  Seismic surface wave dispersion inversion is used widely to study the
subsurface structure of the Earth. The dispersion property is usually measured
by using frequency-phase velocity (f-c) analysis and by picking phase
velocities from the obtained f-c spectrum. However, because of potential
contamination the f-c spectrum often has multimodalities at each frequency for
each mode. These introduce uncertainty and errors in the picked phase
velocities, and consequently the obtained shear velocity structure is biased.
To overcome this issue, in this study we introduce a new method which directly
uses the spectrum as data. We achieve this by solving the inverse problem in a
Bayesian framework and define a new likelihood function, the energy likelihood
function, which uses the spectrum energy to define data fit. We apply the new
method to a land dataset recorded by a dense receiver array, and compare the
results to those obtained using the traditional method. The results show that
the new method produces more accurate results since they better match
independent data from refraction tomography. This real-data application also
shows that it can be applied efficiently since it removes the need to pick
phase velocities, and with relatively little adjustment to current practice
since it uses standard f-c panels to define the likelihood. We therefore
recommend using the energy likelihood function rather than explicitly picking
phase velocities in surface wave dispersion inversion.
