2. Overview of Some Coherent Noise Filtering Methods Overview of Some Coherent Noise Filtering Methods Jianhua Yue, Yue Wang, Gerard Schuster University of Utah

3. Problem: Ground Roll Degrades Signal Offset (ft) Time (sec) 0 35002000 2.5Reflections GroundRoll

4. Problem: PS Waves Degrade Signal Time (sec) 0 4.0 PP Reflections Converted S Waves

5. Time (sec) 4.0 Reflections Converted S Waves 3100 Depth (ft) 2000 0 Time(s) 0.14 Problem: Tubes Waves Obscure PP Reflections Aliased tube waves

6. Radon Filtering Methods Radon Filtering Methods ARCO Field Data Results ARCO Field Data Results Saudi Land Data Saudi Land Data Multicomponent Data Example Multicomponent Data Example Conclusion and Discussion Conclusion and Discussion Outline

7. Two Classes of Coherent Noise Filtering Model Noise and Adaptive Subtraction Filter that Exploit Moveout Differences

8. F-K Dip Filtering F-K Dip Filtering Filtering Methods: Filtering Methods: Moveout Separation Filtering in  - p domain linear  - p linear  - p parabolic  - p parabolic  - p hyperbolic  - p hyperbolic  - p local+adaptive subtraction local+adaptive subtraction Least Squares Migration Filter

9. Distance Time NOISE SIGNALWavenumber Frequency Separation Principle: Exploit Differences in Separation Principle: Exploit Differences in Moveout & Part. Velocity Directions SIGNAL NOISE Transform Overlap Signal & Noise

10. Distance Time V=1/P Tau Transform Sum Tau-P Transform Tau-P Transform

11. Distance Time Transform Tau V=1/P

12. Distance TimeTransform Mute Noise Tau-P Transform Tau-P Transform Tau V=1/P

13. Tau Distance Time Transform Problem: Indistinct Problem: Indistinct Separation Signal/Noise V=1/P Tau-P Transform Tau-P Transform

14. Tau Distance Time Transform V=1/P Hyperbolic Transform Hyperbolic Transform Distinct Separation Distinct Separation Signal/Noise Hi res. Signal/Noise Hi res.

15. Distance Time Breakdown of Hyperbolic Assumptionvvvvvvvvv * Irregular Moveout

16. Distance Time Filtering by LSMF PS d = L m pp d = L m + L m ss P-reflectivity KirchhoffModeler Invert for m & m p s d PP S-Refl. Kirchhoff Modeler

17. LSMF Method 2. Find m by conjugate gradient p d = L m + L m sspp 1. data unknowns d = L m pp 3. Model Coherent Signal

18. Outline Radon Filtering Methods Radon Filtering Methods ARCO Surface Wave Data ARCO Surface Wave Data Saudi Land Data: Local Adapt.+Subt. Saudi Land Data: Local Adapt.+Subt. Multicomponent Data Example Multicomponent Data Example Conclusion and Discussion Conclusion and Discussion

19. RAW DATA OF ARCO Time (s) 0 2.5 1.83.6 X (kft) Raw Data

20. ARCO DATA Time (s) 0 2.5 1.83.61.83.6 X (kft) FKLSMF A B A B

21. ZOOM VIEW OF WINDOW “ A” Time (s) 0.5 1.5 2.03.0 X (kft) 2.03.0 X (kft) FKLSMF

22. Time (s) 1.5 2.5 2.03.45 X (kft) 2.03.45 X (kft) FKLSMF ZOOM VIEW OF WINDOW “ B”

23. Outline Radon Filtering Methods Radon Filtering Methods ARCO Surface Wave Data ARCO Surface Wave Data Saudi Land Data: Local Adapt.+Subt. Saudi Land Data: Local Adapt.+Subt. Multicomponent Data Example Multicomponent Data Example Conclusion and Discussion Conclusion and Discussion

24. 4.0s Aramco Saudi Land Data 0.0s Local tau-p

25. N S + N S + ~ ~Tau-p Tau-p N N S + - = S Adaptive Subtraction

26. Input After Noise Reduction INPUT LOCAL TAU-P INPUT LOCAL TAU-P (courtesy Yi Luo @ Aramco) 4.0s 0.0s

27. Input FK Signal FK F K F K

28. Radon Filtering Methods Radon Filtering Methods ARCO/Saudi Field Data Results ARCO/Saudi Field Data Results Multicomponent Data Example Multicomponent Data Example Graben Example Graben Example Mahagony Example Mahagony Example Conclusion and Discussion Conclusion and Discussion Outline

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

30. Synthetic Data 1.4 0 Time (s) 0 Offset (m) 5000 0 Offset (m) 5000 Horizontal Component Vertical Component PP1 Leak PP1 Leak PP2 Leak PP2 Leak PP3 Leak PP4 Leak PP1PP2 PP3 PP4

31. LSMF Separation 1.4 0 Time (s) 0 Offset (m) 5000 0 Offset (m) 5000 Horizontal Component Vertical Component PP1PP2 PP3 PP4

32. True P-P and P-SV Reflection 1.4 0 Time (s) 0 Offset (m) 5000 0 Offset (m) 5000 Horizontal Component Vertical Component PP1PP2 PP3 PP4

33. F-K Filtering Separation 1.4 0 Time (s) 0 Offset (m) 5000 0 Offset (m) 5000 Horizontal Component Vertical Component PP1PP2 PP3 PP4 PP1 Leak PP1 Leak PP2 Leak PP2 Leak PP3 Leak PP4 Leak

34. Radon Filtering Methods Radon Filtering Methods ARCO/Saudi Field Data Results ARCO/Saudi Field Data Results Multicomponent Data Example Multicomponent Data Example Graben Example Graben Example Mahagony Field Data Mahagony Field Data Conclusion and Discussion Conclusion and Discussion Outline

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

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

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

38. Local tau-p and adaptive subtraction LSMF computes moveout and particle velocity direction based on true physics. velocity direction based on true physics. Don’t use a shotgun to kill a fly Don’t use a shotgun to kill a fly Conclusions Conclusions Filtering signal/noise using: moveout Filtering signal/noise using: moveout difference & particle velocity direction difference & particle velocity direction

39. FKLinearTau-PParabolicTau-P LSMF Simple Filtering YESYESYESYES Complex Filtering No YES/No YES/no YES User Intervention YesYesMildYes Costc$$$$$$ ProvenYESYESYES Yes/No SUMMARY

40. SAUDI DATA Time (s) 0 4.0 882988 X(m) Raw Data

41. SAUDI DATA AFTER FK & LSMF Time (s) 0 4.0 882988 X(m) 882988 X (m) FKLSMF A A B B

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

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

44. ZOOM VIEW OF WINDOW A Time (s) 1.0 2.0 8902088 X (m) FKLSMF 8902088 X (m)

45. ZOOM VIEW OF WINDOW B Time (s) 0.7 2.0 1861189 X (m) FKLSMF 1861189 X (m)

46. SAUDI DATA Time (s) 0 4.0 882089 X(m) Raw Data

47. SAUDI DATA AFTER FK & LSMF Time (s) 0 4.0 882089 X(m) 882089 X (m) FKLSMF BB AA

48. ZOOM VIEW OF WINDOW “A” Time (s) 0.6 2.0 3271370 X (m) FKLSMF 3271370 X (m)

49. ZOOM VIEW OF WINDOW “B” Time (s) 0.4 1.4 186621 X (m) FKLSMF 186621 X (m)

50. Overview of Some Coherent Noise Filtering Merthods Overview of Some Coherent Noise Filtering Merthods Overview There are a number of different coherent noise filtering methods, including FK dip filter, Radon transform, hyperbolic transform, and parabolic transform methods. All of these methods rely upon transforming the signal into a new domain where the signal and noise are more separable. We will show that LSM filtering is another coherent filtering method, but is more precise in defining a transform that separates signal and coherent noise according to the physics of wave propagation. Examples show that this is sometimes a more effective ilter, but it is more costly.

51. Distance Time PS PP Multicomponent Filtering by LSMF Z PP PS ss d = L m + L m pp x ss pp z

52. Signal FK

53. Problem: Out-of-Plane Ground Roll Ground Roll

54. Distance Time PS PP Filtering by LSMF M1M1M1M1 M2M2M2M2 Z X Lp Ls

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

56. Distance Time A B V=1/P Tau Filtering by Parabolic  - p Signal/NoiseOverlap

57. F-X Spectrum of ARCO Data Offset (ft) Frequency (Hz) 0 35002000 50 S. of LSM Filtered Data (V. Const) S. of F-K Filtered Data (13333ft/s)

58. Summary Traditional coherent filtering based on Traditional coherent filtering based on approximate moveout approximate moveout LSMF filtering operators based on LSMF filtering operators based on actual physics separating signal & noise actual physics separating signal & noise Better physics --> Better focusing, more $$$ Better physics --> Better focusing, more $$$