Water Column Sound Speed Profile Research Articles

Source depth estimation with feature matching using convolutional neural networks in shallow water

A feature matching method based on the convolutional neural network (named FM-CNN), inspired from matched-field processing (MFP), is proposed to estimate source depth in shallow water. The FM-CNN, trained on the acoustic field replicas of a single source generated by an acoustic propagation model in a range-independent environment, is used to estimate single and multiple source depths in range-independent and mildly range-dependent environments. The performance of the FM-CNN is compared to the conventional MFP method. Sensitivity analysis for the two methods is performed to study the impact of different environmental mismatches (i.e., bottom parameters, water column sound speed profile, and topography) on depth estimation performance in the East China Sea environment. Simulation results demonstrate that the FM-CNN is more robust to the environmental mismatch in both single and multiple source depth estimation than the conventional MFP. The proposed FM-CNN is validated by real data collected from four tracks in the East China Sea experiment. Experimental results demonstrate that the FM-CNN is capable of reliably estimating single and multiple source depths in complex environments, while MFP has a large failure probability due to the presence of strong sidelobes and wide mainlobes.

Joint trans-dimensional inversion for water-column sound speed and seabed geoacoustic models.

This letter considers joint estimation of the water-column sound-speed profile (SSP) and seabed geoacoustic model through Bayesian inversion of ocean-acoustic data. The inversion is formulated in terms of separate trans-dimensional models for the water column (as an unknown number of nodes of a piecewise-continuous SSP) and seabed (as an unknown number of uniform layers) to intrinsically parameterize each according to the information content of the data. The inversion estimates marginal posterior probability profiles, quantifying the resolution of water-column and seabed structure. To validate the proposed method, modal-dispersion data from the New England Mud Patch, collected using hand-deployable systems, are considered.

Experimental demonstration of low signal-to-noise ratio matched field processing with a geoacoustic model extracted from noise.

Passive localization of a low signal-to-noise ratio (SNR) source in a shallow water waveguide without prior geoacoustic information is accomplished by combining the mode-extraction method modal-MUSIC (multiple signal classification) with range-coherent matched field processing (MFP). Range-coherent MFP coherently combines snapshots from different resolution cells to obtain gain over noise. Modal-MUSIC uses knowledge of the water column sound speed profile (no bottom information) to extract noisy estimates of modal wavenumbers from ship noise recorded on a partially spanning vertical line array (VLA). A geoacoustic model is then fit to the wavenumber estimates extracted from noise with modal-MUSIC and used to compute replicas for range-coherent MFP. The combination of these two methods applied to a 21-element VLA achieves successful source localization at SNR levels as low as -20 dB using ten tonals transmitted during the SWellEx96 experiment.

Geoacoustic Inversion Using Simple Hand-Deployable Acoustic Systems

This article proposes the use of a simple, low-cost, hand-deployable pair of experimental assets to conduct geoacoustic inversion at sea. The system consists of an expendable, fully mechanical acoustic source called a rupture induced underwater sound source (RIUSS) and a new ropeless passive acoustic mooring called a TOSSIT (not an acronym). Used together, RIUSS and TOSSIT enable the collection of acoustic data suitable to perform single-hydrophone geoacoustic inversion. The method is illustrated using data collected on the New England Mud Patch in May 2021 from a relatively small (22 m) and inexpensive chartered fishing vessel. Modal time-frequency dispersion from 15 to 385 Hz is extracted from the TOSSIT/RIUSS data using warping, and used as input for Bayesian transdimensional geoacoustic inversion. The inversion results compare favorably to results obtained with data collected on the same track with traditional assets (e.g., a vertical line array) during the 2017 Seabed Characterization Experiment, even when jointly inverting for the water-column sound speed profile and seabed geoacoustic parameters. This further demonstrates inversion repeatability in a given location using data sets collected years apart, and under different (and potentially unknown) oceanographic conditions.

Joint trans-dimensional inversion for seabed and water-column properties in the seabed characterization experiments

This paper considers the information content to resolve both a geoacoustic model of the seabed and a model of the water-column sound-speed profile (SSP) in the inversion of modal-dispersion data. A Bayesian formulation of the inverse problem allows prior information representing varying levels of independent knowledge to be applied separately to the seabed and water-column models. Issues of interest include the extent to which knowledge (or lack thereof) of the SSP affects geoacoustic inversion, and the ability to remotely estimate the SSP from acoustic data in cases where the seabed is either well known or poorly known. The joint inversion for seabed and water-column properties is formulated in terms of a separate trans-dimensional model for each, providing an automated approach to quantitative model selection. The seabed model is formulated in terms of an unknown number of uniform sediment layers, while the SSP is formulated as an unknown number of depth/sound-speed nodes, with the reciprocal of sound speed squared varying linearly between nodes, as per normal-mode propagation models. The approach is applied to modal-dispersion data measured during the seabed characterization experiments at the New England Mud Patch. [Work supported by the Office of Naval Research]

Mapping of surface-generated noise coherence

The performance of a hydrophone array can be evaluated by its coherent gain, which depends on the spatial correlation of both the signal of interest and the background noise between different array elements, where one hopes to maximize the former while minimizing the latter with array signal processing. In this paper, a computational vertical noise coherence map of the first zero-crossing is generated near Alvin Canyon, south of Martha’s Vineyard, Massachusetts, to study its dependence on the spatial variation in bathymetry, water column sound speed and sediment type. A two and three-dimensional Parabolic Equation propagation model based on reciprocity theory were used for the simulation. The results showed that the seabed parameters have the greatest impact on vertical noise coherence at the array location in the Alvin Canyon area, when compared to 3-D bathymetric and water column sound speed profile variability, especially in the shallower water. The analysis reveals the ideal spacing for a vertical hydrophone array for better signal detection in acoustic experiments. In the continental shelf and slope regions, the ideal spacing lies between 3λ⁄8 in deep water and λ⁄2 in shallow water, and for areas with strong bathymetric variations the ideal spacing can be determined by comprehensive numerical models.

Modal-MUSIC: A passive mode estimation algorithm for partially spanning arrays.

State-of-the-art mode estimation methods either utilize active source transmissions or rely on a full-spanning array to extract normal modes from noise radiated by a ship-of-opportunity. Modal-MUSIC, an adaptation of the MUSIC algorithm (best known for direction-of-arrival estimation), extracts normal modes from a moving source of unknown range recorded on a partially spanning vertical line array, given knowledge of the water column sound speed profile. The method is demonstrated on simulations, as well as on data from the SWellEx-96 experiment. Extracted normal modes from ship noise during the experiment are used to successfully localize a multitone source without any geoacoustic information.

Joint Tracking of Source and Environment Using Improved Particle Filtering in Shallow Water

Shallow water is a complex sound propagation medium, which is affected by the varying spatial–temporal ocean environment. Taking this complexity into account, the classical processing techniques of source localization and environmental inversion may be improved. In this work, a joint tracking approach for the moving source and environmental parameters of the range-dependent and time-evolving environment in shallow water is presented. The tracking scheme treats both the source parameters (e.g., source depth, range, and speed) and the environmental parameters (e.g., water column sound speed profile (SSP) and sediment parameters) at the source location as unknown variables that evolve as the source moves. To counter sample impoverishment and robustly characterize the evolution of the parameters, an improved particle filter (PF), which is an extension of the standard PF, is proposed. Two examples with simulated data in a slowly changing environment and experimental data collected during the ASIAEX experiment are utilized to demonstrate the effectiveness of the joint approach. The results show that we were able to track the source and environmental parameters simultaneously, and the uncertainties were evaluated in the form of time-evolving posterior probability densities (PPDs). The performance comparison confirms that the improved PF is superior to the standard PF, as it can reduce the parameter uncertainties. The tracking capabilities of the improved PF were verified with high accuracy in real-time source localization and well-estimated rapidly varying parameters. Moreover, the influence of different particle numbers on the improved PF tracking performance is also illustrated.

Joint estimation of water-column and seabed properties by inversion of modal-dispersion data

This paper considers the joint estimation of water-column sound-speed profiles (SSP) and seabed geoacoustic models through Bayesian inversion of modal-dispersion data (arrival time as a function of frequency from warping time-frequency analysis). The inversion is formulated in terms of separate trans-dimensional models for the water column and seabed to intrinsically parametrize each according to the information content of the data. An efficient reversible-jump Markov-chain Monte Carlo algorithm is applied to estimate marginal posterior probability profiles, quantifying the resolution of SSP and seabed structure. Measured and simulated dispersion data, as well as varying levels of prior information, are considered to examine the ability to estimate water-column and seabed profiles, and to investigate relationships between these.

Marine Acoustic Zones of Australia

Underwater sound is modelled and mapped for purposes ranging from localised environmental impact assessments of individual offshore developments to large-scale marine spatial planning. As the area to be modelled increases, so does the computational effort. The effort is more easily handled if broken down into smaller regions that could be modelled separately and their results merged. The goal of our study was to split the Australian maritime Exclusive Economic Zone (EEZ) into a set of smaller acoustic zones, whereby each zone is characterised by a set of environmental parameters that vary more across than within zones. The environmental parameters chosen reflect the hydroacoustic (e.g., water column sound speed profile), geoacoustic (e.g., sound speeds and absorption coefficients for compressional and shear waves), and bathymetric (i.e., seafloor depth and slope) parameters that directly affect the way in which sound propagates. We present a multivariate Gaussian mixture model, modified to handle input vectors (sound speed profiles) of variable length, and fitted by an expectation-maximization algorithm, that clustered the environmental parameters into 20 maritime acoustic zones corresponding to 28 geographically separated locations. Mean zone parameters and shape files are available for download. The zones may be used to map, for example, underwater sound from commercial shipping within the entire Australian EEZ.

Estimation of three-dimensional water column sound speed profiles and sediment compressional wave speed and density profiles using a distributed network of buoys.

Broadband data acquired during the Modal Mapping Experiment (MOMAX) V experiment are used to invert simultaneously for the three-dimensional (3D) water column sound speed profiles and the compressional wave speed and density profiles of the seabed in shallow waters off the coast of New Jersey. Linear Frequency Modulation sweep signals in the band 50-300 Hz are transmitted from a nearly stationary source at several discrete positions to a set of freely drifting receivers. Mode travel times are estimated from the signals acquired by the drifting buoys, and these are then used as input data in an inversion algorithm that estimates the acoustic properties of the water column and sediments. The resulting 3D compressional wave speed profiles in the seabed are generally consistent with the one-dimensional profile obtained during the narrowband component of MOMAX V, as well as the results from other experiments in the same area. The validity of the inversion results has also been assessed by the ability of the inverted model to predict the fields measured during the narrowband experiments.

Bayesian inference for water column and sediment inversion in ocean acoustics

Bayesian methods provide inference on unknown parameters given prior beliefs and a statistical model for the observations in a noisy environment. It was shown in “Sequential filtering and linearization for inversion in the Seabed Characterization Experiment,” J. Acoust. Soc. Am. 144, 1913 (2018) how geometry and sediment parameter estimation in an oceanic experiment can be achieved with a two-stage Bayesian approach. Here, we demonstrate the potential of the method for inversion with a different data set presenting challenges in identifying features of the acoustic field necessary for the second stage of the estimation process. The water column sound speed is complex in this experiment, necessitating a complex inverse model. The transmitted signals are lfm pulses and probability densities of multipath arrival times are calculated at the receivers. For the forward and inverse calculations, we compute the Jacobian matrix for sound speed estimation using EOF coefficients. The inversion, employing the direct and surface and bottom paths, leads to inference on the geometry and the water column sound speed profile. Using the computed probability densities and introducing one more arrival, sediment property densities are calculated. Results are in excellent agreement with expected values for the site. [Work supported by ONR.]

Correcting Multibeam Echosounder Bathymetric Measurements for Errors Induced by Inaccurate Water Column Sound Speeds

In this contribution a method for correcting bathymetric measurements affected by inaccurate water column sound speed profiles (SSPs) is presented. The method exploits the redundancy in the multibeam echosounder measurements obtained from the overlap of adjacent swaths by minimizing the difference between depths along overlapping swaths. Two optimization methods are used, i.e., Differential Evolution (DE) and Gauss-Newton (GN). While DE inverts for the sound speed by minimizing the depth variation, GN inverts for both bathymetry and sound speed by minimizing the squared sum of the differences between the modeled and measured travel times. The inversion method assumes a constant SSP in the water column. Applying the method to a salt wedge survey area with large variations in the water column sound speed indicates a good agreement between the original depth measurements and those derived after the inversion with the mean and standard deviation of the depth differences equaling 0.009m and 0.024m, respectively. This indicates that even with a simple parametrization of the sound speed in the water column, the correct bathymetry can be derived from the inversion. The SSP inversion method is also applied to an area with existing refraction artefacts. It corrects the bathymetry and reduces the mean and standard deviation of the depth standard deviation by a factor of around 2.75 compared to the case where the measured SSPs were used. Furthermore, the SSP inversion method neither manipulates the existing morphology nor introduces artificial bathymetric features in the areas where such refraction artefacts are not present. Considering constant SSPs, both DE and GN give almost identical results with GN being faster. However, GN is less flexible with regards to varying sound speed parameterizations.

Modeling AUV Localization Error in a Long Baseline Acoustic Positioning System

This paper develops a linearized Bayesian inversion algorithm for high-precision localization of an autonomous underwater vehicle (AUV) on a test range using time difference of arrival acoustic data. The Bayesian approach allows the uncertainties of several factors, including precise receiver locations and bias and lateral variability of the water-column sound-speed profile, to be included in the inversion as unknown parameters with varying levels of prior information. This provides better estimates of these parameters (and hence AUV locations), and includes the effect of their uncertainties in the uncertainty estimates for AUV locations. A modeling study comparing AUV localization uncertainties from the linearized inversion that results from nonlinear Monte Carlo analysis shows that linearization errors are small, and hence linearized analysis is used to efficiently map AUV localization uncertainty as a function of position over the test range. These approaches are applied to model localization uncertainty for an AUV test range associated with the Victoria Experimental Network Under the Sea cabled observatory system, examining the factors mentioned above as well as the effects of water-column refraction and receiver geometry.

Modeling Fluctuations in Depth-Integrated Acoustic Intensity Induced by Internal Waves Along a 2-D Track

Accurate spatio-temporal prediction of the acoustic field in the presence of internal waves depends largely on one's ability to accurately prescribe water column properties for the acoustic model. This paper describes a data-based methodology for reconstructing a 2-D water column sound-speed profile (WCSSP) from multiple oceanographic moorings deployed in a line. The methodology gives particular attention to reconstructing the 2-D spatio–temporal evolution of the internal wave field. Acoustic predictions are made from an acoustic propagation model with the reconstructed WCSSP as an input. Measured and modeled depth-integrated acoustic intensities are compared to indirectly evaluate the fidelity of the WCSSP reconstruction. Because of the complicated spatio–temporal evolution of the internal wave field in the experimental region, it was found that reconstructing the WCSSP from multiple moorings is important for accurately predicting temporal fluctuations of the depth-integrated acoustic intensity. The average overlap coefficient of 31 pairs of measured/modeled intensity distributions improved from $0.60\pm 0.23$ to $0.79\pm 0.06$ as the number of moorings used in the WCSSP reconstruction increased from one to four.

Inversion of the Sound Speed With Radiated Noise of an Autonomous Underwater Vehicle in Shallow Water Waveguides

In December 2013, an experiment using an autonomous underwater vehicle (AUV) as a moving source for water column sound speed profile (SSP) inversion was conducted in Mogan Lake, China. With in-situ conductivity, temperature, and depth (CTD) measurements, the empirical orthogonal functions were first constructed to represent the SSP. Radiated noises of the AUV were received on a vertical line array and processed via acoustic field matching to obtain estimates of the range-independent SSP, along with the position and velocity of the AUV and the water column depth. The frequency of the radiation noise of the AUV was approximately 14 kHz. At such high frequency, even though the source motion was slow, a significant Doppler shift/broadening was observed. To incorporate the Doppler effects to better match the measured data, a forward acoustic model is derived based on the waveguide Doppler and normal mode theory. An analytical solution of the forward model is obtained for arbitrary signal integration intervals with a monochromatic source, moving radially relative to the receiver. Through simulations and experimental data processing, the feasibility of using an AUV source for water column SSP inversion has been demonstrated; it is also shown that the waveguide Doppler model is more effective compared with the model that does not consider the waveguide Doppler effect.

Underwater source localization using a hydrophone-equipped glider

Buoyancy-driven underwater gliders are autonomous underwater vehicles that were originally developed to collect oceanographic data. CMRE is studying the use of this technology for the characterization of denied areas, including alternate sensor payloads and applications. During the Rapid Environmental Assessment phase of the Noble Mariner 2012 NATO exercise, Conducted in Gulf of Lions in September 2012, an omnidirectional hydrophone was mounted on a shallow water glider to sample the spatial distribution of the acoustic and oceanographic fields at different ranges and depths. This paper presents a study of the potential to localize acoustic sources by using the acoustic and environmental data collected by the glider. During the experiment, a bottom moored acoustic source was deployed in an area with benign bathymetry. Continuous wave and frequency modulated pulses were broadcast for approximately 6 h. The glider was flying along predefined tracks and the distances from the source were typically from 5 to 9 km. A Ray tracing model is used to evaluate the arrival structures of the acoustic signal, and to estimate the source location. The impact of the range dependent water column sound speed profile on the uncertainty of the source localization is also discussed.

Sound speed profile inversion by matching the acoustic intensity striations in shallow water

Estimating the sound speed profile by matching the acoustic intensity striation using a mounted horizontal line array is investigated. Due to constructive and destructive interference between normal modes, the acoustic intensity manifests range-frequency striation structure in shallow water. Using the environment and acoustic data measured during a tomography experiment conducted in the South China Sea in June, 2010, the possibility and validity of inverting for the water column sound speed profile by matching the acoustic intensity striations are studied. The results show that the fluctuation of the sound speed profile has obvious influence on the acoustic intensity striations. The acoustic data recorded in an experiment conducted in the South China Sea in June 2010 are analyzed to invert for the sound speed profiles. The sound speed profile is represented by Empirical Orthogonal Functions (EOFs) to reduce the unknown parameters. The estimates agree well with the measured sound speed profiles and most of the standard deviations are less than 1 m/s.

The measure of environmental sensitivity in detection performance degradation

Existing detection methods have mismatch problem when applyed to the real uncertain ocean, which will lead to the detection performance degradation. However, there has been little work on defining the practical quantitative measures of environmental sensitivity. In this article we define a measure of environmental sensitivity for target detection performance loss in an uncertain ocean for realistic uncertainties in various environmental parameters (water-column sound speed profile and seabed geoacoustic properties). The Monte Carlo approach is used to transfer the environment uncertainty through the forward problem and quantify the resulting variability in the detection performance loss. The computer simulation is based on the Malta Plateau, a well-studied shallow-water region of the Mediterranean Sea. The simulation result shows that 1) the sensitivity is range and depth dependent and in the sound channel the sensitivity is much smaller than in other regions of the ocean; 2) the sound speed profile and the upper seabed layer are most sensitive parameters for the detection performance loss; 3) the sensitivity is frequency dependent. The seabed layer properties such as sediment thickness, density and attenuation coefficient have less influence on the detection as the frequency increases.

Passive sonar target localization and tracking using sequential bayesian filter in uncertain sea environment

A problem of localizing and tracking an acoustic source is researched when ocean environment including water column sound speed profile, ocean depth and seabed property is uncertain. In a Bayesian framework, the source and environmental parameters are regard as random variables with known prior knowledge, then the prior knowledge of parameters and acoustic model are combined with a likelihood function of data to provide posterior probability density functions (PDF) of both south and environmental parameters, target location parameters are estimated by marginalization integrates over the environmental parameters finally. In other hand, the environmental parameters and target location parameters evolve in time or space, which can be described by state-space model. Information on these parameters evolution and uncertainty at preceding steps can be incorporated to determine future probability of parameters with acoustic data being available at current step. A framework of a sequential Bayesian filter is derived naturally based on the model. A Kalman filter or particle filter could be used to implement the sequential Bayesian filter depend on the linear or nonlinear of the measurement equation or/and the state equation. The sequential Bayesian filter is demonstrated to be able to localize and track a source broadcasting a broadband signal in shallow water using both simulated and real data acquired by a towed array.