UCLA

The study of seismic surface waves provides important constraints on the Earth interior and potentially other celestial objects. In this thesis, we present a novel hierarchical transdimensional Bayesian approach to extract phase velocity dispersion and shear-wave velocity (VS) models from a single seismogram. Monte Carlo Markov Chains (MCMC) seek an ensemble of one dimensional (1-D) VS models between a seismic source and a receiver that can explain the observed waveform. The models obtained are used to represent the posterior VS distribution of the 1-D path, which can then be used to invert for three dimensional (3-D) models. An advantage of our approach is that it can also t unknown data noise, which reduces the risk of overtting the data in the seismic inversion problem. A 3-D azimuthally anisotropic VS model is obtained by applying the proposed method to Antarctica. The results show distinct patterns between East Antarctica and West Antarctica in both isotropic and anisotropic terms. We also demonstrate the feasibility to apply our method to Mars using the Mars Structure Service (MSS) blind test data and our own synthetic data, which included realistic noise levels based on the noise recorded by InSight.