1. MULTIPLE PREDICTION & ATTENUATION Ruiqing He University of Utah Feb. 2004 Feb. 2004

2. Outline Introduction Multiple traveltime prediction & examples Multiple traveltime prediction & examples Multiple attenuation & examples Multiple attenuation & examples Conclusion Conclusion

3. Introduction Facts about multiple problems: Multiple removal is important for quality seismic imaging.Multiple removal is important for quality seismic imaging. Multiple waveform prediction is difficult.Multiple waveform prediction is difficult. Multiple traveltime prediction is feasible.Multiple traveltime prediction is feasible. Multiples can be attenuated along their moveout in seismic traces.Multiples can be attenuated along their moveout in seismic traces.

4. Outline Introduction Multiple traveltime prediction & examples Multiple traveltime prediction & examples Multiple attenuation & examples Multiple attenuation & examples Application to synthetic and real data Application to synthetic and real data Conclusion Conclusion

5. Multiple Traveltime Prediction Predict solely from primary traveltimes. Predict solely from primary traveltimes. Recent works: - Landa E., 1999 - Reshef M., 2003 - Schuster G., 2003 This work: Data structure for implementation No subsurface information needed. No subsurface information needed. Valid for regular seismic acquisitions. Valid for regular seismic acquisitions.

6. Naming Convention: Type of Rays 0 1 2 e.g. multiple: SG 21201

7. Prediction of Pegleg Multiple Traveltime by Format’s Principle 0 1 2 SGp e.g. multiple: T 201

8. Prediction of Interbed Multiple Traveltime by Format’s Principle 0 0 1 1 2 2 S S G G p p q q e.g. multiple: T 212

9. Multiple Prediction Algorithm 1.Primary traveltimes are firstly picked and saved. 2.Primary-primary-interference multiples traveltimes are predicted and saved. 3.Primary-multiple-interference multiples traveltimes are predicted and saved. traveltimes are predicted and saved. 4. Multiple-multiple-interference multiples traveltimes are predicted and saved. traveltimes are predicted and saved.

10. Part of The SMAART Model Depth (m) (m) 0 9 km Offset (m) 0 15 km reflector 0 reflector 1 reflector 2 reflector 3

11. Pick Traveltime of Primary 1

12. Picked Primary 1 Picked

13. Prediction of Multiple 101 Picked Predicted

14. Prediction of Multiple 10101 Picked Predicted Newly Predicted

15. Prediction of pegleg 102 Picked Predicted

16. Prediction of pegleg 201 Picked Predicted

17. Order of Peglegs 102 and 201 are different multiples. They are identical to each other only when They are identical to each other only when the model is 1 D, and all the reflectors are the model is 1 D, and all the reflectors are horizontal. horizontal. They are similar in most cases. They are similar in most cases.

18. Prediction of pegleg 323 Picked Predicted

19. Unocal Multiple 101 Time (s) 0.2 4 Offset (km) 0.2 3

20. Unocal Multiple 10101 Time (s) 0.2 4 Offset (km) 0.2 3

21. Outline Introduction Multiple traveltime prediction & examples Multiple traveltime prediction & examples Multiple attenuation & examples Multiple attenuation & examples Conclusion Conclusion

22. Multiple Attenuation Deterministic methods:Deterministic methods: –Multiple waveform prediction Non-deterministic methods:Non-deterministic methods: –Prediction filtering and median filtering

23. NMO of Multiples dt offset T Original window Predictedmultiple dt T offset NMO window Predictedmultiple

24. Prediction Filtering Prediction filter F satisfies: A(x) * F = A(x+d) where, A: amplitude A: amplitude x: offset x: offset d: is prediction distance d: is prediction distance *: is convolution operator *: is convolution operator

25. Multi-channel Prediction Filtering (MPF) Multi-channel Prediction filter F:Multi-channel Prediction filter F: A (t) (x) * F = A (t) (x+d) where, A (t) is the amplitude at the relative time in NMO window. F is the prediction filter for all (time) channels simultaneously.F is the prediction filter for all (time) channels simultaneously. Avoid local anomalies.Avoid local anomalies.

26. Experiments on Synthetic Data Offset (km) Time (s) 1 6 1 32 One part of zero-offset SMAART data

27. Median Filtering Filter length=10 Filter length=20 Linear zone Non-linear zone zone

28. Mul-tichannel Prediction Filtering Linear zone Non-linear zone zone Filter length=5 Filter length=10

29. MPF & Median Filtering Offset (km) Time (s) 1 6 1 32

30. MPF & Median Filtering

31. Unocal Field Data Demultiple Offset (km) Time (s) 1 4 0.2 3

32. Unocal Field Data Demultiple

33. Offset (km) Time (s) 1 4 0.2 3

34. Unocal Field Data Demultiple

35. Outline Introduction Multiple traveltime prediction & examples Multiple traveltime prediction & examples Multiple attenuation & examples Multiple attenuation & examples Conclusion Conclusion

36. Conclusion Without knowing subsurface model, multiple travel-time can be accurately predicted. travel-time can be accurately predicted. By prediction filtering or median filtering, By prediction filtering or median filtering, multiple can be attenuated. multiple can be attenuated. However, there are multiples can not be treatedHowever, there are multiples can not be treated by the proposed method, then a multiple by the proposed method, then a multiple waveform prediction for them is required. waveform prediction for them is required.

37. Reference Houston L., 1998: Multiple suppression using a local 1. Houston L., 1998: Multiple suppression using a local coherence filter, Geophysics, Volume 63, Issue 2, 652-659. coherence filter, Geophysics, Volume 63, Issue 2, 652-659. 2. Landa E., 1999, Multiple prediction and attenuation using wavefront characteristics of multiple-generating using wavefront characteristics of multiple-generating primaries: The Leading Edge, January, 60-64. primaries: The Leading Edge, January, 60-64. 3. Reshef M., et al. 2003, Multiple prediction without prestack data: an efficient tool for interpretive processing: prestack data: an efficient tool for interpretive processing: First Break, Vol. 21, March, 29-37. First Break, Vol. 21, March, 29-37. 4. Schuster G., 2003, Imaging the most bounce out of multiples: UTAM 2002 annual meeting. multiples: UTAM 2002 annual meeting.

38. Thanks 2003 members of UTAM for financial support.2003 members of UTAM for financial support.