Sub-Basalt Imaging Using Long Offset Data in the Tau-P Domain

Author(s):  
G.D. Jones ◽  
P.J. Barton ◽  
S.C. Singh
Keyword(s):  
Geophysics ◽  
2000 ◽  
Vol 65 (2) ◽  
pp. 652-655 ◽  
Author(s):  
Samuel H. Bickel

The parabolic approximation does not accurately model residual moveout for long‐offset marine data. Consequently the focusing power of the parabolic Radon transform is degraded. Maeland (1998) analyzes this problem by deriving the envelope of hyperbolic events in the (τ, q) domain. This note extends Maeland’s analysis to the hyperbolic Radon transform (τ, p) domain.


2004 ◽  
Author(s):  
Hasan Masoomzadeh ◽  
Penny Barton ◽  
Satish C. Singh
Keyword(s):  

Author(s):  
Richard Wright ◽  
James Carter ◽  
Deric Cameron ◽  
Tom Neugebauer ◽  
Jerry Witney ◽  
...  

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. C219-C227 ◽  
Author(s):  
Hanjie Song ◽  
Yingjie Gao ◽  
Jinhai Zhang ◽  
Zhenxing Yao

The approximation of normal moveout is essential for estimating the anisotropy parameters of the transversally isotropic media with vertical symmetry axis (VTI). We have approximated the long-offset moveout using the Padé approximation based on the higher order Taylor series coefficients for VTI media. For a given anellipticity parameter, we have the best accuracy when the numerator is one order higher than the denominator (i.e., [[Formula: see text]]); thus, we suggest using [4/3] and [7/6] orders for practical applications. A [7/6] Padé approximation can handle a much larger offset and stronger anellipticity parameter. We have further compared the relative traveltime errors between the Padé approximation and several approximations. Our method shows great superiority to most existing methods over a wide range of offset (normalized offset up to 2 or offset-to-depth ratio up to 4) and anellipticity parameter (0–0.5). The Padé approximation provides us with an attractive high-accuracy scheme with an error that is negligible within its convergence domain. This is important for reducing the error accumulation especially for deeper substructures.


2014 ◽  
Author(s):  
Qiaofeng Wu* ◽  
Chang-Chun Lee ◽  
Wei Zhao ◽  
Ping Wang ◽  
Yunfeng Li
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document