A Sparse Bayesian Estimation Framework for Conditioning Prior Geologic Models to Nonlinear Flow Measurements
Numerical Analysis
2015-05-14 v1 Data Analysis, Statistics and Probability
Geophysics
Abstract
We present a Bayesian framework for reconstruction of subsurface hydraulic properties from nonlinear dynamic flow data by imposing sparsity on the distribution of the solution coefficients in a compression transform domain.
Cite
@article{arxiv.0911.4961,
title = {A Sparse Bayesian Estimation Framework for Conditioning Prior Geologic Models to Nonlinear Flow Measurements},
author = {Lianlin Li and Behnam Jafarpour},
journal= {arXiv preprint arXiv:0911.4961},
year = {2015}
}