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Improved surface‐wave retrieval from ambient seismic noise by multi‐dimensional deconvolution
Author(s) -
Wapenaar Kees,
Ruigrok Elmer,
van der Neut Joost,
Draganov Deyan
Publication year - 2011
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2010gl045523
Subject(s) - deconvolution , noise (video) , ambient noise level , computer science , function (biology) , seismic noise , point (geometry) , field (mathematics) , blind deconvolution , point source , seismic interferometry , surface wave , surface (topology) , remote sensing , acoustics , geology , algorithm , optics , physics , artificial intelligence , mathematics , telecommunications , seismology , image (mathematics) , interferometry , sound (geography) , geometry , evolutionary biology , pure mathematics , biology
The methodology of surface‐wave retrieval from ambient seismic noise by crosscorrelation relies on the assumption that the noise field is equipartitioned. Deviations from equipartitioning degrade the accuracy of the retrieved surface‐wave Green's function. A point‐spread function, derived from the same ambient noise field, quantifies the smearing in space and time of the virtual source of the Green's function. By multidimensionally deconvolving the retrieved Green's function by the point‐spread function, the virtual source becomes better focussed in space and time and hence the accuracy of the retrieved surface‐wave Green's function may improve significantly. We illustrate this at the hand of a numerical example and discuss the advantages and limitations of this new methodology.