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3-D Parallel Sparse Frequency Wave-Equation Migration

3-D Parallel Sparse Frequency Wave-Equation Migration. Jianhua Yu. University of Utah. Motivation. Sparse Frequency Migration. Numerical Results. Conclusions. OUTLINE. Huge computational cost. Less memory requirement. Compressed data due to band-limited seismic source.

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3-D Parallel Sparse Frequency Wave-Equation Migration

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  1. 3-D Parallel Sparse Frequency Wave-Equation Migration Jianhua Yu University of Utah

  2. Motivation Sparse Frequency Migration Numerical Results Conclusions OUTLINE

  3. Huge computational cost Less memory requirement Compressed data due to band-limited seismic source Limited frequency wavefield extrapolation Features of Migration in Frequency Domain

  4. Saves computational time. How much subsampling before unacceptable degradation of image quality? Purpose for Subsampling Frequencies in Migration

  5. Motivation Sparse Frequency Migration Numerical Results Conclusions OUTLINE

  6. For Shot Loop For Each Depth Sparse frequency wavefield extrapolation by FFD=SSF+FD For Each Frequency Extrapolation source U and receiver D End of Frequency Loop Applying imaging condition: D conjg(U) End of Depth Loop End of Shot Loop Implementation of Migration in Frequency Domain

  7. Kx F Input full wavefield Z Kx F Transform wavefield from frequency domain to time domain Sparse frequency subsampling and wavefield extrapolation Z Yu Strategy:Sparse Wavefield Extrapolation

  8. Apply imaging condition Repeat above steps until maximum depth Sparse Source Wavefield Extrapolation Capture the main energy of wavefield in time domain

  9. Seismic wavefield Seismic wavefield Not even subsampled source wavefield extrapolation Applying Imaging Conditions Pratt Strategy: Optimal frequency migration (Sirgue and Pratt, 2001; Bednar and Neale, 2002)

  10. Data Divided data Cube Parallel Implementation of 3-D Sparse Frequency Migration

  11. Image Cube Computing Nodes Divided data Cube Parallel Implementation of 3-D Sparse Frequency Migration

  12. Motivation Sparse Frequency Migration Numerical Results Conclusions OUTLINE

  13. Motivation Sparse Frequency Migration Numerical Results 3-D SEG/EAGE Salt Model Conclusions OUTLINE

  14. 9 X5 Sources; 201 X 201 Receivers dxshot=dyshot=1 km dxg=dyg=20 m 3-D SEG/EAGE Salt Model 10.2 km 10.2 km 0 0 Imaging: dx=dy=20 m

  15. Mig Cube 2 0 Depth (km) Y (km) X (km) Velocity Cube 2 0 Y (km) X (km)

  16. X (km) Z=1.0 km X (km) Strategy II: 50 freq Standard mig Strategy II: 78 freq Strategy I: 120 freq Y (km) Y (km)

  17. X (km) Z=1.2 km X (km) Strategy II: 50 freq Strategy II: 78 freq Strategy I: 120 freq Y (km) Standard mig Y (km)

  18. X (km) Z=1.6 km X (km) Strategy II: 50 freq Strategy II: 78 freq Strategy I: 120 freq Y (km) Standard mig Y (km)

  19. X (km) Y=6.62 km X (km) Strategy II: 50 freq Strategy II: 78 freq Strategy I: 120 freq Standard mig Y (km) Y (km)

  20. X (km) Y=7.62 km X (km) Strategy II: 50 freq Strategy II: 78 freq Strategy I: 120 freq Standard mig Y (km) Y (km)

  21. X (km) Y=9.12 km X (km) Strategy II: 50 freq Strategy I:120 freq Standard mig Y (km) Strategy II: 78 freq Y (km)

  22. Y (km) x=5.12 km Y (km) Strategy II: 50 freq Strategy II: 78 freq Strategy I: 120 freq Standard mig Y (km) Y (km)

  23. Y (km) x=6.12 km Y (km) Strategy II: 50 freq Standard mig Y (km) Strategy II: 78 freq Strategy I: 120 freq Y (km)

  24. Y (km) x=7.62 km Y (km) Strategy II: 50 freq Standard mig Y (km) Strategy II: 78 freq Strategy I: 120 freq Y (km)

  25. CPU Time , Frequency and Image quality CUP Time 9 Processors, 2GM 1.4GHz 25.0 20.0 CPU Time (Hrs) 15.0 10.0 5.0 376 50 78 120 240 Used Frequency Number

  26. CPU Time , Frequency and Image quality 9 Processors, 2GM 1.4GHz Image quality 376 50 78 120 240 Used Frequency Number

  27. Motivation Sparse Frequency Migration Numerical Results Conclusions OUTLINE

  28. Both sparse frequency strategies are able to reduce computational time. Strategy two is more efficient in computation than strategy one. Computational time can be saved by fraction of about 60 % in Pratt’s strategy with frequency of 78 components    CONCLUSIONS

  29. Reducing CPU time in exchange of lose of subsalt imaging quality even if the optimized frequency components are used. A little communication time in parallel implementation enhances the parallel efficiency   CONCLUSIONS

  30. ACKNOWLEDGMENTS I thank the 2002 sponsors of UTAM Consortium for their financial support

  31. Seismic wavefield Seismic wavefield subsampled source wavefield extrapolation Applying Imaging Conditions Capture the leading portion of wavefield Strategy One:Sparse Wavefield Extrapolation

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