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

Outline Introduction Joint least-squares migration Experiment Conclusion

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

Introduction Kirchhoff migration -

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

Joint least-squares migration of primaries and multiples

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

Multiple condition SG g’ T multiple (S,G) = min g’ [T primary (S,g’)+T primary (g’,G)]

Part of SMARRT model (m/s) Depth (m) Offset (m) 7,000 15,

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

Kirchhoff migration Depth (m) Offset (m) 7,000 15,

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

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

Kirchhoff migration Depth (m) Offset (m) 7,000 15,

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

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.

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