated as a convex optimization problem and the split Bregman iteration is applied to solve the optimization problem. To verify the effectiveness of the proposed method, we test it on several synthetic seismic datasets and real datasets. Compared with three methods (the linear combination of weighted anisotropic total variation and anisotropic second‐order total variation, shearlets and shearlet‐based weighted anisotropic total variation), the numerical experiments indicate that the proposed method attenuates random noises while alleviating artefact and preserving events and features of seismic data. The obtained result also confirms that the proposed method improves the signal‐to‐noise ratio.

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“…In recent years, multi-resolution analysis in the transform domain has opened up new methods for seismic data denoising (Shucong et al 2014;Mei et al 2012;Andrzej et al 2014;Chengming et al 2014). The multi-scale wavelet transform has been widely used in seismic processing (Rongfeng et al 2003;Shucong et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…These shortcomings result in serious distortion, especially around peaks and troughs in the signal (Chengming et al 2014;Ivan et al 2005). More recently, some new methods based on sparse representation have been developed, including Ridgelets (Mei et al 2012), Contourlets (Yang et al 2009), Curvlets (Andrzej et al 2014) and Shearlets (Chengming et al 2014). These offer a promising framework for seismic data denoising, although the non-subsampled property of the Curvlets and Shearlets algorithms increases their time complexity.…”

Section: Introductionmentioning

confidence: 99%

Noise suppression in the dual-tree complex wavelet domain for seismic signal

Meng

2016

J Petrol Explor Prod Technol

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This paper presents a novel method for seismic signal denoising based on the dual-tree complex wavelet transform. The advantages of shift invariance, adequate directions and anti-aliasing enable the dual-tree complex wavelet to effectively represent seismic data. However, the fixed threshold in each sub-band leads to the attenuation of the useful signal or residual noise. In this paper, noise attenuation strategies are discussed for all sub-bands in different directions and scales. In this approach, block matching is combined with singular value decomposition to handle the noisy coefficients in high-frequency scales based on local and non-local similarity. An adaptive threshold is used to divide the signal from the noise at lowfrequency scales. The method is applied to synthetic seismic data and field data to verify its performance. The experimental results illustrate that the proposed method improves the signal-to-noise ratio by 3-4 dB compared with fixed threshold denoising in all sub-bands.

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Application of curvelet denoising to 2D and 3D seismic data — Practical considerations

Cited by 69 publications

References 18 publications

Toward a robust workflow for deep crustal imaging by FWI of OBS data: The eastern Nankai Trough revisited

Toward a robust workflow for deep crustal imaging by FWI of OBS data: The eastern Nankai Trough revisited

A compound method for random noise attenuation

Noise suppression in the dual-tree complex wavelet domain for seismic signal

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