High-dimensional nonlinear Bayesian inference of poroelastic fields from pressure data
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 03, 2023
- Source ID
- 10.1177/10812865221140840
Entities
People
- Kaushik Dayal
- Matteo Pozzi
- Mehrdad Massoudi
- Mina Karimi
Organizations
- Army Research Office
- Carnegie Mellon University
- National Energy Technology Laboratory
- National Science Foundation
- Office of Naval Research