1 / 23

Migration Deconvolution vs. Least Squares Migration

Migration Deconvolution vs. Least Squares Migration. Jianhua Yu University of Utah. Outline. Motivation MD vs. LSM Numerical Tests Conclusions. Amplitude distortion. Footprint. Migration noise and artifacts. Migration Noise Problems. Limited Resolution. Migration Problems. Aliasing.

nuwa
Download Presentation

Migration Deconvolution vs. Least Squares Migration

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Migration Deconvolution vs. Least Squares Migration Jianhua Yu University of Utah

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

  3. Amplitude distortion Footprint Migration noise and artifacts Migration Noise Problems

  4. Limited Resolution Migration Problems Aliasing

  5. Improving resolution Suppressing 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. T -1 m = (L L ) m’ Reflectivity Migration Section MD deblurring 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. Outline • Motivation • MD vs. LSM • Numerical Tests • Conclusions

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

  12. Scatterer Model Kirchhoff Migration 1.0 1.0 0 0 0 Depth (km) 1.8

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

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

  15. X (km) 0 7.0 0 KM Depth (km) 4.5 X (km) 0 7.0 0 LSM 10 4.5

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

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

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

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

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

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

  22. Function Resolution Performance MD = LSM . Conclusions Efficiency MD >> LSM Suppressing noise MD > LSM Robustness MD < LSM

  23. Acknowledgments • Thanks to 2001 UTAM sponsors for their financial support

More Related