Characterising particulate random media from near-surface backscattering: a machine learning approach to predict particle size and concentration

TL;DR

This study demonstrates that machine learning can accurately characterize particle size and concentration in particulate media using backscattering data, even accounting for multiple scattering effects, within a 2D setting.

Contribution

The paper introduces a numerical method to generate precise backscattering datasets and applies machine learning to accurately estimate particle properties from single-source backscattering data.

Findings

Mean backscattered wave field determines particle concentration.

Second moment of backscattering reveals particle radius.

Optimal frequency range identified for size measurement.

Abstract

To what extent can particulate random media be characterised using direct wave backscattering from a single receiver/source? Here, in a two dimensional setting, we show using a machine learning approach that both the particle radius and concentration can be accurately measured when the boundary condition on the particles is of Dirichlet type. Although the methods we introduce could be applied to any particle type. In general backscattering is challenging to interpret for a wide range of particle concentrations, because multiple scattering cannot be ignored, except in the very dilute range. Across the concentration range from 1% to 20% we find that the mean backscattered wave field is sufficient to accurately determine the concentration of particles. However, to accurately determine the particle radius, the second moment, or average intensity, of the backscattering is necessary. We are also able to determine what is the ideal frequency range to measure a broad range of particles sizes. To get rigorous results with supervised machine learning requires a large, highly precise, dataset of backscattered waves from an infinite half-space filled with particles. We are able to create this dataset by introducing a numerical approach which accurately approximates the backscattering from an infinite half-space.