Some problems in the application of inverse scattering series method

2009 ◽  
Author(s):  
Xiang Li ◽  
Tianyue Hu
Keyword(s):  
Inverse Scattering ◽  
Series Method ◽  
Inverse Scattering Series

Inverse scattering theory: Inverse scattering series method for one dimensional non-compact support potential

2014 ◽  
Vol 55 (12) ◽  
pp. 123512 ◽  
Author(s):  
Jie Yao ◽  
Anne-Cécile Lesage ◽  
Bernhard G. Bodmann ◽  
Fazle Hussain ◽  
Donald J. Kouri
Keyword(s):  
Inverse Scattering ◽  
Compact Support ◽  
Scattering Theory ◽  
Series Method ◽  
One Dimensional ◽  
Inverse Scattering Series ◽  
Inverse Scattering Theory

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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