Simultaneous source separation using a robust Radon transform

We adopted the robust Radon transform to eliminate erratic incoherent noise that arises in common receiver gathers when simultaneous source data are acquired. The proposed robust Radon transform was posed as an inverse problem using an [Formula: see text] misfit that is not sensitive to erratic noise. The latter permitted us to design Radon algorithms that are capable of eliminating incoherent noise in common receiver gathers. We also compared nonrobust and robust Radon transforms that are implemented via a quadratic ([Formula: see text]) or a sparse ([Formula: see text]) penalty term in the cost function. The results demonstrated the importance of incorporating a robust misfit functional in the Radon transform to cope with simultaneous source interferences. Synthetic and real data examples proved that the robust Radon transform produces more accurate data estimates than least-squares and sparse Radon transforms.

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Simultaneous source separation using dithered sources

10.1190/1.3063928 ◽

2008 ◽

Cited By ~ 43

Author(s):

Ian Moore ◽

Bill Dragoset ◽

Tor Ommundsen ◽

David Wilson ◽

Camille Ward ◽

...

Keyword(s):

Source Separation ◽

Simultaneous Source

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Regularization by Denoising for simultaneous source separation

Geophysics ◽

10.1190/geo2021-0068.1 ◽

2021 ◽

pp. 1-56

Author(s):

Breno Bahia ◽

Rongzhi Lin ◽

Mauricio Sacchi

Keyword(s):

Inverse Problem ◽

Inverse Problems ◽

Data Processing ◽

Spectrum Analysis ◽

Numerical Experiments ◽

Source Separation ◽

Singular Spectrum Analysis ◽

Source Data ◽

Blended Data ◽

Simultaneous Source

Denoisers can help solve inverse problems via a recently proposed framework known as regularization by denoising (RED). The RED approach defines the regularization term of the inverse problem via explicit denoising engines. Simultaneous source separation techniques, being themselves a combination of inversion and denoising methods, provide a formidable field to explore RED. We investigate the applicability of RED to simultaneous-source data processing and introduce a deblending algorithm named REDeblending (RDB). The formulation permits developing deblending algorithms where the user can select any denoising engine that satisfies RED conditions. Two popular denoisers are tested, but the method is not limited to them: frequency-wavenumber thresholding and singular spectrum analysis. We offer numerical blended data examples to showcase the performance of RDB via numerical experiments.

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