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New Migration Deconvolution Filters

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.

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New Migration Deconvolution Filters

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  1. New Migration Deconvolution Filters Jianhua Yu University of Utah

  2. Contents New MD Filters Numerical Examples Motivation: MD Prob. & Soln. Summary

  3. Motivation New MD Filters Examples Summary Contents New MD Filters Numerical Examples Motivation Summary

  4. Migration Deconvolution Reflectivity T r G G Migration image = Migration Modeling Migration Green’s function Motivation New MD Filters Examples Summary = m

  5. Migration Deconvolution T T T r G G G G G G = -1 -1 [ [ ] ] 1 1 Motivation New MD Filters Examples Summary = m

  6. Migration Deconvolution T G G -1 [ ] 1 Motivation New MD Filters Examples Summary r = m =

  7. 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

  8. 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

  9. Motivation New MD Filters Examples Summary Contents New MD Filters Numerical Examples Motivation Summary

  10. 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

  11. 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

  12. 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

  13. Motivation New MD Filters Examples Summary Contents New MD Filters Numerical Examples Motivation Discussion & Conclusions

  14. Motivation New MD Filters Examples Summary Kirchhoff Mig Beylkin Kirchhoff MD Gaussian Beam MD FFD MD

  15. 0 0 0 Y (km) Y (km) Y (km) 3 3 3 Motivation New MD Filters Examples Summary WE MD 3 0 X (km)

  16. 0 0 0 Y (km) Y (km) Y (km) 3 3 3 Motivation New MD Filters Examples Summary Kirchhoff MD 3 0 X (km)

  17. X (km) 0 20 Motivation New MD Filters Examples Summary Migration Image by FFD 3 Depth (km) 10

  18. X (km) 0 Wave Equation Migration 20 Motivation New MD Filters Examples Summary 3 Depth (km) 10

  19. X (km) 0 Wave Equation Migration after Kirch. MD 20 Motivation New MD Filters Examples Summary 3 Depth (km) 10

  20. X (km) 0 Wave Equation Migration after Beam MD 20 Motivation New MD Filters Examples Summary 3 Depth (km) 10

  21. X (km) 0 20 Motivation New MD Filters Examples Summary Wave Equation Migration (AGC) 2.5 Depth (km) 10

  22. X (km) Wave Equation migration after Kirch MD (AGC) 0 20 Motivation New MD Filters Examples Summary 2.5 Depth (km) 10

  23. X (km) 0 Wave Equation migration after Kirch MD (AGC) 20 Motivation New MD Filters Examples Summary 2.5 Depth (km) 10

  24. No AGC No AGC Motivation New MD Filters Examples Summary WE Mig Depth (km) Beam MD 10

  25. 5 AGC AGC Motivation New MD Filters Examples Summary WE Mig. Depth (km) Beam MD 10

  26. Motivation New MD Filters Examples Summary Contents New MD Filters Numerical Examples Motivation Summary

  27. 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

  28. Thanks to SMAART Joint Venture 2003 UTAM Sponsors Alan Leeds (ChevronTexaco) CHPC

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