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

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

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

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

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

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

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

Short introduction to SI by MDD

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

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

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

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

Geometry

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

Comparison of results CC Reference

Comparison of results MDD Reference

Comparison of results

CC Reference

Comparison of results MDD Reference

Comparison of results

CC MDD

Comparison of results

CC Reference

Comparison of results MDD Reference

Comparison of results

CC Reference

Comparison of results MDD Reference

Comparison of results

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