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

Abstract

A non-linear Bayesian Monte-Carlo method is presented to estimate a Vsv model beneath
\nstations by jointly interpreting Rayleigh wave dispersion and receiver functions and associated
\nuncertainties. The method is designed for automated application to large arrays of broad-band
\nseismometers. As a testbed for the method, 185 stations from the USArray Transportable
\nArray are used in the IntermountainWest, a region that is geologically diverse and structurally
\ncomplex. Ambient noise and earthquake tomography are updated by applying eikonal and
\nHelmholtz tomography, respectively, to construct Rayleighwave dispersion maps from 8 to 80 s
\nacross the study region with attendant uncertainty estimates.Amethod referred to as ‘harmonic
\nstripping method’ is described and applied as a basis for quality control and to generate
\nbackazimuth independent receiver functions for a horizontally layered, isotropic effective
\nmedium with uncertainty estimates for each station. A smooth parametrization between (as
\nwell as above and below) discontinuities at the base of the sediments and crust suffices to fit most
\nfeatures of both data types jointly across most of the study region. The effect of introducing
\nreceiver functions to surface wave dispersion data is quantified through improvements in
\nthe posterior marginal distribution of model variables. Assimilation of receiver functions
\nquantitatively improves the accuracy of estimates of Moho depth, improves the determination
\nof the Vsv contrast across Moho, and improves uppermost mantle structure because of the
\nability to relax a priori constraints. The method presented here is robust and can be applied
\nsystematically to construct a 3-D model of the crust and uppermost mantle across the large
\nnetworks of seismometers that are developing globally, but also provides a framework for
\nfurther refinements in the method.