High-dimensional nonlinear Bayesian inference of poroelastic fields from pressure data

We investigate solution methods for large-scale inverse problems governed by partial differential equations (PDEs) via Bayesian inference. The Bayesian framework provides a statistical setting to infer uncertain parameters from noisy measurements. To quantify posterior uncertainty, we adopt Markov Chain Monte Carlo (MCMC) approaches for generating samples. To increase the efficiency of these approaches in high-dimension, we make use of local information about gradient and Hessian of the target potential, also via Hamiltonian Monte Carlo (HMC). Our target application is inferring the field of soil permeability processing observations of pore pressure, using a nonlinear PDE poromechanics model for predicting pressure from permeability. We compare the performance of different sampling approaches in this and other settings. We also investigate the effect of dimensionality and non-gaussianity of distributions on the performance of different sampling methods.

Automated summary

We explore the use of Bayesian inference for solving large-scale inverse problems governed by partial differential equations (PDEs). Markov Chain Monte Carlo (MCMC) methods are employed to generate samples and estimate posterior uncertainty. To improve efficiency in high-dimensional problems, the gradient and Hessian of the target potential are utilized through Hamiltonian Monte Carlo (HMC). Specifically, we apply this framework to infer the field of soil permeability from pore pressure observations using a nonlinear PDE poromechanics model. The performance of different sampling approaches, as well as the impact of dimensionality and non-Gaussian distributions, are studied.