Entropy-based inhomogeneity detection in porous media

TL;DR

This paper introduces an entropy-based method for detecting inhomogeneities in porous media, specifically fiber directions in reinforced polymers, by identifying abrupt changes through local entropy estimation.

Contribution

It presents a novel change-point detection approach using entropy to identify inhomogeneities in stochastic fiber processes within porous materials.

Findings

Effective detection of fiber direction changes in polymers.

Entropy-based method identifies inhomogeneities accurately.

Applicable to high fiber density materials.

Abstract

We study a change-point problem for random fields based on a univariate detection of outliers via the $3\sigma$-rule in order to recognize inhomogeneities in porous media. In particular, we focus on fibre reinforced polymers modeled by stochastic fibre processes with high fibre intensity and search for abrupt changes in the direction of the fibres. As a measure of change, the entropy of the directional distribution is locally estimated within a window that scans the region to be analyzed.