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Abstract The confinement of waves in open systems represents a fundamental phenomenon extensively explored across various branches of wave physics. Recently, significant attention is directed toward bound states in the continuum (BIC), a class of modes that are trapped but do not decay in an otherwise unbounded continuum. Here, the theoretical investigation and experimental demonstration of the existence of quasi‐bound states in the continuum (QBIC) for ultrasonic waves are achieved by leveraging an elastic Fabry–Pérot metasurface resonator. Several intriguing properties of the ultrasound quasi‐bound states in the continuum that are robust to parameter scanning are unveiled, and experimental evidence of a remarkable Q‐factor of 350 at ≈1 MHz frequency, far exceeding the state‐of‐the‐art using a fully acoustic underwater system is presented. The findings contribute novel insights into the understanding of BIC for acoustic waves, offering a new paradigm for the design of efficient, ultra‐high Q‐factor ultrasound devices.

Abstract Multibeam imaging SONARs have been used for a range of measurement applications, such as measurements of fish lengths. This study aimed to quantify the accuracy of imaging SONAR systems, that varied in frequency and beam geometry, to measure the length of synthetic targets positioned perpendicularly. Blueprint Oculus imaging SONAR systems, with four different (centre) frequencies (750 kHz, 1.2 MHz, 2.1 MHz and 3 MHz), were used to measure the length of three targets of nominal lengths: 10 cm, 20 cm and at ranges between 1 and 15.5 m. The effect of beam geometry on measurement error was then examined using regression analysis. This study found that there was an overestimation of the actual length of the target for all measurements that was inversely proportional to the horizontal beamwidth. The measurement error can be reduced by normalising for beamwidth. However, the variation of measurements (i.e. the precision) was found to also increase with range, which was attributed to the increasing beam separation. It is important that the effect of beamwidth on the accuracy of target length measurements, by imaging SONARs is acknowledged in future studies. One approach to mitigating this problem, is to limit the range at which length measurements are made to a beamwidth that produces an estimated level of error that is acceptable for that study.

Abstract The superhydrophobicity of submerged surfaces typically pertains to the trapped air film at the liquid–solid interface, subject to wettability transitions from a Cassie–Baxter state to more unstable states that gradually collapse to high retention regimes, which are energetically more favorable. In this work, the dynamic evolution of those transient metastable states is correlated to the underwater acoustic performance of laser textured superhydrophobic surfaces, resolving the dependence of the ultrasound spectral response with the immersion time to capture the genuine contribution of the hierarchical subwavelength morphology, regardless of the air layer effects. Acoustic wave attenuation of the incident ultrasound energy is extensively quantified in transmission, accounting for instantaneous broadband sound blocking (>30 dB) within the spectral range 0.5–1.5 MHz. As a result of the air layer detachment with the immersion time, transmission coefficients increase accordingly, while acoustic fields in reflection unexpectedly evolve toward stealthiness and naïve acoustic camouflage, mostly ascribable to dissipative mechanisms at air layer interfaces. The intrinsic decay of the air layer effect is tentatively determined at different frequencies, since quantitative understanding of the transient lifetime governing underwater surface wettability is critical to design stable superhydrophobic character of laser induced subwavelength metastructures on the most promising acoustic materials – from eco‐friendly natural to artificial.

This paper describes the Santos Basin Underwater Soundscape Monitoring Project (PMPAS-BS), a Brazilian ocean soundscape monitoring initiative. The main objective of the project is to quantify and assess hydroacoustic noise of anthropogenic origin in a large sedimentary basin extending from 23° S to 28° S on the southeastern Brazilian continental margin of the South Atlantic Ocean. Noise associated with oil and gas (O&G) exploration and production activities is the primary target, but this oceanic region also has busy shipping lanes for commercial, military, and fishing vessels. The two main hubs of Brazil’s export and import of goods by sea are located in this region: Santos and Rio de Janeiro ports. The project has three measurement components: mobile monitoring based on gliders and drifting acoustic profilers, fixed shallow-water monitoring based on acoustic measurements at coastal stations near shipping lanes associated with exploration and production activities in the Santos Basin, and fixed oceanic monitoring based on deep-water mooring lines equipped with passive autonomous acoustic recorders near production units, shipping lanes, and areas with lower intensity of O&G activities (pristine or reference sites). Numerical modeling of anthropogenic underwater acoustic noise has also been included as a fourth project component. The PMPAS-BS covers an area of more than 251,000 km2 and uses several instruments with different methods and sensors for acoustic measurements. Its results provide current sound levels over a very large region of the western South Atlantic, both in areas more and less affected by anthropogenic activities.

Non-cooperative underwater acoustic signal detection and recognition technologies occupy an important position in the marine communication environment. Traditional methods require threshold adjustments based on the environment, making the application less robust. This article addresses the technical challenges of automatic detection and recognition of non-cooperative underwater communication signals. It utilizes two pre-trained models, SSD(Single Shot Detector)-MobileNet-V2-FPNlite(Feature Pyramid Network lite) and EfficientDet, which can simultaneously perform signal classification and automatic drawing of signal bounding boxes on the time-frequency graph. SSD-MobileNet-V2-FPNlite uses the Linear Bottleneck layers and the Inverted Residual structure, which improve the capture of multi-scale representations of signals. EfficientDet performs bidirectional feature fusion to enhance the connections and information flow between signal features. After fine-tuning with simulated data, SSD-MobileNet-V2-FPNlite achieves high detection accuracy on both simulated and real lake test data without transfer learning. EfficientDet also performs well on simulated data and, after transfer learning, shows high accuracy in recognizing and drawing bounding boxes on the lake test data. Both methods exhibit strong generalization ability, feature fewer model parameters, and have lower computational complexity.

The presence of natural ambient noise interferes with the system for locating and identifying underwater targets. This paper suggests that a Dual-Path Transformation Network (DPTN) reduces ambient noise in underwater acoustic signals. First, the input acoustic signals’ higher-order non-linear features are extracted using a multi-scale convolutional encoder neural network. Second, sub-vectors with the same length are created according to the time dimension from the higher-order non-linear features. The sub-vectors are stitched together to form a three-dimensional tensor. Third, a neural network transformer based on the feed-forward network is constructed. Further, to capture long-term series features and separate the target signal from the noisy signals, the three-dimensional tensor is used as the input of the transformer-based masking network. Finally, overlap-add and transpose are used to obtain discernible target signals. The experimental results verify the effectiveness of the proposed underwater acoustic signal denoising algorithm and demonstrate that the proposed DPRN method can obtain higher output signal-to-noise ratio (SNR) and the scale-invariant signal-to-noise ratio (SI-SNR) compared with the other classical algorithms.

Since the industrial revolution the ocean has become noisier. The global increase in shipping is one of the main contributors to this. In some regions, shipping contributed to an increase in ambient noise of several decibels, especially at low frequencies (10 to 100 Hz). Such an increase can have a substantial negative impact on fish, invertebrates, marine mammals and birds interfering with key life functions (e.g. foraging, mating, resting, etc.). Consequently, engineers are investigating ways to reduce the noise emitted by vessels when designing new ships. At the same time, since the industrial revolution (starting around 1760) greenhouse gas emissions have increased the atmospheric carbon dioxide fraction x(CO2) by more than 100 μmol mol-1. The ocean uptake of approximately one third of the emitted CO2 decreased the average global surface ocean pH from 8.21 to 8.10. This decrease is modifying sound propagation, especially sound absorption at the frequencies affected by shipping noise lower than 10 kHz, making the future ocean potentially noisier. There are also other climate change effects that may influence sound propagation. Sea surface warming might alter the depth of the deep sound speed channel, ice melting could locally decrease salinity and more frequent storms and higher wind speed alter the depth of the thermocline. In particular, modification of the sound speed profile can lead to the appearance of new ducts making specific depths noisier. In addition, ice melting and the increase in seawater temperature will open new shipping routes at the poles increasing anthropogenic noise in these regions. This review aims to discuss parameters that might change in the coming decades, focusing on the contribution of shipping, climate change and economic and technical developments to the future underwater soundscape in the ocean. Examples are given, contrasting the open ocean and the shallow seas. Apart from the changes in sound propagation, this review will also discuss the effects of water quality on ship-radiated noise with a focus on propeller cavitation noise.

The extraction of typical features of underwater target signals and excellent recognition algorithms are the keys to achieving underwater acoustic target recognition of divers. This paper proposes a feature extraction method for diver signals: frequency−domain multi−sub−band energy (FMSE), aiming to achieve accurate recognition of diver underwater acoustic targets by passive sonar. The impact of the presence or absence of targets, different numbers of targets, different signal−to−noise ratios, and different detection distances on this method was studied based on experimental data under different conditions, such as water pools and lakes. It was found that the FMSE method has the best robustness and performance compared with two other signal feature extraction methods: mel frequency cepstral coefficient filtering and gammatone frequency cepstral coefficient filtering. Combined with the commonly used recognition algorithm of support vector machines, the FMSE method can achieve a comprehensive recognition accuracy of over 94% for frogman underwater acoustic targets. This indicates that the FMSE method is suitable for underwater acoustic recognition of diver targets.

As sound is a propagating pressure wave, it is important to obtain the hydrodynamic pressure oscillations in the fluid to calculate propeller noise. Numerical hydroacoustic simulations generally assume incompressible flow. Time delays in sound propagation are neglected due to the incompressibility assumption, leading to physically infeasible results in the far field. However, recent works have shown that incompressible solvers can comfortably be used in the near field. This work focused on the effect of distance on the accuracy of the incompressible solver and investigated the hydrodynamic and hydroacoustic properties of a model-scale Duisburg Test Case (DTC) propeller by the finite volume-based computational method. Open-water experiments on a 1/59.407 model-scale DTC propeller were carried out at the Ata Nutku Ship Model Testing Laboratory in Istanbul Technical University. Open-water numerical simulations were performed to determine the hydrodynamic and hydroacoustic properties of the propeller and validated with the hydrodynamic performance of the open-water propeller. Thrust and torque coefficients and open-water efficiency were compared with experiments. The Ffowcs-Williams and Hawkings equation was coupled with the incompressible solver using impermeable surfaces in hydroacoustic predictions of the hybrid solver. Pressure oscillations in the time domain at 21 receivers were used to calculate the sound pressure levels in the vicinity of the propeller. Results of incompressible and hybrid solvers were compared to determine the reach of incompressible solvers for hydroacoustic predictions. It was revealed that discrepancy starts after a 1.5-2D propeller.

Plastic pollution has become an enormous environmental problem, endangering ecosystems, livelihoods, safety and human health. Large quantities of plastics are trapped in or transported by rivers. Monitoring methods mostly focus on plastics floating at the surface or deposited on riverbanks, while a substantial part of plastics may be transported below the water surface. Available underwater monitoring methods rely on nets and large equipment, making them labour-intensive, expensive and invasive. The measurements are, therefore, limited to occasional point measurements. In this paper, we explore the potential of echo sounding for the monitoring of suspended macroplastic (plastic items bigger than 5 mm). We performed tests in a controlled (basin), a semi-controlled (harbour) and an uncontrolled (river) environment using the high-end Acoustic Doppler Current Profiler (ADCP). This device is already in use for the estimation of flow velocity and suspended sediment concentrations using the wide network of ADCPs in the Netherlands and other countries. In the undisturbed controlled environment, 25 items varying in size, material, and orientation could be detected up to at least 4.6 m from the ADCP. The semi-controlled experiments showed that most of these items can also be detected among other naturally occurring scatterers, such as aquatic life, organic material and air bubbles. The field tests under natural conditions, combining ADCP and net measurements, showed that ADCP data can be calibrated towards a correct order of magnitude estimate of plastic transport. The coupling of the ADCP data to item characteristics such as size, material and orientation is still challenging, but more research into, for example, the signature of items may enable distinguishing item characteristics. This fundamental knowledge, combined with repetitions of validated field measurements under different flow conditions, is needed for the development of a robust monitoring method. Such a method may enable continuous or cross-sectional monitoring of suspended plastics and give insight into historic and plastic transport through 30-year long datasets. These insights can help improve and determine the effect of current mitigation and cleaning efforts.

Since the industrial revolution, oceans have become substantially noisier. The noise increase is mainly caused by increased shipping, resource exploration, and infrastructure development affecting marine life at multiple levels, including behavior and physiology. Together with increasing anthropogenic noise, climate change is altering the thermal structure of the oceans, which in turn might affect noise propagation. During this century, we are witnessing an increase in seawater temperature and a decrease in ocean pH. Ocean acidification will decrease sound absorption at low frequencies (500 km). In the case of the Northeast Atlantic Ocean, this sub-surface duct will only be present during winter, leading to similar total mean square pressure level (SPLtot) values in the summer for both (2018 to 2022) and (2094 to 2098). We observed a strong and similar correlation for the two climate change scenarios, with an increase of the top 200 m SPLtot and a slowdown of Atlantic Meridional Overturning Circulation (AMOC) leading to an increase of SPLtot at the end of the century by 7 dB.

ABSTRACTIn this paper, the discussion trend on Underwater Radiated Noise (URN) from Ships being discussed at the International Maritime Organization (IMO) meeting was shared. In some countries, such as Canada and United States, marine mammals such as whales are severely affected by URN from Ships and Offshore plant facilities. In relation to the regulation of URN, internationally agreed regulations such as greenhouse gas reduction from Ships have not been developed, but awareness of seriousness of the URN is gradually increasing. Accordingly, international policy trends and technology trends for URN from Shipping, which are being dealt with in the IMO meeting, were reviewed. The technology for the URN is divided into measurement and reduction, and the reduction of propeller cavitation is being dealt with as a major technical element. Currently, the actual regulation to reduce the URN from ships is the “restriction on the speed of a ship” implemented at the individual national level. In order to develop coherent international rules and regulations, methods for measuring URN and technologies for URN reduction should be continuously developed.

This study proposes a novel application of neural computing based on deep learning for the real-time prediction of motion parameters for underwater maneuvering object. The intelligent strategy utilizes the capabilities of Scaled Conjugate Gradient Neural Intelligence (SCGNI) to estimate the dynamics of underwater target that adhere to discrete-time Markov chain. Following a state-space methodology in which target dynamics are combined with noisy passive bearings, nonlinear probabilistic computational algorithms are frequently used for motion parameters prediction applications in underwater acoustics. The precision and robustness of SCGNI are examined here for effective motion parameter prediction of a highly dynamic Markov chain underwater passive vehicle. For investigating the effectiveness of the soft computing strategy, a steady supervised maneuvering route of undersea passive object is designed. In the framework of bearings-only tracking technology, system modeling for parameters prediction is built, and the effectiveness of the SCGNI is examined in ideal and cluttered marine atmospheres simultaneously. The real-time location, velocity, and turn rate of dynamic target are analyzed for five distinct scenarios by varying the standard deviation of white Gaussian observed noise in the context of mean square error (MSE) between real and estimated values. For the given motion parameters prediction problem, sufficient Monte Carlo simulation results support SCGNI’s superiority over typical generalized pseudo-Bayesian filtering strategies such as Interacting Multiple Model Extended Kalman Filter (IMMEKF) and Interacting Multiple Model Unscented Kalman Filter (IMMUKF).

With the extensive application of sensor technology in scientific ocean research, ocean resource exploration, underwater engineering construction, and other fields, underwater target positioning technology has become an important support for the ocean field. This paper proposes a robust positioning algorithm that combines the disadvantages of distributed estimation and particle swarm optimization, which can solve the large localization error problem caused by uncertainties in underwater acoustic communication and sampling processes. Considering the presence of ranging anomalies and sampling packet loss in underwater acoustic measurements, a weighted coupling filling method is used to correct the outliers in an underwater acoustic ranging signal. Based on the mapping model from the element array to the underwater acoustic responder, an unconstrained optimization algorithm for one-time localization estimation of underwater acoustic targets was established. Based on the one-time localization estimation results of underwater acoustic targets, an improved multi-particle swarm optimization estimation based on interactive search is proposed, which improves the accuracy of underwater target localization. The numerical results show that the positioning accuracy of the proposed algorithm can be effectively enhanced in cases of distance measurement errors and azimuth measurement errors. Compared with the positioning error before optimization, the positioning error can be reduced after optimization. Additionally, the experiment was carried out in the underwater environment of Hangzhou Qiandao Lake, which verified the positioning performance of the proposed algorithm.

Carbon dioxide produced by human activities (i.e., use of fossil fuels, deforestation, and livestock farming) is the main greenhouse gas causing global warming. In 2020, the concentration in the atmosphere exceeded the pre-industrial level by 48% (before 1750). The study of natural CO2 (carbon dioxide) emissions due to volcanic activity through innovative measurement techniques is the main goal of the IPANEMA project. These studies are both essential for the evaluation of natural CO2 emissions and for the development of future carbon capture and storage in underground geological formations to ensure that there are no leaks from the storage sites. Through the installation of two underwater acoustic stations, i.e., one in Panarea and one in the Gulf of Catania, we want to investigate techniques for estimating the flux of CO2 emitted by natural sources, locating emission sources, and, in general, monitoring volcanic activity.

The precision of acoustic ranging and navigation depends on the accuracy of the information about the sound speed field in the area of interest. Large-scale inhomogeneities in the bottom relief and water column can significantly affect the horizontal rays corresponding to vertical modes (in the framework of Burridge–Weinberg formalism), which can lead to delays in the acoustic signal modal components, as compared to propagation along the geodesics on the Earth’s surface. In this study, the influence of horizontal refraction on the delay times of the modal components is considered. In particular, it is studied to what extent the presence of a synoptic eddy near the source–receiver path increases the effective propagation distances due to horizontal refraction. The elongation of horizontal eigenrays relative to the geodesic connecting the source and the receiver is also estimated. The influence of hydrological inhomogeneities on the propagation time of different modal components of a broadband acoustic signal is investigated. This is accomplished by the integration of the group slowness (reciprocal to the group speed) along the horizontal eigenrays connecting the locations of the source and the receiver. Implications for improving the accuracy of the solution of acoustic ranging problems are discussed.

Underwater acoustic networks have recently risen as an effective support to several marine and oceanic applications. However, the potential presence of a large number of nodes simultaneously connected has pushed the scientific community to address the matter of communication resources management, hence to provide the best possible performance for each link. Since underwater acoustic communications (UWACs) suffer from limited bandwidth and long propagation delays, medium access control results really challenging. The best known schemes, that are time division, frequency division and code division multiple access have been considered to handle this problem in underwater scenarios, even though they suffer from some weaknesses. Furthermore, spatial division multiple access achievable in Multiple-Input Multiple-Output (MIMO) systems has emerged as a promising technique able to fit with the multipath propagation typically characterizing UWACs. Dealing with underwater medium access control, we investigate the feasibility of a novel hybrid multiple access technique that works in a bi-dimensional resources domain, namely space and frequency. This solution is aimed to mitigate the multi-user interference by exploiting spatial diversity and, whenever necessary, by applying frequency reuse. Finally, we discuss the feasibility and potential of massive MIMO paradigm, conveniently recast into the underwater context.

In this research, a new application using broadband ship noise as a source-of-opportunity to estimate the scattering field from the underwater targets is reported. For this purpose, a field trial was conducted in collaboration with JASCO Applied Sciences at Duncan’s Cove, Canada in September 2020. A hydrophone array was deployed in the outbound shipping lane at a depth of approximately 71 m to collect broadband noise data from different ship types and effectively localize the underwater targets. In this experiment, a target was installed at a distance (93 m) from the hydrophone array at a depth of 25 m. In this study, a matched field processing (MFP) algorithm is utilized for localization. Different propagation models are presented using Green’s function to generate the replica signal; this includes normal modes in a shallow water waveguide, the Lloyd-mirror pattern for deep water, as well as the image model. We use the MFP algorithm with different types of underwater environment models and a proposed estimator to find the best match between the received signal and the replica signal. Finally, by applying the scatter function on the proposed multi-channel cross correlation coefficient time-frequency localization algorithm, the location of target is detected.

Acoustic tomography systems provide an integrated, synoptic measurement of ocean temperature. By recording the time it takes for a sound signal to travel from a sound source to a receiver, the depth- and range-average sound speed along the geodesic path between the sound source and the receiver can be obtained through inversion. Sound speed and ocean temperature are empirically related; salinity plays a negligible role.The ACOBAR acoustic tomography experiment in central Fram Strait was carried out from September 2010 to September 2012. It consisted of 3 moorings with sound sources and receivers forming a triangle, and one mooring with only receivers in the middle. The steel-sphere flotation of the northernmost mooring imploded in the start of the experiment, so that mooring was not recovered. The three remaining moorings formed a smaller triangle that provided travel time measurements along three paths. Measurements were taken 8 times a day for two of the paths, 8 times every other day for the other paths. The distances covered by the acoustic measurements are 188 – 201 km.Complex data processing was used to determine peaks in the acoustic arrival coda and to correct them for mooring motion and clock drift; travel-time accuracy is O(10) ms. The travel time measurements were inverted to obtain range-depth average sound speed using a statistical approach. The sound speed obtained from each section was then converted to mean ocean temperature.The mean ocean temperature data are published as a set of 8 NetCDF files, compliant with Climate and Forecast (CF) [1] and OceanSITES metadata conventions [2]. Each file contains one year of measurements from one of the sections. The files contain the ocean temperature data, together with theoretical and statistical error estimates and metadata such as discovery metadata and adequate-use metadata. Each data point is provided with a statistical quality measure and a quality flag based on this measure.

Underwater acoustic spiral sources can generate spiral acoustic fields where the phase depends on the bearing angle. This allows estimating the bearing angle of a single hydrophone relative to a single source and implementing localization equipment, e.g., for target detection or unmanned underwater vehicle navigation, without requiring an array of hydrophones and/or projectors. A spiral acoustic source prototype made out of a single standard piezoceramic cylinder, which is able to generate both spiral and circular fields, is presented. This paper reports the prototyping process and the multi-frequency acoustic tests performed in a water tank where the spiral source was characterized in terms of the transmitting voltage response, phase, and horizontal and vertical directivity patterns. A receiving calibration method for the spiral source is proposed and showed a maximum angle error of 3° when the calibration and the operation were carried out in the same conditions and a mean angle error of up to 6° for frequencies above 25 kHz when the same conditions were not fulfilled.

In this study formal derivation of mode coupling equations in underwater acoustics is revisited. This derivation is based on the method of multiple scales from which modal expansion of the field emerges, and the vectorized WKBJ equation for the coefficients in this expansion are obtained in an automatic way. Asymptotic analysis accomplished in this work also establishes a connection between coupled mode parabolic equations in three-dimensional case and the generalized WKBJ solution that emerges as its two-dimensional counterpart. Despite the fact that similar mode coupling equations can be found in literature, in our study a new systematic and formalized approach to their derivation is proposed. A theorem that guarantees asymptotic conservation of the energy flux in the considered two-dimensional waveguide is also proven.

Fine-grained ship-radiated noise recognition methods of different specific ships are in demand for maritime traffic safety and general security. Due to the high background noise and complex transmission channels in the marine environment, the accurate identification of ship radiation noise becomes quite complicated. Existing ship-radiated noise-based recognition systems still have some shortcomings, such as the imperfection of ship-radiated noise feature extraction and recognition algorithms, which lead to distinguishing only the type of ships rather than identifying the specific vessel. To address these issues, we propose a fine-grained ship-radiated noise recognition system that utilizes multi-scale features from the amplitude–frequency–time domain and incorporates a multi-scale feature adaptive generalized network (MFAGNet). In the feature extraction process, to cope with highly non-stationary and non-linear noise signals, the improved Hilbert–Huang transform algorithm applies the permutation entropy-based signal decomposition to perform effective decomposition analysis. Subsequently, six learnable amplitude–time–frequency features are extracted by using six-order decomposed signals, which contain more comprehensive information on the original ship-radiated noise. In the recognition process, MFAGNet is designed by applying unique combinations of one-dimensional convolutional neural networks (1D CNN) and long short-term memory (LSTM) networks. This architecture obtains regional high-level information and aggregate temporal characteristics to enhance the capability to focus on time–frequency information. The experimental results show that MFAGNet is better than other baseline methods and achieves a total accuracy of 98.89% in recognizing 12 different specific noises from ShipsEar. Additionally, other datasets are utilized to validate the universality of the method, which achieves the classification accuracy of 98.90% in four common types of ships. Therefore, the proposed method can efficiently and accurately extract the features of ship-radiated noises. These results suggest that our proposed method, as a novel underwater acoustic recognition technology, is effective for different underwater acoustic signals.

This paper describes the design, characterization, and testing of a compact hydrophone capable of measuring acoustic signals from cardiomyocytes. Our hydrophone consists of a nanofabricated photonic-crystal diaphragm externally-mounted to the facet of an optical fiber to form a pressure-sensitive Fabry-Pérot cavity. Our hydrophone can operate in small liquid volumes less than 5 mm deep and incorporates a microchannel to vent air during immersion. The venting channel is designed to optimize bandwidth and sensitivity. Modeling and experimental results in water show a bandwidth from 50 Hz to 18 kHz and a minimum detectable pressure of $3~\mu Pa/ \sqrt {Hz}$ . We demonstrate the sensitivity to simulated bio-acoustic sources with nanometer-scale displacements.

Underwater sound in the oceans has been significantly rising in the past decades due to an increase in human activities, adversely affecting the marine environment. In order to assess and limit the impact of underwater noise, the European Commission's Marine Strategy Framework Directive (MSFD) included the long-term monitoring of low-frequency underwater sound as a relevant indicator to achieve a good environmental status. There is a wide range of commercial hydrophones and observing platforms able to perform such measurements. However, heterogeneity and lack of standardization in both hydrophones and observing platforms makes the integration and data management tasks time-consuming and error-prone. Moreover, their power and communications constraints need to be addressed to make them suitable for long-term ocean sound monitoring. Measured underwater sound levels are challenging to compare because different measurement methodologies are used, leading to a risk of misunderstandings and data misinterpretation. Furthermore, the exact methodology applied is not always public or accessible, significantly reducing ocean sound data re-usability. Within this work, a universal architecture for ocean sound measurement is presented, addressing hydrophone integration, real-time in situ processing and data management challenges. Emphasis is placed on generic and re-usable components, so it can be seamlessly replicated and deployed in new scenarios regardless of the underlying hardware and software constraints (hydrophone model, observing platform, operating system, etc.). Within the proposed architecture, a generic implementation of an underwater sound algorithm based on underwater noise measurement best practices is provided. Standardized and coherent metadata with emphasis on strong semantics is discussed, providing the building blocks for FAIR (Findable, Accessible, Interoperable, Reusable) ocean sound data management.

During the past decade, attacks by grey seals on fishing nets in the Baltic Sea have caused considerable loss of fish catch and damage to fishing gears. One of the approaches to reduce the number of seal attacks on fishing nets is to use acoustic deterrent devices (ADDs). Unfortunately, most of the commercially available ADDs are not well suited to the deployment in the sea and require considerable additional investments. The objective of the present research is to develop a compact and cost-efficient ADD for deployment in the sea environment. This paper is devoted to the design of acoustic signals for a prototype ADD. Signals from other experimental and commercially available ADDs are studied and compared. Moreover, limitations imposed by the underwater environment, transducers, battery power, and fish hearing are analysed and considered during the development of signal patterns. The results of tests conducted in an artificial reservoir and in the sea are presented.

Abstract Climate change is a relevant threat on a global scale, leading to impacts on ecosystems and ocean biodiversity. A considerable fraction of marine life depends on sound. Marine mammals, in particular, exploit sound in all aspects of their life, including feeding and mating. This work explores the impact of climate change in sound propagation by computing the three‐dimensional global field of underwater sound speed. The computation was performed based on present conditions (2006–2016) and a “business‐as‐usual” future climate scenario (Representative Concentration Pathway 8.5), identifying two “acoustic hotspots” where larger sound speed variations are expected. Our results indicate that the identified acoustic hotspots will present substantial climate‐change‐induced sound speed variations toward the end of the century, potentially affecting the vital activities of species in the areas. Evidence is provided of the impact of such variation on underwater sound transmission. As an example of a species impacted by underwater transmission, we considered one marine mammal endangered species, the North Atlantic right whale (Eubalaena glacialis), in the northwestern Atlantic Ocean. To the best of our knowledge, this is the first global‐scale data set of climate‐induced sound speed changes expected under a future scenario. This study provides a starting point for policies oriented research to promote the conservation of marine ecosystems and, in particular, endangered marine mammals.

Underwater acoustics, which is the study of the phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. Underwater acoustics is mainly applied in the field of remote sensing, wherein information on a target object is acquired indirectly from acoustic data. Presently, machine learning, which has recently been applied successfully in a variety of research fields, is being utilized extensively in remote sensing to obtain and extract information. In the earlier parts of this work, we examined the research trends involving the machine learning techniques and theories that are mainly used in underwater acoustics, as well as their applications in active/passive SONAR systems (Yang et al., 2020a; Yang et al., 2020b; Yang et al., 2020c). As a follow-up, this paper reviews machine learning applications for the inversion of ocean parameters such as sound speed profiles and sediment geoacoustic parameters.

Underwater acoustics, which is the study of phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and environments, and noise measurement and analysis. The main objective of underwater acoustic remote sensing is to obtain information on a target object indirectly by using acoustic data. Presently, various types of machine learning techniques are being widely used to extract information from acoustic data. The machine learning techniques typically used in underwater acoustics and their applications in passive SONAR systems were reviewed in the first two parts of this work (Yang et al., 2020a; Yang et al., 2020b). As a follow-up, this paper reviews machine learning applications in SONAR signal processing with a focus on active target detection and classification.

Underwater acoustics, which is the domain that addresses phenomena related to the generation, propagation, and reception of sound waves in water, has been applied mainly in the research on the use of sound navigation and ranging (SONAR) systems for underwater communication, target detection, investigation of marine resources and environment mapping, and measurement and analysis of sound sources in water. The main objective of remote sensing based on underwater acoustics is to indirectly acquire information on underwater targets of interest using acoustic data. Meanwhile, highly advanced data-driven machine-learning techniques are being used in various ways in the processes of acquiring information from acoustic data. The related theoretical background is introduced in the first part of this paper (Yang et al., 2020). This paper reviews machine-learning applications in passive SONAR signal-processing tasks including target detection/identification and localization.

Underwater acoustics that is the study of the phenomenon of underwater wave propagation and its interaction with boundaries, has mainly been applied to the fields of underwater communication, target detection, marine resources, marine environment, and underwater sound sources. Based on the scientific and engineering understanding of acoustic signals/data, recent studies combining traditional and data-driven machine learning methods have shown continuous progress. Machine learning, represented by deep learning, has shown unprecedented success in a variety of fields, owing to big data, graphical processor unit computing, and advances in algorithms. Although machine learning has not yet been implemented in every single field of underwater acoustics, it will be used more actively in the future in line with the ongoing development and overwhelming achievements of this method. To understand the research trends of machine learning applications in underwater acoustics, the general theoretical backgroundof several related machine learning techniques is introduced in this paper.

A new acoustic navigation system was developed to determine the position and speed of moving underwater objects such as divers and underwater vehicles. The path of an object and its speed were determined by the Doppler shifts of acoustic signals emitted by a transmitter placed on the object and received by four hydrophones installed at the periphery of the monitored body of water. The position and speed measurements were affected by errors mainly caused by acoustic reflections (returns) from the water body boundaries and surface reverberations. This paper discusses the source of the disturbances with the results of a simulation test and experimental measurements. It was demonstrated that the magnitude of the errors could be acceptable in most of the potential applications of the acoustic navigation system.

Both source localization and environmental inversions are practical problems for long-standing applications in underwater acoustics. This paper presents an approach of the moving source localization and sound speed field (SSF) inversion in shallow water. The approach is formulated in a state-space model with a state equation for both the source parameters (e.g., source depth, range, and speed) and SSF parameters (first three empirical orthogonal function coefficients, EOFs) and a measurement equation that incorporates underwater acoustic information via a vertical line array (VLA). As a sequential processing algorithm that operates on nonlinear systems with non-Gaussian probability densities, an improved sequential importance resampling type particle filtering (SIR PF) is proposed to counter degeneracy. The improved PF performs tracking of source and SSF parameters simultaneously, and evaluates their uncertainties in the form of time-evolving posterior probability densities (PPDs). The performance of improved PF is illustrated with well-tracked simulations of real-time source localization and time-varying SSF inversion. Moreover, the influence of different particle numbers on PF tracking accuracy and computational cost is also demonstrated. Simulation results show that the high-particle-number PF has an outperform performance. For a given hardware system, the reasonable compromise between accuracy and computational cost is a matter of tradeoff.

Multiple layer anisotropic fluid medium is critical to the realization of transformation acoustic devices, such as cloak or bend waveguide. Pentamode metamaterials have attracted extensive attention as a solid artificial version with anisotropic modulus to approximate liquids. In this paper, we present an approach to realize fluid-like anisotropic density by using pentamode materials, and an underwater bend acoustic waveguide with anisotropic density is designed and fabricated to demonstrate the effectiveness of it. Simulation results indicate that, compared with anisotropic-modulus design by using pentamode materials, wider bandwidth acoustic modulation effect can be obtained. An in-depth and comprehensive analysis of the mechanisms of the broadband characteristics is provided by calculating the band structure of the pentamode metamaterials constituting the acoustic waveguides and analyzing their vibration modes. Finally, remarkable wavefront manipulation for underwater acoustics based on the acoustic waveguide with anisotropic density is experimentally verified.

This work is devoted to increasing the computational efficiency of numerical methods for the one-way Helmholtz Equation (higher-order parabolic equation) in a heterogeneous underwater environment. The finite-difference rational Padé approximation of the propagation operator is considered, whose artificial computational parameters are the grid cell sizes and reference sound speed. The relationship between the parameters of the propagation medium and the artificial computational parameters is established. An optimized method for automatic determination of the artificial computational parameters is proposed. The optimization method makes it possible to account for any propagation angle and arbitrary variations in refractive index. The numerical simulation results confirm the adequacy and efficiency of the proposed approach. Automating the selection process of the computational parameters makes it possible to eliminate human errors and avoid excessive consumption of computational resources.

Anthropogenic noise from navigation is a major contributor to the disturbance of the acoustic soundscape in underwater environments containing noise-sensitive life forms. While previous studies mostly developed protocols for the empirical determination of noise source levels associated with the world’s commercial fleet, this work explores the radiated noise emitted by small recreational vessels that thrive in many coastal waters, such as in the St. Lawrence Estuary beluga population’s summer habitat. Hydrophone-based measurements in the Saguenay River (QC, Canada) were carried out during the summers of 2021 and 2022. Shore-based observations identified 45 isolated transits of small, motorized vessels and were able to track their displacement during their passage near the hydrophone. Received noise levels at the hydrophone typically fell below the hearing audiogram of the endangered St. Lawrence Estuary beluga. Monopole source levels at low frequencies (0.1–≲2 kHz) held on average twice the acoustic power compared to their mid-frequency (≳2–30 kHz) counterparts. The speed over ground of recreational vessel showed a positive correlation with the back-propagated monopole source levels. Estimations of the mid-frequency noise levels based on low-frequency measurements should be used moderately.

Underwater acoustic target recognition is very complex due to the lack of labeled data sets, the complexity of the marine environment, and the interference of background noise. In order to enhance it, we propose an attention-based residual network recognition method (AResnet). The method can be used to identify ship-radiated noise in different environments. Firstly, a residual network is used to extract the deep abstract features of three-dimensional fusion features, and then a channel attention module is used to enhance different channels. Finally, the features are classified by the joint supervision of cross-entropy and central loss functions. At the same time, for the recognition of ship-radiated noise in other environments, we use the pre-training network AResnet to extract the deep acoustic features and apply the network structure to underwater acoustic target recognition after fine-tuning. The two sets of ship radiation noise datasets are verified, the DeepShip dataset is trained and verified, and the average recognition accuracy is 99%. Then, the trained AResnet structure is fine-tuned and applied to the ShipsEar dataset. The average recognition accuracy is 98%, which is better than the comparison method.

Fluvial biological habitat types are classified using the diversity in physical characteristics of a water channel. Recent ecological management studies have highlighted the potential of underwater sound as a quantitative indicator of habitat characteristics. We investigate the relationship between underwater acoustic characteristics and hydraulic factors of 12 habitat types in the Namdae Stream in Yangyang, Korea, namely riffles, pools, and step riffle habitats. In the riffles and pools, the underwater sound levels were measured as sound pressure levels (SPLs). SPL(RMS) and 1/3 octave band have been measured in the frequency range between 8 Hz and 20 kHz. Among riffles, high SPL corresponded to the descending level of flow velocity. Pools generally had a low SPL. Low-frequency sound waves in the upper regions are better transmitted in the deeper water. To quantitatively analyze the water depth and flow velocity, we used a regression between the observed water depth, flow velocity, and acoustic SPL. The application of this study was certificated. The correlation coefficients between SPL and flow velocity/water depth revealed specific frequency bands with very strong positive correlations between SPL and flow rate in riffles and very strong negative correlations between SPL and pool water depth. Consequently, underwater sound can be used as an alternative for evaluating biological habitats.

A class of problems of wave propagation in waveguides consisting of one or several layers that are characterized by linear variation of the squared refractive index along the normal to the interfaces between them is considered in this paper. In various problems arising in practical applications, it is necessary to efficiently solve the dispersion relations for such waveguides in order to compute horizontal wavenumbers for different frequencies. Such relations are transcendental equations written in terms of Airy functions, and their numerical solutions may require significant computational effort. A procedure that allows one to reduce a dispersion relation to an ordinary differential equation written in terms of elementary functions exclusively is proposed. The proposed technique is illustrated on two cases of waveguides with both compact and non-compact cross-sections. The developed reduction method can be used in applications such as geoacoustic inversion.

Computational predictions of acoustic transmission loss (TL) in ocean environments depend on the relevant environmental characteristics, such as the sound speed field, bathymetry, and seabed properties. When databases are used to obtain estimates of these properties, the resulting predictions of TL are uncertain, and this uncertainty can be quantified via the probability density function (PDF) of TL. A machine learning technique for quickly estimating the PDF of TL using only a single, baseline TL calculation is presented here. The technique shifts the computational burden from present-time Monte-Carlo (MC) TL simulations in the environment of interest to ahead-of-time training of a neural network using equivalent MC TL simulations in hundreds of ocean environments. An environmental uncertainty approach which draws information from global databases is also described and is used to create hundreds of thousands of TL-field examples across 300 unique ocean environments at ranges up to 100 km for source frequencies between 50 and 600 Hz. A subset of the total dataset is used to train and compare neural networks with various architectures and TL-PDF-generation methods. Finally, the remaining dataset examples are used to compare the machine-learning technique’s accuracy and computational effort to that of prior TL-uncertainty-estimation techniques.

In this work, a mode parabolic equation method with interacting modes accounting for the weak elasticity at the bottom is developed. An important feature of the proposed method is that computations of elastic modes are avoided and that the solution is obtained in the form of expansion over acoustic modes. A numerical technique for solving resulting mode parabolic equations is developed, and the accuracy and efficiency of the resulting solution is validated by a direct comparison against source image solutions in the 3D wedge benchmark problem. Satisfactory agreement of the two solutions is achieved for sufficiently small values of shear wave speed that are typical for soft sediments of the sea bottom. The developed approach may be used for solving 3D problems of sound propagation with the elastic properties of bottom sediments taken into account.

Anthropogenic underwater noise has been identified as one of the main pressures on the marine environment. Considerable research efforts have been made to quantify acoustic soundscapes on different spatial and temporal scales in order to identify trends and investigate how this may impact the marine environment. Measures to reduce noise input into the seas from anthropogenic sources are under discussion, including the reduction of vessel speed or re-routing of shipping lanes. The decline in maritime transport as a consequence of the Covid-19 pandemic provides an opportunity to examine the associated extent of noise reduction. Here, we present the results of a “Before-After-Control-Impact” study where we analyzed acoustic data sets from two monitoring stations in the German Baltic Sea. Data were collected between 2013 and 2020. As part of an international initiative, coordinated by the International Quiet Ocean Experiment, monthly statistics (20 average sound pressure levels per 1/3 octave bands) were calculated from acoustic data collected during the pre-pandemic period (2013–2019), and were compared with data from the year 2020, during the Covid-19 pandemic. To account for varying natural conditions the measurements were sorted into categories of same prevailing sea state. Through this approach, measurements with equivalent natural noise impact are compared and any resulting differences are likely due to the variability in the anthropogenic noise. A decline in sound pressure of 13% (1.2 dB) for low frequencies (10 Hz–1 kHz) was observed at both stations, which corresponds to the reduced level of shipping activity.

Underwater acoustic signal propagation is greatly affected by source-receiver motion, surface variations, and water column variabilities, which make the underwater acoustic communications very challenging. A passive time reversal (pTR) system is widely used for underwater communication systems due to its focusing property, which minimizes the time spreading of the underwater channel. The performance of the pTR system degrades when the underwater channel varies rapidly. During the experiment in TrondheimFjord on September 22, 2016, some environmental variations are observed which resulted in channel variations and the performance of the pTR system degraded. A time diversity pTR system is proposed and it uses the time diversity of the channel to compensate for the channel variations. The proposed system is tested at two receiver positions and it provides 2-3-dB gain in terms of mean square error in the presence of rapid channel variations.

A convolutionally coded M-ary frequency shift keying (MFSK) modulation scheme for underwater acoustic communication is introduced. It uses a rate 1/log2 M inner convolutional code, whose coded symbols are used as transmission symbols. An interleaver in the frequency domain is applied to improve the average channel clearing time. It thereby achieves almost the same data rate as a comparable uncoded frequency hopped frequency shift keying (FH-FSK) modulation scheme, but obtains the error correcting properties of the convolutional code. Bit and packet error rates are evaluated on the additive white Gaussian noise (AWGN) channel and the so-called Watermark channel model, which is a benchmark for underwater acoustic modulation schemes based on sea trial measurements. The JANUS standard which uses convolutionally coded FH-FSK is used for comparison. On the AWGN channel, the proposed scheme achieves a gain of 4dB with respect to the required Eb/N0 for a given packet error rate (PER) compared with JANUS. In the Watermark scenarios, the best proposed-scheme achieves a PER -3 at more than 10 dB lower Eb/N0 than the JANUS implementation.

Monitoring coral reefs is vital to the conservation of these at-risk ecosystems. While most current monitoring methods are costly and time-intensive, passive acoustic monitoring (PAM) could provide a cost-effective, large scale reef monitoring tool. However, for PAM to be reliable, the results must be field tested to ensure that the acoustic methods used accurately represent the certain ecological components of the reef being studied. For example, recent acoustic studies have attempted to describe the diversity of coral reef fish using the Acoustic Complexity Index (ACI) but despite inconsistent results on coral reefs, ACI is still being applied to these ecosystems. Here, we investigated the potential for ACI and sound pressure level (SPL – another common metric used), to accurately respond to biological sounds on coral reefs when calculated using three different frequency resolutions (31.2 Hz, 15.6 Hz, and 4 Hz). Acoustic recordings were made over two to three-week periods in 2017 and 2018 at sites around Kiritimati (Christmas Island), in the central equatorial Pacific. We hypothesized that SPL would be positively correlated with the number of nearby fish sounds in the low frequency band and with snapping shrimp snaps in the high frequency band, but that ACI would rely on its settings, specifically its frequency resolution, to describe sounds in both frequency bands. We found that nearby fish sounds were partially responsible for changes in low frequency SPL in the morning, during crepuscular chorusing activity, but not at other times of day. Snapping shrimp snaps, however, were responsible for large changes in high frequency SPL. ACI results were reliant on the frequency band chosen, with the 31.2 Hz frequency resolution models being chosen as the best models. In the low frequency band, the effect of fish knocks was positive and significant only in the 31.2 Hz and 15.6 Hz models while in the high frequency band snapping shrimp snaps were negatively associated with ACI in all frequency resolutions. These results contribute to a growing body of evidence against the continued use of ACI without standardization on highly energetic underwater ecosystems like coral reefs and highlight the importance of extensive field testing of new acoustic metrics prior to their adoption and proliferation.

This paper presents a comprehensive overview of current deep-learning methods for automatic object classification of underwater sonar data for shoreline surveillance, concentrating mostly on the classification of vessels from passive sonar data and the identification of objects of interest from active sonar (such as minelike objects, human figures or debris of wrecked ships). Not only is the contribution of this work to provide a systematic description of the state of the art of this field, but also to identify five main ingredients in its current development: the application of deep-learning methods using convolutional layers alone; deep-learning methods that apply biologically inspired feature-extraction filters as a preprocessing step; classification of data from frequency and time–frequency analysis; methods using machine learning to extract features from original signals; and transfer learning methods. This paper also describes some of the most important datasets cited in the literature and discusses data-augmentation techniques. The latter are used for coping with the scarcity of annotated sonar datasets from real maritime missions.

The Global Navigation Satellite System-Acoustic ranging combination technique (GNSS-A) is a seafloor geodetic technique that enables precise global seafloor positioning to detect subseafloor geophysical phenomena. The technique requires a sea surface observation platform that combines GNSS positioning and acoustic ranging. Currently, a survey vessel is used as the platform, which entails substantial financial and human resources costs, which makes increasing observation frequency difficult. It is possible to detect long-term average seafloor movement at the centimeter level, but it is difficult to detect short-term variation due to the insufficiency of observation frequency. The terrestrial GNSS observation network has detected temporal changes in crustal deformation fields. These precise observations provide useful information on the megathrust seismogenic zone. To detect such phenomena on the seafloor, the temporal resolution of the GNSS-A observation needs to be improved. Advances in vessel equipment technology are crucial for increasing observation frequency. In this paper, we review the historical development of the Japan Coast Guard’s GNSS-A observation system, focusing on technological developments of on-board equipment installed on a sea surface platform, and explain how such improvements have increased observation frequency over time. In the present, ranging frequency has improved from 40–60 s to 15–20 s by the introduction of the multiple ranging system, resulting in more frequent observations up to five times per year for an individual site.

3D sound propagation modeling in the context of acoustic noise monitoring problems is considered. A technique of effective source spectrum reconstruction from a reference single-hydrophone measurement is discussed, and the procedure of simulation of sound exposure level (SEL) distribution over a large sea area is described. The proposed technique is also used for the modeling of pulse signal waveforms at other receiver locations, and results of a direct comparison with the pulses observed in the experimental data is presented.