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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.
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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 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
B A B A Time (s) Short reminder of what is SI by CC t1 t2
B A Short reminder of what is SI by CC B A B t1 => t2 Time (s)
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 • Assumes lossles medium • Requires homogeneous and well-sampled source distridution • Needs only one receiver at each of xA and xB • Relatively fast to compute
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
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 xA • 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
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
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 Reference CC
Comparison of results Reference MDD
Comparison of results Reference CC
Comparison of results Reference MDD
Comparison of results CC MDD
Comparison of results Reference CC
Comparison of results Reference MDD
Comparison of results Reference CC
Comparison of results Reference MDD
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