A physically informed deep-learning approach for locating sources in a waveguide

Adar Kahana*, Symeon Papadimitropoulos, Eli Turkel, Dmitry Batenkov

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Inverse source problems are central to many applications in acoustics, geophysics, non-destructive testing, and more. Traditional imaging methods suffer from the resolution limit, preventing distinction of sources separated by less than the emitted wavelength. In this work we propose a method based on physically informed neural-networks for solving the source refocusing problem, constructing a novel loss term which promotes super-resolving capabilities of the network and is based on the physics of wave propagation. We demonstrate the approach in the setup of imaging an a priori unknown number of point sources in a two-dimensional rectangular waveguide from measurements of wavefield recordings along a vertical cross section. The results show the ability of the method to approximate the locations of sources with high accuracy, even when placed close to each other.

Original languageEnglish
Pages (from-to)2553-2563
Number of pages11
JournalJournal of the Acoustical Society of America
Volume154
Issue number4
DOIs
StatePublished - 1 Oct 2023

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