Geoacoustic Parameters Research Articles

Extracting bottom reflection parameters from the vertical correlation of ambient noise in shallow water

A method is introduced to extract reflection characteristics at smaller angles from the vertical correlation of surface-generated noise in shallow water, since the relationship of the two is analyzed. By presenting the vertical correlations of the ambient noise data measured in one experiment in China, the geo-acoustics parameters are estimated. It is shown that these parameters are mutual coupled, therefore a segmentation inversion algorithm has been developed. The reflection phase shift is obtained according to curve fitting or empirical expressions, while the reflection energy is inferred subsequently. The simulation calculated using the inverted geo-acoustic parameters is in good agreement with the explosive sources measurement. There is a discrepancy of about 10% in the prediction values. It is effective for acoustic field prediction by extracting bottom reflection parameters from the noise vertical correlation. The advantage of using reflection parameters is not limited. In addition, it is simple and precision to predict acoustical field, as a result of containing interface scattering in the reflection loss.

Bayesian ambient noise inversion for geoacoustic uncertainty estimation.

The noise produced by wind-driven breaking waves in shallow water provides a method for probing the seabed, and drifting vertical arrays have been deployed for remote sensing of geoacoustic parameters by estimating the frequency- and angle-dependent reflection coefficients. In addition, techniques such as spectral factorization allow obtaining the impulse response of the multilayered seabed environment. This impulse response carries information of the sediment acoustic properties that can be extracted and passed as prior information to a Bayesian framework for the estimation of geoacoustic parameters and its corresponding uncertainties, which ultimately determine the resolution of the method. The Bayesian formulation estimates a joint posterior probability density function, from which marginal density functions, moments, and covariances between geoacoustic parameters of interest can be quantified. In this work, Bayesian inversion based on Markov-chain Monte Carlo sampling is applied to simulated ambient noise data for the estimation of layer thicknesses, compressional sound speed, density, and sediment attenuation, and the resolution of the method is explored as a function of qualities of the data such as array design and wind speed. The approach is applied to experimental data collected near Sicily.

Bottom parameter behavior in slightly range-dependent shallow water.

An examination of the matched field processing behavior of key geoacoustic parameters as a function of frequency (25–100 Hz), range (250 m–2 km), and sediment layer thickness (12, 22, and 40 m) in a systematic manner can be very helpful in the design of inversion methods. Even in the presence of small expected geometric errors (in source depth, phone depths, and source range) plus small errors in ocean sound-speed and bottom depths (with a slight range dependence), some low frequencies and close ranges can be surprisingly sensitive to bottom properties while still very effectively and efficiently indicating correct bottom values. Simple averaging over all such frequencies and ranges may not be optimal for maximizing sensitivity and, thus, for finding geoacoustic parameters.

Sequential trans-dimensional Monte Carlo for range-dependent geoacoustic inversion

This paper develops a sequential trans-dimensional Monte Carlo algorithm for geoacoustic inversion in a strongly range-dependent environment. The algorithm applies advanced Markov chain Monte Carlo methods in combination with sequential techniques (particle filters) to carry out geoacoustic inversions for consecutive data sets acquired along a track. Changes in model parametrization along the track (e.g., number of sediment layers) are accounted for with trans-dimensional partition modeling, which intrinsically determines the amount of structure supported by the data information content. Challenging issues of rapid environmental change between consecutive data sets and high information content (peaked likelihood) are addressed by bridging distributions implemented using annealed importance sampling. This provides an efficient method to locate high-likelihood regions for new data which are distant and ∕ or disjoint from previous high-likelihood regions. The algorithm is applied to simulated reflection-coefficient data along a track, such as can be collected using a towed array close to the seabed. The simulated environment varies rapidly along the track, with changes in the number of layers, layer thicknesses, and geoacoustic parameters within layers. In addition, the seabed contains a geologic fault, where all layers are offset abruptly, and an erosional channel. Changes in noise level are also considered.

Bayesian inversion of seabed scattering data.

Reverberation modeling and sonar performance predictions in shallow water require good estimates of seabed scattering and reflection as well as an understanding of scattering processes in a particular region. This talk considers the ability to resolve scattering parameters (e.g., scattering strength and roughness) and geoacoustic parameters (layer thicknesses, sound speed, density, and attenuation) using Bayesian inversion and a forward model based on first-order perturbation scattering theory and multilayer reflection coefficients. Results are considered in terms of marginal posterior probability distributions, which quantify the effective data information content to resolve scattering/geoacoustic parameters. Inversions are applied to synthetic data and to direct-path scattering measurements from shallow-water test beds. These measurements probe the seabed on an intermediate spatial scale (patch-size radius of ∼500 m for both reflection and scattering), which reduces the effects of ocean variability (associated with sound speed profile, seabed, and biologics) and uncertainty relative to long-range reverberation measurements. [This work is supported by the ONR.]

Hierarchical autoregressive error models in trans‐dimensional matched‐field geoacoustic inversion.

This paper applies a general trans‐dimensional matched‐field inversion algorithm to data with strongly correlated errors, which are addressed by augmenting the forward model with a hierarchical autoregressive error model. This approach accounts for the limited knowledge of the optimal seabed parametrization and of the data‐error statistics in the resulting geoacoustic parameter uncertainties. The assumed seabed parametrization influences estimates of parameter values and uncertainties since different parametrizations lead to different ranges of data predictions. The data support for a particular model is often non‐unique and trans‐dimensional formulations account for this by including an unknown model indexing parameter to consider groups of models (indexed by this parameter) in the posterior results. The hierarchical autoregressive error model accounts for a wide range of possible error correlations with few parameters, with no requirement to explicitly specify a covariance matrix (for which an inverse a...

Bayesian inversion of seabed reverberation and scattering data.

This paper describes a Bayesian approach to the inversion of ocean acoustic reverberation data for scattering and geoacoustic parameters of the seabed. The seabed is modeled as a sediment layer over a semi‐infinite basement. Interface scattering occurs at the (rough) upper and lower boundaries of the sediment layer, and volume scattering occurs within the layer (the scattering mechanisms are considered to be independent and are modeled using perturbation theory and the Born approximation). Unknown parameters include geoacoustic properties (sediment‐layer thickness and sound speeds, densities, and attenuations for sediments and basement) and scattering properties (roughnesses and scattering strengths for upper and lower sediment boundaries, and volume scattering strength). One dimensional (1‐D) and two‐dimensional (2‐D) marginal probability distributions are computed from the multidimensional posterior probability density (PPD) using Metropolis–Hastings sampling applied in a principal‐component parameter space to provide efficient sampling of correlated parameters. Results indicate that reverberation inversion is a strongly nonlinear inverse problem, with highly multimodal marginal distributions and strong interparameter correlations. Addressing this nonlinearity is of key importance to understanding the information content of reverberation data. [Work funded by the ONR.]

Effect of the Geoacoustic Parameters on the Range-Frequency Interference in Shallow-Water Waveguide

In this study, range-frequency interference is analyzed in the propagation of acoustic waves in shallow-water waveguides. The interference pattern is examined using image processing techniques to produce the waveguide-invariant parameter β. The variability of the β-distributions of four geoacoustic parameters in a sediment layer and four typical sediment types is characterized by skewness and kurtosis. The result shows that a low value of skewness and a high value of kurtosis appear in the hard-sediment case and a high value of skewness and a low value of kurtosis appear in the soft-sediment case. This analysis technique will be useful in geoacoustic parameter inversion.

Geoacoustic Inversion Using Backpropagation

This paper presents inversion results of the 2006 Shallow Water Experiment (SW06) data measured on a vertical line array. A low-frequency (100-900 Hz) chirp source was towed along two tracks (circle, straight line) at 30-m depth. For the inversions, a three-step optimization scheme is applied to the data using very fast simulated reannealing (VFSR). The objective function is defined by the energy of the backpropagated signal from the array to the source. At each step, water-column sound-speed profile (SSP), experimental geometry, and geoacoustic parameters are inverted successively. An environmental model is employed consisting of a linear segmented SSP in the water column, a sediment layer, and a half-space. The geometric parameter inversion results show good agreement with in situ measurements. Finally, the estimated geoacoustic parameters show that the experimental site near the vertical line array (VLA) is fairly homogeneous in bottom properties consisting of a 21-m-thick sediment layer with sound speed of around 1600 m/s over a hard basement whose sound speed is approximately 1750 m/s.

Theory of wave propagation in marine sediments.

Saturated, unconsolidated marine sediments support sound waves and shear waves, each of which is dispersive in that its phase speed varies with frequency and the corresponding attenuation scales as the first power of frequency over an extended frequency range. A theory of sound wave and shear wave propagation in unconsolidated marine sediments has recently been developed in which the dissipation is based on a particular form of intergranular interaction involving the molecularly thin layer of pore fluid at grain contacts. As grain-to-grain sliding progresses, the contact is postulated to become progressively stiffer, a phenomenon known as strain-hardening, adapted from the literature on creep in metals and metal alloys. The theory returns algebraic expressions for the phase speed and attenuation of both types of wave, and these expressions depend on frequency as well as the geoacoustic parameters of the sediment. A comparison of the theoretical dispersion relations with data obtained during the ONR-supported SAX99 experiment shows good agreement over the frequency range range from 2–400 kHz. The theoretical expressions also fit measurements, at a fixed frequency, of phase speeds and attenuations versus porosity, overburden pressure, and mean grain diameter. [Research supported by ONR.]

Coupled Ocean–Acoustic Prediction of Transmission Loss in a Continental Shelfbreak Region: Predictive Skill, Uncertainty Quantification, and Dynamical Sensitivities

In this paper, we quantify the dynamical causes and uncertainties of striking differences in acoustic transmission data collected on the shelf and shelfbreak in the northeastern Taiwan region within the context of the 2008 Quantifying, Predicting, and Exploiting Uncertainty (QPE 2008) pilot experiment. To do so, we employ our coupled oceanographic (4-D) and acoustic (Nx2-D) modeling systems with ocean data assimilation and a best-fit depth-dependent geoacoustic model. Predictions are compared to the measured acoustic data, showing skill. Using an ensemble approach, we study the sensitivity of our results to uncertainties in several factors, including geoacoustic parameters, bottom layer thickness, bathymetry, and ocean conditions. We find that the lack of signal received on the shelfbreak is due to a 20-dB increase in transmission loss (TL) caused by bottom trapping of sound energy during up-slope transmissions over the complex and deeper bathymetry. Sensitivity studies on sediment properties show larger but isotropic TL variations on the shelf and smaller but more anisotropic TL variations over the shelfbreak. Sediment sound-speed uncertainties affect the shape of the probability density functions of the TLs more than uncertainties in sediment densities and attenuations. Diverse thicknesses of sediments lead to only limited effects on the TL. The small bathymetric data uncertainty is modeled and also leads to small TL variations. We discover that the initial transport conditions in the Taiwan Strait can affect acoustic transmissions downstream more than 100 km away, especially above the shelfbreak. Simulations also reveal internal tides and we quantify their spatial and temporal effects on the ocean and acoustic fields. One type of predicted waves are semidiurnal shelfbreak internal tides propagating up-slope with wavelengths around 40–80 km, horizontal phase speeds of 0.5–1 m/s, and vertical peak-to-peak displacements of isotherms of 20–60 m. These waves lead to variations of broadband TL estimates over 5–6-km range that are more isotropic and on bearing average larger (up to 5–8-dB amplitudes) on the shelf than on the complex shelfbreak where the TL varies rapidly with bearing angles.

Low‐frequency normal mode propagation in shallow water and its application to the acoustic inverse problem.

Acoustic normal mode theory is useful to describe low‐frequency sound propagation in shallow water. In this talk, normal‐mode propagation effects will be reviewed, as well as acoustic inverse approaches based on modal propagation theory. The normal modes of low frequency sound in shallow water are geometrically dispersive due to the presence of surface and bottom boundaries. One can exploit this modal dispersion to solve some acoustic inverse problems, such as passive acoustic localization and geoacoustic inversion. The approaches of matched field processing and backpropagation are commonly used. In addition, the modal wavenumber spectrum contains information on seabed properties that can be used to infer the geoacoustic parameters. Underwater sound propagation is strongly affected by water‐column fluctuations, and internal waves are one of the significant effects in shallow water environment. An overview of internal wave effects on normal mode propagation will be presented here, along with their consequence on acoustic inversions.

Proof of principle for inversion of vector sensor array data

Acoustic data collected from a 22 m long horizontal line array with elements comprised of omni-directional hydrophone sensors co-located with acceleration vector sensor triplets are analyzed for seabed geo-acoustic information. Broadband and continuous wave tone data were recorded from the passage of a surface vessel source, the R/V Montague, in an August 2006 experiment conducted in Monterey Bay. The water depth at the array and along the source track was approximately 85 m. Simultaneous inversions for source track and geo-acoustic parameters were performed with 18 tones from the 62-415 Hz tone data for source ranges 0.5-2.0 km. The efficacy of inversions with vector sensor data is demonstrated by the fact that data from vector sensor acceleration components parallel to the array line of bearing produced inversion solutions approximately identical to the solutions obtained from the inversion of data for the same tones from the hydrophone array components. In addition the source spectra derived from these inversions are in nominal agreement with the source spectra obtained from an independent measurement.

Bayesian geoacoustic inversion and source tracking for horizontal line array data

This thesis develops non-linear Bayesian methods for three-dimensional tracking of a moving acoustic source in shallow water despite environmental uncertainty, with application to data from a horizontal line array (HLA) of hydrophones. Focalization-tracking maximizes the posterior probability density (PPD) over track and environmental parameters. Synthetic test cases show that the algorithm substantially outperforms tracking with poor environmental estimates and generally obtains results close to those achieved with exact environmental knowledge. Marginalization-tracking integrates the PPD over environmental parameters to obtain joint marginal distributions over source coordinates, from which track uncertainty estimates and the most probable track are extracted. Both approaches are applied to HLA data from a shallow-water experiment conducted in the Barents Sea. Focalization-tracking successfully estimates the tracks of a towed source and a surface ship in cases where simpler tracking algorithms fail. Marginalization-tracking generally outperforms focalization-tracking and gives uncertainty estimates that encompass the true tracks. As a precursor, Bayesian geoacoustic inversion is applied (to both controlled-source and ship-noise data) to estimate seabed model parameters and their uncertainties. It is demonstrated that combining data from multiple, independent time-series segments in the inversion can significantly reduce geoacoustic parameter uncertainties. Geoacoustic uncertainties are also shown to depend on ship range and orientation.

Bayesian geoacoustic inversion.

This paper describes a general Bayesian approach to estimating seabed geoacoustic parameters from ocean acoustic data, which is also applicable to other inverse problems. Within a Bayesian formulation, the complete solution is given by the posterior probability density (PPD), which includes both data and prior information. Properties of the PPD, such as optimal parameter estimates, variances/covariances, correlations, and marginal probability distributions, are computed numerically for nonlinear problems using Markov-chain Monte Carlo methods. However, in many practical cases, both an appropriate model parametrization and the data error distribution are unknown and must be estimated as part of the inversion. These problems are linked, since the resolving power of the data is affected by the data uncertainties. Model selection is carried out by evaluating Bayesian evidence (parametrization likelihood given the data), or a point estimate thereof such as the Bayesian information criterion, which provides the simplest parametrization consistent with the data. The error covariance matrix (including off-diagonal terms, as needed) is estimated from residual analysis under the assumption of a simple, physically reasonable distribution form, such as a Gaussian or Laplace distribution. The validity of the above assumptions and estimates is examined a posteriori using both qualitative and quantitative statistical tests.

Maximum entropy inference method applied to shallow-water ocean waveguides.

The principle of maximum entropy (MaxEnt) is used to analyze the uncertainty that is associated with determining geo-acoustic waveguide parameters for shallow-water ocean environments. Marginal probability distributions and statistics, such as the mean and standard deviation, for each geo-acoustic parameter are obtained using MaxEnt methods. Both theoretical and numerical components of the MaxEnt method are addressed. The method is applied to experimental acoustic measurements taken on the New Jersey continental shelf on a sand ridge where a priori information includes sound velocity measurements in the water column and chirp seismic reflection grams that provide geophysical ground truth on the seabed layering structure. The acoustic data used in the geo-acoustic inference result from a towed multi-frequency source. The effect of discretization choices for the geo-acoustic representation on the marginal probability distributions is investigated. The effect of changing a priori information (such as the assumed sound velocity profile in the water column) on the marginal probability distributions of the geo-acoustic parameters is also explored.

A New Multistep Algorithm for Geoacoustic Inversion via Subspace Extraction

This communication presents a new multistep matched-field algorithm for geoacoustic inversion by subspace extraction with a threshold. In this algorithm, according to the varying sensitivities of geoacoustic parameters, parameters are separated into several subsets (or subspaces). Then, inversions are carried out in each sensitive subspace using an optimization algorithm, and for each inversion, a sub-subspace is extracted where values of objective functions are lower than a given threshold. Finally, in all the extracted sub-subspaces combined with the subspace of insensitive parameters, an inversion is performed for all parameters to find the optimal solution. After the extracting process, the search space is greatly reduced, and generally, the true parameter values will not be excluded from the sub-subspace if a reasonable threshold is designed. Thus, higher efficiency and accuracy can be obtained when compared with other algorithms. Simulation is carried out on synthetic data and results indicate that the new algorithm's performance is significantly superior to those of other algorithms.

The impact of ocean sound speed variability on the uncertainty of geoacoustic parameter estimates

This paper investigates the influence of water column variability on the estimates of geoacoustic model parameters obtained from matched field inversions. The acoustic data were collected on the New Jersey continental shelf during shallow water experiments in August 2006. The oceanographic variability was evident when the data were recorded. To quantify the uncertainties of the geoacoustic parameter estimates in this environment, Bayesian matched field geoacoustic inversion was applied to multi-tonal continuous wave data. The spatially and temporally varying water column sound speed is parametrized in terms of empirical orthogonal functions and included in the inversion. Its impact on the geometric and geoacoustic parameter estimates is then analyzed by the inter-parameter correlations. Two different approaches were used to obtain information about the variation of the water sound speed. One used only the profiles collected along the experimental track during the experiment, and the other also included observations collected over a larger area. The geoacoustic estimates from both the large and small sample sets are consistent. However, due to the diversity of the oceanic sound speed, more empirical orthogonal functions are needed in the inversion when more sound speed profile samples are used.

Bayesian inversion of reverberation and propagation data for geoacoustic and scattering parameters

This paper applies nonlinear Bayesian inference theory to quantify the information content of reverberation and short-range propagation data, both individually and in joint inversion, to resolve seabed geoacoustic and scattering properties. The inversion of reverberation data alone is shown to poorly resolve seabed properties because of strong multi-dimensional correlations between parameters. Inversion of propagation data alone is limited by different correlations, but better constrains the geoacoustic parameters. However, propagation data are insensitive to scattering parameters such as Lambert's scattering coefficient. In each case the parameter correlations are inherent in the physics of the forward problem (reverberation and propagation) and cannot be overcome by processing or inversion techniques; rather, the inversion of more informative data is required. This is accomplished here by joint inversion of reverberation and propagation data, weighted according to their respective maximum-likelihood error estimates. Joint inversion of reverberation and propagation data collected on the Malta Plateau (Strait of Sicily) resolves both geoacoustic and scattering properties and achieves smaller uncertainties for all parameters than obtained by the inversion of either data set alone.

Particle filter source tracking in a changing geoacoustic environment.

This paper addresses the problem of tracking the acoustic source parameters such as the depth, range, and speed in evolving geoacoustic environments. It is well known that inaccurate knowledge about the environmental parameters such as the sound speed profile (SSP), water depth, sediment, and bottom parameters may result in significant errors in source parameters. To counter this, a particle filtering (PF) approach is adopted here where the geoacoustic parameters are tracked together with the source location and speed in a range-dependent environment. This allows accurate, real-time updating of the environment the ship is moving in and hence source can be located at any time accurately. As a sequential Monte Carlo technique that can operate on nonlinear systems with non-Gaussian probability densities, the PF is an ideal tracking algorithm to perform tracking of source and environmental parameters and their evolving probability distributions. The algorithm is tested on a sloping environment with the SSP, water depth, and sediment parameters evolving as the ship moves. The change in the water depth created the well-known “source mirage effect,” but the PF was still able to track the true source, geoacoustic parameters, and their evolving densities in this spatially varying environment. [Work supported by ONR.]