Article ID Journal Published Year Pages File Type
4548960 Journal of Marine Systems 2009 11 Pages PDF
Abstract

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.

Related Topics
Physical Sciences and Engineering Earth and Planetary Sciences Oceanography
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