On analysis-based two-step interpolation methods for randomly sampled seismic data

Yang, Pengliang; Gao, Jinghuai; Chen, Wenchao

doi:10.1016/j.cageo.2012.07.023

Cited by 28 publications

(4 citation statements)

References 36 publications

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“…Eq. (13) with L 0 norm constraint can be solved using the POCS method with hard threshold (Gao et al 2010(Gao et al , 2012Yang et al 2012Yang et al , 2013Wang et al 2014a,b), and the detailed derivation is shown in Appendix A. The exact formula is shown in eq.…”

Section: Pocs Methods and Its Extensionmentioning

confidence: 99%

“…There is an implicit assumption in the POCS formula that observed seismic data should have high signal-to-noise ratio (SNR) because of the insertion of observed seismic data. However, observed seismic data is always noisy, therefore some authors use the weighted strategy in noisy situations (Oropeza & Sacchi 2011;Gao et al 2012;Yang et al 2013). Eq.…”

Section: Pocs Methods and Its Extensionmentioning

confidence: 99%

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Simultaneous seismic data interpolation and denoising with a new adaptive method based on dreamlet transform

Wang

Chen³

et al. 2015

Geophysical Journal International

135

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Interpolation and random noise removal is a prerequisite for multichannel techniques because the irregularity and random noise in observed data can affect their performances. Projection Onto Convex Sets (POCS) method can better handle seismic data interpolation if the data's signal-to-noise ratio (SNR) is high, while it has difficulty in noisy situations because it inserts the noisy observed seismic data in each iteration. Weighted POCS method can weaken the noise effects, while the performance is affected by the choice of weight factors and is still unsatisfactory. Thus, a new weighted POCS method is derived through the Iterative Hard Threshold (IHT) view, and in order to eliminate random noise, a new adaptive method is proposed to achieve simultaneous seismic data interpolation and denoising based on dreamlet transform. Performances of the POCS method, the weighted POCS method and the proposed method are compared in simultaneous seismic data interpolation and denoising which demonstrate the validity of the proposed method. The recovered SNRs confirm that the proposed adaptive method is the most effective among the three methods. Numerical examples on synthetic and real data demonstrate the validity of the proposed adaptive method.

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Section: Pocs Methods and Its Extensionmentioning

confidence: 99%

Section: Pocs Methods and Its Extensionmentioning

confidence: 99%

Simultaneous seismic data interpolation and denoising with a new adaptive method based on dreamlet transform

Wang

Chen³

et al. 2015

Geophysical Journal International

135

View full text Add to dashboard Cite

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“…Therefore, in the case of deblending, we prefer to use the thresholding operator rather than the mask operator. The selection criteria of γ i in the above definitions of thresholding Deblending using shaping regularization V181 operators (soft and hard) leads to different kinds of thresholding strategies, which may result in different thresholding performances (convergence rate and quality) (Yang et al, 2013). Some of the common iterative thresholding strategies are constant-value thresholding, linear-decreasing thresholding, exponential-decreasing thresholding, etc.…”

Section: Shaping Operatormentioning

confidence: 99%

Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization

2014

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We used a novel iterative estimation scheme for separation of blended seismic data from simultaneous sources. The scheme is based on an augmented estimation problem that can be solved by iteratively constraining the deblended data using shaping regularization in the seislet domain. We formulated the forward modeling operator in the commonreceiver domain, in which two sources were assumed to be blended using a random time-shift dithering approach. The nonlinear shaping-regularization framework offered some freedom in designing a shaping operator to constrain the model in an underdetermined inverse problem. We designed the backward operator and the shaping operator for the shaping-regularization framework. The backward operator can be optimally chosen as half of the identity operator in the two-source case, and the shaping operator can be chosen as coherency-promoting operator. The high performance deblending effect of the iterative framework was tested on three numerically blended synthetic data sets and one numerically blended field data set. Compared with alternative f-k domain thresholding and f-x predictive filtering, seislet-domain soft thresholding exhibits the most robust behavior.

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“…(2) Reconstruction with certain types of transforms such as the Fourier transform [7], the Radon transform [8], and the curvelet transform [9]. Some well known algorithms which fall under this umbrella can handle the non-uniformly sampled seismic records, including iterative soft thresholding method [9], minimum weighted norm interpolation (MWNI) method [10], anti-leakage Fourier transform (ALFT) method [11], projection onto convex sets (POCS) method [12,13,14] etc.…”

Section: Introductionmentioning

confidence: 99%

Irregularly sampled seismic data interpolation using iterative half thresholding regularization

Yang

Gao

Chen

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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The recent progress of compressive sensing (CS) theory shows that perfect reconstruction is possible when the data are sampled randomly and compressed greatly. This revolutionary theory strongly advocates the irregular sampling pattern in the process of seismic data acquisition, leading to a highly reduced acquisition cost. The complete seismic record can be reconstructed from the sparsely few sampled seismic data. In this paper, an analysis version of iterative half thresholding algorithm is introduced to interpolate the seismic data, using the tight frame dictionary. Inspired by the newly proposed spectral compressive sensing theory and the favorable characteristics of tight frame, we employ the redundant Fourier transform to sparsely represent the oscillating seismic data. A double zeropadding strategy in spatial direction of seismic data is suggested to further enhance the reconstruction quality, allowing for the improved interpolation performance and the increased computational cost and storage requirement. The validity of the proposed method is demonstrated by experimental result.

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On analysis-based two-step interpolation methods for randomly sampled seismic data

Cited by 28 publications

References 36 publications

Simultaneous seismic data interpolation and denoising with a new adaptive method based on dreamlet transform

Simultaneous seismic data interpolation and denoising with a new adaptive method based on dreamlet transform

Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization

Irregularly sampled seismic data interpolation using iterative half thresholding regularization

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