|Full-field tomography and Kalman tracking of the range-dependent sound speed field in a coastal water environment|Carriere, O.; Hermand, J.P.; Le Gac, J.C.; Rixen, M. (2009). Full-field tomography and Kalman tracking of the range-dependent sound speed field in a coastal water environment. J. Mar. Syst. 78: S382-S392. https://dx.doi.org/10.1016/j.jmarsys.2009.01.036
In: Journal of Marine Systems. Elsevier: Tokyo; Oxford; New York; Amsterdam. ISSN 0924-7963; e-ISSN 1879-1573, meer
Data assimilation; Shallow water; Kalman filter; Acoustic inversion
|Auteurs|| || Top |
- Carriere, O.
- Hermand, J.P.
- Le Gac, J.C.
- Rixen, M.
The monitoring, assessment and prediction of dynamic processes in shallow water constitute an attractive challenge. The availability of targeted observations enable high-resolution ocean forecasting to develop the 4D environmental picture. In particular, range-resolving acoustic tomography data constitute an effective way to reduce the non-uniform distribution and sparsity of standard hydrographic observations. In this paper a Kalman filtering scheme is investigated for tracking the time variations of a range-dependent sound-speed field in a vertical slice of a shallow water environment from full-field acoustic data and a propagation model taking into account the acoustic properties of the seafloor and subseafloor. The basic measurement setup for each radial of a tomography system consists of a broadband, multifrequency sound source and a vertical receiver array spanning most of the water column. The state variables represent the main features of the sound-speed field in a low dimensional parameterization scheme using empirical orthogonal functions. To test the algorithm acoustic data are synthesized from ocean model predictions obtained in support of the MREA/BP07 experiment southeast of the island of Elba, Italy. Bottom geoacoustic parameters obtained from previous acoustic inversion experiments are input to a normal mode propagation model as a background dataset. Additional data such as sea-surface temperature data from satellite or in situ hydrographic observations provide a priori approximate information about the range dependency of the subsurface structure and an estimation of the sea-surface sound speed. The evolution of the entire sound-speed field in the vertical slice is then sequentially estimated by the inversion processor. The results show that the daily space and time variations of the simulated sound-speed field can be effectively tracked with an extended Kalman filter. The depth-integrated sound-speed error (RMS) remains lower than 0.3 m/s (0.09°C) when the benchmark environment is completely determined in the parameter space and lower than 0.7 m/s (0.22°C)for an approximate environment parameterization.