Multi-task Unscented Kalman Inversion for joint inversion of receiver function and surface wave dispersion
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.
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