The inverse scattering series for tasks associated with primaries: direct non‐linear inversion of 1D elastic media

2006 ◽  
Author(s):  
Haiyan Zhang ◽  
Arthur B. Weglein
Keyword(s):  
Inverse Scattering ◽  
Elastic Media ◽  
Linear Inversion ◽  
Non Linear ◽  
Inverse Scattering Series

Dual regularization in non-linear inverse scattering problems

2016 ◽  
Vol 24 (7) ◽  
pp. 1215-1239 ◽  
Author(s):  
Konstantin P. Gaikovich ◽  
Petr K. Gaikovich ◽  
Yelena S. Maksimovitch ◽  
Alexander I. Smirnov ◽  
Mikhail I. Sumin
Keyword(s):  
Inverse Scattering ◽  
Scattering Problems ◽  
Inverse Scattering Problems ◽  
Dual Regularization ◽  
Non Linear

scholarly journals Relating source-receiver interferometry to an inverse-scattering series to derive a new method to estimate internal multiples

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. Q27-Q40 ◽  
Author(s):  
Katrin Löer ◽  
Andrew Curtis ◽  
Giovanni Angelo Meles
Keyword(s):  
Inverse Scattering ◽  
Computational Cost ◽  
Synthetic Data ◽  
Data Set ◽  
Inverse Scattering Series ◽  
Internal Multiples ◽  
Multiple Prediction ◽  
New Formula ◽  
3D Applications ◽  
Insight Into

We have evaluated an explicit relationship between the representations of internal multiples by source-receiver interferometry and an inverse-scattering series. This provides a new insight into the interaction of different terms in each of these internal multiple prediction equations and explains why amplitudes of estimated multiples are typically incorrect. A downside of the existing representations is that their computational cost is extremely high, which can be a precluding factor especially in 3D applications. Using our insight from source-receiver interferometry, we have developed an alternative, computationally more efficient way to predict internal multiples. The new formula is based on crosscorrelation and convolution: two operations that are computationally cheap and routinely used in interferometric methods. We have compared the results of the standard and the alternative formulas qualitatively in terms of the constructed wavefields and quantitatively in terms of the computational cost using examples from a synthetic data set.

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