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This document presents innovative migration deconvolution filters developed by Jianhua Yu from the University of Utah. It explores the motivation, numerical examples, and solutions associated with new migration deconvolution techniques, focusing on reflectivity, modeling, and Green's functions. The content includes various examples such as Gaussian Beam migration and Kirchhoff methods, along with practical applications for complex geological structures. The findings aim to enhance subsalt imaging and offer superior quality in geophysical data processing.
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New Migration Deconvolution Filters Jianhua Yu University of Utah
Contents New MD Filters Numerical Examples Motivation: MD Prob. & Soln. Summary
Motivation New MD Filters Examples Summary Contents New MD Filters Numerical Examples Motivation Summary
Migration Deconvolution Reflectivity T r G G Migration image = Migration Modeling Migration Green’s function Motivation New MD Filters Examples Summary = m
Migration Deconvolution T T T r G G G G G G = -1 -1 [ [ ] ] 1 1 Motivation New MD Filters Examples Summary = m
Migration Deconvolution T G G -1 [ ] 1 Motivation New MD Filters Examples Summary r = m =
Migration Deconvolution Problems T G G V(z) assumption For moderately complex models Migration Green’s function based on ray theory -1 [ ] Motivation New MD Filters Examples Summary r = m
Migration Deconvolution Solution T G G • Replace G by a • WE Propagator • Gaussian Beam Propagator • Beylkin Inverse Determinant -1 [ ] Motivation New MD Filters Examples Summary r = m
Motivation New MD Filters Examples Summary Contents New MD Filters Numerical Examples Motivation Summary
T G G [ ] òò * * G ( r r ) G ( r r ) G ( r r ) G ( r r ) d r d r g s g o o s g s -1 [ ] Motivation New MD Filters Examples Summary Fourier Finite Difference MD r = m
T G G [ ] òò * * G ( r r ) G ( r r ) G ( r r ) G ( r r ) d r d r g s g o o s g s 1 ~ r ò = w w G ( | r , t ) Re[ F ( ) U ( r , r , ) -1 g s g s p [ ] Motivation New MD Filters Examples Summary Gaussian Beam MD r = m
T G G z v ( x ) | h ( x , ) | òò ò = z w w 2 r ( 0 x ) d d F ( ) p 3 8 A ( s , r ) -1 [ ] Motivation New MD Filters Examples Summary Beylkin Deblurring r = m
Motivation New MD Filters Examples Summary Contents New MD Filters Numerical Examples Motivation Discussion & Conclusions
Motivation New MD Filters Examples Summary Kirchhoff Mig Beylkin Kirchhoff MD Gaussian Beam MD FFD MD
0 0 0 Y (km) Y (km) Y (km) 3 3 3 Motivation New MD Filters Examples Summary WE MD 3 0 X (km)
0 0 0 Y (km) Y (km) Y (km) 3 3 3 Motivation New MD Filters Examples Summary Kirchhoff MD 3 0 X (km)
X (km) 0 20 Motivation New MD Filters Examples Summary Migration Image by FFD 3 Depth (km) 10
X (km) 0 Wave Equation Migration 20 Motivation New MD Filters Examples Summary 3 Depth (km) 10
X (km) 0 Wave Equation Migration after Kirch. MD 20 Motivation New MD Filters Examples Summary 3 Depth (km) 10
X (km) 0 Wave Equation Migration after Beam MD 20 Motivation New MD Filters Examples Summary 3 Depth (km) 10
X (km) 0 20 Motivation New MD Filters Examples Summary Wave Equation Migration (AGC) 2.5 Depth (km) 10
X (km) Wave Equation migration after Kirch MD (AGC) 0 20 Motivation New MD Filters Examples Summary 2.5 Depth (km) 10
X (km) 0 Wave Equation migration after Kirch MD (AGC) 20 Motivation New MD Filters Examples Summary 2.5 Depth (km) 10
No AGC No AGC Motivation New MD Filters Examples Summary WE Mig Depth (km) Beam MD 10
5 AGC AGC Motivation New MD Filters Examples Summary WE Mig. Depth (km) Beam MD 10
Motivation New MD Filters Examples Summary Contents New MD Filters Numerical Examples Motivation Summary
Beylkin inverse determinant for moderate complex structure Cost: WEMD >> Beam MD Quality: Beam MD >> Kirch. MD WEM+BMD for subsalt imaging Quality: Beam MD = WE MD Summary
Thanks to SMAART Joint Venture 2003 UTAM Sponsors Alan Leeds (ChevronTexaco) CHPC