Search Results (61)

Growing concern over anthropogenic underwater noise, highlighted by initiatives like the Marine Strategy Framework Directive (MSFD) and its Technical Group on Underwater Noise (TG Noise), emphasizes regions like the Western Black Sea, where increasing activities threaten marine habitats. This region is experiencing rapid growth in maritime traffic and resource exploitation, which is intensifying concerns over the noise impacts on its unique marine habitats. While machine learning offers promising solutions, a research gap persists in comprehensively evaluating diverse ML models within an integrated framework for complex underwater acoustic data, particularly concerning real-world data limitations like class imbalance. This paper addresses this by presenting a multi-faceted framework using passive acoustic monitoring (PAM) data from fixed locations (50–100 m depth). Acoustic data are processed using advanced signal processing (broadband Sound Pressure Level (SPL), Power Spectral Density (PSD)) for feature extraction (Mel-spectrograms for deep learning; PSD statistical moments for classical/unsupervised ML). The framework evaluates Convolutional Neural Networks (CNNs), Random Forest, and Support Vector Machines (SVMs) for noise event classification, alongside Gaussian Mixture Models (GMMs) for anomaly detection. Our results demonstrate that the CNN achieved the highest classification accuracy of 0.9359, significantly outperforming Random Forest (0.8494) and SVM (0.8397) on the test dataset. These findings emphasize the capability of deep learning in automatically extracting discriminative features, highlighting its potential for enhanced automated underwater acoustic monitoring. Full article

This study investigates the combined effects of mesoscale eddies and rough sea surfaces on acoustic propagation in the eastern Arabian Sea and Gulf of Aden during summer monsoon conditions. Utilizing three-dimensional sound speed fields derived from CMEMS data, sea surface spectra from the SWAN wave model validated by Jason-3 altimetry, and the BELLHOP ray-tracing model, we quantify their synergistic impacts on underwater sound. A Monte Carlo-based dynamic sea surface roughness model is integrated with BELLHOP to analyze multiphysics interactions. The results reveal that sea surface roughness significantly influences surface duct propagation, increasing transmission loss by approximately 20 dB compared to a smooth sea surface, while mesoscale eddies deepen the surface duct and widen convergence zones by up to 5 km. In deeper waters, eddies shift convergence zones and reduce peak sound intensity in the deep sound channel. These findings enhance sonar performance and underwater communication in dynamic, monsoon-influenced marine environments. Full article

Accurate acoustic identification of marine mammals is vital for monitoring ocean health and human impacts. Existing methods often struggle with limited single-feature representations or suboptimal fusion of multiple features. This paper proposes an Evaluation-Adaptive Weighted Multi-Head Fusion Network that integrates CQT and STFT features via a dual-branch ResNet architecture. The model enhances intra-branch features using channel attention and adaptive weighting of each branch based on its validation accuracy during training. Experiments on the Watkins Marine Mammal Sound Database show that the proposed method achieves superior performance, reaching 96.05% accuracy and outperforming baseline and attention-based fusion models. This approach offers an effective solution for multi-feature acoustic recognition in complex underwater environments. Full article

Monitoring dolphins in the open sea is essential for understanding their behavior and the impact of human activities on the marine ecosystems. Passive Acoustic Monitoring (PAM) is a non-invasive technique for tracking dolphins, providing continuous data. This study presents a novel approach for classifying dolphin vocalizations from a PAM acoustic recording using a convolutional neural network (CNN). Four types of common bottlenose dolphin (Tursiops truncatus) vocalizations were identified from underwater recordings: whistles, echolocation clicks, burst pulse sounds, and feeding buzzes. To enhance classification performances, edge-detection filters were applied to spectrograms, with the aim of removing unwanted noise components. A dataset of nearly 10,000 spectrograms was used to train and test the CNN through a 10-fold cross-validation procedure. The results showed that the CNN achieved an average accuracy of 95.2% and an F1-score of 87.8%. The class-specific results showed a high accuracy for whistles (97.9%), followed by echolocation clicks (94.5%), feeding buzzes (94.0%), and burst pulse sounds (92.3%). The highest F1-score was obtained for whistles, exceeding 95%, while the other three vocalization typologies maintained an F1-score above 80%. This method provides a promising step toward improving the passive acoustic monitoring of dolphins, contributing to both species conservation and the mitigation of conflicts with fisheries. Full article

In the present study, a simple immersion boundary method was developed to numerically simulate the fluid-structure-acoustic coupling problem of underwater vehicles and their towed super long cables. A typical underwater vehicle connected with different cable models at different positions was created in this study. The length of the vehicle is 4356 mm, the cables are approximately 4 and 6 times the vehicle length, i.e., 17,424 mm and 26,136 mm, and the freestream velocity is 7.72 m/s (15 kts). In the simulation, the freestream velocities are 9.26 m/s (18 kts), 7.72 m/s (15 kts), and 5.14 m/s (10 kts), respectively. The models are numerically simulated by a simple immersion boundary method to solve the flow field structure, the velocity profile, and the transverse flow near the towed cable, compute the pressure pulsation of the cable models with huge lengths and extremely small diameters, and analyze their flow noise. The results show that the towed cables with different lengths have a relatively small impact on the velocity distribution around the underwater vehicle system; however, the transverse flow occurs near the cable, thereby affecting the pressure pulsation changes and causing significant flow noise problems. Furthermore, it was also found that the closer the connection position of the towed cable is to the center position, the more significant the impact on the downstream flow fields and the higher the sound pressure level of the flow noise. Full article

Anthropogenic noise pollution is increasingly acknowledged as a major threat to marine ecosystems, especially for sound-sensitive species, such as the large yellow croaker (Larimichthys crocea). While the effects of underwater noise on fish behavior and physiology have been well-documented, its influence on oxygen metabolism across varying temperatures remains poorly understood. This study examines the impact of boat noise on the oxygen consumption rate (OCR) of juvenile large yellow croakers at different temperatures, a key factor in their metabolic activity. The underwater noise generated by a fishing boat spans a broad frequency range, with a peak spectrum level of 130 dB re 1 µPa at low frequencies between 100 and 200 Hz. Our findings reveal that boat noise significantly elevates the OCR of juvenile fish, with mass-specific OCR increasing by 65.0%, 35.3%, and 28.9% at 18 °C, 25 °C, and 30 °C, respectively. Similarly, individual OCR rose by 60.7%, 35.3%, and 17.1% at these temperatures. These results demonstrate that boat noise triggers a stress response in fish, resulting in heightened metabolic demands across different seasonal conditions. Notably, the impact of boat noise on respiratory metabolism is most significant at lower temperatures. In aquatic environments with stable oxygen levels, the noise-induced rise in oxygen consumption could lead to hypoxia and provoke maladaptive behavioral changes in fish. Full article

The sound speed in the ocean has a considerable impact on the characteristics of underwater acoustic propagation. The swift gathering of the underwater three-dimensional (3D) sound speed field is essential for target detection, underwater acoustic communication, and navigation. Currently, the reconstruction of the underwater sound speed utilizing satellite remote sensing data of the sea surface has emerged as a significant area of research. However, dynamic activities within the ocean result in varying degrees of perturbation in the sound speed structure. Relying solely on sea surface information will restrict the accuracy of sound speed reconstruction. In response to this issue, by utilizing multi-source satellite remote sensing data alongside Argo profiles, we first implemented the random forest (RF) algorithm to establish the statistical mapping relationship from the sea surface temperature (SST), sea level anomaly (SLA), and absolute dynamic topography (ADT) to the density, and thus, reconstructed a 3D density field. Subsequently, based on the sea surface environmental information, we introduced the underwater vertical density as a novel input for sound speed calculations and proposed a new model for 3D sound speed field reconstruction (RF-SDR). The experimental results indicate that utilizing both the sea surface environmental variables and underwater density as inputs yielded an average root-mean-square error (RMSE) of 1.51 m/s for the reconstructed sound speed, along with an average mean absolute error (MAE) of 0.85 m/s. Following the incorporation of density into the reconstruction inputs, the two error metrics exhibited reductions of 31% and 35%, respectively. And the proposed RF-SDR model demonstrated a reduction in the RMSE by 36% and in the MAE by 43% when compared with the commonly utilized single Empirical Orthogonal Function regression (sEOF-r) method. Furthermore, simulations of the sound propagation with both the reconstructed sound speed and Argo sound speed demonstrated a high degree of consistency in the computed acoustic propagation losses. The correlation coefficients consistently exceeded 0.7, thereby reinforcing the validity of the reconstructed sound speed. Full article

This paper theoretically and experimentally studies the effect of underwater waveguide interface scattering on the nonlinear sound field characteristics of parametric array (PA) radiation. Based on the image source method, the components of the sound field in the waveguide are first analyzed. Then, a non-paraxial model is developed to account for the influence of interface scattering. This model enables accurate calculation of the wide-angle sound field. The impact of the sound source depth and the interface reflection coefficient on the distribution of the difference-frequency wave (DFW) sound field in the waveguide is studied. The interface alters the phase distribution of the DFW’s virtual source density function, thereby affecting the sound field accumulation process. Waveguide interfaces with different absorption coefficients influence the amplitude oscillation caused by interface reflection and change the sidelobe size of the DFW beam. The DFW sound field distribution is measured at three typical frequencies. Simulation and experimental results show that the attenuation of the DFW’s axial sound pressure level in the waveguide oscillates, and the DFW’s beamwidth gradually widens as the frequency decreases. The calculated results from the proposed model agree well with the measured data, with average errors along the sound axis and depth being less than 3 dB and 6 dB, respectively. This demonstrates the model’s superior applicability compared to the existing free-field model. Full article

With the rapid advancement of underwater communication and unmanned aerial vehicle (UAV) technologies, the potential applications of cross-medium communication in environmental monitoring, maritime Internet of Things (IoTs), and rescue operations, in particular, have attracted great attention. This study explores the feasibility of achieving cross-medium direct acoustic communication through the air–water interface. Specifically, it investigates challenges such as acoustic impedance mismatches and signal attenuation caused by energy loss during interface transmission, aiming to understand their impact on communication performance. Experimental tests employed underwater acoustic transducers as signal transmitters to propagate sound waves directly into the air, attempting to establish communication links with aerial UAV nodes. Preliminary experimental results indicate that even conventional underwater acoustic transducers can achieve information exchange between underwater nodes and UAVs, laying a foundation for further research and application of cross-medium communication systems. Full article

Unlike terrestrial radio, the speed of sound in the ocean is relatively slow, which results in mobile underwater M-ary spread spectrum communication typically exhibiting significant and variable multipath effects along with strong Doppler effects, leading to rapid carrier phase shifts in the received signal that severely impact decoding accuracy. This study aims to address the issue of rapid carrier phase shifts caused by significant time-varying Doppler shifts during mobile underwater M-SS communication. This paper innovatively proposes a method for updating matched filters based on correlation cost factors. By calculating the correlation cost factors for each received symbol, the method guides the direction of Doppler estimation and updates the matched filters. After identifying the optimal match, the received symbols are shifted, correlated, and decoded. Simulation and sea trial results indicate that this method demonstrates higher computational efficiency and improved decoding accuracy compared to traditional Doppler estimation matched filters under low signal-to-noise ratio conditions, and exhibits greater robustness under complex motion conditions. Full article

Since the Industrial Revolution, underwater soundscapes have become more complex and contaminated due to increased cumulative human activities. Anthropogenic underwater sources have been growing in number, and shipping noise has become the primary source of chronic acoustic exposure. However, global data on current and historic noise levels is lacking. Here, using the Listening to the Deep-Ocean Environment network, we investigated the baseline shipping noise levels in thirteen observatories (eight stations from ONC Canada, four from the JAMSTEC network, and OBSEA in the Mediterranean Sea) and, in five of them, animal presence. Our main results show yearly noise variability in the studied locations that is not dominated by marine traffic but by natural and biological patterns. The halt in transportation due to COVID was insignificant when the data were recorded far from shipping routes. In order to better design a legislative framework for mitigating noise impacts, we highlight the importance of using tools that allow for long-term acoustic monitoring, automated detection of sounds, and big data handling and management. Full article

The Black Sea, a unique semi-enclosed marine ecosystem, is the eastern maritime boundary of the European Union and holds significant ecological importance. The present study investigates anthropogenic noise pollution in the context of the Marine Strategy Framework Directive’s Descriptor 11, with a particular emphasis on the criteria for impulsive sound (D11C1) and continuous low-frequency sound (D11C2) in Romanian ports, which handle a substantial share of regional cargo traffic, and impact maritime activities and associated noise levels. The noise levels from shipping activity vary across Romanian waters, including territorial waters, the contiguous zone, and the Exclusive Economic Zone. These areas are classified by high, medium, and low ship traffic density. Ambient noise levels at frequencies of 63 Hz and 125 Hz, dominated by shipping noise, were established, along with their hydrospatial distribution for the 2019–2020 period. Furthermore, predictive modeling techniques are used in this study to assess underwater noise pollution from human sources. This modeling effort represents the first initiative in the region and utilizes the BELLHOP ray-tracing method for underwater acoustic channel modeling in shallow-water environments. The model incorporates realistic bathymetry, oceanography, and geology features for environmental input, allowing for improved prediction of acoustic variability due to time-varying sea variations in shallow waters. The study’s findings have important implications for understanding and mitigating anthropogenic noise pollution’s impact on the Black Sea marine ecosystem. Full article

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. Full article

Due to the physical characteristics of acoustic channels, the performance of underwater acoustic communication networks (UACNs) is more susceptible to the impacts of multipath and Doppler effects. Channel quality can serve as a measure of the reliability of underwater communication links. A cross-layer routing protocol based on channel quality (CLCQ) is proposed to improve the overall network performance and resource utilization. First, the BELLHOP ray model is used to calculate the channel impulse response combined with the winter sound speed profile data of a specific sea area. Then, the channel impulse response is integrated into the communication system to evaluate the channel quality between nodes based on the bit error rate (BER). Finally, during the selection of the next hop node, a reinforcement learning algorithm is employed to facilitate cross-layer interaction within the protocol stack. The optimal relay node is determined by the channel quality index (BER) from the physical layer, the buffer state from the data link layer, and the node residual energy. To enhance the algorithm’s convergence speed, a forwarding candidate set selection method is proposed which takes into account node depth, residual energy, and buffer state. Simulation results show that the packet delivery rate (PDR) of the CLCQ is significantly higher than that of Q-Learning-Based Energy-Efficient and Lifetime-Extended Adaptive Routing (QELAR) and Geographic and Opportunistic Routing (GEDAR). Full article

In the shift toward sustainable energy production, offshore wind power has experienced notable expansion. Several projects to install floating offshore wind farms in European waters, ranging from a few to hundreds of turbines, are currently in the planning stage. The underwater operational sound generated by these floating turbines has the potential to affect marine ecosystems, although the extent of this impact remains underexplored. This study models the sound radiated by three planned floating wind farms in the Strait of Sicily (Italy), an area of significant interest for such developments. These wind farms vary in size (from 250 MW to 2800 MW) and environmental characteristics, including bathymetry and seabed substrates. Propagation losses were modeled in one-third-octave bands using JASCO Applied Sciences’ Marine Operations Noise Model, which is based on the parabolic equation method, combined with the BELLHOP beam-tracing model. Two sound speed profiles, corresponding to winter and summer, were applied to simulate seasonal variations in sound propagation. Additionally, sound from an offshore supply ship was incorporated with one of these wind farms to simulate maintenance operations. Results indicate that sound from operating wind farms could reach a broadband sound pressure level (L_p) of 100 dB re 1 µPa as far as 67 km from the wind farm. Nevertheless, this sound level is generally lower than the ambient sound in areas with intense shipping traffic. The findings are discussed in relation to local background sound levels and current guidelines and regulations. The implications for environmental management include the need for comprehensive monitoring and mitigation strategies to protect marine ecosystems from potential acoustic disturbances. Full article