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A statistical estimation of source location incorporating uncertainty in ocean environmental model parameters is derived using a Bayesian approach. From a previous geoacoustic inversion, a posterior probability distribution of the environmental parameters that reflects uncertainty in the ocean environment is obtained. This geoacoustic uncertainty then is mapped into uncertainty in the acoustic pressure field and is propagated through the Bartlett matched-field processor for source localization. Using data from the ASIAEX 2001 East China Sea experiment, the estimated source location and variability over time are compared with the known source positions.

The effect of correlated data errors on matched-field geoacoustic inversion for vertical array data is examined. The correlated errors stem from the inability to model the inhomogeneities in the environment resulting in an additional error term beyond ambient noise. Simulated data with these correlated errors are generated and then inverted with or without using the proper covariance matrix. Results show that the correlated error has a negative impact on geoacoustic parameter estimation if not accounted for properly.

The problem of coherent reflection of an acoustic plane wave from a rough seabed with a randomly inhomogeneous sediment layer overlying a uniform elastic basement is considered in this analysis. The randomness of the sound field is attributable to the roughness of the seabed and the sound-speed perturbation in the sediment layer, resulting in a joint rough surface and volume scattering problem. An approach based upon perturbation theory, combined with a derived Green's function for a slab bounded above and below by a fluid and an elastic half-space, respectively, is employed to obtain an analytic solution for the coherent field in the sediment layer. Furthermore, a boundary perturbation theory developed by Kuperman and Schmidt (1989) is applied to treat the problem of rough surface scattering. A linear system is then established to facilitate the computation of the coherent reflection field. The coherent reflection coefficients for various surface roughness, sediment randomness, frequency, sediment thickness, and basement elasticity have been generated numerically and analyzed. It was found that the higher/larger size of surface and/or medium randomness, frequency, thickness, and shear-wave speed, the lower the coherent reflection. Physical interpretations of the various results are provided.

Acoustic wave fields in an ocean waveguide with a sediment layer having continuously varying density and sound speed overlying an elastic subbottom are considered in this analysis. The objective of this study is to investigate the effects of seabed acoustic properties, including the density and sound speed of the sediment layer and subbottom, on the characteristics of the wave fields. Examination of the reflection coefficient, wavenumber spectrum, and noise intensity of the sound field through numerical analysis has shown that the variation in the acoustic properties in the sediment layer is an important factor in determining the reflected or noise sound fields. In particular, the sediment thickness-to-wavelength ratio and the types of variation of acoustic properties inside the layer give rise to many characteristics that potentially allow for acoustic inversion of the seabed properties. With regard to the wave-field components in a shallow-water environment, the various types of waves existing in a seismo-acoustic waveguide have been illustrated. The results indicate that the effects of the sediment properties on the wavenumber spectrum are primarily on the continuous and evanescent regimes of the wave field. The noise intensity generated by distributive random monopoles at various depths, together with the effect of refractive sound-speed distribution in the water column, has been obtained and analyzed.

Acoustic scattering of a plane wave incident upon a rough surface over a transition fluid layer within which both the density and sound speed vary with depth is considered. A theory based upon a boundary perturbation method has been applied to a typical seabed environment to study the power spectral density representing the energy distribution of the scattered field over the space. The effects of frequency and roughness properties, including the rms roughness height, spatial correlation, and power spectrum, on the power spectral density have been investigated. The results demonstrate that the power spectral density of the scattered field depends upon all the aforementioned parameters, particularly the correlation length and the power spectrum of the rough surface, a conclusion in distinct contrast to the results for the coherent field obtained in an earlier study. It was found that the constituents of the rough surface such as the correlation length and wavenumber spectrum dominate the angular distribution of the scattered energy. These results indicate that it is crucial to employ a suitable topological model in the study of rough seabed scattering.

A Marine Test Field (MTF) in the Sizihwan coastal area near Kaohsiung, Taiwan has been developed since the summer of 2006. It represents an effort on a long-term investment for the study of underwater acoustics in a very-shallow-water and/or harbor environment. The process for developing the MTF is first to analyze the marine environment through detailed survey and analysis, including the geographical, morphological, and geological properties of the seabed, followed by the prediction and analysis of the acoustic sound fields based upon the measured environmental data. A series of benchmarking experiments have been carried out since the summer of 2007, including acoustic transmission experiment in a very shallow water environment, underwater anti-intrusion experiment in an inner harbor area, and shallow water networking and communication experiment.The purpose of this paper is to report the results from the above-mentioned experiments, which demonstrated that the concepts of establishing an MTF in the Sizihwan Bay area is feasible and realizable. Furthermore, environmental sensing, monitoring and communication underwater have been a challenge for scientists who study the oceans. To facilitate advanced research in underwater wireless sensor network technology, we are currently in the process of developing underwater distributed networked system in the MTF.

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.

Geoacoustic inversion estimates environmental parameters from measured acoustic fields (e.g., received on a towed array). The inversion results have some uncertainty due to noise in the data and modeling errors. Based on the posterior probability density of environmental parameters obtained from inversion, a statistical estimation of transmission loss (TL) can be performed and a credibility level envelope or uncertainty band for the TL generated. This uncertainty band accounts for the inherent variability of the environment not usually contained in sonar performance prediction model inputs. The approach follows [Gerstoft et al. IEEE J. Ocean. Eng. 31, 299–307 (2006)] and is demonstrated with data obtained from the MAPEX2000 experiment conducted by the NATO Undersea Research Center using a towed array and a moored source in the Mediterranean Sea in November 2000. Based on the geoacoustic inversion results, the TL and its variability are estimated and compared with the measured TL.