Joint inversion of surface wave dispersion and receiver functions: a Bayesian Monte-Carlo approach

A non-linear Bayesian Monte-Carlo method is presented to estimate a Vsv model beneath stations by jointly interpreting Rayleigh wave dispersion and receiver functions and associated uncertainties. The method is designed for automated application to large arrays of broad-band seismometers. As a testbed for the method, 185 stations from the USArray Transportable Array are used in the Intermountain West, a region that is geologically diverse and structurally complex. Ambient noise and earthquake tomography are updated by applying eikonal and Helmholtz tomography, respectively, to construct Rayleigh wave dispersion maps from 8 to 80 s across the study region with attendant uncertainty estimates. A method referred to as 'harmonic stripping method' is described and applied as a basis for quality control and to generate backazimuth independent receiver functions for a horizontally layered, isotropic effective medium with uncertainty estimates for each station. A smooth parametrization between (as well as above and below) discontinuities at the base of the sediments and crust suffices to fit most features of both data types jointly across most of the study region. The effect of introducing receiver functions to surface wave dispersion data is quantified through improvements in the posterior marginal distribution of model variables. Assimilation of receiver functions quantitatively improves the accuracy of estimates of Moho depth, improves the determination of the Vsv contrast across Moho, and improves uppermost mantle structure because of the ability to relax a priori constraints. The method presented here is robust and can be applied systematically to construct a 3-D model of the crust and uppermost mantle across the large networks of seismometers that are developing globally, but also provides a framework for further refinements in the method.