1. Migration Deconvolution vs Least Squares Migration Jianhua Yu, Gerard T. Schuster University of Utah

2. Outline MotivationMotivation MD vs. LSMMD vs. LSM Numerical TestsNumerical Tests ConclusionsConclusions

3. Migration Noise Problems Footprint Migration noise and artifacts Time

4. Migration Problems Recording footprints Aliasing Limited resolution Amplitude distortion

5. Motivation Investigate MD and LSM: Improve resolution Suppress migration noise Computational cost Robustness

6. Outline MotivationMotivation MD vs. LSMMD vs. LSM Numerical TestsNumerical Tests ConclusionsConclusions

7. m = (L L ) L d TT Least Squares Migration Reflectivity Modeling operator Seismic data Migration operator

8. m = (L L ) L d TT Migration Deconvolution Reflectivity Modeling operator Migrated datam’

9. Solutions of MD Vs. LSM m = (L L ) L d TT LSM: T m = (L L ) m’ MD: Migrated image Data

10. I/O of 3-D MD Vs. LSM Huge volume LSM: Relative samll cube MD:

11. Outline MotivationMotivation MD Vs. LSMMD Vs. LSM Numerical TestsNumerical Tests ConclusionsConclusions

12. Numerical Tests Point Scatterer ModelPoint Scatterer Model 2-D SEG/EAGE overthrust model poststack MD and LSM2-D SEG/EAGE overthrust model poststack MD and LSM

13. Scatterer Model Krichhoff Migration Depth (km) 1.8 0 1.0 0 0

14. MDLSM Iter=10 Depth (km) 1.8 0 1.0 0 0

15. Depth (km) 1.8 0 1.0 0 LSM Iter=15 1.0 0 LSM Iter=20

16. Point Scatterer ModelPoint Scatterer Model 2-D SEG/EAGE Overthrust Model Poststack MD and LSM2-D SEG/EAGE Overthrust Model Poststack MD and LSM Numerical Tests

17. KM Depth (km) 4.5 0 07.0 0 X (km) 4.5 0 LSM 15

18. KM Depth (km) 4.5 0 07.0 0 X (km) 4.5 0 MD

19. Depth (km) 4.5 0 07.0 0 X (km) 4.5 0 MD LSM 15

20. MD KM 2 3.5 Depth (km) LSM 19 2 3.5 Depth (km) Zoom View

21. Depth (km) 4.5 0 07.0 Why does MD perform better than LSM ? 4.5 MD LSM 19 0 X (km)

22. Outline MotivationMotivation MD Vs. LSMMD Vs. LSM Numerical TestsNumerical Tests ConclusionsConclusions

23. Conclusions Efficiency MD >> LSMFunction Performanc e Resolution MD < LSM (?) Suppressing noise MD = LSM (?) Robustness MD < LSM

24. Acknowledgments Thanks UTAM (http://utam.gg.utah.edu) sponsors for the financial supportThanks UTAM (http://utam.gg.utah.edu) sponsors for the financial supporthttp://utam.gg.utah.edu