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Interferometric Prediction and Least Squares Subtraction of Surface Waves

Interferometric Prediction and Least Squares Subtraction of Surface Waves. Shuqian Dong and Ruiqing He University of Utah. Land Field Data Test. OUTLINE. Motivation: Surface Wave Filtering. Interfer. Surface Wave Theory. Conclusions. Land Field Data Test. OUTLINE.

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Interferometric Prediction and Least Squares Subtraction of Surface Waves

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  1. Interferometric Prediction and Least Squares Subtraction of Surface Waves Shuqian Dong and Ruiqing He University of Utah

  2. Land Field Data Test OUTLINE • Motivation: Surface Wave Filtering • Interfer. Surface Wave Theory • Conclusions

  3. Land Field Data Test OUTLINE • Motivation: Surface Wave Filtering • Interfer. Surface Wave Theory • Conclusions

  4. Motivation A CSG with Strong Surface Waves A CSG with Strong Surface Waves 0 0 Time (s) Time (s) • Solution: Interfer. Predict. + Least Squares Subtraction. Accounts for dispersion. 1.0 1.0 7200 7200 0 Offset (m) 0 Offset (m) • Problem: Surface waves = strong coherent noise blurs seismogram. Moveout-based filtering not always effective for dispersive waves.

  5. Land Field Data Test OUTLINE • Motivation: Surface Wave Filtering • Interfer. Surface Wave Theory • Conclusions

  6. A B B C A B C • Prediction of multiples by convolution (SRME) * • Prediction of Primaries by Crosscorrelation (Interferometry) ⊕

  7. e e ikx ikx u (s,g) u (s,g’) u (s,g’)= A(s,g’) u (s,g)= A(s,g) sg sg’ ⊕ τ g g’ S ik(x x gg’ Sg’ u(g,g’) } * u (s,g’) u (s,g) u(g,g’) = -x sg e ) τ = A(s,g’) A(s,g) g g’ • Predict Surface Waves by Crosscorrelation ⊕

  8. A B C B B B C C A A’ C B B A’ • Predict Surface Waves by Crosscorrelation ⊕ + ⊕

  9. … S S 2 N S 1 g g’ g g’ • Coherent Stacking: surface waves (all src pts = stationary) Incoherent Stacking: primaries ⊕ • Coherent Stacking: FS Multiples? Avoid stationary source points

  10. Offset (m) 3600 0 Offset (m) 3600 0 0 0 Time (s) Time (s) 2.0 2.0 Predcted Surface Waves Original Data 1 Amplitude 0 -1 Time (s) 2.0 0 Surface Waves Prediction

  11. Refl. Surf. d d d (t) (t) (t) = + Pred. Refl. d d f - d (t) (t) * (t) ≈ (t) f (t) * = - Least Square Matching Filter

  12. Surface Waves Filtering Results Original Data Filtered Data 0 0 Time (s) Time (s) 2.0 2.0 0 Offset (m) 7200 0 Offset (m) 7200

  13. Result Comparison Results of f-k method Results of interferometric method 0 0 Time (s) Time (s) 2.0 2.0 Offset (m) 7200 Offset (m) 0 0 7200

  14. Conclusions • Preliminary results promising for interfer. Prediction + subtraction surface waves. • Future work: iterative prediction + subtraction.

  15. Can Interferometric Prediction+Subtraction work for Irregular 3D Arrays? Answer?: Irregular S. Calif. Earthquake Array Predicted Surface Waves 400 38 N Predicted Surface Waves Stations Station Offset (km) Latitude 32 N 0 -200 200 Time (s) Longitude 120 W 115 W (Andrew Curtis, The Leading Edge, 2006)

  16. Acknowledgements We thank the UTAM sponsors for the support of the research.

  17. Thanks

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