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PRESTACK TARGET-ORIENTED LEAST SQUARES MIGRATION

PRESTACK TARGET-ORIENTED LEAST SQUARES MIGRATION. Zhiyong Jiang and Min Zhou. Geology and Geophysics Department University of Utah. Outline. Least Squares Migration Prestack Target-Oriented LSM Prestack Numerical Examples Conclusions. Standard Migration. LSM. d=Lm. d=Lm.

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PRESTACK TARGET-ORIENTED LEAST SQUARES MIGRATION

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  1. PRESTACK TARGET-ORIENTED LEAST SQUARES MIGRATION Zhiyong Jiang and Min Zhou Geology and Geophysics Department University of Utah

  2. Outline • Least Squares Migration • Prestack Target-Oriented LSM • Prestack Numerical Examples • Conclusions

  3. Standard Migration LSM d=Lm d=Lm mmig=LTd mmig=[LTL]-1LTd m : reflectivity model mmig: migration image d : observed data L : forward operator

  4. A Simple Model Distance (km) 1 0 0 Depth (Km) 1.2 True Reflectivity Model

  5. Common Shot Gathers Distance (km) Distance (km) 1 1 0 0 0 Time (s) 1 CSG 51 CSG 101

  6. Distance (km) 1 0 0 Depth (Km) 1.2 Standard LSM-35 Standard Migration vs. LSM Distance (km) 1 0 0 Depth (Km) 1.2 Standard Migration

  7. Limitation of LSM Requires 10 or more iterations,where each iteration requires several forward modeling and migration steps Impractical to implement for current 3-D prestack imaging of large data sets

  8. Outline • Least Squares Migration • Prestack Target-Oriented LSM • Prestack Numerical Examples • Conclusions

  9. Target-Oriented Prestack LSM Prestack LSM Data Model

  10. Answer: Smaller Model Fast Convergence Why faster convergence & better resolution? Simple Model Revisited Distance (km) Distance (km) 1 1 0 0 0 Depth (Km) 1.2 Target Oriented LSM-20 Standard LSM-35

  11. UNOCAL POSTSTACK DATA, 2003 Distance (km) 0 14.4 a 0.8 D (Km) 1.5 0.8 b D (Km) 1.5 (a) Standard Migration (b) TO LSM-5

  12. UNOCAL POSTSTACK DATA, 2003 Distance (km) 0 14.4 a 0.8 D (Km) 1.5 0.8 b D (Km) 1.5 (a) Standard Migration (b) TO LSM-10

  13. Outline • Least Squares Migration • Prestack Target-Oriented LSM • Prestack Numerical Results • Conclusions

  14. MOBIL MARINE DATA X (m) 10,356 22,856 0 V (m/s) 3643 Depth (m) 1500 3500 Velocity Model

  15. X (m) X (m) 10,356 22,856 10,356 22,856 500 Depth (m) 2500 Kirchhoff Migration Standard LSM-6

  16. X (m) 10,356 22,856 1000 Depth (m) Kirchhoff 1800 1000 Standard LSM-6 Depth (m) 1800

  17. Trace NO. Trace NO. 0 120 0 120 0 Time (s) Field Data CSG 600 Modeled Data CSG 600 6

  18. X (m) 10,356 22,856 1000 Depth (m) Kirchhoff 1800 1000 Target Oriented LSM-3 Depth (m) 1800

  19. X (m) 10,356 22,856 1000 Standard LSM-6 Depth (m) 1800 1000 Target Oriented LSM-3 Depth (m) 1800

  20. Outline • Least Squares Migration • Prestack Target-Oriented LSM • Prestack Numerical Results • Conclusions

  21. LSM is effective in suppressing the migration artifacts, but expensive Prestack Target-oriented LSM improves the image quality in a few iterations Prestack Target-oriented LSM greatly reduces CPU time Conclusion

  22. Future Work Eliminate the noise Implement preconditioning and regularization for LSM Test the poststack/prestack target-oriented LSM on 3-D data

  23. Acknowledgements We thank Utah Tomography and Modeling/Migration Consortium sponsors for their financial support

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