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Multiple attenuation in the image space

Multiple attenuation in the image space. Paul Sava & Antoine Guitton Stanford University SEP. Goal. Method feasible in 3-D Less expensive Dense data requirement Exploit the data/imaging mismatch Data: two-way propagation Migration: one-way extrapolation. Key technology.

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Multiple attenuation in the image space

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  1. Multiple attenuation in the image space Paul Sava & Antoine Guitton Stanford University SEP paul@sep.stanford.edu

  2. Goal • Method feasible in 3-D • Less expensive • Dense data requirement • Exploit the data/imaging mismatch • Data: two-way propagation • Migration: one-way extrapolation paul@sep.stanford.edu

  3. Key technology • Migration by wavefield extrapolation (WEM) • Angle-domain common-image gathers • High resolution Radon Transforms paul@sep.stanford.edu

  4. The big picture Image Image RT & Mute S/N separation WE migration & ADCIG RT & Mute S/N separation NMO WE prediction Data Data paul@sep.stanford.edu

  5. Multiple attenuation by RTs Moveout analysis NMO Moveout analysis WE migration • S/N separation • RT + Mute • S/N separation • RT + Mute paul@sep.stanford.edu

  6. 3-D depth imaging WE migration Multi-arrival Angle-gathers Single-valued Kirchhoff migration Single-arrival Offset-gathers Multi-valued y x g z g Biondi et al. (2003) Stolk & Symes (2002) paul@sep.stanford.edu

  7. Synthetic example: data vs. image CIG CMP paul@sep.stanford.edu

  8. Which Radon transform? g q g(g) z Generic Radon Transform Parabolic Tangent Biondi & Symes (2003) paul@sep.stanford.edu

  9. Synthetic example: RTs Parabolic Tangent paul@sep.stanford.edu

  10. Synthetic example: S/N separation primaries & multiples ART ART + mute multiples primaries paul@sep.stanford.edu

  11. BP synthetic example paul@sep.stanford.edu

  12. BP synthetic example primaries & multiples ART multiples primaries paul@sep.stanford.edu

  13. BP synthetic example: stacks primaries & multiples multiples primaries paul@sep.stanford.edu

  14. GOM example paul@sep.stanford.edu

  15. GOM example: CIG 1 primaries & multiples ART ART + mute multiples primaries paul@sep.stanford.edu

  16. GOM example paul@sep.stanford.edu

  17. GOM example: CIG 2 primaries & multiples ART ART + mute multiples primaries paul@sep.stanford.edu

  18. GOM example paul@sep.stanford.edu

  19. GOM example: zoom 1 primaries & multiples paul@sep.stanford.edu

  20. GOM example: zoom 1 primaries paul@sep.stanford.edu

  21. GOM example: zoom 1 primaries & multiples paul@sep.stanford.edu

  22. GOM example: zoom 1 multiples paul@sep.stanford.edu

  23. GOM example paul@sep.stanford.edu

  24. GOM example: zoom 2 primaries & multiples paul@sep.stanford.edu

  25. GOM example: zoom 2 primaries paul@sep.stanford.edu

  26. GOM example: zoom 2 primaries & multiples paul@sep.stanford.edu

  27. GOM example: zoom 2 multiples paul@sep.stanford.edu

  28. RT comparison Image space RT Data space RT paul@sep.stanford.edu

  29. Discussion PROs Cheap & robust 3-D Simple primaries Migration artifacts CONs Velocity model? Moveout function? Interactive mute Inner angles RT artifacts paul@sep.stanford.edu

  30. Summary Image Image RT & Mute S/N separation WE migration & ADCIG RT & Mute S/N separation NMO WE prediction Data Data paul@sep.stanford.edu

  31. Summary • Multiple attenuation after migration • WE migration • Angle gathers • Cost/accuracy • Complex propagation • Cheap separation • RT limitations • filtering approach paul@sep.stanford.edu

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