A model inversion framework is proposed for the recovery of the depth profile of a rough surface. A broadband sound source is placed above the surface of interest and the scattered sound pressure is measured at a microphone array. The problem is modelled analytically using the Kirchhoff approximation, which provides a computationally efficient forward model, with reasonable accuracy at high frequencies or in the far field. The inverse problem is formulated in a statistical sense within the Bayesian framework and sampled using a Markov chain Monte Carlo algorithm. In order to shorten the burn-in sampling phase, an initial solution obtained by deterministic optimisation is used. Special attention is devoted to modelling the smoothness of the surface using a prior probability distribution. The procedure is demonstrated experimentally on a surface with one-dimensional roughness.
A statistical inverse method for the reconstruction of rough surfaces from acoustic scattering / Cuenca, J.; Lähivaara, T.; Johnson, M. D.; Dolcetti, G.; Alkmim, M.; De Ryck, L.; Krynkin, A.. - In: PROCEEDINGS OF FORUM ACUSTICUM. - ISSN 2221-3767. - (2023). (Intervento presentato al convegno 10th Convention of the European Acoustics Association, EAA 2023 tenutosi a Torino, Italy nel September 11 - 15, 2023) [10.61782/fa.2023.1288].
A statistical inverse method for the reconstruction of rough surfaces from acoustic scattering
Dolcetti G.;
2023-01-01
Abstract
A model inversion framework is proposed for the recovery of the depth profile of a rough surface. A broadband sound source is placed above the surface of interest and the scattered sound pressure is measured at a microphone array. The problem is modelled analytically using the Kirchhoff approximation, which provides a computationally efficient forward model, with reasonable accuracy at high frequencies or in the far field. The inverse problem is formulated in a statistical sense within the Bayesian framework and sampled using a Markov chain Monte Carlo algorithm. In order to shorten the burn-in sampling phase, an initial solution obtained by deterministic optimisation is used. Special attention is devoted to modelling the smoothness of the surface using a prior probability distribution. The procedure is demonstrated experimentally on a surface with one-dimensional roughness.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione