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An unscented Kalman filter (UKF) for geoacoustic inversion using scalar and vector sound fields created by a passing ship is discussed in this paper. The continuous sound field emitted by a ship of opportunity is processed by the sequential filtering technique to estimate slowly changing environmental properties along the source range. The inversion problem is solved by the UKF with a random-walk parameter model, which is expected to perform well when dealing with highly nonlinear problems. Synthetic geoacoustic inversions are performed using multi-frequency pressure, vertical particle velocity and waveguide impedance (a ratio between pressure and vertical particle velocity) data for the geoacoustic model of a mud environment offshore at the mouth of the Amazon river in Brazil (CANOGA 12). For the preliminary tests, the sound source is composed of a flat spectrum. Numerical results demonstrate that the sequential filtering technique is capable of estimating the evolution of environmental properties along the source range. In practice, ship data have complex time-varying spectral characteristics that can greatly limit the accuracy of broadband or multi-frequency passive applications. Since the vertical waveguide impedance is independent of the source spectral level, it is preferred for environmental characterization by the sound field generated from a ship of opportunity. Because of this independence property, the vertical waveguide impedance is expected to yield a more reliable inversion than that of pressure or vertical particle velocity field.

In the context of the recent Maritime Rapid Environmental Assessment/Blue Planet 2007 sea trial (MREA/BP07), this paper presents a range-resolving tomography method based on ensemble Kalman filtering of full-field acoustic measurements, dedicated to the monitoring of environmental parameters in coastal waters. The inverse problem is formulated in a state-space form wherein the time-varying sound-speed field (SSF) is assumed to follow a random walk with known statistics and the acoustic measurements are a nonlinear function of the SSF and the bottom properties. The state-space form enables a straightforward implementation of a nonlinear Kalman filter, leading to a data assimilation problem. Surface measurements augment the measurement vector to constrain the range-dependent structure of the SSF. Realistic scenarios of vertical slice shallow-water tomography experiments are simulated with an oceanic model, based on the MREA/BP07 experiment. Prior geoacoustic inversion on the same location gives the bottom acoustic properties that are input to the propagation model. Simulation results show that the proposed scheme enables the continuous tracking of the range-dependent SSF parameters and their associated uncertainties assimilating new measurements each hour. It is shown that ensemble methods are required to properly manage the nonlinearity of the model. The problem of the sensitivity to the vertical array (VA) configuration is also addressed.

In the framework of the European Seas Observatory Network (ESONET) a research objective is to couple physical oceanography and ocean acoustics into a data inversion and assimilation scheme for monitoring dynamic processes in shallow waters at multiple scales. In this paper the use of ensemble Kalman filtering scheme is investigated for tracking the time variations of a sound-speed field in a vertical section of a shallow water environment, taking into account the seafloor and sub-seafloor acoustic properties. The measurement setup consists of a broadband, multi-frequency sound source and a vertical receiving array spanning a significant part of the water column. In this preliminary study the state-space variables represent the main features of the sound-speed profiles in a low dimensional parameterization scheme, i.e., thickness of mix layer and thermocline vs. range. To test the algorithm full-field acoustic data are synthesized from multi-model super ensemble ocean predictions obtained in support of the BP07 MREA experiment southeast of Elba, Italy. Bottom geoacoustic properties obtained from previous geoacoustic inversion are input to the propagation model as a background dataset. Sea-surface temperature (SST) data from satellite and in-situ CTD observations provide a priori rough information about the rangedependent subsurface structure and an estimation of the sea-surface sound speed. The evolution of the entire sound-speed field is then sequentially estimated in an ensemble Kalman filtering scheme to handle correctly the non-linearity of the inverse problem.