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Mitigation of RTM Artifacts with Migration Kernel Decomposition

Mitigation of RTM Artifacts with Migration Kernel Decomposition. Ge Zhan* and Gerard T. Schuster. King Abdullah University of Science and Technology. June 7, 2012. Outline. 0. 0. Introduction Method Examples Two-layer model BP salt model Conclusions. 2 km/s. Depth ( km ).

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Mitigation of RTM Artifacts with Migration Kernel Decomposition

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  1. Mitigation of RTM Artifacts with Migration Kernel Decomposition Ge Zhan* and Gerard T. Schuster King Abdullah University of Science and Technology June 7, 2012

  2. Outline 0 0 • Introduction • Method • Examples • Two-layer model • BP salt model • Conclusions 2 km/s Depth (km) Depth (km) 3 km/s 12.5 4 0 0 25 8 X (km) X (km)

  3. Outline • Introduction • Method • Examples • Two-layer model • BP salt model • Conclusions

  4. Introduction --- Reverse-time migration (RTM) Benefits: Images any dipping structure; Accounts for multiple arrivals; and etc. Problems: intensive computational costs severe migration artifacts

  5. Introduction --- RTM artifacts artifacts contaminate image RTM artifacts usually present as strong-amplitude, low-frequency noises in the migration image. Various remedies have been proposed to suppress RTM artifacts:  Smooth the velocity model before migration (Loewenthal et al., 1987);  Low-cut filtering on migrated images (Mulder and Plessix, 2003);  Directional damping to non-reflection wave equation (Fletcher et al., 2005);  Least-squares migration (Nemeth et al., 1999; Guitton et al., 2006);  Migration deconvolution (Hu et al., 2001; Yu et al., 2006);  Poynting-vector imaging condition (Yoon and Marfurt, 2006);  Wavefield decomposition using Hilbert transform (Liu et al., 2007; 2011).

  6. Outline • Introduction • Method • Examples • Two-layer model • BP salt model • Conclusions

  7. Method --- Seismic Survey

  8. Method --- Seismic Modeling d(r|s) s r G(x|s) G(r|x) x Recorded seismic data

  9. Method --- Reverse Time Migration (RTM) d(r|s) s r s r G*(x|s) G*(r|x) x x Migration of seismic data

  10. Method --- Reverse Time Migration (RTM) d(r|s) s r s r G*(x|s) G*(r|x) x x Migration of seismic data

  11. Method --- Reverse Time Migration (RTM) d(r|s) s r s r G*(x|s) G*(r|x) x x Migration of seismic data

  12. Method --- Reverse Time Migration (RTM) d(r|s) s r s r G*(x|s) G*(r|x) x x Migration of seismic data

  13. Method --- Reverse Time Migration (RTM) d(r|s) s r s r G*(x|s) G*(r|x) x x Migration of seismic data

  14. Method --- Reverse Time Migration (RTM) d(r|s) s r s r G*(x|s) G*(r|x) x x Migration of seismic data

  15. Method --- Generalized Diffraction Migration (GDM) d(r|s) s r s r D*(x|s) U*(r|x) x x Migration of seismic data

  16. Method --- GDM Workflow Compute & save Green’s functions for a given migration velocity; s s r G(x|s)G(x|r) G(x|r) G(x|s) Filter the Green’s functions into downgoing and upgoing components in F-K domain; r Convolve the appropriate components of filtered Green’s function to form the migration kernel; x x x shotgather Migration Kernel Dot product of the migration kernel with the recorded seismic data to get the migration image. T T x x

  17. Outline • Introduction • Method • Examples • Two-layer model • BP salt model • Conclusions

  18. Examples --- two-layer model 0 0 0 0 0 2 km/s Depth (km) Depth (km) Depth (km) Depth (km) Depth (km) 3 km/s 4 4 4 4 4 0 0 0 0 0 8 8 8 8 8 X (km) X (km) X (km) X (km) X (km) RTM GDM

  19. Outline • Introduction • Method • Examples • Two-layer model • BP salt model • Conclusions

  20. Examples --- BP salt model 0 0 0 0 Vp 1-shot RTM image km/s 1.5 Depth (km) Depth (km) Depth (km) Depth (km) 4.5 12.5 12.5 12.5 12.5 0 0 0 0 25 25 25 25 X (km) X (km) X (km) X (km) Stacked RTM image High-pass-filtered RTM image

  21. Examples --- BP salt model 0 0 0 0 0 Depth (km) Depth (km) Depth (km) Depth (km) Depth (km) 12.5 12.5 12.5 12.5 12.5 0 0 0 0 0 25 25 25 25 25 X (km) X (km) X (km) X (km) X (km) Standard RTM w/ filtering Horizontal GDM image Stacked GDM image Vertical GDM image

  22. Outline • Introduction • Method • Examples • Two-layer model • BP salt model • Conclusions

  23. Conclusions 1). The kernel of RTM imaging operator is decomposed into products of downgoingand upgoingGreen’s functions. 2). This decomposition leads to an imaging algorithm with fewerartifactsand a higher-qualityRTM image. 3). Advantage: deterministic filtering of RTM kernel can be directly applied to reduce migration artifacts, mitigate multiplesandeliminate aliasing artifacts. 4). Drawback: significantly more storage capacity and I/O time than standard RTM. 5). There are still some residual artifacts, which can be further eliminated by least-squares migration.

  24. Acknowledgments We thank the sponsors of the Center for Subsurface Imaging and Fluid Modeling (CSIM)at KAUST for their support. We also thank BP for making the BP 2007 salt model available.

  25. Thank you for your attention! Question or Suggestion?

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