Separation of Signal and Coherent Noise

1. Separation of Signal and Coherent Noise Gerard T. Schuster University of Utah

2. Outline • Problem and Solution • Methodology • ARCO Field Data Results • Multicomponent Data Example • Conclusion and Discussion

3. Problem: - Data with signal polluted by coherent noise Solution: - Separate signal from coherent noise using traditional filtering method and LSMF.

4. Outline • Problem and Solution • Methodology • ARCO Field Data Results • Multicomponent Data Example • Conclusion and Discussion

5. Traditional Filtering Method - F-K dip filtering - Filtering in  - p domain linear  - p parabolic  - p hyperbolic  - p

6. Central Idea of Migration Filtering 1. <= observed data <= model estimates 2. <= reconstructed signal 3. - signal - signal model estimate - coherent noise - coherent noise model estimate - signal modeling operator - coherent noise modeling operator

7. Outline • Problem and Solution • Methodology • Moveout Difference • Particle Velocity Direction Difference • Separation Process • ARCO Field Data Results • Multicomponent Data Example • Conclusion and Discussion

8. B A Move-Out Difference * v v v v v v v v v Time Distance

9. F-K Filtering B Frequency Time A Wavenumber Distance

10. A B Muting Time Distance F-K Filtering Frequency Wavenumber

11. Filtering by Linear - p B Time Time A p Distance

12. A B Time Distance Filtering by Linear - p Muting Time p

13. Filtering by Parabolic - p B Time Time A p Distance

14. B Time A Distance Filtering by Parabolic - p Muting Time p

15. B A Filtering by LSMF * v v v v v v v v v Time Distance

16. Filtering by Parabolic - p B Time Time A p Distance

17. -1 L1 -1 L2 M1 M2 Filtering by LSMF B Time A Distance

18. B L2 Filtering by LSMF Time Distance M2

19. Outline • Problem and Solution • Methodology • Moveout Difference • Particle Velocity Direction Difference • Separation Process • ARCO Field Data Results • Multicomponent Data Example • Conclusion and Discussion

20. P S Particle Velocity Direction Difference * v v v v v v v v v Time Distance

21. Multicomponent Data - F-K and linear  - p filtering plane wave assumption possible for P and S separation - Other filtering in  - p domain impossible for P and S separation

22. Multicomponent Data - LSMFfiltering raypath known from modeling operator possible for P and S separation

23. Outline • Problem and Solution • Methodology • Moveout Difference • Particle Velocity Direction Difference • Separation Process • ARCO Field Data Results • Multicomponent Data Example • Conclusion and Discussion

24. Muting Muting Process B Time Time A p Distance

25. LSMF Process -1 L1 B M1 Time -1 L2 A M2 Distance

26. Separation Process - Traditionalfiltering muting based on a range of parameters noise residual and signal damage - LSMF filtering operators based on physics automatically separating S/N

27. Outline • Objective • Methodology • ARCO Field Data Results • Multicomponent Data Example • Conclusion and Discussion

28. ARCO Field Data (0.3 sec AGC) Offset (ft) 2000 3500 0 Time (sec) 2.5

29. F-X Spectrum of ARCO Data Offset (ft) 2000 3500 0 Frequency (Hz) 50

30. Predicted Surface Waves Offset (ft) 2000 3500 0 Time (sec) 2.5

31. F-X Spectrum of Surface Waves Offset (ft) 2000 3500 0 Frequency (Hz) 50

32. LSM Filtered Data (V. Const.) Offset (ft) 2000 3500 0 Time (sec) 2.5

33. S. of LSM Filtered Data (V. Const) Offset (ft) 2000 3500 0 Frequency (Hz) 50

34. F-K Filtered Data (13333ft/s) Offset (ft) 2000 3500 0 Time (sec) 2.5

35. S. of F-K Filtered Data (13333ft/s) Offset (ft) 2000 3500 0 Frequency (Hz) 50

36. Outline • Objective • Methodology • ARCO Field Data Results • Multicomponent Data Example • Graben Example • Mahogony Example • Conclusion and Discussion

37. Graben Velocity Model X (m) 0 5000 0 V1=2000 m/s V2=2700 m/s V3=3800 m/s Depth (m) V4=4000 m/s V5=4500 m/s 3000

38. Synthetic Data Offset (m) Offset (m) 5000 0 5000 0 0 PP1 PP2 Time (s) PP3 PP4 1.4 Horizontal Component Vertical Component

39. LSMF Separation 5000 0 Offset (m) 5000 0 Offset (m) 0 Time (s) 1.4 Horizontal Component Vertical Component

40. True P-P and P-SV Reflection 5000 0 Offset (m) 5000 0 Offset (m) 0 Time (s) 1.4 Horizontal Component Vertical Component

41. F-K Filtering Separation 5000 0 Offset (m) 5000 0 Offset (m) 0 PP1 PP2 Time (s) PP3 PP4 1.4 Horizontal Component Vertical Component

42. Outline • Objective • Methodology • ARCO Field Data Results • Multicomponent Data Example • Graben Example • Mahogony Field Data • Conclusion and Discussion

43. CRG1 Data after Using F-K Filtering 0 Time (s) 4 CRG1 (Vertical component)

44. CRG1 Raw Data 0 Time (s) 4 CRG1 (Vertical component)

45. CRG1 Data after Using LSMF 0 Time (s) 4 CRG1 (Vertical component)

46. CRG2 Data after Using F-K Filtering (vertical component) 0 Time (s) 4 CRG2 (Vertical component)

47. CRG2 Raw Data (vertical component) 0 Time (s) 4 CRG2 (Vertical component)

48. CRG2 Data after Using LSMF (vertical component) 0 Time (s) 4 CRG2 (Vertical component)

49. Outline • Objective • Methodology • ARCO Field Data Results • Multicomponent Data Example • Conclusion and Discussion

50. Conclusions - Filtering signal/noise using: moveout difference particle velocity direction - Traditional filtering is cheaper than LSMF. - LSMF computes moveout and particle velocity direction based on true physics.