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Migration Deconvolution vs Least Squares Migration

Migration Deconvolution vs Least Squares Migration. Jianhua Yu, Gerard T. Schuster University of Utah. Outline. Motivation MD vs. LSM Numerical Tests Conclusions. Footprint. Migration noise and artifacts. Migration Noise Problems. Time. Aliasing. Recording footprints.

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Migration Deconvolution vs Least Squares Migration

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

  2. Outline • Motivation • MD vs. LSM • Numerical Tests • Conclusions

  3. Footprint Migration noise and artifacts Migration Noise Problems Time

  4. Aliasing Recording footprints Amplitude distortion Limited resolution Migration Problems

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

  6. Outline • Motivation • MD vs. LSM • Numerical Tests • Conclusions

  7. -1 T T m = (L L ) Ld Reflectivity Seismic data Modeling operator Migration operator Least Squares Migration

  8. -1 T T m = (L L ) Ld m’ Reflectivity Migrated data Modeling operator Migration Deconvolution

  9. -1 T T LSM: m = (L L ) Ld T -1 m = (L L ) m’ MD: Migrated image Data Solutions of MD Vs. LSM

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

  11. Outline • Motivation • MD Vs. LSM • Numerical Tests • Conclusions

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

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

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

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

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

  17. X (km) 0 7.0 0 KM Depth (km) 4.5 X (km) 0 7.0 0 LSM 15 4.5

  18. X (km) 0 7.0 0 KM Depth (km) 4.5 X (km) 0 7.0 0 MD 4.5

  19. X (km) 0 7.0 0 LSM 15 Depth (km) 4.5 X (km) 0 7.0 0 MD 4.5

  20. Zoom View KM LSM 15 2 Depth (km) 3.5 LSM 19 MD 2 Depth (km) 3.5

  21. Why does MD perform better than LSM ? X (km) 0 7.0 0 Depth (km) LSM 19 4.5 0 MD 4.5

  22. Outline • Motivation • MD Vs. LSM • Numerical Tests • Conclusions

  23. Function Resolution Performance MD < LSM (?) Conclusions Efficiency MD >> LSM Suppressing noise MD = LSM (?) Robustness MD < LSM

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

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