Ocean Ambient Noise Research Articles

Assessment of the effectiveness of ship machinery noise reduction measures using a test platform in a water basin

Underwater radiated noise (URN) from commercial shipping is partly responsible for increased ocean ambient noise levels in the last decades. To preserve marine wildlife, there is a need to reduce it. Machinery noise is the dominant URN source at lower speeds. Mitigation technologies exist to reduce it, but a lack of quantitative data regarding their effectiveness results in limited practical ship applications since the cost-to-benefit ratio is imprecise. A small ship-like structure (test platform) representative of a ship section is designed and constructed to conduct measurements in a controlled environment and at a lower cost than actual on-ship testing. The platform is deployed in a water basin whose acoustic response is first characterized by reverberation measurements. Vibroacoustic sources simulate structure-borne and airborne noise, while hydrophones and sensors measure the response in the water basin and of the platform. Measurements with and without standard mitigation technologies installed in the platform are conducted to quantify the insertion loss. Up to 37 and 20 dB URN reductions are obtained with elastic mounts and mineral wool, respectively. The results obtained with the platform and the developed methodology can support and guide the implementation of mitigation measures in current and future ship constructions.

Tidally modulated seismic velocity changes observed using submarine dark fiber and the virtual-source method

Natural cyclical stress perturbations, including wet and solid earth tides, coupled with repeatable seismic monitoring approaches, provide a tool for understanding the elastic properties of rocks at depth. Ocean tides, in particular, perturb the overburden and pore pressure, affecting hydraulic and elastic rock properties. The recent emergence of distributed acoustic sensing (DAS) and seismology using existing dark telecom fiber offers an unprecedented opportunity to instrument the seas and oceans with seismic monitoring networks of large aperture, fine spatial resolution, and broadband sensitivity. We use a submarine dark-fiber array to methodologically assess whether the changes in seafloor seismic velocities are modulated by ocean tidal loading. Considering oceanic ambient noise (AN) in the frequency range between 1 and 5 Hz, we find it possible to retrieve reliable surface-wave estimates using relatively short time windows (approximately 60 min). Next, using the power of linear arrays to capture AN energy from the stationary-phase zones and AN processing techniques commonly used in DAS applications for subsurface monitoring in urban settings, we introduce a methodological approach to turn each segment of dark fiber into a short-offset monitoring array (2 km), providing redundant, hourly, surface-wave estimates. Using four days of AN data recorded by the Monterey Accelerated Research System (MARS) 20 km section offshore cable, we generate the virtual-source gathers for 10 segments spanning the whole MARS cable. We then use virtual-source data from one segment to estimate the hourly velocity changes that are then quantitatively compared with the tidal data. The results suggest that changes in seafloor seismic velocities are likely modulated by tidal height, even in regions without large tidal excursions. This advance demonstrates a new application of submarine telecom fibers for monitoring the seismic stress response of near-seafloor sediments.

Enhanced Underwater Single Vector-Acoustic DOA Estimation via Linear Matched Stochastic Resonance Preprocessing

Underwater acoustic vector sensors (UAVSs) are increasingly utilized for remote passive sonar detection, but the accuracy of direction-of-arrival (DOA) estimation remains a challenging problem, particularly under low signal-to-noise ratio (SNR) conditions and complex background noise. In this paper, a comprehensive theoretical analysis is conducted on UAVS signal preprocessing subjected to gain-phase uncertainties for average acoustic intensity measurement (AAIM) and complex acoustic intensity measurement (CAIM)-based vector DOA estimation, aiming to explain the theoretical restrictions of intensity-based vector acoustic preprocessing approaches. On this basis, a generalized vector acoustic preprocessing optimization model is established in which the principle can be described as “maximizing the denoising performance under the constraints of an equivalent amplitude-gain response and phase-bias response”. A novel vector acoustic preprocessing method named linear matched stochastic resonance (LMSR) is proposed within the framework of matched stochastic resonance theory, which can naturally guarantee the linear gain-phase restrictions, as well achieving effective denoising performance. Numerical analyses demonstrate the superior vector DOA estimation performance of our proposed LMSR-AAIM and LMSR-CAIM methods in comparison to classical intensity-based AAIM and CAIM methods, especially under low-SNR conditions and non-Gaussian impulsive noise circumstances. Experimental verification conducted in the South China Sea further verifies its the effectiveness for practical application. This work can lay a solid foundation to break through the challenges of underwater remote vector acoustic DOA estimation under low-SNR conditions and complex ocean ambient noise and can provide important guidance for future research work.

A time-delay neural network for ship-radiated noise recognition based on residual block and attention mechanism

Ship-radiated noise recognition remains a challenging issue in underwater acoustic signal processing for a variety of reasons. The ocean ambient noise influences the signal quality, and the scarcity of samples limits the performance of deep learning algorithms. In this study, a self-attention mechanism and residual block-based time-delay neural network (SRTDNN) is proposed to improve the recognition performance of ship-radiated noise. The noise samples are first segmented and transformed into three-dimensional Mel-scale frequency cepstral coefficients (3D-MFCC), which combine static and dynamic characteristics of targets through the concatenation of MFCC, Delta MFCC and Delta-Delta MFCC. Subsequently, 3D-MFCC are fed as input to the SRTDNN. The SRTDNN extracts time-domain features of inputs through the TDNN layers and then assigns different weights to the frame and channel dimensions of the feature maps through two attention mechanisms. The Res Connections and Res2Block are interspersed to facilitate convergence. During training, a time-domain and frequency-domain data augmentation pipeline is designed to alleviate the lack of samples. Experiments with public and real sea experiment datasets are conducted to compare the SRTDNN with acknowledged high-quality recognition methods. The results show that the SRTDNN has a significant improvement in recognition performance. Furthermore, the ablation tests demonstrate the effectiveness of the proposed method.

Discrete Distribution Model of Ship Noise Source in Shipping Channel

Ship noise plays an important role in low-frequency ocean ambient noise. With the increasingly busy sea traffic, the randomness of the spatiotemporal distribution characteristics of ship noise is also increasing. However, most ships sail in a relatively fixed range in different sea areas. Most of the previous studies assumed that the ship noise sources in shipping channel satisfy a uniformly distribution, actually all kinds of large ships are not uniformly distributed in the shipping channel, and there are also many small boats distributed on the channel that contribute significantly to low-frequency ocean ambient noise. The channel within a certain sea area will be divided into different regions, and ship information (AIS) in different regions will be analyzed statistically in this paper. It is assumed that the ship noise sources in different channel regions are discrete point noise sources that obey a certain spatiotemporal statistical distribution, combined with the noise source level models of different types of ships to form a discrete source model of ship noise in shipping channel. Using the ray propagation theory considering the horizontal refraction of sound energy, the vertical distribution of the ship noise level in this sea area is calculated, and compared with the vertical noise level distribution of ship noise source uniform distribution model, the variation range of noise level vertical distribution of two models is close calculated noise level (such as maximum and minimum values) is consistent with the measured data. The discrete source model of ship noise established in this paper may provide a technical support for predicting low-frequency ambient noise characteristics near the channel.

SSANet: normal-mode interference spectrum extraction via SSA algorithm-unrolled neural network

In ocean acoustic fields, extracting the normal-mode interference spectrum (NMIS) from the received sound intensity spectrum (SIS) plays an important role in waveguide-invariant estimation and underwater source ranging. However, the received SIS often has a low signal-to-noise ratio (SNR) owing to ocean ambient noise and the limitations of the received equipment. This can lead to significant performance degradation for the traditional methods of extracting NMIS at low SNR conditions. To address this issue, a new deep neural network model called SSANet is proposed to obtain NMIS based on unrolling the traditional singular spectrum analysis (SSA) algorithm. First, the steps of embedding and singular value decomposition (SVD) in SSA is achieved by the convolutional network. Second, the grouping step of the SSA is simulated using the matrix multiply weight layer, ReLU layer, point multiply weight layer and matrix multiply weight layer. Third, the diagonal averaging step was implemented using a fully connected network. Simulation results in canonical ocean waveguide environments demonstrate that SSANet outperforms other traditional methods such as Fourier transform (FT), multiple signal classification (MUSIC), and SSA in terms of root mean square error, mean absolute error, and extraction performance.

Long-term statistics and wind dependence of near-bottom and deep-sea ambient noise in the northwest South China Sea

Research on ocean ambient noise is highly important for environment monitoring, marine mammal protection, underwater communication and navigation. In this paper, we present the long-term statistics and wind dependence of near-bottom and deep-sea ambient noise in the northwest South China Sea, at a depth of 1240 m. The data were collected from 11th July 2022 to 31st December 2022 together with local wind speeds ranging from 1 to 58 knots (two typhoons involved), and the processing frequency band is between 20 and 2000 Hz. The long-term mean noise level is calculated along with its skewness, kurtosis and percentile distributions. Diurnal and monthly average of noise levels are analyzed, and the large fluctuations in lower (≤100 Hz) and higher (≥400 Hz) frequencies are respectively caused by the variation of the number of nearby and distant ships and the diverse distributions of the windspeeds in individual months. We find that the noise level in winter (Dec.) is 10~11 dB higher than that in summer (Jul.) at higher frequencies. The probability densities of noise levels in the situation of a fixed wind speed are likely to obey the Burr distributions in low frequencies (50 and 100 Hz) and the Weibull distributions in high frequencies (400 and 1000 Hz). In addition, the mean noise levels for different Beaufort scales match well with the 5-dB-addtion Wenz curves, and a mathematic relationship is acquired between the noise level and wind speed in the experimental site. The results are of great representativeness, and are significant to data-driven noise modelling, evaluation and improvement of sonar performance in the region of South China Sea with an incomplete deep-water sound channel.

Analysis and application of spatio-temporal correlation characteristics of measured ocean ambient noise

Gain in sonar signal processing is closely linked to the spatio-temporal coherence characteristics of the ocean ambient noise field. The gain relies on the spatio-temporal coherence coefficient of the signal and noise, with the aim of maximizing the former and minimizing the latter. Currently, there has been ample research on the spatial characteristics of the noise field, while studies on the temporal coherence characteristics of single-point noise fields are limited. In this paper, a temporal and spatial coherence model of wind-induced noise is established based on the normal mode theory, and the impact of bandwidth and spectral structure on the time coherence coefficient of wind-induced noise is analyzed.To measure the environmental noise in 103 m of shallow water in the South China Sea, a 64-element vertical array was utilized. The results demonstrate that the measured vertical spatial correlation coefficient aligns closely with the theoretical findings when wind-induced noise is predominant. The measured temporal correlation coefficient of narrowband noise exhibits a cosine function with gradual attenuation of the envelope, and the correlation radius remains independent of frequency. As the bandwidth increases, the measured broadband noise temporal correlation radius gradually decreases. Wind speed has minimal influence on the temporal and spatial correlation coefficients of wind-induced noise. In the presence of interferences such as ships and pulses, the measured temporal and spatial correlation coefficients of wind-induced noise exhibit larger values in certain intervals.Lastly, leveraging the characteristic that the temporal coherence radius of noise is significantly smaller than that of the line spectrum signal, a single-hydrophone cross-spectrum method is proposed to process the line spectrum signal. This approach effectively enhances the signal-to-noise ratio of the line spectrum.

Estimation of seabed properties from ocean ambient noise in shallow water: A review

Ocean ambient noise is the sound persisting in the ocean, which includes contributions from natural and anthropogenic sources, such as rain, wind, shipping, and marine mammals. Multiple interactions of the sound with the surface and bottom in shallow water affect the spatial characteristics of ambient noise. Thus, the spatial noise properties, coherence, and directionality of ambient noise depend on the characteristics of the shallow water environment, such as depth, sound speed profile, and seabed sediment properties. Acoustic properties of seafloor sediments can be extracted from ambient noise based on these noise properties using various types of noise data and geo-acoustic inversion methods. This paper presents a review of literature concerning investigations into available methods, the implementations of these methods, and their verification and validation. The efficacy and the applicability of these inversion methods for a wide range of ocean noise data are also examined in this review.

Evidence of oceanic and atmospheric tides in underwater ambient noise time records

Multiyear time records of low-frequency ocean ambient noise show clear evidence of a modulating influence by major semi-diurnal and diurnal gravitational tides. These are displayed by means of a variance spectral analysis of noise time records obtained from the United Nations Comprehensive Nuclear Test Ban Treaty Organization for hydrophone stations at Wake Island, Ascension Island and Diego Garcia. Narrow-band noise frequencies ranged from ½ to 10 Hz. There is also evidence in the noise records of a diurnal atmospheric tide related to solar radiation and atmospheric loading. This same tidal presence is seen in wind speed records as well. Part of the challenge in the analysis is to distinguish between expected diurnal behavior in wind speed, with its accompanying effect on ambient noise, and the presence of actual tidal lines. A brief history of earlier observations of diurnal variation in underwater acoustics is presented, along with some suggestions of causal mechanisms.

From ocean ambient noise to soundscape ecology

Ambient noise in the ocean has been studied for more than 80 years but recently there has been a tendency to use the word “soundscape” for what appears to be the same phenomenon. Ambient noise is usually defined as the background noise from all sources, excluding sounds from individual identifiable sources and is an important limitation on sonar performance and the use of sound by marine animals. The term “soundscape” has been used for decades outside of underwater acoustics with varying definitions depending on the application. Probably the most relevant is “soundscape ecology,” which comes from terrestrial ecology and includes the acoustic interaction between animals and between animals and their environment. A soundscape, therefore, includes all sounds in an environment, not just the background or ambient noise. Sounds from individual identifiable sources may have particular interest to marine animals especially if they are from conspecifics, predators, or prey. This paper discusses the value of applying the broader concepts of soundscapes and soundscape ecology to the underwater environment, the advantages of recognizing the distinction between soundscapes and ambient noise and the importance of soundscape ecology in understanding animal acoustic behavior and the effects of anthropogenic noise.

Estimation of seafloor properties in shallow oceans using minimally processed vertical directivity of surface-generated ambient noise

Typically, data from an array with vertical aperture will be beam-formed to provide the level and variation of spectrally averaged ocean ambient noise as a function of vertical angle. This paper considers the potential for such measurements to provide information about the acoustical properties and layering of a seafloor in a shallow ocean. First, with the assumption of dipole surface sources, it is feasible to describe the ambient noise in the near-horizontal in terms of the Weston α parameter (Weston, J. Sound Vib. 18, 271–287, 1971). Furthermore, measurements of ambient noise in the positive and negative vertical directions may be used to provide a spectrally averaged determination of bottom loss. With the presence of a surficial seafloor layer, each of the near-horizontal and near-vertical noise intensities will exhibit spectral variation. Techniques are described by which such relative variations in noise directivity, over a frequency span of some octaves, enable a reasonable estimation of, first, the thickness of a seafloor layer, and second, the acoustical properties of the layer and the seafloor basement.

Ocean ambient noise field modelling and the optimized noise term

The objective of this thesis is to determine the frequency and wind-wave forcing dependent effective sea surface noise source level per unit area extracted from the hourly minimum sound power levels of six month-long acoustic recordings. The effect of the propagation environment is accounted for using Bellhop. The simulated environment is configured using climatological sound velocity profiles to capture seasonal effects and bottom sound speed estimates made from seabed sediment maps. Hourly meteorological data were extracted from ERA5 providing relevant wind and wave parameters from which noise levels may be predicted. A weighted composite model consisting of neutral wind and significant wave height leveraging the two-term exponential regression function proved to maximize model R2. Received level data originating from 16 hydrophone stations in the North Atlantic and Labrador Sea were combined with the Bellhop TL simulations in order to produce estimates of the effective noise source level per unit area (NSL/A) for changing surface environmental conditions and inter-compared. Hourly minimum sound power level derived model-data comparisons using horizontal wind speed magnitude 10 m above sea level expressed a decrease in NSL/A estimates versus Kewley (1990) by 10 to 15 dB from 1 to 3 kHz.

Fast estimation of distance between two hydrophones using ocean ambient noise in multi-ship scenarios

Accurately estimating the bearing of a target with two hydrophones requires knowing the precise distance between them. However, in practice, it is difficult to measure this distance accurately due to the influence of current. To solve this problem, we propose a method for extracting the time-domain Green’s function between two points in multi-ship scenarios and for extracting the time-domain waveform arrival structure between two hydrophones in real-time based on long samples of ship radiation noise cross-correlation. Using the cross-correlation function of the radiated noise from any ship located in the end-fire direction of the two hydrophones, we can estimate the distance between the hydrophones in real-time. To verify the accuracy of our estimation, we compare the result of azimuth estimation with the actual azimuth based on the azimuth estimation of a cooperative sound source in the maritime environment. Our experimental results show that the proposed method correctly estimates the distance between two hydrophones that cannot be directly measured and estimates the position of a cooperative sound source 4 km away with an average deviation of less than 1.2°.

Assessing the liquefaction potential of seabed soils based on ocean ambient noise in the Yellow River Delta

Seabed soils can undergo liquefaction under cyclic loading, resulting in a rapid decrease in strength and stiffness, which may lead to the destruction of offshore structures. Therefore, the assessment of seabed soil liquefaction will become an important factor in disaster prevention and risk analysis in coastal and offshore engineering construction. In this study, the ocean ambient noise with low-frequency, long-wavelength, and wide-band characteristics was used to conduct and analysis noise based on the horizontal-to-vertical spectral ratio method. The shear wave velocity of the seabed soil was obtained by inverting the ocean ambient noise dataset. Then, we proposed a shear wave velocity threshold that can be used for liquefaction assessment of Holocene unconsolidated fine-grained soils by statistical analysis, and the liquefaction potential of the soils was evaluated according to 1-D shear wave velocity structures and 2-D shear wave velocity profiles. The results showed that the distribution of the shear wave velocity obtained by inverting ocean ambient noise was generally consistent with the measured shear wave velocity in the field, indicating that the inversion results have a certain degree of accuracy. A shear wave velocity threshold of 200 m/s was proposed for liquefaction assessment, determining that the soils within 0-10 m depth in the coastal area of Yellow River Delta have liquefaction potential. This result is in accordance with the assessment based on the critical shear wave velocity, indicating that this threshold is applicable to the assessment of seabed soil liquefaction in the Yellow River Delta. The in-situ observations of ocean ambient noise provide a more convenient, economical, and environmentally friendly method, which can help to investigate marine geology disasters and serve marine engineering construction.

Ocean ambient noise on the Chukchi Plateau and its environmental correlates

Conducting research on ocean ambient noise under different sea ice conditions is highly important for the comprehension of the rapidly changing Arctic. We present the first results of ambient noise and its relationship to environmental forcing during the open-water, ice transition and ice-covered periods on the Chukchi Plateau. The ambient noise level (ANL) in the 20 Hz to 2 kHz band is higher, intermediate and lower during the open-water, ice transition and ice-covered periods, respectively. During the ice-covered period, the ambient noise is dominated by the ice-generated noise due to sea ice activities and shows a negative correlation with temperature. Therefore, when the temperature decreases, the sea ice is prone to shrinking and cracking, thus increasing the sea ice activities and resulting in increased ice-generated noise; when the temperature rises and is relatively high in May and June, the ANL is lowest for the sea ice inhibition to wind waves and decreased sea ice activities induced by temperature rise. Sea ice is the most predominant environmental factor affecting Arctic ocean ambient noise, and the ANL can potentially increase due to a reduction in Arctic sea ice and increase in human activities caused by global climate change.

The analysis of the depth dependence of ambient noise in the South China Sea

A 64-element vertical array was deployed in the South China Sea for ocean ambient noise observation. A rainstorm passed over the area during the experiment, lasting a total of 35 minutes. The differences between the depth distribution and vertical coherence of wind-driven noise and rain noise are compared, and the influence of rainfall rates on them is analyzed. The wind-driven noise intensity decreases with increasing depth, and it is basically consistent with the theoretical results. The depth distribution of rain noise is different from that of wind-driven noise. When the rainfall rate exceeds 48mm/h, the rain noise intensity in the frequency band above 3200Hz increases with increasing depth. Compared with wind-driven noise, the vertical coherence function depth distribution of rain noise is more uniform. The vertical coherence function of rain noise is in good agreement with the simulation results when the noise source directivity index take a larger value.

Spectral Level Prediction Model of Ocean Ambient Noise Based on GA-LM-BP Neural Network

Spectral Level Prediction Model of Ocean Ambient Noise Based on GA-LM-BP Neural Network

Low frequency ambient noise dynamics and trends in the Indian Ocean, Cape Leeuwin, Australia

Examination of 18 years of nearly continuous low frequency deep ocean ambient noise offshore Cape Leeuwin, Australia, finds evidence of a decreasing nonlinear trend suggestive of long-term cyclic dynamics. The nonlinear trend is found to be consistent with trends in oceanographic sea surface temperature, which are thought to drive changes in Antarctic sea ice extent. Assessment of oscillatory dynamics finds causal covariance between ambient noise levels and Indian Ocean sea surface temperature dipoles. Dynamics of annual ambient noise and Antarctic sea ice extent are examined suggesting a phase-locked relationship revealing nonlinear characteristics of the presumed dependence. Collectively, the hypotheses that deep water ambient noise dynamics in the Indian Ocean are influenced by Antarctic sea ice extent and melt dynamics and that linear models do not fully capture long-term ambient noise trends and dynamics are supported.

Open ocean ambient noise data in the frequency band of 100 Hz–50 kHz from the Pacific Ocean: A legacy of Jeffrey A. Nystuen

Ocean ambient noise, spanning from a few hertz to tens of kilohertz, is often the limiting factor for sonar performance in target detection, location, and identification. In this frequency band, wind generated surface breaking waves produce bubbles near the surface that are the dominant ambient noise source. In this work, results from a long-term collaboration between NOAA/ NASA and ambient noise study pioneer, Jeffrey A. Nystuen, are presented. Specifically, two-decades of ambient noise data from six deep ocean moorings with companion surface meteorological measurements are used to validate ambient noise models. Excluding data during rainy periods, the ambient noise level is investigated under different wind speed ranges. For wind speeds exceeding 15 m/s, the ambient noise level displays a sharp drop-off and creates a “cross-over” as the spectral level at higher wind speeds and frequencies becomes lower than that at lower wind speeds. Data-model comparisons show a mismatch, as existing models are monotonic in nature, i.e., the modeled spectral level increases with increasing wind speed for all frequencies. This mismatch, currently under investigation, is likely due to attenuation when ambient sound propagates through the deeper and denser bubble layer under high sea conditions. [Work supported by NOAA, NASA, and ONR.]