1. LEAST-SQUARES MIGRATION OF BOTH PRIMARIES AND MULTIPLES Ruiqing He, Gerard Schuster University of Utah Oct. 2003

2. Outline Introduction Joint least-squares migration Experiment Conclusion

3. Former works Brown (2002) Duquet and Marfurt (1999) Liu (1998) Nemeth (1999) Wang (1998)

4. Introduction Kirchhoff migration -

5. Least-squares migration - - Iterative solution Conjugate Gradient (CG) method

6. Joint least-squares migration of primaries and multiples

7. Modeling Operators Travel-times Geometric spreading Reflectance (angle-dependent) Non-linear

8. Multiple condition 0 1 2 SG g’ T multiple (S,G) = min g’ [T primary (S,g’)+T primary (g’,G)]

9. Part of SMARRT model (m/s) 4500 1500 3000 Depth (m) Offset (m) 7,000 15,000 0 0

10. Synthetic zero-offset data Offset (m) 7,000 0 Time (sec.) 8.8 0

11. Kirchhoff migration Depth (m) Offset (m) 7,000 15,000 0 0

12. Joint least-squares migration Depth (m) Offset (m) 7,000 15,000 0 0

13. Stack-of-scattering data Time (sec.) 8.8 0 0 Offset (m) 7,000

14. Kirchhoff migration Depth (m) Offset (m) 7,000 15,000 0 0

15. Joint least-squares migration Depth (m) Offset (m) 7,000 15,000 0 0

16. Conclusion Primary migration is improved. It is possible to attenuate multiple migration. Accurate forward modeling is vital. Optimum iteration number is a balance. It is costly.

17. Thanks  Thank you.  2002 members of UTAM for financial support.