Site Characterization Through Hierarchical Bayesian Model Updating Using Dispersion and H/V Data

Abstract

In this study, a hierarchical Bayesian model inversion method is developed for site characterization and site response using surface wave measurements and H/V spectral ratios. The hierarchical Bayesian method estimates a velocity profile with uncertainty assessment using both surface wave derived dispersion curves and ambient noise derived H/V spectral ratios. In the proposed hierarchical inversion framework, prior distributions are assumed for the soil parameters and shear wave velocity distribution at each soil layer is estimated. The proposed inversion process is evaluated experimentally when applied to field measurements at a site in the greater Boston area. In this application, soil stratigraphy is assumed from a boring log a-priori and prior Gaussian distributions are assumed for the soil parameters using SPT blowcounts and/or local shear wave velocity data. The prior probability distributions of shear wave velocities are updated to their posterior distributions through the hierarchical Bayesian inference where the mean and covariance of model parameters are estimated as hyperparameters. The Metropolis-Hastings algorithm and Gibbs sampler is used for estimating the updating parameters and hyperparameters. The hierarchical Bayesian approach provides a mechanism to use both the dispersion curve and the H/V spectral ratio in the shear wave velocity inversion. The estimated uncertainty bounds derived from the hierarchical Bayesian approach using dispersion curves and H/V spectral ratios are more informative than those using classical Bayesian inference schemes and dispersion curves alone.