Volume 122, Number 5, June 2018
|Number of page(s)||6|
|Section||Electromagnetism, Optics, Acoustics, Heat Transfer, Classical Mechanics, and Fluid Dynamics|
|Published online||18 July 2018|
Characterising particulate random media from near-surface backscattering: A machine learning approach to predict particle size and concentration
1 School of Mathematics, University of Manchester - Oxford Road, Manchester, M13 9PL, UK
2 LTCI, Télécom Paristech, Université Paris-Saclay - 75013, Paris, France
3 Isaac Newton Institute for Mathematical Sciences - 20 Clarkson Road, Cambridge, CB3 0EH, UK
Received: 6 April 2018
Accepted: 23 June 2018
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.
PACS: 42.25.Dd – Wave propagation in random media / 43.20.Fn – Scattering of acoustic waves / 05.10.Ln – Monte Carlo methods
© EPLA, 2018
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