Exploring the model space and ranking a best class of models in surface- wave dispersion inversion: Application at European strong-motion sites

The inversion of surface-wave dispersion curve to derive shear-wave velocity profile is a very delicate process dealing with a non-unique problem, which is strongly dependent on the model space parameterization. When independent and reliable information are not available, the selection of most representative models within the ensemble produced by the inversion is often difficult. We present a strategy in the inversion of dispersion curves able to investigate the influence of the parameterization of the model space, and to select a ‘’best’’ class of models. We analyze surface-wave dispersion curves measured at 14 European strong-motion sites within the EC-project NERIES. We focus on the inversion task exploring the model space by means of four distinct parameterization classes composed of layers progressively added over a half-space. The classes differ in the definition of the shear-wave velocity profile; we consider models with uniform velocity as well as models with increasing velocity with depth. At each site and for each model parameterization, we perform an extensive surface-wave inversion (200100 models for 5 seeds) using the conditional neighbourhood algorithm.
We address the model evaluation following the corrected Akaike’s Information Criterion (AICc) which combines the concept of misfit to the number of degrees of freedom (dof) of the system. The misfit is computed as least-squares estimation between theoretical and observed dispersion curve. The model complexity is accounted in a penalty term by AICc. By applying such inversion strategy on 14 strong-motion sites, we find that the best parameterization of the model space is mostly 3-4 layers over a half-space; where the shear-wave velocity of the uppermost layers can follow uniform or power-law dependence with depth. The shear-wave velocity profiles derived by inversion agree with shear-wave velocity profiles provided by borehole surveys at approximately 80% of the sites.