Underwater Sound Research Articles

Impact localization on a metal plate using matched field processing and a microphone arraya).

Acoustic waves are well-suited for remote sensing applications and structural health monitoring because they convey information about their source and can be recorded using non-contacting methods. An important structural health monitoring task is localization of an impact excitation. However, traditional array signal processing techniques for source localization are ill-suited for many structural engineering applications because of geometrical complexity, dispersive acoustic wave propagation in structures, and the coupling of the vibrating structure and the surrounding medium. Plus, many traditional methods use contacting sensors, which can permanently alter the structure. This study utilizes Bartlett matched field processing (MFP), a localization technique initially developed for underwater acoustics, to localize an impact source on a metal plate. A 14-microphone array recorded the sound radiated by a 0.64-cm-thick 91.4-cm-diameter round aluminum plate after the impact of a 1.3-cm-diameter stainless-steel ball bearing. MFP and a physics-based finite-element acoustic environment model were used to localize the impact on the plate. Results are presented as ambiguity surfaces where the predicted source location was typically found to be within 1.1 cm of the true source location. Localization performance was also assessed in a noisy environment, with success down to a signal-to-noise ratio of -7.5 dB.

Acoustic interaction of submerged thin-shell structures considering seabed reflection effects

This paper presents a novel approach for simulating acoustic-shell interaction, specifically focusing on seabed reflection effects. The interaction between acoustic waves and shell vibration is crucial in various engineering applications, particularly in underwater acoustics and ocean engineering. The study employs the finite element method (FEM) with Kirchhoff-Love shell elements to numerically analyze thin-shell vibrations. The boundary element method (BEM) is applied to simulate exterior acoustic fields and seabed reflections, using half-space fundamental solutions. The FEM and BEM are coupled to model the interaction between acoustic waves and shell vibration. Furthermore, the FEM-BEM approach is implemented within an isogeometric analysis (IGA) framework, where the basis functions used for geometric modeling also discretize the physical fields. This ensures geometric exactness, eliminates meshing, and enables the use of Kirchhoff-Love shell theory with high-order continuous fields. The coupled FEM-BEM system is accelerated using the fast multipole method (FMM), which reduces computational time and memory storage. Numerical examples demonstrate the effectiveness and efficiency of the proposed algorithm in simulating acoustic-shell interaction with seabed reflection.

Underwater Acoustics for All: Expanding Capacity with Education and Low-Cost Sensors

Sound is a persistent yet dynamic component of the marine environment, reflecting both physical and biological properties. Whereas light can only travel tens of meters in the ocean, sound is able to travel tens to thousands of kilometers under certain conditions, revealing information at specific times and places. In addition, underwater sound provides opportunities for sustainable development and blue economy growth that aren’t readily available with other technologies. For example, the melting rate of Arctic ice and the health of coral reefs can be estimated from acoustic measurements (Becker et al., 2023). However, underwater acoustics is a complex topic, requiring specialized education and equipment. To expand the capacity of underwater acousticians requires dedicated educational opportunities and low-cost equipment and analysis resources.

Underwater Acoustic Preamble Detection via End-to-End Complex-Valued Synchrosqueezed Wavelet Neural Network

Underwater Acoustic Preamble Detection via End-to-End Complex-Valued Synchrosqueezed Wavelet Neural Network

Enhancing Time-Domain Interference Alignment for Underwater Acoustic Networks with Cross-Layer Design.

In exploiting large propagation delays in underwater acoustic (UWA) networks, the time-domain interference alignment (TDIA) mechanism aligns interference signals through delay-aware slot scheduling, creating additional idle time for improved transmission at the medium access control (MAC) layer. However, perfect alignment remains challenging due to arbitrary delays. This study enhances TDIA by incorporating power allocation into its transmission scheduling framework across the physical and MAC layers, following the cross-layer design principle. The proposed quasi-interference alignment (QIA) mechanism enables controlled interference on useful signals by jointly optimizing the transmission schedule and power. The formulated optimization problem to maximize network throughput is divided into two sub-problems: one for coarse slot scheduling and another for refining both scheduling and power allocation. The simulation results validate the QIA framework's superiority over the traditional TDIA and genetic algorithm benchmarks.

Approaching merchant ships elicit behavioral changes in Atlantic sturgeon (Acipenser oxyrinchus) in the St. Lawrence River, Canada.

There are gaps in our understanding of sturgeon's response to anthropogenic sounds and the spatial scales at which they occur. We measured spatial displacement of Atlantic sturgeon in the St. Lawrence River at various distances of approaching merchant ships. This fish population is designated as "threatened," although anthropogenic noise is not currently considered a direct threat. For several years, Atlantic sturgeon migrations have been monitored by the Quebec government using acoustic transmitters and a network of telemetry receivers in the St. Lawrence River. We combined fish telemetry data with merchant ship positions to detect co-occurrences between Atlantic sturgeons that remained in the vicinity of the receivers and approaching ships. Numerical simulations reveal that the probability of masking of transmitters (69 kHz) by ship noise was infinitesimal and that the disappearance of the transmitter signal was related to fish movement. When the ships approached, a significant spatial displacement was detected with ships at distances between 0.5 and 5 km from the receivers. After emitter signal loss, over 61% of sturgeons took at least 30 min to be detected again or did not return at all in the vicinity of the receivers. Furthermore, the median time to redetection after a ship transit was longer than when no ship was approaching (31 vs. 18 min). Our results show that sturgeons alter their position due to approaching ships at greater trigger distances than previously documented, which are too far away to be attributed to visual cues alone. We also found that the long-distance propagation of low-frequency sounds from large ships through water should not be heard by Atlantic sturgeon at distances of 1 km and longer based on current knowledge of sturgeons hearing. These results suggest that behavioral responses in Atlantic sturgeons are modulated not only by visual cues but can also be triggered by underwater sounds at relatively long distances, although the precise mechanism is still unknown.

Estimation of the spatial variability of the New England Mud Patch geoacoustic properties using a distributed array of hydrophones and deep learning

This article presents a spatial environmental inversion scheme using broadband impulse signals with deep learning (DL) to model a single spatially-varying sediment layer over a fixed basement. The method is applied to data from the Seabed Characterization Experiment 2022 (SBCEX22) in the New England Mud-Patch (NEMP). Signal Underwater Sound (SUS) explosive charges generated impulsive signals recorded by a distributed array of bottom-moored hydrophones. The inversion scheme is first validated on a range-dependent synthetic test set simulating SBCEX22 conditions, then applied to experimental data to predict the lateral spatial structure of sediment sound speed and its ratio with the interfacial water sound speed. Traditional geoacoustic inversion requires significant computational resources. Here, a neural network enables rapid single-signal inversion, allowing the processing of 1836 signals along 722 tracks. The method is applied to both synthetic and experimental data. Results from experimental data suggest an increase in both absolute compressional sound speed and sound speed ratio from southwest to northeast in the NEMP, consistent with published coring surveys and geoacoustic inversion results. This approach demonstrates the potential of DL for efficient spatial geoacoustic inversion in shallow water environments.

Study on the Vibration-Damping Mechanism of a New Phononic Crystal Suspension Equipped on Underwater Gliders

The vibration caused by the movement of internal actuating components within an acoustic underwater glider can interfere with onboard sensors. However, as a new vibration-damping material, phononic crystals can effectively reduce this impact. Using simulation and an underwater test, this work studied the vibration-damping mechanism of the phononic crystal suspension (PCS) designed by Tianjin University, China. The bandgaps and the modes of PCS were calculated first, which offered basic data for the following simulation. Then, the relationship between the modes and attenuation zones (AZs) were broadly considered to reveal the variation law of the AZs with the change in modes, both in the air and under water. Finally, an underwater test was carried out to verify the good vibration-damping effect of the PCS. The results show that the cutoff frequency of the AZs could be predicted by finding the relevant modes. The PCS showed a good vibration-damping effect from 170 Hz to 5000 Hz in the underwater test, with a maximum decrease of 6 dB at 2000 Hz. Finally, the damping of the PCS could suppress the overlap of modes that resulted from Bragg scattering. This work will also provide theoretical guidance for further study on the optimization of phononic crystal mechanisms for vibration damping.

A Novel Embedded Side Information Transmission Scheme Based on Polar Code for Peak-to-Average Power Ratio Reduction in Underwater Acoustic OFDM Communication System.

In this paper, we proposed an embedded side information (SI) transmission scheme based on polar code construction for PAPR reduction using the PTS scheme in the underwater Acoustic (UWA) Orthogonal Frequency Division Multiplexing (OFDM) communication system. We use polar codes due to the ability of the arbitrarily designed code rate. Additionally, polar codes can be employed to establish a nested code structure consisting of multiple subsets. The SI bits can be embedded in a polar codeword by exploiting these features. Thus, the approach does not occupy existing data rates or cause additional loss in data transmission rates. At the same time, it embeds m-sequence into the polar code as an indicator vector for the blind SI detector, which makes the blind SI detector able to autonomously discriminate SI at the receiver. Simulation and tank experiment results indicate that the proposed embedded SI transmission scheme has the potential to significantly decrease the likelihood of whole-symbol error caused by SI errors. Meanwhile, the proposed PTS scheme eliminates the need to wait for the entire packet to be received before obtaining the SI, thereby preventing waste of data storage devices and ensuring real-time performance of the Underwater Acoustic Communication (UAC) OFDM system. This achieves symbol-level real-time calculation for the system.

Data-Driven Analysis of Ocean Fronts’ Impact on Acoustic Propagation: Process Understanding and Machine Learning Applications, Focusing on the Kuroshio Extension Front

Ocean fronts, widespread across the global ocean, cause abrupt shifts in physical properties such as temperature, salinity, and sound speed, significantly affecting underwater acoustic communication and detection. While past research has concentrated on qualitative analysis and small-scale research on ocean front sections, a comprehensive analysis of ocean fronts’ characteristics and their impact on underwater acoustics is lacking. This study employs high-resolution reanalysis data and in situ observations to accurately identify ocean fronts, sound speed structures, and acoustic propagation features from over six hundred thousand Kuroshio Extension Front (KEF) sections. Utilizing marine big data statistics and machine learning evaluation metrics such as out-of-bag (OOB) error and Shapley values, this study quantitatively assesses the variations in sound speed structures across the KEF and their effects on acoustic propagation shifts. This study’s key findings reveal that differences in sound speed structure are significantly correlated with KEF strength, with the channel axis depth and conjugate depth increasing with front strength, while the thermocline intensity and depth excess decrease. Acoustic propagation features in the KEF environment exhibit notable seasonal variations.

Performance Analysis of Underwater Acoustic Channel Amid Jamming by Random Jammers

Performance Analysis of Underwater Acoustic Channel Amid Jamming by Random Jammers

Underwater acoustic target recognition model based on transformer and knowledge distillation

Abstract Traditional methods for feature extraction in underwater acoustics heavily rely on domain expertise and handcrafted features, which are often challenged by the complex and variable ocean environment. In contrast, deep learning models, particularly those based on CNNs and Transformers, offer significant advantages in automatic feature extraction and classification tasks. This study introduces a Spectrogram-Vision Transformer (SViT) model, specifically designed for spectrogram classification, leveraging pre-trained Vision Transformer (ViT) models. The SViT model is further utilized as a teacher model in a knowledge distillation framework, where its robust feature extraction capabilities are transferred to a lightweight ResNet50 student model. Experimental results on the DeepShip dataset demonstrate that the proposed method significantly improves the recognition accuracy and generalization capability of ResNet50, achieving performance close to the more computationally intensive SViT model. This research highlights the effectiveness of Transformers in underwater target recognition and underscores the potential of knowledge distillation in enhancing the performance of lightweight models for real-world applications, particularly in resource-constrained environments.

Deep learning aided underwater acoustic OFDM receivers: Model-driven or data-driven?

The Underwater Acoustic (UWA) channel is bandwidth-constrained and experiences doubly selective fading. It is challenging to acquire perfect channel knowledge for Orthogonal Frequency Division Multiplexing (OFDM) communications using a finite number of pilots. On the other hand, Deep Learning (DL) approaches have been very successful in wireless OFDM communications. However, whether they will work underwater is still a mystery. For the first time, this paper compares two categories of DL-based UWA OFDM receivers: the Data-Driven (DD) method, which performs as an end-to-end black box, and the Model-Driven (MD) method, also known as the model-based data-driven method, which combines DL and expert OFDM receiver knowledge. The encoder-decoder framework and Convolutional Neural Network (CNN) structure are employed to establish the DD receiver. On the other hand, an unfolding-based Minimum Mean Square Error (MMSE) structure is adopted for the MD receiver. We analyze the characteristics of different receivers by Monte Carlo simulations under diverse communications conditions and propose a strategy for selecting a proper receiver under different communication scenarios. Field trials in the pool and sea are also conducted to verify the feasibility and advantages of the DL receivers. It is observed that DL receivers perform better than conventional receivers in terms of bit error rate.

A small variable rate underwater CAN-fiber data transmission system

Abstract Currently, the primary data transmission methods underwater include optical fibers, radio waves, blue-green light, underwater acoustics, electric fields, and inductive coupling electric fields. Optical fibers are extensively utilized for underwater data transmission due to their benefits, such as long transmission distances, strong anti-interference capabilities, good environmental adaptability, and minimal limitations from navigating carriers. However, optical fibers face challenges in meeting high-speed transmission requirements beyond 10km due to communication distance constraints. As a result, optical fibers are often used as a replacement for the CAN communication system, but they come with drawbacks such as large volume and fixed speed. They lack dynamic testing and release-length experiments, supporting only one-time use. To tackle these issues, this paper presents a comprehensive design of a variable (adaptive) baud rate optoelectronic data transmission system for underwater applications with limited space. Lake trials were conducted under various conditions, demonstrating the system’s excellent performance. It ensures high-speed data transmission without interference and extends communication distances. This system holds significant reference value for the engineering implementation of composite data transmission systems constrained by space.

A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning

As sound speed is a fundamental parameter of ocean acoustic characteristics, its prediction is a central focus of underwater acoustics research. Traditional numerical and statistical forecasting methods often exhibit suboptimal performance under complex conditions, whereas deep learning approaches demonstrate promising results. However, these methodologies fall short in adequately addressing multi-spatial coupling effects and spatiotemporal weighting, particularly in scenarios characterized by limited data availability. To investigate the interactions across multiple spatial scales and to achieve accurate predictions, we propose the STA-ConvLSTM framework that integrates spatiotemporal attention mechanisms with convolutional long short-term memory neural networks (ConvLSTM). The core concept involves accounting for the coupling effects among various spatial scales while extracting temporal and spatial information from the data and assigning appropriate weights to different spatiotemporal entities. Furthermore, we introduce an interpolation method for ocean temperature and salinity data based on the KNN algorithm to enhance dataset resolution. Experimental results indicate that STA-ConvLSTM provides precise predictions of sound speed. Specifically, relative to the measured data, it achieved a root mean square error (RMSE) of approximately 0.57 m/s and a mean absolute error (MAE) of about 0.29 m/s. Additionally, when compared to single-dimensional spatial analysis, incorporating multi-spatial scale considerations yielded superior predictive performance.

A fast temporal multiple sparse Bayesian learning-based channel estimation method for time-varying underwater acoustic OFDM systems

In this paper, a fast temporal multiple sparse Bayesian learning (FTMSBL)-based channel estimation method for underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) systems is proposed, which is optimized using the fast marginalized likelihood maximization method. The algorithm fully uses the consistent sparse structure and time-domain correlation properties of channels to improve the reconstruction performance and computational efficiency, offering better performance and higher computational efficiency than the traditional Bayesian learning algorithms. At the same time, the FTMSBL algorithm does not require computing the inverse of large matrices and consumes very little storage resources in the operation, making it suitable for hardware implementation. Simulation and sea trial results show that the FTMSBL-based underwater channel estimation algorithm achieves higher channel estimation accuracy than the orthogonal matching tracking algorithm, and the system bit error rate (BER) is significantly reduced; specifically, the FTMSBL algorithm can achieve optimal performance in strong time-dependent channels.

Abundance Distribution Pattern of Zooplankton Associated with the Eastern Arabian Sea Monsoon System as Detected by Underwater Acoustics and Net Sampling

Abundance Distribution Pattern of Zooplankton Associated with the Eastern Arabian Sea Monsoon System as Detected by Underwater Acoustics and Net Sampling

Exploiting Compress Sensing in Training of Deep Neural Network for Self-Noise Cancellation in Underwater Acoustics

Exploiting Compress Sensing in Training of Deep Neural Network for Self-Noise Cancellation in Underwater Acoustics

Metagratings for underwater acoustic wavefront manipulation

Metagratings are flat periodic assemblies of small scatterers, engineered to achieve effects such as steering, beamforming, or absorption. They have recently received attention in both electromagnetism and acoustic fields. In this work, we report the design, modeling, and experimental characterization of metagratings to steer underwater acoustic wavefronts towards anomalous directions (i.e. not classically allowed by Snell-Decartes relationships) with high efficiency (close to 100%). They are build from simple assemblies of small brass cylinders, hold by 3D-printed plastic supports, and can redirect ultrasonic wavefronts either in reflection mode (when placed in front of a reflective surface like e.g. the water / air interface) or in transmission mode. Furthermore, we demonstrate how metagratings build from an asymmetrical pattern of multiple basic elements can achieve near perfect asymmetrical transmission: a wavefront incident from side of the structure is fully transmitted, while a similar wavefront incoming from the other side is fully reflected, achieving an acoustic one-way mirror effect. This work combine theoretical analysis, finite element modeling, and experimentation in water tanks to explore and demonstrate the potential of such metagratings for various applications in underwater acoustics, like communication, noise mitigation, and stealth.

Characterization of the scattered pressure from a cylindrical shell close to a free surface and understanding of the depth-dependent phenomenon

The characterization of the scattered pressure field of a submerged cylindrical target is of interest for underwater acoustics applications. The aim of this work is to study the vibroacoustic behavior of an immersed cylindrical shell excited by a plane wave, particularly in close proximity to a free surface. To achieve this goad, finite element simulation and experimental investigation are carried out on a mock-up. The effect of the free surface is highlighted by comparing results to those obtained in a free field situation, in both time and frequency domains. Additional echoes are observed on the time signals due to the presence of the free surface. In fact, after being scattered by the target, these echoes reflect on the surface, implying a time shift between echoes directly from the target and those that have travelled a longer path. The spectrum is also impacted as the form function shows additional oscillations. These observations are depth-dependent. To determine wave trajectories, a study based on flight time and ray theory is conducted.