Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/8240
AuthorsCauchie, L.* 
Saccorotti, G.* 
TitleProbabilistic inversion of Rayleigh-wave dispersion data: an application to Mt. Etna, Italy
Issue Date2013
Series/Report no./17 (2013)
DOI10.1007/s10950-012-9323-6
URIhttp://hdl.handle.net/2122/8240
KeywordsSurface waves
Volcanic tremor
Dispersion curves
Nonlinear inversion
Etna volcano
Subject Classification04. Solid Earth::04.06. Seismology::04.06.06. Surveys, measurements, and monitoring 
04. Solid Earth::04.06. Seismology::04.06.09. Waves and wave analysis 
AbstractWe present a methodology for determining the elastic properties of the shallow crust from inversion of surface wave dispersion characteristics through a fully nonlinear procedure. Using volcanic tremor data recorded by a small-aperture seismic array on Mount Etna, we measured the surface waves dispersion curves with the multiple signal classification technique. The large number of measurements allows the determination of an a priori probability density function without the need of making any assumption about the uncertainties on the observations. Using this information, we successively conducted the inversion of phase velocities using a probabilistic approach. Using a wave-number integration method, we calculated the predicted dispersion function for thousands of 1-D models through a systematic grid search investigation of shear-wave velocities in individual layers. We joined this set of theoretical dispersion curves to the experimental probability density function (PDF), thus obtaining the desired structural model in terms of an a posteriori PDF of model parameters. This process allowed the representation of the objective function, showing the non-uniqueness of the solutions and providing a quantitative view of the uncertainties associated with the estimation of each parameter. We then compared the solution with the surface wave group velocities derived from diffuse noise Green’s functions calculated at pairs of widely spaced (~5–10 km) stations. In their gross features, results from the two different approaches are comparable, and are in turn consistent with the models presented in several earlier studies.
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