The effect of ocean sound speed uncertainty on matched-field geoacoustic inversion is investigated using data from the SW06 experiment along a nearly range-independent bathymetric track. Significant sound speed differences were observed at the source and receiving array and several environmental parameterizations were investigated for the inversion including representing the ocean sound speed at both source and receivers with empirical orthogonal function (EOF) coefficients. A genetic algorithm-based global optimization method was applied to the candidate environmental models. Then, a Bayesian inversion technique was used to quantify uncertainty in the environmental parameters for the best environmental model, which included an EOF description of the ocean sound speed.

Ocean acoustic noise can be processed efficiently to extract Green’s function information between two receivers. By using noise array-processing techniques, it has been demonstrated that a passive array can be used as a fathometer [Siderius, et al., J. Acoust. Soc. Am. 120, 1315–1323 (2006)]. Here, this approach is derived in both frequency and time domains and the output corresponds to the reflection sequence. From this reflection sequence, it is possible to extract seabed layering. In the ocean waveguide, most of the energy is horizontally propagating, whereas the bottom information is contained in the vertically propagating noise. Extracting the seabed information requires a dense array, since the resolution of the bottom layer is about half the array spacing. If velocity sensors are used instead of pressure sensors, the array spacing requirement can be relaxed and simulations show that just one vertical velocity sensor is sufficient.

Quantifying uncertainty for parameter estimates obtained from matched-field geoacoustic inversions using a Bayesian approach requires estimation of the uncertainties in the data due to ambient noise as well as modeling errors. In this study, the variance parameter of the Gaussian error model, hereafter called error variance, is assumed to describe the data uncertainty. In practice, this parameter is not known a priori, and choosing a particular value is often difficult. Hence, to account for the uncertainty in error variance, several methods are introduced for implementing both the full and empirical Bayesian approaches. A full Bayesian approach that permits uncertainty of the error variance to propagate through the parameter estimation processes is a natural way of incorporating the uncertainty of error variance. Due to the large number of unknown parameters in the full Bayesian uncertainty analysis, an alternative, the empirical Bayesian approach, is developed, in which the posterior distributions of model parameters are conditioned on a point estimate of the error variance. Comparisons between the full and empirical Bayesian inferences of model parameters are presented using both synthetic and experimental data.

A high frequency experiment was conducted in the Woods Hole Harbor in Massachusetts to evaluate the effectiveness of Doppler sonar for discriminating targets from reverberation. Using a pulsed linear frequency modulated signal, one finds that the matched filtered outputs are filled with high-level discrete backscattered returns, referred to as clutter, which are often confused with the target echo. The high level non-target returns have an amplitude distribution that is heavy-tailed. Using a Doppler-sensitive binary-phase-shift-keying signal coded with an m-sequence, the target echo and clutter can be separated by Doppler and delay, and tracked using the Doppler spectrogram (Dopplergram). The Doppler filtered time series show a background reverberation with a Rayleigh-like amplitude distribution, with an improved signal-to-(peak) reverberation ratio compared with that without Doppler filtering. The reduced reverberation level with Doppler processing decreases the probability of false alarm (Pfa) for a given threshold level. Conversely, for a given Pfa, the higher signal-to-(peak) reverberation ratio implies a higher probability of detection. Transmission loss measurement was conducted to estimate some of the system parameters, e.g., the source level and target strength relative to the noise level.

Distributed underwater sensors are expected to provide oceanographic monitoring over large areas. As fabrication technology advances, low cost sensors will be available for many uses. The sensors communicate to each other and are networked using acoustic communications. This paper first studies the performance of such systems for current measurements using tomographic inversion approaches to compare with that of a conventional system which distributes the sensors on the periphery of the area of interest. It then proposes two simple signal processing methods for ocean current mapping (using distributed networked sensors) aimed at real-time in-buoy processing. Tomographic inversion generally requires solving a challenging high dimensional inverse problem, involving substantial computations. Given distributed sensors, currents can be constructed locally based on data from neighboring sensors. It is shown using simulated data that similar results are obtained using distributed processing as using conventional tomographic approaches. The advantage for distributed systems is that by increasing the number of nodes, one gains a much more improved performance. Furthermore, distributed systems use much less energy than a conventional tomographic system for the same area coverage. Experimental data from an acoustic communication and networking experiment are used to demonstrate the feasibility of acoustic current mapping.

This study investigates techniques for the acoustic mapping of ocean currents in a shallow-water environment using moored and ship-towed stations. The currents are estimated using the differential travel times (DTTs) observed in the reciprocal acoustic transmissions between those stations. Due to the relative motion induced by the ship-towed station, the Doppler shift estimated from the measured channel impulse response is used to compensate for the arrival patterns and to correct the influence of the relative speed on the DTTs. Furthermore, to estimate the DTTs from the arrival patterns received in the reciprocal directions, a method is devised which involves the time-evolving cross-correlation function of the reciprocal arrival patterns. A feasibility test was carried out at Sizhiwan Marine Test Field, Kaohsiung, Taiwan in September of 2015. The currents were estimated using the data collected from one ship-towed and four moored stations. The estimated currents are consistent with the direct measurements from a shipboard acoustic Doppler current profiler and reveal the spatial distribution of the currents.