3:30 PM - 3:45 PM
[SEM14-06] Probabilistic inversion of magnetotelluric data and uncertainty quantification
★Invited Papers
Keywords:Magnetotelluric, Probabilistic inversion, Uncertainty quantification, Transdimensional MCMC, Hamiltonian Monte Carlo
In this study, we have developed a Bayesian inversion framework for probabilistic inversion of 2D MT data. By postulating the inverse problem into a sampling-based Bayesian inference framework, we can rigorously estimate model uncertainty associated with the inverted model parameters using a large ensemble of models sampled from the posterior distribution of model parameters. Here, we present two Bayesian algorithms: the transdimensional Markov chain Monte Carlo (MCMC) and Hamiltonian Monte Carlo (HMC) methods for inverting MT data. The transdimensional MCMC uses a flexible parameterization where the electrical resistivity model is partitioned by a variable number of Voronoi cells, thus the level of model complexity will be automatically determined and adapted to the spatial resolution of the observed data during the inversion. However, it surfers from the slow convergence of the Markov chains, especially in high dimensional model space. In contrast, the HMC method can efficiently explore the model space and produce samples with much higher acceptance probabilities than MCMC by making use of gradient information of the posterior distribution, but the model parameterization is kept fixed during the HMC inversion. We demonstrate and compare the performance of these two algorithms with synthetic and field MT datasets, and discuss their main benefits and drawbacks. In addition, we explore some useful techniques such as parallel tempering and surrogate modeling to accelerate the convergence of the Markov chains and alleviate the computational burden, making the probabilistic inversions more tractable for practical MT applications.