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    Wave-equation dispersion inversion

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    Type
    Article
    Authors
    Li, Jing cc
    Feng, Zongcai cc
    Schuster, Gerard T. cc
    KAUST Department
    Center for Subsurface Imaging and Fluid Modeling
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    KAUST Grant Number
    OCRF-2014-CRG3-2300
    Date
    2016-12-10
    Online Publication Date
    2016-12-10
    Print Publication Date
    2017-03-01
    Permanent link to this record
    http://hdl.handle.net/10754/623009
    
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    Abstract
    We present the theory for wave-equation inversion of dispersion curves, where the misfit function is the sum of the squared differences between the wavenumbers along the predicted and observed dispersion curves. The dispersion curves are obtained from Rayleigh waves recorded by vertical-component geophones. Similar to wave-equation traveltime tomography, the complicated surface wave arrivals in traces are skeletonized as simpler data, namely the picked dispersion curves in the phase-velocity and frequency domains. Solutions to the elastic wave equation and an iterative optimization method are then used to invert these curves for 2-D or 3-D S-wave velocity models. This procedure, denoted as wave-equation dispersion inversion (WD), does not require the assumption of a layered model and is significantly less prone to the cycle-skipping problems of full waveform inversion. The synthetic and field data examples demonstrate that WD can approximately reconstruct the S-wave velocity distributions in laterally heterogeneous media if the dispersion curves can be identified and picked. The WD method is easily extended to anisotropic data and the inversion of dispersion curves associated with Love waves.
    Citation
    Li J, Feng Z, Schuster G (2016) Wave-equation dispersion inversion. Geophysical Journal International 208: 1567–1578. Available: http://dx.doi.org/10.1093/gji/ggw465.
    Sponsors
    We thank the financial support from the sponsors of the Consortium of Subsurface Imaging and Fluid Modeling (CSIM). We also thank KAUST for providing funding by the CRG grantOCRF-2014-CRG3-2300. For computer time, this research used the resources of the IT Research Computing Group and the Supercomputing Laboratory at KAUST. We thank them for providing the computational resources required for carrying out this work.
    Publisher
    Oxford University Press (OUP)
    Journal
    Geophysical Journal International
    DOI
    10.1093/gji/ggw465
    Additional Links
    https://academic.oup.com/gji/article-lookup/doi/10.1093/gji/ggw465
    ae974a485f413a2113503eed53cd6c53
    10.1093/gji/ggw465
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Olduvai Gorge Project; Center for Subsurface Imaging and Fluid Modeling (CSIM)

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