1. 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

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. A B Time (s) A B t1t1 t2t2 Short reminder of what is SI by CC

4. A B Time (s) t1t1 B t2t2 => A B 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 Needs only one receiver at each of x A and x B Relatively fast to compute Assumes lossles medium Requires homogeneous and well- sampled source distridution

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

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 x A 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. 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 Modelling parameters

18. 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 CC Reference

26. Comparison of results MDD Reference

27. Comparison of results

28. CC Reference

29. Comparison of results MDD Reference

30. Comparison of results

31. CC MDD

32. Comparison of results

34. CC Reference

35. Comparison of results MDD Reference

36. Comparison of results

37. CC Reference

38. Comparison of results MDD Reference

39. Comparison of results

40. 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 Conclusions