Mode separation with one hydrophone in shallow water: A sparse Bayesian learning approach based on phase speed

An approach of broadband mode separation in shallow water is proposed using phase speed extracted from one hydrophone and solved with sparse Bayesian learning (SBL). The approximate modal dispersion relation, connecting the horizontal wavenumbers (phase velocities) for multiple frequencies, used to build dictionary matrix SBL. Given a multi-frequency pressure vector on hydrophone, SBL estimates set coefficients large number atoms dictionary. With estimated corresponding atoms, separated normal modes are retrieved. presented method can be impulsive or known-form signals shallow-water environment while no bottom information required. simulation results demonstrate that adapted where both reflected refracted coexist, whereas performance time warping transformation degrades significantly this scenario.