A Sparse Bayesian Estimation Framework for Conditioning Prior Geologic Models to Nonlinear Flow Measurements

TL;DR

This paper introduces a Bayesian method that uses sparsity in a transformed domain to reconstruct subsurface hydraulic properties from nonlinear flow data, improving model conditioning.

Contribution

It proposes a novel sparse Bayesian estimation framework specifically designed for conditioning prior geologic models to nonlinear flow measurements.

Findings

Effective reconstruction of subsurface properties from flow data.

Enhanced model conditioning through sparsity constraints.

Potential for improved accuracy in geologic modeling.

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.