Acoustic inversion with a modified state-space model using ROMS

The inhomogeneities and uncertainties of shallow water environment make sound speed profile (SSP) difficult to estimate. To track the spatial-temporal evolution of the SSP, the sound field and dynamic environment coupled state-space model is proposed, in which the state evolution equation is built via the random term modelled by a first-order Markov state-space model and the stable term predicted by the Regional Ocean Model System (ROMS). With the combination of the acoustic pressure observation equation and the state equations with information from ROMS, the state-space model is updated with both the sound field and dynamic environment processes by the weighted ensemble Kalman Filter (WEnKF), which can alleviate the Gaussian limit of the ensemble samples for the ensemble Kalman Filter (EnKF). Due to the non-Gaussian nature of the SSP evolution, WEnKF has advantages in the inversion problems with strong time variability. The simulation and experimental data verify that the proposed method can effectively improve the accuracy of the SSP inversion under the space-time varying shallow water environment.