Uncertainty quantification in crustal radial anisotropy models based on a transdimensional Bayesian inversion of surface wave dispersion and receiver functions: A case study in Sri Lanka

Radial seismic anisotropy (RA) designates the difference between the speeds of vertically and horizontally polarized shear waves. Since the amplitude of anisotropic is smaller than the variation of velocity, it is more difficult to distinguish that RA anomalies are driven by the structure or uncertainty. Hence, a lack of consideration of uncertainty in radial anisotropy may lead to divergent geodynamical interpretations. The hierarchical transdimensional Bayesian approach provides uncertainty estimates taking fully into account the nonlinearity of the forward problem. Under the Bayesian framework, the mean and the variance of the ensemble containing a large set of models are interpreted as the reference solution and a measure of the model error respectively. In our study, we applied a two-step RA inversion of surface wave dispersion and receiver function based on Bayesian Monte Carlo search with coupled uncertainty propagation to a temporary broadband array covering all of Sri Lanka. First, we constructed Rayleigh and Love wave phase velocity and error maps at periods ranging from 0s to 20s. To remove outliers, data uncertainty distribution was expressed as a mixture of a Gaussian and a uniform distribution. This was followed by a joint inversion method to invert the local dispersion curves and receiver functions at each station for the shear wave velocity and RA of the crust. Model errors were propagated from the first step to the joint inversion as relative uncertainties. The method effectively quantifies the uncertainty of the final crustal shear wave velocity and RA model and shows robust results. The negative RA -5~0% (Vsv > Vsh) with low uncertainty found in the mid-lower crust of Central Sri Lanka may indicate the existence of the vertically strained crust during the amalgamation of Gondwana.