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Review of Coherent Noise Suppression Methods

Review of Coherent Noise Suppression Methods. Gerard T. Schuster University of Utah. Problem: Ground Roll Degrades Signal. Offset (ft). 2000. 3500. 0. Reflections. Time (sec). Ground Roll. 2.5. Problem: PS Waves Degrade Signal. 0. Reflections. Time (sec). Converted S Waves.

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Review of Coherent Noise Suppression Methods

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

  2. Problem: Ground Roll Degrades Signal Offset (ft) 2000 3500 0 Reflections Time (sec) Ground Roll 2.5

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

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

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

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

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

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

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

  10. Tau-P Transform Tau-P Transform Transform Tau Time P Distance

  11. Mute Noise Tau-P Transform Tau-P Transform Transform Tau Time P Distance

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

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

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

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

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

  17. -1 L s -1 L p Filtering by LSMF PP Time Z PS X Distance M1 M2

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

  19. Multicomponent Filtering by LSMF PP d = L m +L m PS p p x s s d = L m +L m p p z s s PS PP Time Z Distance

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

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

  22. ARCO Field Data Offset (ft) 2000 3500 0 Time (sec) 2.5

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

  24. F-K Filtered Data (13333ft/s) LSM Filtered Data (V. Const.) Offset (ft) 2000 3500 0 Time (sec) 2.5

  25. F-X Spectrum of ARCO Data S. of LSM Filtered Data (V. Const) S. of F-K Filtered Data (13333ft/s) Offset (ft) 2000 3500 0 Frequency (Hz) 50

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

  27. 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

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

  29. PP1 PP2 PP3 PP4 LSMF Separation 5000 0 Offset (m) 5000 0 Offset (m) 0 Time (s) 1.4 Horizontal Component Vertical Component

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

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

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

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

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

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

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

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

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

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

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

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