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LEAST-SQUARES MIGRATION OF BOTH PRIMARIES AND MULTIPLES Ruiqing He, Gerard Schuster University of Utah Oct. 2003
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Outline Introduction Joint least-squares migration Experiment Conclusion
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Former works Brown (2002) Duquet and Marfurt (1999) Liu (1998) Nemeth (1999) Wang (1998)
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Introduction Kirchhoff migration -
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Least-squares migration - - Iterative solution Conjugate Gradient (CG) method
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Joint least-squares migration of primaries and multiples
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Modeling Operators Travel-times Geometric spreading Reflectance (angle-dependent) Non-linear
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Multiple condition 0 1 2 SG g’ T multiple (S,G) = min g’ [T primary (S,g’)+T primary (g’,G)]
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Part of SMARRT model (m/s) 4500 1500 3000 Depth (m) Offset (m) 7,000 15,000 0 0
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Synthetic zero-offset data Offset (m) 7,000 0 Time (sec.) 8.8 0
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Kirchhoff migration Depth (m) Offset (m) 7,000 15,000 0 0
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Joint least-squares migration Depth (m) Offset (m) 7,000 15,000 0 0
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Stack-of-scattering data Time (sec.) 8.8 0 0 Offset (m) 7,000
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Kirchhoff migration Depth (m) Offset (m) 7,000 15,000 0 0
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Joint least-squares migration Depth (m) Offset (m) 7,000 15,000 0 0
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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.
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Thanks Thank you. 2002 members of UTAM for financial support.
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