Learning about random media from near-surface backscattering: using machine learning to measure particle size and concentration

Abstract :

We ask what can be measured from a random media by using backscattered waves, emitted from and received at one source. We show that in 2D both the particle radius and concentration can be accurately measured for particles with Dirichlet boundary conditions. This is challenging to do for a wide range of particle volume fractions, 1% to 21%, because for high volume fraction the effects of multiple scattering are not completely understood. Across this range we show that the concentration can be accurately measured just from the mean backscattered wave, but the particle radius requires the backscattered variance, or intensity. We also show that using incident wavenumbers 0 ≤ k ≤ 0.8 is ideal to measure particle radius between 0 and 2. To answer these questions we use supervised machine learning (kernel ridge regression) together with a large, precise, dataset of simulated backscattered waves. One long term aim is to develop a device, powered by data, that can characterise random media from backscattering with little prior knowledge. Here we take the first steps towards this goal.

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https://hal.telecom-paristech.fr/hal-02287766
Contributor : Telecomparis Hal <>
Submitted on : Friday, September 13, 2019 - 5:19:28 PM
Last modification on : Thursday, October 17, 2019 - 12:37:02 PM

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  • HAL Id : hal-02287766, version 1

Citation

Artur L. Gower, Robert M. Gower, Jonathan Deakin, William J. Parnell, I. David Abrahams.. Learning about random media from near-surface backscattering: using machine learning to measure particle size and concentration. arXiv:1801.05490, 2018. ⟨hal-02287766⟩

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