1 / 31

Multiple attenuation in the image space

This paper discusses methods for multiple attenuation in image space, focusing on the feasibility of applying wavefield extrapolation migration (WEM) in 3D scenarios. It highlights the advantages of dense data requirements and cost-effective solutions while addressing data and imaging mismatches. The research employs high-resolution Radon transforms to improve signal-to-noise separation and migration accuracy. Several synthetic examples illustrate the application of multiple attenuation techniques, demonstrating their potential for enhancing subsurface imaging quality in geophysical data.

erin-scott
Télécharger la présentation

Multiple attenuation in the image space

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. 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

More Related