1 / 40

Neustadt July 8, 2009

Seismic Interferometry by cross-correlation (CC) and by multi-dimensional deconvolution (MDD) using ambient seismic noise. Deyan Draganov, Elmer Ruigrok, Jan Thorbecke, Jürg Hunziker, Joost v. d. Neut, Kees Wapenaar. Neustadt July 8, 2009. SI by CC and MDD using ambient seismic noise.

lada
Télécharger la présentation

Neustadt July 8, 2009

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Seismic Interferometry by cross-correlation (CC) and by multi-dimensional deconvolution (MDD) using ambient seismic noise Deyan Draganov, Elmer Ruigrok, Jan Thorbecke, Jürg Hunziker, Joost v. d. Neut, Kees Wapenaar Neustadt July 8, 2009

  2. SI by CC and MDD using ambient seismic noise Outline of the presentation • Short reminder of what is SI by CC • Advantages and limitations of SI by CC • Short introduction to SI by MDD • Advantages and limitations of SI by MDD • Numerical examples with homogeneous and inhomogeneous illumination • Modelling parameters and geometry • Comparison of results • Conclusions

  3. B A B A Time (s) Short reminder of what is SI by CC t1 t2

  4. B A Short reminder of what is SI by CC B A B t1 => t2 Time (s)

  5. Short reminder of what is SI by CC

  6. Short reminder of what is SI by CC

  7. SI by CC and MDD using ambient seismic noise Outline of the presentation • Short reminder of what is SI by CC • Advantages and limitations of SI by CC • Short introduction to SI by MDD • Advantages and limitations of SI by MDD • Numerical examples with homogeneous and inhomogeneous illumination • Modelling parameters and geometry • Comparison of results • Conclusions

  8. Advantages and limitations of SI by CC • Assumes lossles medium • Requires homogeneous and well-sampled source distridution • Needs only one receiver at each of xA and xB • Relatively fast to compute

  9. SI by CC and MDD using ambient seismic noise Outline of the presentation • Short reminder of what is SI by CC • Advantages and limitations of SI by CC • Short introduction to SI by MDD • Advantages and limitations of SI by MDD • Numerical examples with homogeneous and inhomogeneous illumination • Modelling parameters and geometry • Comparison of results • Conclusions

  10. Short introduction to SI by MDD

  11. Short introduction to SI by MDD

  12. Short introduction to SI by MDD

  13. Short introduction to SI by MDD

  14. SI by CC and MDD using ambient seismic noise Outline of the presentation • Short reminder of what is SI by CC • Advantages and limitations of SI by CC • Short introduction to SI by MDD • Advantages and limitations of SI by MDD • Numerical examples with homogeneous and inhomogeneous illumination • Modelling parameters and geometry • Comparison of results • Conclusions

  15. Advantages and limitations of SI by MDD • Does not assume lossless medium • Does not require homogeneous source distribution • Require a well-sampled array at xA • More computationally expensive

  16. SI by CC and MDD using ambient seismic noise Outline of the presentation • Short reminder of what is SI by CC • Advantages and limitations of SI by CC • Short introduction to SI by MDD • Advantages and limitations of SI by MDD • Numerical examples with homogeneous and inhomogeneous illumination • Modelling parameters and geometry • Comparison of results • Conclusions

  17. Modelling parameters • We model surface waves propagating in a layered elastic medium • We model a dispersion curve for the top 300 km of the PREM model • The dispersion curve is used to model fundamental-mode Rayleigh waves • The surface waves are convolved with white noise at each source position • The obtained ambient noise peaks at 0.2 Hz • The receiver arrays recorded about 42 hours of noise

  18. Geometry

  19. Geometry

  20. Geometry

  21. Geometry

  22. Geometry

  23. Geometry

  24. SI by CC and MDD using ambient seismic noise Outline of the presentation • Short reminder of what is SI by CC • Advantages and limitations of SI by CC • Short introduction to SI by MDD • Advantages and limitations of SI by MDD • Numerical examples with homogeneous and inhomogeneous illumination • Modelling parameters and geometry • Comparison of results • Conclusions

  25. Comparison of results Reference CC

  26. Comparison of results Reference MDD

  27. Comparison of results

  28. Comparison of results Reference CC

  29. Comparison of results Reference MDD

  30. Comparison of results

  31. Comparison of results CC MDD

  32. Comparison of results

  33. Comparison of results

  34. Comparison of results Reference CC

  35. Comparison of results Reference MDD

  36. Comparison of results

  37. Comparison of results Reference CC

  38. Comparison of results Reference MDD

  39. Comparison of results

  40. Conclusions • We showed an application of SI by MDD to surface waves • We compared results from SI by CC and by MDD • When the source illumination is inhomogeneous • the CC results are distorted • the MDD compensates for the illumination problems and improves on the CC results

More Related