  Based on the recently developed theory of Unscented Kalman Inversion in
computational mathematics, we proposed a Bayesian joint inversion framework,
i.e., Multi-task Unscented Kalman Inversion (MTUKI), and apply it to the joint
inversion of receiver function (RF) and surface wave dispersion (SWD). This
method can share information between different observations in a
derivative-free way and provide an efficient Gaussian approximation to the
posterior distribution of model parameters (thickness and S-wave velocity in
each layer of media). The theory and experiments show that our proposed
framework demonstrates superior performance in terms of robustness, accuracy,
and high efficiency.

Geophysics