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.
A physically informed deep-learning approach for locating sources in a waveguide
Adar Kahana, Symeon Papadimitropoulos, Eli Turkel, Dmitry Batenkov; A physically informed deep-learning approach for locating sources in a waveguide. J. Acoust. Soc. Am. 1 October 2023; 154 (4): 2553–2563. https://doi.org/10.1121/10.0021889
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