Sound Speed Variations Research Articles

GNSS-A Geodetic Positioning with Global Gridded Marine Environment Dataset Replacing In-Field Sound Speed Measurement

In-field sound speed measurement used to correct the effect of sound speed variation in GNSS-A observation not only increases the cost of seafloor geodetic observation but also limits the timeliness of various seafloor geodetic applications. We investigate the seafloor geodetic positioning with the global gridded marine environment dataset (GGMED) replacing the in-field sound speed measurement. Three typical GGMEDs, including WOA18, EN4, and GLORYS12, were evaluated for seafloor geodetic positioning on the Japan GNSS-A dataset. The experimental results show that the three typical GGMEDs can be applied in high-precision seafloor geodesy. GLORYS12 has the highest positioning accuracy evaluated by the positioning results of the in-field sound speed profile (SSP), with the root mean square (RMS) of the positioning difference of 0.0021, 0.0020, and 0.0244 m in the E, N, and U directions, respectively. The experimental results indicate that GGMEDs have the potential to replace in-field sound speed measurement in the future to reduce seafloor geodetic observation costs.

The Parameterization of the Sound Speed Profile in the Sea of Japan and Its Perturbation Caused by a Synoptic Eddy

This study presents the description of the parameterization of sound speed distribution in the Sea of Japan in the presence of a synoptic eddy. An analytical representation of the background sound speed profile (SSP) on its periphery is proposed. The perturbation of sound speed directly associated with the presence of an eddy is investigated. The proposed parameterization of the background SSP leads to a Sturm–Liouville problem for normal mode computation, which is equivalent to the eigenvalue problem for the Schrödinger equation with the Morse potential. This equivalence leads to simple analytical formulae for normal modes and their respective horizontal wavenumbers. It is shown that in the presence of an eddy causing moderate variations in sound speed, the standard perturbation theory for acoustic modes can be applied to describe the variability in horizontal wavenumbers across the area in which the eddy is localized. The proposed parameterization can be applied to the sound propagation modeling in the Sea of Japan.

On the accuracy of distance estimates by propagation time of sound signals on the arctic shelf

As part of numerical modeling, estimates are made of the accuracy of determining the distance between underwater sources and sound receivers located at a distance of several kilometers from each other in the Kara Sea in the autumn. It is assumed that the main source of possible errors in determining the distance is the lack of accurate data on the vertical profile of the sound speed along the propagation path of acoustic signals. Data from September and November were analyzed, in the interval between which significant changes in the profile take place, when the vertical gradient of sound speed changes from negative to positive values. Characteristic values of sound speed variations were obtained by statistical processing of hydrological data taken from the World Ocean Database. The results obtained are important for analyzing the capabilities of underwater acoustic navigation.

Two-dimensional visualisation of a travelling sound wave over porous medium

Prior research has demonstrated a variation in sound speed and a wave bending in the air when an acoustic wavefront approaches a porous medium at a grazing angle. The impact is found to be explicitly dependent on the frequency of the sound and the nature of the adjacent absorber. The current study expands the investigations by using a distributed array of eight microphones mounted on a single-axis motorised mover. The measurement setup allowed for an extensive collection of impulse responses within a confined area near a horizontally placed porous panel. The sensors captured a frequency sweep every 10 millimetres on multiple horizontal planes above the air-material boundary. Moreover, the array configuration allowed for simultaneous capture of wave propagation both above and outside the projection area of the absorbing material. The progression of a travelling wavefront is reconstructed as an image-sequence-based 2D animation on four discrete vertical planes, 60 cm by 50 cm each, traversing or passing outside the porous material.

Localisation for underwater acoustic networks with a piece-wise linear sound speed profile

ABSTRACT Underwater agent localisation is a precondition for plenty of ocean applications. The variation of the underwater sound speed (USS) is one of the main factors that constrain the performance of underwater localisation. In this paper, a scheme is designed to reduce the agent localisation error arising from the USS variation in deep water and an agent localisation algorithm named ‘localisation with a piecewise linear USS' (LPL) is proposed. According to the characteristics of a real USS in deep water, a piecewise linear USS model is introduced to approximate the real USS. The agent above under the ‘sound fixing and ranging’ (SOFAR) channel are both considered. The LPL algorithm consists of three parts: initial value estimation, horizontal distance estimation and localisation. Considering the piecewise linear USS profile, the proposed LPL algorithm improves the performance for localisation with 60% more precision than the state-of-the-art methods.

Active acoustic measurement of sound speed for quantifying underwater gaseous bubble emissions

Artificial or natural processes can cause underwater gas leakage, which often forms upwelling bubbly plume. The interaction of gaseous plume with acoustic waves would result in the variation of sound speed, which makes it possible to monitor the upwelling process via active acoustics. In this work, we build a numerical model to simulate the transport of gaseous bubbles and the sound speed response to the plume in the acoustic channel. The effectiveness of the model is validated by conducting a tank-based experiment, in which the sound speed variations were measured by varying gas flow rates. Meanwhile, signals at six frequencies (1 to 8 kHz) were transmitted and propagated through the medium part of which was occupied by the gaseous plume. Moreover, the comparison results also give a matched bubble size distribution (BSD), which further confirms the validity of the proposed model.

Learning-based sound speed estimation and aberration correction for linear-array photoacoustic imaging

Photoacoustic (PA) image reconstruction involves acoustic inversion that necessitates the specification of the speed of sound (SoS) within the medium of propagation. Due to the lack of information on the spatial distribution of the SoS within heterogeneous soft tissue, a homogeneous SoS distribution (such as 1540 m/s) is typically assumed in PA image reconstruction, similar to that of ultrasound (US) imaging. Failure to compensate for the SoS variations leads to aberration artefacts, deteriorating the image quality. Various methods have been proposed to address this issue, but they usually involve complex hardware and/or time-consuming algorithms, hindering clinical translation. In this work, we introduce a deep learning framework for SoS estimation and subsequent aberration correction in a dual-modal PA/US imaging system exploiting a clinical US probe. As the acquired PA and US images were inherently co-registered, the estimated SoS distribution from US channel data using a deep neural network was incorporated for accurate PA image reconstruction. The framework comprised an initial pre-training stage based on digital phantoms, which was further enhanced through transfer learning using physical phantom data and associated SoS maps obtained from measurements. This framework achieved a root mean square error of 10.2 m/s and 15.2 m/s for SoS estimation on digital and physical phantoms, respectively and structural similarity index measures of up to 0.88 for PA reconstructions compared to the conventional approach of 0.69. A maximum of 1.2 times improvement in the signal-to-noise ratio of PA images was further demonstrated with a human volunteer study. Our results show that the proposed framework could be valuable in various clinical and preclinical applications to enhance PA image reconstruction.

Classification of sound speed fields in the Western Pacific Ocean based on tensor decomposition

The three-dimensional (3-D) sound speed structure of the ocean is a fundamental environmental element in studying underwater sound propagation modeling and forecasting. The accurate classification of the sound speed fields (SSFs) provides a comprehensive understanding and analysis of the sound propagation pattern. To take advantage of the 3-D structure of the ocean SSFs, this paper presents a quick method based on tensor decomposition for classifying the ocean 3-D SSFs. Utilizing the WOA18 dataset, High Order Iterative Orthogonal (HOOI) decomposition of the 3-D SSFs is executed so as to accurately extract the characteristic information of the SSFs. The Fuzzy C-Means clustering (FCM) method is applied to classify the feature tensors, partitioning of regional categories in different seasons and revealing the typical SSFs structures. By combining the BELLHOP model with analysis of the characteristics of the first convergence zone of each category, it is concluded that there are six categories of SSFs in the Western Pacific Ocean. The SSFs across all categories are primarily latitudinally distributed, featuring distinct sound channel axes and surface sound speed variations.

Estimation of Temperature and Salinity from Marine Seismic Data—A Two-Step Approach

Ocean-water temperature and salinity are two vital properties that are required for weather-, climate-, and marine biology-related research. These properties are usually measured using disposable instruments at sparse locations, typically from tens to hundreds of kilometers apart. Laterally interpolating these sparse measurements provides smooth temperature and salinity distributions within the oceans, although they may not be very accurate. Marine seismic data, on the other hand, show visible reflections within the water-column which are primarily controlled by subtle sound-speed variations. Because these variations are functions of the temperature, salinity, and pressure, estimating sound-speed from marine seismic data and relating them to temperature and salinity have been attempted in the past. These seismically derived properties are of much higher lateral resolution (less than 25 m) than the sparse measurements and can be potentially used for climate and marine biology research. Estimating sound-speeds from seismic data, however, requires running iterative seismic inversions, which need a good initial model. Currently practiced ways to generate this initial model are computationally challenging, labor-intensive, and subject to human error and bias. In this research, we outline an automated method to generate the initial model which is neither computational and labor-intensive nor prone to human errors and biases. We also use a two-step process of, first, estimating the sound-speed from seismic inversion data and then estimating the salinity and temperature. Furthermore, by applying this method to real seismic data, we demonstrate the feasibility of our approach and discuss how the use of machine learning can further improve the computational efficiency of the method and make an impact on the future of climate modeling, weather prediction, and marine biology research.

Optoacoustic interferometric characterization system (OPTICS) for the evaluation of fuel quality through speed of sound measurements

Ultrasonic techniques have been applied to assess crucial physical parameters in various types of fuels including the speed of sound (SoS), the bulk modulus and the acoustic attenuation coefficient. Such investigations may have important practical significance as the knowledge of fuel properties is directly related to the analysis of combustion characteristics, engine’s overall performance and exhaust emission in the environment. Nevertheless, typical pulse-echo acoustic methods, require the exact determination of both acoustic source – reflector distance and the time of flight of a finite temporal width ultrasonic pulse, setting thus an upper limit as regards the accuracy of the measurements. To encounter these challenges, we present a novel technology implemented through a low-cost and potentially portable optoacoustic interferometric characterization system (OPTICS) for the investigation of SoS variations in common fuels including automotive diesel, hydrous ethanol and gasoline. At 25 °C, diesel/kerosene blends demonstrated a SoS variation ranging from 1322.91 m/s (0.6 diesel volume fraction) to 1349.79 m/s (diesel fuel only), whereas hydrous ethanol samples varied between 1199.92 m/s (0.95 ethanol volume fraction) to 1149.39 m/s (pure ethanol only). Finally, assessments for 95 and 100 research octane number (RON) gasoline blends showed a SoS range from 1134.42 m/s (RON 95) to 1159.86 m/s (RON 100). The high precision and repeatability (relative uncertainty: ∼10-4) of the performed SoS measurements in controlled samples, has demonstrated the promising potential of OPTICS for the evaluation of fuel physical properties as well as the potential detection of contamination with adulterants.

A modular neural network to quickly approximate the modal dispersion in coastal waters

Low-frequency acoustic propagation modeling in coastal waters usually relies on numerical models based on modal theory such as Kraken and Orca. These models compute the modal parameters (e.g., modal wavenumbers and depth functions) that can be used in the calculation of the acoustic field. Their repeated use in broadband applications, or for inversion purposes, comes with a notable computational cost. To mitigate this, a modular neural network (NN) was trained to approximate modal parameters for varying modes and frequencies, across diverse environments, with variable water sound speed profile and variable seabed geoacoustic parameters. The training dataset is generated using Kraken and the NN is evaluated on many environments not seen during training. Once trained, the NN can make broadband predictions without prior knowledge on the number of modes, even when the number of modes changes over the frequency-band of interest. This approach reduces computation time compared to the original forward propagation model, while maintaining high precision. The effectiveness of our method is demonstrated through transmission loss calculations and a simulated geoacoustic inversion scenario.

Passive acoustic localization using dictionary learning

Ray-trace simulations are used to demonstrate the use of Dictionary Learning to improve passive localization of a source monitored with a vertical linear acoustic array. The learned dictionary, generated using historical sound velocity profile (SVP) data from a region of interest, is an over complete, sparse representation of the SVP training set. By minimizing an objective function that measures the differences between multipath reception intervals detected at a receiver for propagation through candidate and baseline SVP profiles, we show that an optimal SVP match to the “unknown” baseline can be reconstructed by an efficient dictionary search. Ray traces back-propagated through the reconstructed SVP according to beamformed receive angles computed with the baseline SVP are then found to well estimate the source position as the centroid of a cluster of multipath signal intersections. The accuracy for small unit-sparsity dictionaries of size up to 50 was evaluated on a randomly sampled, 30 SVP testing set obtained from an area close to that of the training set, demonstrating mean location errors less than 5% in both distance and depth. Five representative profiles spanning the range of observed sound speed variations were used to assess localization performance at source depths from 50 to 450 m and at source ranges from 2000 to 4500 m. Compared to using the average sound speed profile to estimate source position, using the learned dictionary produced a general performance increase in position accuracy.

Compression AutoEncoder for High-Resolution Ocean Sound Speed Profile Data

High-resolution ocean sound speed profile (HROSSP) data is essential for ocean acoustic modeling and sonar performance evaluation. However, the large volume and storage requirements of this data severely restrict its practical application in ocean acoustics. In this paper, we propose a compression autoencoder specifically designed for managing HROSSP data (CAE-HROSSP) and investigate the optimal network structure. Experimental results demonstrate that by using the min-max normalization method for input data and the corresponding inverse normalization for output data, along with employing the LeakyReLU function as the final activation layer, the accuracy of decompressed data reconstruction can be significantly improved. To tackle the challenges of fitting the distribution of surface sound speed data caused by significant variations and noise, we propose two loss functions: slice mean square error and elemental mean square error. These loss functions are combined with mean squared error through weighted summation to enhance CAE-HROSSP’s ability to fit the distribution of surface sound speed values and minimize the reconstruction errors of compressed data. Performance evaluation experiments reveal that CAE-HROSSP outperforms two existing methods in compressing HROSSP data, achieving superior performance with smaller data reconstruction errors at higher compression ratios. Furthermore, transfer learning is utilized to enhance the training of CAE-HROSSP, employing HROSSP data from the area where the mesoscale eddy is situated, as well as at the convergence of cold and warm ocean currents. The compression performance of both the training set and the validation set is comparable in the sea, where the structure of the sound speed profile varies greatly. This indicates that CAE-HROSSP can compress highly variable sound speed profile data in more sea areas using transfer learning, and has the potential to be extended globally. The findings and insights obtained from this study provide guidance for future endeavors in utilizing autoencoders to compress HROSSP data.

Performance study of ray-based ocean acoustic tomography methods for estimating submesoscale variability in the upper ocean.

Ocean acoustic tomography (OAT) methods aim at estimating variations of sound speed profiles (SSP) based on acoustic measurements between multiple source-receiver pairs (e.g., eigenray travel times). This study investigates the estimation of range-dependent SSPs in the upper ocean over short ranges (<5 km) using the classical ray-based OAT formulation as well as iterative or adaptive OAT formulations (i.e., when the sources and receivers configuration can evolve across successive iterations of this inverse problem). A regional ocean circulation model for the DeSoto Canyon in the Gulf of Mexico is used to simulate three-dimensional sound speed variations spanning a month-long period, which exhibits significant submesoscale variability of variable intensity. OAT performance is investigated in this simulated environment in terms of (1) the selected source-receivers configuration and effective ray coverage, (2) the selected OAT estimator formulations, linearized forward model accuracy, and the parameterization of the expected SSP variability in terms of empirical orthogonal functions, and (3) the duration over which the OAT inversion is performed. Practical implications for the design of future OAT experiments for monitoring submesoscale variability in the upper ocean with moving autonomous platforms are discussed.

GNSS-A Network Solution with Zenith Acoustic Delay Estimation

The ocean sound speed changes drastically with time and space. It is almost impossible to achieve centimeter-level-precision Global Navigation Satellite System (GNSS)-Acoustic (GNSS-A) positioning without regarding spatio-temporal variations of sound speed. Inspired by the atmospheric delay estimation, we define zenith acoustic delay (ZAD) as the zenith-direction ranging error sourced by the sound speed variations, and then it is decomposed into one temporal component and two horizontal components for the sound speed variations in time and space, respectively. We propose a network solution with the three ZAD components of each seafloor geodetic point as common parameters to be estimated, and then present a piece-wise estimation to characterize their time-varying nature. To remedy the ray bending effect this study is implemented in the context of the ray tracing algorithm based on a reference sound speed profile (SSP). Experimental tests show that the proposed network solution improves ZAD estimation and results in a better position determination as compared to the single-point positioning (SPP) solution. The proposed network solution for a single campaign can achieve a horizontal positioning precision better than 5 cm (1-sigma) for the seafloor geodetic array centroid. The temporal variation of ZAD is up to one meter while the horizontal variations vary within a few decimeters in the time domain and show an azimuthal asymmetry in space.

Sequential GNSS-Acoustic seafloor point positioning with modeling of sound speed variation

Sequential GNSS-Acoustic seafloor point positioning with modeling of sound speed variation

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.

On the role of vertical resolution for resolving mesoscale eddy dynamics and the prediction of ocean sound speed variability

This work investigates how vertical resolution affects the prediction of ocean sound speed through a suite of regional simulations covering the DeSoto Canyon in the Gulf of Mexico. Simulations have identical horizontal resolution of 0.5 km, partially resolving submesoscale dynamics, and vertical resolution from 30 to 200 terrain-following layers. The focus is on mesoscale eddies and how modeled sound speeds vary whenever more vertical baroclinic modes are resolved. While domain-averaged sound speed profiles do not differ substantively, the standard deviation increases for increasing resolution due to the sharper representation of mesoscale circulations underneath the mixed layer and their associated density anomalies.

Shallow Water Seafloor Geodesy With Wave Glider‐Based GNSS‐Acoustic Surveying of a Single Transponder

AbstractDue to the blockage of seawater, seafloor displacement cannot be directly measured by space geodesy. The combination of Global Navigation Satellite Systems‐acoustic ranging (GNSS‐A) has been used to overcome the electromagnetic barrier, so that a GNSS‐determined sea surface vessel's coordinates can be transformed to seafloor benchmarks in a global reference frame. Due to the high cost and science priorities, previous GNSS‐A studies mainly targeted relatively deep water and a minimum of three transponders were used to form an array, equivalent to a precision geodetic station. With recent developments in unmanned autonomous surface vessels, low cost GNSS‐A surveys are poised to become practical. Here we demonstrate that with a carefully designed surveying trajectory, Wave Glider‐based GNSS‐A surveying of a single transponder in shallow water can provide centimeter‐level accuracy on horizontal seafloor positioning, even if the sound speed model deviates from the actual value by a few meters per second. Results from a nine‐month experiment conducted at ∼54 m water depth show that the repeatability of the seafloor horizontal positioning is better than 2 cm. When conditions allow, the acoustic observations should be collected symmetrically about the transponder and data redundancies are recommended to reduce the error associated with time‐dependent variations in sound speed.

Full-Bayes GNSS-A solutions for precise seafloor positioning with single uniform sound speed gradient layer assumption

A systematic analysis methodology for precise seafloor positioning using the GNSS-A has been constructed and implemented in the open-source software GARPOS. It introduces a linearized perturbation field model for extraction of the 4-dimensional sound speed variation, and solves the perturbation parameters simultaneously with the seafloor position based on the empirical Bayes approach. Although it can provide the solutions stably and almost analytically, it has less expandability when imposing additional nonlinear constraint parameters in the observation equation. Even though such parameters can be optimized by applying some information criteria, information on the details of the joint posterior probability would be lost and only the conditional posterior can be estimated. To overcome the above limitations, we implemented full-Bayes estimation using the Markov-Chain Monte Carlo algorithm. This approach can not only help evaluate the dependency of the existing constraint parameters on the seafloor position, but also let us discuss the effects of the additionally imposed constraints. We imposed a constraint under the assumption that a temporally-variable gradient layer steadily lies at a certain depth in the observation scale (typically < 10 km × 10 km, < 1 day). This models the cases with temperature gradients due to a large-scale structure such as the Kuroshio current or internal waves with long-wavelength. The constraint narrows the posterior of the horizontal position and provides a better solution for many datasets, especially in the Nankai Trough region. For the other datasets, the constraint emphasized bias errors, which can also provide information on the possibility of instrumental and modelling errors.