Sound-speed Profile Measurement Research Articles

Centimeter-level-precision seafloor geodetic positioning model with self-structured empirical sound speed profile

In-field Sound Speed Profile (SSP) measurement is still indispensable for achieving centimeter-level-precision Global Navigation Satellite System (GNSS)-Acoustic (GNSS-A) positioning in current state of the art. However, in-field SSP measurement on the one hand causes a huge cost and on the other hand prevents GNSS-A from global seafloor geodesy especially for real-time applications. We propose an Empirical Sound Speed Profile (ESSP) model with three unknown temperature parameters jointly estimated with the seafloor geodetic station coordinates, which is called the 1st-level optimization. Furthermore, regarding the sound speed variations of ESSP we propose a so-called 2nd-level optimization to achieve the centimeter-level-precision positioning for monitoring the seafloor tectonic movement. Long-term seafloor geodetic data analysis shows that, the proposed two-level optimization approach can achieve almost the same positioning result with that based on the in-field SSP. The influence of substituting the in-field SSP with ESSP on the horizontal coordinates is less than 3 mm, while that on the vertical coordinate is only 2–3 cm in the standard deviation sense.

A meta-deep-learning framework for spatio-temporal underwater SSP inversion

Sound speed distribution, represented by a sound speed profile (SSP), is of great significance because the nonuniform distribution of sound speed will cause signal propagation path bending with Snell effect, which brings difficulties in precise underwater localization such as emergency rescue. Compared with conventional SSP measurement methods via the conductivity-temperature-depth (CTD) or sound-velocity profiler (SVP), SSP inversion methods leveraging measured sound field information have better real-time performance, such as matched field process (MFP), compressed sensing (CS) and artificial neural networks (ANN). Due to the difficulty in measuring empirical SSP data, these methods face with over-fitting problem in few-shot learning that decreases the inversion accuracy. To rapidly obtain accurate SSP, we propose a task-driven meta-deep-learning (TDML) framework for spatio-temporal SSP inversion. The common features of SSPs are learned through multiple base learners to accelerate the convergence of the model on new tasks, and the model’s sensitivity to the change of sound field data is enhanced via meta training, so as to weaken the over-fitting effect and improve the inversion accuracy. Experiment results show that fast and accurate SSP inversion can be achieved by the proposed TDML method.

Spherical wave scattering from rough surfaces and array processing: Application to sound-speed profile measurement uncertainty analysis.

A three-dimensional modeling of spherical wave reflection on layered media with rough interfaces is used to study the roughness influence in sediment sound-speed profile measurements. The configuration of the numerical experiment consists of a point source and a horizontal array of hydrophones. In addition, theoretical uncertainties on the angle of arrival and travel time of a spherical wave reflected over a slightly rough interface are derived by including the array processing in the Helmholtz-Kirchhoff integral. The sound-speed profile sensitivity to those parameters is used to compare theory and numerical experiments. The agreement is good and shows a surprisingly strong influence of the roughness on the sound-speed profile measurement.

Sound speed profile measurement uncertainties due to rough interfaces: A parametric study using the Langston-Kirchhoff model

In the context of sound-speed profile measurement by the image source method, interface roughnesses are responsible for result uncertainties. The image source method models the reflected wave from a layered media as a collection of images which are the mirror reflections of the real source over the interfaces. From image source positions, one can deduce the sound-speed profile. Interface roughnesses might blur these image sources and reduce the accuracy of their localization. Using the Langston-Kirchhoff model of a 3D layered media with rough interfaces, it is possible to perform a parametric study of these uncertainties as a function of roughness parameters. In the aim of performing roughness parameter inversion, theoretical uncertainties are calculated and compared with estimated uncertainties from numerical experiments.

Spherical wave reflection in layered media with rough interfaces: Three-dimensional modeling.

In the context of sediment characterization, layer interface roughnesses may be responsible for sound-speed profile measurement uncertainties. To study the roughness influence, a three-dimensional (3D) modeling of a layered seafloor with rough interfaces is necessary. Although roughness scattering has an abundant literature, 3D modeling of spherical wave reflection on rough interfaces is generally limited to a single interface (using Kirchhoff-Helmholtz integral) or computationally expensive techniques (finite difference or finite element method). In this work, it is demonstrated that the wave reflection over a layered medium with irregular interfaces can be modeled as a sum of integrals over each interface. The main approximations of the method are the tangent-plane approximation, the Born approximation (multiple reflection between interfaces are neglected) and flat-interface approximation for the transmitted waves into the sediment. The integration over layer interfaces results in a method with reasonable computation cost.

Sensitivity analysis of the image source method to roughness and volume heterogeneities

In the context of the sediment characterization, the image source method provides a fast and automated sound-speed profile measurement of the seafloor. This technique is based on the analysis of the seafloor reflected acoustic wave as a collection of image sources whose positions are linked with the thicknesses and the sound speed of the sediment stack. The presence of interface roughness and volume inhomogeneities will reduce phase coherence between the receivers and thus may reduce the ability to precisely obtain the image source position. However, “blurring” of the image source position may provide useful clues about the roughness and/or volume heterogeneities. Recent measurements were obtained using an Autonomous Underwater Vehicle (AUV) towing a broadband source (frequency band from 1600 to 3500 Hz) and a linear array of hydrophones. Based on that configuration, a sensitivity study of the effect of roughness and volume heterogeneities on the image source method is presented and discussed.

Scattering objects imaging using an autonomous underwater vehicle towing a source and an horizontal array

Recently, seabed sound-speed profile measurement by the image source method had been performed using an Autonomous Underwater Vehicle (AUV) towing a broadband source (frequency band from 1600 to 3500 Hz) and a linear array of hydrophones. This method provides an automatic process by the use of the semblance function. In that communication, the semblance ability to detect coherent reflections is used to image scattering objects such as mud volcanoes by integrating the results of successive measurement along the AUV track. Due to the horizontal array configuration, there is an ambiguity on the scatterers localization that could be solved by the use of hydrophone triplets.

Range dependent sediment sound speed profile measurements using the image source method

This paper presents a range dependent sediment sound speed profile measurement obtained using the image source method. This technique is based on the analysis of the seafloor reflected acoustic wave as a collection of image sources which positions are linked with the thick-nesses and the sound speed of the sediment stack. The data used were acquired by the NURC in 2009 during the Clutter09 experiment. The equipment used was an autonomous undersea vehicle towing a 1600-3500 Hz frequency band source and a 32 m horizontal line array of 32 hydrophones at 12 m above the seabed. Under the assumption of locally range independent seabed properties, the moving horizontal array provides successive range independent sediment sound speed profiles along a track to obtain the range and depth dependent structure of the seafloor. Two key steps include recovery of the time-varying unknown array shape from the data and spatial filtering of the successive sound speed profiles. A comparison of the image source method result and seismic data along nearly the same 14 km track indicates that the seabed stratigraphy is correctly mapped by this method.

Finding Acoustically Stable Areas Through Empirical Orthogonal Function (EOF) Classification

Validity of sonar performance models is generally limited by environmental uncertainty, and particularly uncertainty in the sound-speed profile (SSP). Rapid environmental assessment (REA) missions, e.g., using gliders, and advanced ocean models may be used to reduce this uncertainty before sonar operation in hostile waters. This work shows how data from ocean models may be used for planning of REA missions. The area of operation is divided into oceanographically stable subareas using empirical orthogonal functions (EOFs) and different methods of clustering analyses on SSPs from the ocean model. The acoustic stability of each subarea is assessed using sonar performance modeling. Acoustically unstable areas are divided into smaller subareas. Acoustically stable groups are represented by a single SSP. A map of acoustically stable areas in the area of operation is the main output. Large, geographically contiguous groups indicate acoustically stable areas where frequent SSP measurements are unnecessary, e.g., low concentration of gliders. Small and noncontiguous groups indicate the opposite. Other applications include determination of suitable locations for sonar tests that require stable sonar conditions, and efficient optimization of sonar operation in acoustically stable areas.

Acoustic research in the Hudson River Estuary at the Stevens Maritime Security Laboratory.

Stevens Institute of Technology has established a Maritime Security Laboratory (MSL) as a national laboratory resource for government, industry, and universities to advance technologies for the protection of USN maritime infrastructure. Experiment instrumentation includes research vessels, a multiplicity of hydrophones and emitters, stand alone acoustic buoys, diver acoustic simulators, unmanned underwater vehicles (UUVs), and precision instrumentation placement capabilities. The in‐river experiments are controlled remotely from a Visualization Center on campus. Acoustic research is supported by sound speed profile measurements, integrated video and acoustic tracking of surface events, and global positionlng system tracking of live divers. Recent results include determination of parameters defining the detection distance of a threat: source level, transmission loss, and ambient noise. The combination of acoustic noise with video data for different kinds of ships in the Hudson River enables estimation of sound attenuation in a wide frequency band. The establishment of a library of various estuarine signatures, including divers, boats, ships, UUVs, construction equipment, and so forth, is underway. This knowledge can be used in a variety of intruder detection scenarios and for optimal methods of threat detection. [This work was supported by ONR Project N00014‐05‐1‐0632: Navy Force Protection Technology Assessment Project.]

Adaptive sampling based on the waveguide invariant for determining environmental provinces

The waveguide invariant (related to the striations seen in range-frequency plots of sonar data) can be used as a tool to determine provinces in adaptive sampling schemes used to plan the placement of environmental measurements. The quantity from which the waveguide invariant is derived is used to demark spatial regions over which the environment can be considered to be range and bearing independent from an acoustic point of view. This allows significant speedup in acoustic propagation calculations. It also provides guidance for the placement of environmental measurements—particularly water column sound speed profile measurements. Results from the application of this technique to simulated data and to data from the Sea of Japan are shown and are compared to results of another method based on the adiabatic condition. [This work was supported by ONR with technical guidance provided by the Naval Research Laboratory, Stennis Space Center, MS.]

Precise Multibeam Acoustic Bathymetry

The maximum error in ocean depth measurement as specified by the International Hydrographic Organization is 1% for depth greater than 30m. Current acoustic multibeam bathymetric systems used for depth measurement are subject to errors from various sources which may significantly exceed this limit. The lack of sound speed profiles may be one significant source of error. Because of the limited ability of sound speed profile measurement, depth values are usually estimated using an assumed profile. If actual sound speed profiles are known, depth estimate errors can be corrected using ray-tracing methods. For depth measurements, the calculation of the location at which a sound pulse impinges on the sea bottom varies with the variation of the sound speed profile. We demonstrate that this location is almost unchanged for a family of sound speed profiles with the same surface value and the same area under them. Based on this observation, we can construct a simple constant-gradient equivalent sound speed profile to correct errors. Compared with ray-tracing methods, the equivalent sound speed profile method is more efficient. If a vertical depth is known (or independently measured), then depth correction for a multibeam system can be accomplished without knowledge of the actual sound speed profile. This leads to a new type of precise acoustic multibeam bathymetric system.

Environmental data assimilation in matched-field processing with a random normal mode model

Numerical acoustic propagation models used in matched-field processing typically assume that ocean environmental parameters, such as the range-dependent sound-speed profile, are either known exactly or are characterized by stationary prior statistics. However, neither of these modeling approaches is well suited for accurately assimilating measurements of the correct ocean realization into the calculation of point-source acoustic wave front statistics. In this paper, environmental data is assimilated into a random adiabatic normal mode propagation model by calculating the conditional correlation matrix of an acoustic wave front given a set of contemporaneous sound-speed profile measurements made at different points in space. First-order perturbation theory is used to map the conditional statistics of Gaussian-distributed excess sound-speed variations into an estimate of the conditional point-source wave front correlation matrix. Minimum variance beamforming with environmental perturbation constraints [J. L. Krolik, J. Acoust. Soc. Am. 92, 1408–1419 (1992)] conditioned on current environmental measurements is demonstrated as a means of exploiting this random wave front model to achieve improved matched-field source localization performance. [Work supported by ONR.]