1. Review of Coherent Noise Suppression Methods Gerard T. Schuster University of Utah

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

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

4. 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

5. Problem: Dune Waves Obscure PP Dune Waves

6. Coherent Filtering Methods Coherent Filtering Methods ARCO Field Data Results ARCO Field Data Results Multicomponent Data Example Multicomponent Data Example Conclusion and Discussion Conclusion and Discussion Outline

7. F-K Dip Filtering F-K Dip Filtering Filtering in  - p domain Filtering in  - p domain linear  - p linear  - p parabolic  - p parabolic  - p hyperbolic  - p hyperbolic  - p Least Squares Migration Filter Least Squares Migration Filter Traditional Filtering Methods

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

9. Distance Time P Tau Transform Sum Tau-P Transform Tau-P Transform

10. Distance Time Transform P Tau

11. Distance Time Transform P Tau Mute Noise

12. Tau Distance Time Transform Problem: Indistinct Problem: Indistinct Separation Signal/Noise P Tau-P Transform Tau-P Transform

13. Tau Distance Time Transform P Hyperbolic Transform Hyperbolic Transform Distinct Separation Distinct Separation Signal/Noise Signal/Noise

14. Distance Time Breakdown of Hyperbolic Assumptionvvvvvvvvv * A B Irregular Moveout

15. Distance Time A B p Time Filtering by Parabolic  - p Signal/NoiseOverlap

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

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

18. Distance Time PS PP Z ss d = L m + L m pp x ss M1M1M1M1 M2M2M2M2 X pp z

19. 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 $$$ Better physics --> Better focusing, more $$$

20. Outline Coherent Filtering Methods Coherent Filtering Methods ARCO Surface Wave Data ARCO Surface Wave Data Multicomponent Data Example Multicomponent Data Example Conclusion and Discussion Conclusion and Discussion

21. ARCO Field Data Offset (ft) Time (sec) 0 35002000 2.5

22. LSM Filtered Data (V. Const.) Offset (ft) Time (sec) 0 35002000 2.5 ARCO Field Data

23. F-K Filtered Data (13333ft/s) Offset (ft) Time (sec) 0 35002000 2.5 LSM Filtered Data (V. Const.)

24. 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)

25. Coherent Filtering Methods Coherent Filtering Methods ARCO Field Data Results ARCO Field Data Results Multicomponent Data Example Multicomponent Data Example Graben Example Graben Example Mahogony Example Mahogony Example Conclusion and Discussion Conclusion and Discussion Outline

26. 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

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

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

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

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

31. Coherent Filtering Methods Coherent Filtering Methods ARCO Field Data Results ARCO Field Data Results Multicomponent Data Example Multicomponent Data Example Graben Example Graben Example Mahogony Field Data Mahogony Field Data Conclusion and Discussion Conclusion and Discussion Outline

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

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

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

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

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

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

38. Coherent Filtering Methods Coherent Filtering Methods ARCO Field Data Results ARCO Field Data Results Multicomponent Data Example Multicomponent Data Example Conclusion and Discussion Conclusion and Discussion Outline

39. Filtering signal/noise using: moveout Filtering signal/noise using: moveout difference & particle velocity direction difference & particle velocity direction - Traditional filtering $ vs $$$$ LSMF LSMF computes moveout and particle LSMF computes moveout and particle velocity direction based on true physics. velocity direction based on true physics. Conclusions