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Advanced Noise Suppression Methods in Seismic Data Analysis

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donnadonovan
39 diapositives · 430 Views · Jan 06, 2020

Explore advanced coherent filtering methods to suppress noise and enhance signal quality in seismic data analysis. Learn about F-K filtering, LSMF techniques, and their applications in separating signal from noise effectively.

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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: Dune Waves Obscure PP Dune Waves

  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 s PP Time PS Distance

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

  18. PP d = L m +L m PS p p x s s d = L m +L m X M1 M2 p p z s s Filtering by LSMF Time Z Distance

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

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

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

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

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

  24. 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

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

  26. 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

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

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

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

  30. 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

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

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

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

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

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

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

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

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

  39. 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.