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Seismic surface wave dispersion inversion is used widely to study thesubsurface structure of the Earth. The dispersion property is usually measuredby using frequency-phase velocity (f-c) analysis and by picking phasevelocities from the obtained f-c spectrum. However, because of potentialcontamination the f-c spectrum often has multimodalities at each frequency foreach mode. These introduce uncertainty and errors in the picked phasevelocities, and consequently the obtained shear velocity structure is biased.To overcome this issue, in this study we introduce a new method which directlyuses the spectrum as data. We achieve this by solving the inverse problem in aBayesian framework and define a new likelihood function, the energy likelihoodfunction, which uses the spectrum energy to define data fit. We apply the newmethod to a land dataset recorded by a dense receiver array, and compare theresults to those obtained using the traditional method. The results show thatthe new method produces more accurate results since they better matchindependent data from refraction tomography. This real-data application alsoshows that it can be applied efficiently since it removes the need to pickphase velocities, and with relatively little adjustment to current practicesince it uses standard f-c panels to define the likelihood. We thereforerecommend using the energy likelihood function rather than explicitly pickingphase velocities in surface wave dispersion inversion.

Surface wave dispersion inversion using an energy likelihood function