Estimation Of Geoacoustic Parameters Research Articles

Geoacoustic inversion of wide-angle reflection-coefficient data to estimate sediment shear properties

This paper considers Bayesian geoacoustic inversion of broadband, wide-angle reflection-coefficient data including shear parameters in the seabed model to investigate the ability to estimate these parameters as well as effects on the estimation of other geoacoustic parameters, particularly compressional-wave attenuation. The seabed parameterization is based on the viscous grain-shearing (VGS) sediment-acoustic model, including the grain-to-grain shear modulus as an unknown parameter. VGS sediment parameters are transformed to density and frequency-dependent compressional- and shear-wave speeds and attenuations. Data prediction involves spherical-wave reflection-coefficient calculations. Trans-dimensional inversion, which samples probabilistically over the number of layers in the seabed model, is applied to combine quantitative model selection with parameter/uncertainty estimation. The inversion is applied to reflection-coefficient data sets collected on the New England Mud Patch, and inversion results are compared to those obtained under the common assumption of negligible shear effects (i.e., inverting for a fluid sediment model).

Geoacoustic Inversion with a Single Vector Sensor and Multichannel Dispersion Curves

This paper discusses the value added by using a single vector sensor over a conventional pressure-only hydrophone for geoacoustic inversions. Inversion methods based on genetic algorithms are used to estimate the seabed properties. Synthetic signals of impulsive arrivals first are modeled using KRAKEN and RAM propagation models, each being modified to predict components of the vector field. While KRAKEN is utilized to directly compute dispersion curves, RAM provides full-field results that require the application of time warping to separate the modal arrivals. Combinations of dispersion curves utilizing all vector sensor channels are compared to curves estimated with the pressure-only channel. Within the time warping analysis, both binary masking and band-pass filter masking methods are applied to compare stability of results. The environment modeled for the synthetic analysis and inversion method utilize sound speed profiles measured during the Monterey Bay 2019 at-sea experiment and assume a sediment layer of constant thickness overlying a deeper sub-bottom type. White noise is added to the synthetic data at different signal-to-noise ratios to evaluate the impact of signal excess on the results. A hybrid optimization approach is used to improve the results of the genetic algorithm method. The analysis with synthetic data is consistent with the analysis of broadband, impulsive data collected from the experiment, indicating that the additional information from the vertical velocity channel further improves the geoacoustic parameter estimates.

Surficial sediment sound speed estimation from wide-angle multipath arrivals.

Multipath acoustic arrivals on fixed receivers from a moving source in an underwater waveguide include wide-angle bottom reflections that are affected by sediment properties. Hence, one can use them as input data for seabed geoacoustic parameter estimation. In this study, the effects of seabed properties on such measurements are theoretically analyzed. The result shows that one can utilize time delays and source distances at the critical angle points of multipath arrivals to determine the sediment sound speed. The effect of sediment attenuation is also discussed, and the proposed method is demonstrated with experimental data.

Probabilistic Estimation of Merchant Ship Source Levels in an Uncertain Shallow-Water Environment

The estimation of ship source levels (SSLs) in shallow-water environments can be complicated by sound interaction with the seabed. Uncertainty in seabed properties influences SSL estimates, and it is of interest to mitigate and quantify such effects. This article proposes a probabilistic approach to ship radiated noise recorded on a vertical line array (VLA) of hydrophones to infer SSL and properties of a mud-sand shallow water seabed on the New England Shelf. The approach, trans-dimensional Bayesian marginalization, samples probabilistically over complex spectral source strengths, source depths/ranges, and number of seabed layers and geoacoustic parameters of each layer. The Bayesian information criterion is applied to determine the appropriate number of (point) sources used to describe a ship. Radiated noise due to two merchant ships passing the VLA at beam aspect at 3.2−3.4-km range is considered. The SSL estimates agree well with reference spectra from shallow-water studies on large ensembles of merchant ships. The average SSL uncertainty (in terms of one-half the interquartile range interval) is 3.2 dB/Hz for low-frequency narrowband (20−120 Hz) and 1.8 dB/Hz for broadband noise (190−590 Hz). Seabed layering and geoacoustic parameter estimates agree reasonably well with mud-over-sand seabed models from other inversions in the area.

Uncertainty reduction in matched field inversion using Gaussian Processes

Gaussian Processes have been shown to be an effective tool for predicting an acoustic field in the ocean at a densely populated virtual array. This property is employed in this work for pre-processing acoustic data before these are used for source localization and geoacoustic parameter estimation using matched-field inversion (MFI). The process increases the accuracy of MFI as uncertainty in the estimation process is reduced. Via the application of Gaussian Processes, the data are denoised and interpolated and the new, enhanced fields are used as input to MFI along with replicas calculated at the virtual receivers at which field predictions are made. Kernel functions, implicit in the implementation of Gaussian Processes, quantify the correlation among field values at neighboring spatial points. Employing different kernels, the approach is tested on synthetic and real data with both an exhaustive search and genetic algorithms and is found to be superior to conventional Bartlett MFI in source localization and geoacoustic inversion. [Work supported by ONR.]

Matched-field geoacoustic inversion using Gaussian Processes for field prediction

Matched-field geoacoustic inversion is the process of maximizing a correlation measure between received data at deployed hydrophones and replica fields calculated via an acoustic model for a set of values for environmental parameters, frequently along with source range and depth. The values that produce the maximum correlation form the geoacoustic parameter and source location estimates. Matched-field inversion performance improves as the number of receiving hydrophones increases and the water column is sampled in a dense manner. In order to achieve dense water column sampling when only a few phones are available, we predict the field at a large number of virtual depths within a given aperture using Gaussian Processes. Similarly to classical matched-field inversion, the predicted field, computed after the choice of a suitable kernel and its hyperparameters, is correlated with replicas calculated at the same phone depths. It is demonstrated with both synthetic and real data that the Gaussian Process matched-field inversion approach outperforms conventional processing. The effect is more pronounced as the Signal-to-Noise Ratio attains lower values. [Work supported by ONR.]

Geoacoustic inversion with generalized additive models.

Geoacoustic parameter estimation is presented as a non-linear regression problem where prediction is performed using generalized additive models applied to features extracted from broadband acoustic time-series in a machine learning framework. Qualitatively, it can be seen that signals that have propagated in different environments have distinct structures: in some cases, a single mode is identified, in others, multiple modes can be seen; signals can also be distinguished by different energy levels. Features that are employed here comprise relative amplitudes of distinct peaks in the signals, signal kurtosis, signal strength, decay of the time-series with time, and time difference between distinct peaks of the received signals. Functions are sought that relate sediment sound speed and attenuation to these features. A multivariate generalized additive model is proposed using smoothing splines for the nonlinear regression problem of predicting geoacoustic properties using the features. The spline functions are estimated using noise-free training patterns from known environments. After this training step, the geoacoustic properties are predicted in an efficient manner using noisy testing patterns from a variety of different areas.

Reflected seismic waves radiated by the apex of a wedge-shaped ocean

A sound propagation experiment has been carried out near the coast in Qingdao to investigate underwater sound propagation characteristics over a sloping bottom. Hydrophones and Ocean Bottom Seismometers (OBSs) were arranged in different place to receive the signal radiated by explosive source. In the temporal signals received by the OBSs, some weak and mystical “tails” following the direction waves were observed but the hydrophones only received the direction waves without the “tails.” Analyzing signal of different OBSs at different distance, it is indicated that the “tails” is the seismic waves coming from a fixed location. The temporal signal propagating in elastic wedge-shaped ocean is simulated with spectral-element method. The results of the model show that, when the acoustic waves propagates to the apex of the wedge, the boundary among the surface, the seabed and the coast, the apex will radiate the “tail” seismic waves. The reflection seismic waves can be apply in geoacoustic parameter estimation and Underwater Topographic Survey.

Bottom characterization in the northern Gulf of Mexico using chirp sonar and ship noise data

As a part of Prof. George Ioup’s multi-institutional research initiative, chirp sonar bottom surveys were conducted near the Mississippi Canyon in the northern Gulf of Mexico during the Littoral Acoustic Demonstration Center (LADC) 2001 experiment. Bottom geoacoustic properties were estimated using a shallow-towed 2–12 kHz chirp sonar that provided sub-meter resolution and 60 m penetration. The inversion results showed that the experimental site is mainly covered with clayey sediment. In 2010, a second bottom characterization experiment was conducted at ∼25 miles NE of the LADC 2001 experiment site using a deep-towed chirp sonar. Deep-towed chirp sonar provided geoacoustic property estimations as well as low-grazing-angle bottom-loss measurements. As a source of opportunity, own-ship noise data were also used for geoacoustic parameter estimation and bottom-loss measurements. Geoacoustic parameter estimations and bottom loss measurements from the chirp-sonar and ship-noise data provided similar results that were also in agreement with those from the LADC 2001 experiment. [Work supported by ONR.]

Inversion of geoacoustic parameters from transmission loss measurements in the presence of swim bladder bearing fish in the Santa Barbara Channel

It is well established that attenuation due to swim bladder bearing fish can have profound effects on transmission loss (TL) in continental shelf and slope environments (Weston, 1967; Diachok, 1999). Realistic estimates of geoacoustic parameters from TL measurements in such environments may be derived by application of the concurrent bio and geo inversion method (Diachok and Wales, 2005; Diachok and Wadsworth, 2014). This method assumes that the biological environment may be characterized by bio-alpha (attenuation coefficient within the layer), layer depth, and layer thickness. Application of this method to broadband TL measurements between 0.3 and 5 kHz in the Santa Barbara Channel resulted in realistic estimates of geo and bioacoustic parameters. Inverted geoacoustic parameters were consistent with coincident core and chirp sonar measurements. Inverted bioacoustic parameters were consistent with concurrent measurements of fish depths and length distributions of dominant species. The latter permitted cal...

Inversion of geoacoustic parameters from transmission loss measurements in the presence of swim bladder bearing fish in the Yellow Sea

Realistic estimates of geoacoustic parameters from transmission loss (TL) measurements in the presence of swim bladder bearing fish may be derived by application of the concurrent bio and geo inversion method (Diachok and Wales, 2005; Diachok and Wadsworth, 2014). This method was applied to Qiu’s, (1999) TL measurements in the Yellow Sea. Qiu’s measurements were made in August, south of the Shandong Peninsula, where the bottom is composed of sand, and the concentration of anchovies, the dominant species in the Yellow Sea, is high, at night when anchovies are dispersed near the surface and bio-alpha is relatively high. The measured attenuation coefficient (TS—cylindrical spreading—absorption) was nearly 8 dB/km at the resonance frequency of anchovies. Inverted values of bioacoustic parameters were consistent with bioacoustic parameters of anchovies. Inverted geoacoustic parameters were consistent with geoacoustic properties of sand. Disregard of the effects of bio-alpha on TL resulted in unrealistically hi...

Geoacoustic inversion to study spatial variability and uncertainty along a 14-km seabed survey on the Malta Plateau

Seabed variability of continental shelves is well understood at kilometer and centimeter scales; however, mesoscales of several meters are poorly understood. While vertical seismic profiling can provide insights into layer geometries, geoacoustic parameter values are not estimated, and structural images are averaged over ~100 m and typically distorted. Here, we apply automated sequential inversion to seabed reflectivity data recorded using an autonomous underwater vehicle (AUV) on the Malta Plateau. The AUV tows a 32-hydrophone array and a source emitting signals at regular intervals along a 14-km survey track in two frequency bands (900-1300 and 1900-3600 Hz). The reflection data are processed in terms of reflection coefficients which results in ~1600 data sets, each with a seabed footprint of <20 m. For efficient uncertainty quantification, a particle filter is applied. The inversion provides rich seabed information with resolution and geoacoustic parameter estimates significantly better than possible with vertical profiling. The survey reveals a low-velocity (<1500 m/s) wedge with low attenuation of initially 1.2-m thickness, thinning towards the Sicilian coast and disappearing after 8 km. An erosional, high-velocity boundary is increasingly buried by low-velocity material towards the coast. This boundary is rougher in shallower water and depressions are filled with material of lower velocity. [Data: CLUTTER JRP, a collaboration of ARL-PSU, DRDC, CMRE, and NRL. Research supported by SERDP and ONR ocean acoustics.]

Geoacoustic inversion using low frequency broadband acoustic measurements from L-shaped arrays in the Shallow Water 2006 Experiment.

When using geoacoustic inversion methods, one objective function may not result in a unique solution of the inversion problem because of the ambiguity among the unknown parameters. This paper utilizes acoustic normal mode dispersion curves, mode shapes, and modal-based longitudinal horizontal coherence to define a three-objective optimization problem for geoacoustic parameter estimation. This inversion scheme is applied to long-range combustive sound source data obtained from L-shaped arrays deployed on the New Jersey continental shelf in the summer of 2006. Based on the sub-bottom layering structure from the Compressed High-Intensity Radiated Pulse reflection survey at the experimental site, a two-layer (sand ridge overlaying a half-space basement) range-independent sediment model is utilized. The ambiguities of the sound speed, density, and depth of the sand ridge layer are partially removed by minimizing these objective functions. The inverted seabed sound speed over a frequency range of 15-170 Hz is comparable to the ones from direct measurements and other inversion methods in the same general area. The inverted seabed attenuation shows a nonlinear frequency dependence expressed as αb=0.26f1.55(dB/m) from 50 to 500 Hz or αb=0.32f1.65(dB/m) from 50 to 250 Hz, where f is in kHz.

Fast computation of seabed spherical-wave reflection coefficients in geoacoustic inversion.

This paper develops a fast numerical approach to computing spherical-wave reflection coefficients (SWRCs) for layered seabeds, which provides substantial savings in computation time when used as the forward model for geoacoustic inversion of broadband seabed reflectivity data. The approach exploits the Sommerfeld-integral representation of SWRCs as the Hankel transform of a function proportional to the plane-wave reflection coefficient (PWRC), and applies Levin integration to the rapidly oscillating integrand cast as the product of a (pre-computed) media-independent matrix and a vector involving PWRCs at a sparse sampling of integration angles. Compared to conventional Simpson's rule integration for computation of the SWRC, the Levin integration yields speed-up factors of an order of magnitude or more. Further, it results in reduced memory requirements for storage of pre-computed quantities, a desirable property when a graphics processing unit (GPU) is used for parallel computation of SWRCs. The paper applies trans-dimensional Bayesian inversion to investigate the impact of forward modeling in terms of PWRCs and SWRCs on the estimation of geoacoustic parameters and uncertainties. Model comparisons are quantified in simulated- and measured-data inversions by comparing the estimated geoacoustic parameters to the true parameters or core measurements, respectively, and by calculating the deviance information criterion for model selection.

Estimating Geoacoustic Properties of Surficial Sediments in the North Mien-Hua Canyon Region With a Chirp Sonar Profiler

Acoustic reflection coefficients and attenuation rolloff rates of the seabed near North Mien-Hua Canyon, northeast of Taiwan, were measured from chirp sonar echoes during the Quantifying, Predicting, and Exploiting (QPE) Uncertainty Initiative Experiment in 2009. Using these measurements and the Biot theory with a fluid approximation, the depth-averaged sound speed, density, and medium attenuation of the surficial sediment layer were estimated. The sediment types in the chirp sonar survey area vary from fine sand to coarse silt. To capture this spatial variability, the 1-D geoacoustic parameter estimates along the chirp sonar track were interpolated onto a 2-D grid using an objective mapping technique. The 2-D maps of surficial sediment properties, along with interpolation errors, can be further applied to underwater sound propagation models in the experimental area.

A true-depth passive fathometer

This paper applies a sequential trans-dimensional (trans-D) Monte Carlo algorithm for geoacoustic inversion to bottom-loss data estimated from wind-driven ambient noise at a drifting vertical array. The approach explored in this work provides range-dependent estimates of geoacoustic parameters and true-depth layering structure of the seabed, together with corresponding uncertainties. The Bayesian inversion is applied to incoherent estimates of seabed bottom loss, computed as the array drifts along a range-dependent track. The method adopts a layered representation of the seabed, where each layer is determined by sound speed, density, attenuation, and thickness. The number of layers is also included as an unknown parameter, which allows data-driven parametrization rather than an arbitrary choice of the parametrization for the seabed model. The trans-D Bayesian inversion method samples the joint posterior probability density of all model parameters to provide parameter estimates and uncertainties. A particle filter is used to update the estimated geoacoustic parameters from one array position to the next. The sequential inversion approach is demonstrated using data from the Boundary 2003 experiment, and compared to images of the seabed layering structure obtained by an active seismic system.

A new broadband matched field processor?

It is well known that the use of multiple frequencies helps to reduce uncertainty and to improve resolution for matched field processing. This can be extremely important for the estimation of geoacoustic (bottom) parameters and usually means averaging ambiguity surfaces either incoherently or coherently (if the source spectrum is known) for the linear or minimum variance processors. However, a new approach requires that only the minimum processor values be retained over all the frequencies considered. This presentation will show how this method improves resolution and sidelobe reduction (for the simple incoherent method) both for the linear and for the minimum variance processing. Of course, one never gets something for nothing. This improvement comes at the cost of using only those frequencies which show high-matched field values, usually the low frequencies (25–75 Hz), for geoacoustic inversion. If some frequency component is strongly degraded, then the method will not work. The behavior of this new processor (to be called the “minimization broadband processor”) is demonstrated on simulated shallow water data.

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.

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.

Parameter Estimate Biases in Geoacoustic Inversion From Neglected Range Dependence

This paper shows that neglecting environmental range dependence in matched-field inversion (MFI) results in biased theory errors that lead to biased geoacoustic parameter estimates using standard inversion methods. Two approaches are used to investigate this issue. The first considers the distribution of optimal parameter estimates obtained from a large number of range-independent inversions of synthetic data generated for random range-dependent environments. The second applies Bayesian inversion and computes marginal uncertainty distributions for geoacoustic parameters, neglecting environmental range dependence. Both hard- and soft-bottom environments are considered at a number of scales of lateral variability for water depth and seabed sound speed. While the use of multifrequency data reduces the variability of the parameter estimates, it does not generally reduce parameter biases and increases biases in some cases. The biases appear to result from additional losses in range-dependent propagation, which are compensated for in range-independent inversion by adjusting geoacoustic parameters to decrease the seabed reflection coefficient. The effects of range dependence differ for different environments, with the soft-bottom case sensitive to range-dependent sound speed and the hard-bottom case sensitive to range-dependent water depth.