Incoherent noise suppression with curvelet‐domain sparsity

Kumar, Vishal; Herrmann, Felix J.

doi:10.1190/1.3255557

Cited by 11 publications

(2 citation statements)

References 8 publications

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“…Furthermore, in this context, dictionary training approaches have also been developed in order to build transformation bases that enable describing the signals at hand. Domain-transform based methods for interpolation and denoising of seismic data are generally processed using one of the following transform-domains: curvelet [8], pocs [9] and dreamlet [10]. Additionally, dictionary learning approaches for noise reduction were reported in [11], [12] and [13].…”

Section: Special Issue On Seismic Imagingmentioning

confidence: 99%

Interpolation and denoising of seismic signals using orthogonal matching pursuit algorithm: An aplication in VSP and refraction data

Sandoval-Flórez

Paredes

Vivas

et al. 2018

CT&F Cienc. Tecnol. Futuro

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An implementation of the Orthogonal Matching Pursuit (OMP) algorithm was used and the results obtained therefrom are presented for simultaneous interpolation and denoising from seismic signals in the framework of sparse signal representation. OMP is an algorithm for sparse signal representation based on orthogonal projections underlying the signal over an over-complete dictionary. This over-complete dictionary was designed using K-times Singular Values Decomposition (K-SVD). In each iteration, OMP calculates a new signal approximation and the approximation error is used in the next iteration to determine the new element. The new element corresponds to the largest magnitude of the inner products between the current residual and the original elements in the dictionary. The implemented algorithm was applied to VSP seismic data and refraction seismic data; results for the application in restored missing traces and denoise signals are presented.

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Section: Special Issue On Seismic Imagingmentioning

confidence: 99%

Interpolation and denoising of seismic signals using orthogonal matching pursuit algorithm: An aplication in VSP and refraction data

Sandoval-Flórez

Paredes

Vivas

et al. 2018

CT&F Cienc. Tecnol. Futuro

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“…The fi nal, noise-free reconstructed image is given by m = C T x. Further information and discussion concerning curvelet denoising and its application to seismic-refl ection data are given in Kumar (2009) and from V. Kumar (2010, personal commun. ).…”

Section: Processing Of the Refl Ection Datamentioning

confidence: 99%

Nature of the Moho transition in NW Canada from combined near-vertical and wide-angle seismic-reflection studies

Oueity

Clowes

2010

Lithosphere

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Although the year 2009 marked one century since the discovery of the Moho, or crust-mantle boundary, the exact nature of that boundary and the manner in which it formed remain major uncertainties in lithospheric studies. In northwestern Canada, sharp Moho refl ections at both near-vertical and wide-angle incidence have been imaged beneath the Great Bear magmatic arc. They show a remarkably fl at Moho and do not refl ect the complex tectonic history of the Wopmay orogen, of which the Great Bear arc is one part. In order to understand the origin of these refl ections and the nature of the Moho, we calculated near-vertical and wide-angle synthetic seismograms for a number of crust-mantle transition models using one-and two-dimensional wave propagation algorithms. Only laterally and vertically heterogeneous models can properly simulate the observed seismic signature recorded on both near-vertical and wide-angle refl ection data. The heterogeneity is achieved by either laterally discontinuous layering or a lamellae structure with randomly distributed ellipses. These models suggest that the Moho represents a thermal/metamorphic front, a regional décollement, or both.

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