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Multisource Least-Squares Migration of Marine Streamer Data with Frequency-Division Encoding

Multisource Least-Squares Migration of Marine Streamer Data with Frequency-Division Encoding. Yunsong Huang and Gerard Schuster KAUST. Outline. Multisource LSM Problem with Marine Data Multisource LSM with Frequency Division Numerical results Conclusions. Multisource. vs.

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Multisource Least-Squares Migration of Marine Streamer Data with Frequency-Division Encoding

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  1. Multisource Least-Squares Migration of Marine Streamer Data with Frequency-Division Encoding YunsongHuang and Gerard Schuster KAUST

  2. Outline Multisource LSM Problem with Marine Data Multisource LSM with Frequency Division Numerical results Conclusions

  3. Multisource vs Benefit: Reduction in computation and memory Liability: Crosstalk noise …

  4. Multisource (2) d1 +d2 m~ [L1+L2](d1+d2) T T migrate = L1d1+L2d2+ L1d2+L2d1 T T T T mmig T T =[L +L ](d + d ) Time 1 d1 d2 d1+d2 = [L1+L2]m vs standard mig. ~ ~ crosstalk d L blended forward modeling operator blended data

  5. Multisource LSM Inverse problem: 1 ~ ~ m || d – L m ||2 arg min J = 2 d misfit Iterative update: ~T m(k+1) = m(k) + aL d K=1 K=10

  6. Outline Multisource LSM Problem with Marine Data Multisource LSM with Frequency Division Numerical results Conclusions

  7. Problem with Marine Data erroneous misfit misfit = observed data simulated data

  8. Outline Multisource LSM Problem with Marine Data Multisource LSM with Frequency Division Numerical results Conclusions

  9. Solution • Every source sends out a unique identifier that survives LTI operations • Every receiver acknowledge the contribution from the ‘correct’ sources. observed simulated

  10. Frequency Division R(w) Nwfrequency bands of source spectrum: Nw= 5 ttravfpeak w Group 1 152 sources/group 2.2 km

  11. Outline Multisource LSM Problem with Marine Data Multisource LSM with Frequency Division Numerical results (2D) Conclusions

  12. Migration images (input SNR = 10dB) 304 shots in total an example shot and its aperture 0 0 b) Standard Migration a) Original Z (km) Z (km) 1.48 1.48 • d) 304 shots/gather • 26 iterations c) Standard Migration with 1/8 subsampled shots 6.75 6.75 X (km) X (km) 0 0

  13. Convergence curves. Input SNR = 10dB 1 Conjugate gradient Encoding anew and resetting search direction 0.5 0.4 304 shots/gather 0.3 Normalized data misfit 0.2 38 shots/gather 0.1 0 6 9 15 21 30 39 3 Iteration number

  14. Sensitivity to input noise level 9.4 8.0 SNR=30dB 6.6 5.4 SNR=10dB 3.8 Computational gain SNR=20dB Conventional migration: 1 38 76 152 304 Shots per supergather

  15. I/O considerations • Ns: # shots subsumed in a supergather • Nit: # of iterations that call for new encoding (i.e., new frequency division scheme) i) If data is stored on hard disk • The I/O cost of our proposed method is Nit/Ns times that of standard migration. ii) If data is stored on tape • The I/O cost of our proposed method is 1+ etimes that of standard migration.

  16. I/O cost Data on hard disk ii) Data on tape • Conventional • migration • Proposed method

  17. Stacked migration vssuccessive least-squares 1 1 1 3 3 3 successive least-squares: stacked migration: 0 2 2 2

  18. Outline Multisource LSM Problem with Marine Data Multisource LSM with Frequency Division Numerical results (3D) Conclusions

  19. SEG/EAGE Model+Marine Data • sources in total 40m 100 m 16 swaths, 50% overlap 256 sources a swath 6 km 20 m 3.7 km 16 cables 13.4 km

  20. Numerical Results True reflectivities Conventional migration 6.7 km 256shots/super-gather,16iterations 3.7 km 13.4 km 8 x gain in computational efficiency

  21. What have we empirically learned? Stnd. MigMultsrc. LSM IO 1 ~1/36 1 ~0.1 Cost Migration SNR ~1 1 Resolution dx1 ~double Cost vs Quality: Can I<<S? Yes.

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