Modeling and interpretation of bistatic bottom reverberation in deep water near seamounts.

The received reverberation signal can be beamformed by utilizing a vertical array, generating a vertical-angle time record (VATR) that enables analysis of spatiotemporal distribution characteristics. Due to the influence of multipath propagation effects, deep-sea reverberation exhibits highly complex characteristics, especially in a seabed with significant depth variation. In a recent bistatic reverberation experiment with a vertical array receiver, peculiar bright stripes were observed in the VATR. These stripes are the result of scattering caused by large-scale bottom structures and are closely associated with seamounts. To accurately model and interpret these stripes, a bistatic reverberation model is initially established to reproduce the VATR. This model enables us to numerically simulate the spatiotemporal distribution of reverberation in the VATR, offering a qualitative explanation for these stripes. However, the model alone is incapable of predicting the specific stripe structure associated with a particular seamount. To address this limitation, an equation system is introduced to calculate the stripe parameters based on the seamount parameters. By analyzing and deducing the dependency of the stripes on the seamount, conclusions were drawn using the equation system. Ultimately, the presented model and equation system successfully reproduce and comprehensively explain the observed abnormal stripes from the experiment.

A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition.

As is well-known, ship-radiated noise (SN) signals, which contain a large number of ship operating characteristics and condition information, are widely used in ship recognition and classification. However, it is still a great challenge to extract weak operating characteristics from SN signals because of heavy noise and non-stationarity. Therefore, a new mono-component extraction method is proposed in this paper for taxonomic purposes. First, the non-local means algorithm (NLmeans) is proposed to denoise SN signals without destroying its time-frequency structure. Second, adaptive chirp mode decomposition (ACMD) is modified and applied on denoised signals to adaptively extract mono-component modes. Finally, sub-signals are selected based on spectral kurtosis (SK) and then analyzed for ship recognition and classification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance.

Spatiotemporal variations, surface inventory, and cross regional impact of current-use organoamine pesticides in Chinese Marginal Seas.

Current-use Organoamine Pesticides (CUOAPs) are a growing concern as emerging pesticide pollutants that were initially discovered on a large scale in Chinese Marginal Seas (CMSs). The highest level was detected in the East China Sea in the late spring and decreased in the following order: East China Sea (early spring) > the South China Sea > the Bohai Sea. The crucial role played by the Yangtze and Yellow rivers as significant terrestrial sources were established. The fluctuations in the land application and riverine input led to variations in the source, distribution, and seasonal patterns of CUOAPs. Terrestrial-exported CUOAPs were transported and redistributed spatially by the surface ocean currents, resulting in significant regional disparities. The results displayed a pronounced terrestrial source signature and a cross-regional impact. By the ocean current transport, CMSs will likely become a secondary source region for the surrounding seas.

A deep-water ray-based blind deconvolution for a near-surface source with a bottom-moored short-aperture vertical array.

A deep-water ray-based blind deconvolution (DW-RBD) method for estimating the channel impulse response of a near-surface source with a bottom-moored vertical array is developed. The proposed DW-RBD is an alternative when the original RBD suffers from performance degradation due to the insufficient beam resolution. The signal-processing scheme coherently utilizes the information of multipath time-delay implied in the conventional wideband beamforming output. A time-delay-related compensation term is then derived based on image theory and introduced into the original RBD to enhance multipath separation. Both simulation and experimental results demonstrate the effectiveness of the proposed method.

Robust shallow water reverberation reduction methods based on low-rank and sparsity decomposition.

Using the characteristics of low rank for reverberation and sparsity for the target echo in multi-ping detection, the low-rank and sparsity decomposition method can effectively reduce reverberation. However, in the case of highly sparse reverberation or a stationary target, the distinctions in the characteristics between the reverberation and target echo become ambiguous. As a result, the reverberation reduction performance is degraded. To guarantee a meaningful decomposition based on the random orthogonal model and random sparsity model, the identifiability condition (IC) for the decomposition was derived from the perspective of the low-rank matrix and sparse matrix, respectively. According to the IC, sparsity compensation for the low-rank matrix was proposed to address the false alarm probability inflation (FAPI) induced by highly sparse reverberation. In addition, increasing the dimension of the sparse matrix was also proposed to manage the detection probability shrinkage caused by a stationary target. The robust reverberation reduction performance was validated via simulations and field experiments. It is demonstrated that FAPI can be eliminated by increasing the sparse coefficient of the low-rank matrix to 0.30 and a stationary target could be detected with a large ping number, i.e., a high dimension, of the sparse matrix.

Prediction uncertainty of wind-generated noise spectra from wind speed.

The prediction of wind-generated noise spectral levels at one frequency is typically based on a linear regression function, which is defined over the logarithm of the 10-m wind speed. However, despite its widespread success, the linear regression model does not pay attention to its prediction uncertainty because it makes point predictions. The main reasons for the uncertainty in the predicted value of the wind-generated noise level are that it cannot be uniquely determined by 10-m wind speed and its measurements may be corrupted by other sources of ambient noise. To quantify the uncertainty in predictions in this scenario, a Bayesian treatment of linear regression models and its associated predictive distribution are applied, making distribution predictions instead of point predictions. Once the predictive distribution for one frequency has been fixed, its linear variants are used to obtain predictive distributions for other frequencies. The data for the ocean ambient noise and 10-m wind speed are collected from two deep-water experiments, conducted in the South China Sea, and reanalysis data sets of the European Centre For Medium-Range Weather Forecasts, respectively. Empirical expressions for the predictive distribution of noise spectra (0.5-10 kHz) at wind speeds from 3.3 to 14 m/s have been developed. The results indicate decreasing uncertainties with an increasing wind speed.

A novel inner surface enhancement method for holes utilizing ultrasonic cavitation.

Holes in housings, shafts and flanges lead to stress concentrations when the components are working under high dynamic loads. Peening methods are commonly used to improve the stress concentration and extend the working life. These methods, however, are difficult to treat the inner surface of the holes in the components because these surfaces are fully shadowed and limit the access of the shot streams, water jets or laser beams. A new developed method using ultrasonic cavitation can be expected to solve these problems by using a sonotrode with a special shape. The working principle is that the fluid enters through a narrow gap between the sonotrode and the inner surface to create a cavitation. In this paper, a new sonotrode was designed and manufactured, then tested at a resonance frequency of 23.8 kHz. The sono-chemiluminescence experiments were carried out to detect the cavitation intensity on the inner surfaces. The stainless-steel tubes were treated, and their surface properties were evaluated as well. The results show that the cavitation intensity is strongest at the working distance of 1 mm. The hardness increased by about 12% without the significant change of surface roughness.

Use of multipath time-delay ratio for source depth estimation with a vertical line array in deep water.

In this paper, a method for the problem of depth estimation of a broadband source via reliable acoustic path propagation is presented for the case using a vertical line array (VLA). The estimates are determined by two kinds of multipath time-delay ratios, namely, the ratio of direct-surface-reflected (D-SR) to direct-direct time-delays and the ratio of D-SR to surface-reflected-surface-reflected time-delays. The innovation of ratio behavior is that it provides a mechanism for obtaining a useful depth interval with the assumption of plane-wave propagation. The estimation accuracy of a depth interval relies on the degree to which the actual acoustic propagation characteristic can be modeled by image theory. Furthermore, the variability of depth interval due to the approximation made in the derivation method allows one to achieve binary discrimination of both the source depth and source range with only a minimal amount of prior environmental knowledge. The methodology of multipath time-delay estimation is first reviewed and improved, followed by an illustration of the source depth estimation and a discussion of the performance analysis using results from numerical simulations. Finally, the proposed method is demonstrated with experimental data collected in the South China Sea in which a short-aperture VLA is deployed near the sea bottom.

Line spectrum extraction based on autoassociative neural networks.

Line spectrum is an important feature for the detection and classification of underwater targets. This letter presents a method for extracting the line spectrum submerged in underwater ambient noise through autoassociative neural networks (AANN). Compared with the traditional methods, the proposed method based on AANN can directly enhance the line spectrum from the raw time-domain noise data without relying on prior information and spectral features. Moreover, the proposed method can suppress the background noise while extracting the line spectrum. Both the numerical simulation and experimental data test results demonstrate that the proposed method provides a good ability to extract the line spectrum from the strong background noise.

Reverberation reduction based on multi-ping association in a moving target scenario.

Conventional reverberation reduction methods are conducted with single-ping data and may fail in a low signal-to-reverberation ratio (SRR) environment. To improve the performance of reverberation reduction, multi-ping data are fully considered in this paper. The reverberation can be treated as a combination of the steady component of reverberation and reverberation fluctuations, and then an alternating direction multiplier method is proposed to reduce the steady component of the reverberation. By exploiting the evolution of the target location along multiple pings, the reverberation fluctuation is reduced by the probabilistic data association method. The proposed method was verified by the field data, and the results show that compared with the accelerated proximal gradient method, the sparse coefficient is improved by a factor of 1.23, and the signal excess is improved by an average value of 2.0 dB. In addition, the performance of the proposed method is found to be closely related to the signal-to-reverberation-fluctuation ratio rather than only the SRR.

A denoising representation framework for underwater acoustic signal recognition.

To suppress the noise interference in underwater acoustic signals for recognition, a practical denoising representation and recognition method is proposed. This algorithm first generates the multi-images between marine noise and target signal by correlation and "dropout" processing, adaptively. Second, a convolutional denoising autoencoder is designed to train the segmented multi-images in parallel to acquire denoising features. Finally, to improve the classification accuracy of random forest (RF), the weight fusion is exploited to initialize parallel RF classifier. Numerical experiments are shown that demonstrate superiority to three other methods in feature denoising and classification under underwater acoustic scenes.

Probability density function of ocean noise based on a variational Bayesian Gaussian mixture model.

Extensive ocean noise records have kurtoses markedly different from the Gaussian distribution and therefore exhibit non-Gaussianity, which influences the performance of many sonar signal processing methods. To model the amplitude distribution, this paper studies a Bayesian Gaussian mixture model (BGMM) and its associated learning algorithm, which exploits the variational inference method. The most compelling feature of the BGMM is that it automatically selects a suitable number of effective components and then can approximate a sophisticated distribution in practical applications. The probability density functions (PDFs) of three types of noise in different frequency bands collected in the South China Sea-ambient noise, ship noise, and typhoon noise-are modeled and the goodness of fit is examined by applying the one-sample Kolmogorov-Smirnov test. The results demonstrate that: (i) Ambient noise in the low-frequency band may be slightly non-Gaussian, ship noise in each considered band is apparently non-Gaussian, and typhoons affect the noise in the low-frequency band to make it apparently non-Gaussian, while the noise in the high-frequency band is less affected and appears to be Gaussian. (ii) BGMM has higher goodness of fit than the Gaussian or Gaussian mixture model. (iii) In the non-Gaussian case, despite some components having small mixing coefficients, they are of great significance for describing the PDF.

Deep learning classification for improved bicoherence feature based on cyclic modulation and cross-correlation.

This paper aims to present an improved bicoherence spectrum (IBS) combined with cyclic modulation spectrum (CMS) and cross-correlation that is suitable for classification of hydrophone signals involving deep learning (DL). First, the proposed feature utilizes the all-phase fast Fourier transform to modify the spectrum leakage caused by CMS; this can be used to detect line spectra with low signal-to-noise ratios (SNRs). Second, the cross-correlation and bispectrum are both exploited to suppress non-periodic line spectra interference from CMS. Based on numerous numerical simulations and experimental verification, compared with CMS and conventional bispectrum, the prominent characteristics of IBS include: detecting higher-precision periodic harmonics without single-line interference, superior robustness under low SNR, and greatly reducing the data redundancy. In addition, to test the performance of IBS for DL application, three deep belief network (DBN)-based classifiers-DBN-softmax, DBN-support vector machine, and DBN-random forest-are introduced and employed for five experimental scenarios (including ships and underwater source). The results indicate that benefiting from DBN pre-training, the IBS classification accuracy of DBN-based models is generally higher than 80%.

Mapping sea surface observations to spectra of underwater ambient noise through self-organizing map method.

This letter presents a model for mapping sea surface observations to the spectra of underwater ambient noise through the self-organizing map method (SOM). The data used to train and test the proposed model include observations of wind speed, atmospheric pressure, and significant wave height from public databases, as well as observations of ambient noise from two deep-water experiments. SOM extracts nonlinear relations from the data and is more suitable for the study of nonlinear dynamics in the ocean than conventional methods. Results indicate the proposed model is reliable with coefficients of determination above 0.9 and root-mean-square errors below 1 dB.

Localization of Two Sound Sources Based on Compressed Matched Field Processing with a Short Hydrophone Array in the Deep Ocean.

Passive multiple sound source localization is a challenging problem in underwater acoustics, especially for a short hydrophone array in the deep ocean. Several attempts have been made to solve this problem by applying compressive sensing (CS) techniques. In this study, one greedy algorithm in CS theory combined with a spatial filter was developed and applied to a two-source localization scenario in the deep ocean. This method facilitates localization by utilizing the greedy algorithm with a spatial filter at several iterative loops. The simulated and experimental data suggest that the proposed method provides a certain localization performance improvement over the use of the Bartlett processor and the greedy algorithm without a spatial filter. Additionally, the effects on the source localization caused by factors such as the array aperture, number of hydrophones or snapshots, and signal-to-noise ratio (SNR) are demonstrated.

Passive broadband source localization based on a Riemannian distance with a short vertical array in the deep ocean.

A passive broadband source localization approach in the deep ocean is proposed based on the coherent matched-field processing combined with a Riemannian distance. The spatial coherence between hydrophones over frequency bandwidth is utilized for eliminating the unknown source spectrum. Three Riemannian distances are introduced for the measurement of the similarity between the data and replica matrices. The experimental results indicate that the proposed processors with a short array can effectively suppress the sidelobe and improve the localization performance. The source localization influenced by the factors such as the aperture, number of hydrophones, and desired bandwidth is also demonstrated.

A performance study of acoustic interference structure applications on source depth estimation in deep water.

A recent publication by Duan et al. [J. Acoust. Soc. Am. 142(3), EL245-EL250 (2017)] analyzed the interference pattern observed in the plot of acoustic intensity versus source-receiver range and frequency in deep water. This paper extends this work by applying an equivalent acoustic intensity surface versus the vertical angle of arrivals and frequency to depth estimation of broadband sources. A matched-interference-structure (MIS) method that processes the whole acoustic intensity surface is proposed and formulated as a filtration of the surface where the binominal filter weights ±1 for each surface grid point and presumed source depth are based on model-based interference structures. For comparison, the conventional method of matching multipath delays is reformulated and applied to process the surface. Because the numerical prediction of interference structures can be costly, analytical expressions are derived to improve the efficiency. Monte Carlo simulations are carried out to compare the performance and experimental data of broadband random sequences and pulse sources are used to illustrate the methods. The MIS method has a higher resolution and can work under a lower signal-to-noise ratio in the case of weak source depth fluctuation but is not as robust to the large source depth fluctuation as the other method.

A simple method for depth estimation of a sound source at known range in the deep sea.

Based on the distribution of acoustic intensity at different depths at a fixed distance, a simple method is proposed to estimate the sound source depth at known range in the deep sea. First, the method calculates the acoustic intensity distribution of all the depths at a receiving distance. Second, the depths with the strongest acoustic intensity are selected. Sound sources are set at the selected depths to calculate the transmission loss (TL) at the same distance through the acoustic model, and the depth of the minimum superimposed TL is considered as the depth of the original sound source. The simulation and experiment verify the feasibility and reliability of the method.

Particle filter for multipath time delay tracking from correlation functions in deep water.

This paper presents a particle filtering-based approach for tracking multipath time delays from correlation function, such as autocorrelation, cross-correlation, and matched-filter output. The proposed approach exploits the continuous evolution with time of the correlations between multipath arrivals masked by the background noise to track time delays. The prominent feature of this approach is tracking the signal-related peaks (single points) instead of correlation pulses adopted in conventional approaches. To do so, the correlation function with only local peaks is introduced in the model of the measurement equation. This allows no assumption on the reference signal used to match the correlation pulse and no a priori knowledge of the covariance of the background noise. The time-evolving marginal posterior probability densities are also extracted by filtering to reveal the uncertainty of the time delays in every step of tracking. The approach is performed on both simulated data in reliable acoustic path propagation and experimental data collected during two deep water experiments; the results demonstrate significant advantages of the proposed method over a conventional state-space approach, the multiple hypothesis tracking, and a modified peak amplitude detection method.

Striation-based source depth estimation with a vertical line array in the deep ocean.

A striation-based method with a vertical line array is proposed for source depth estimation. Broadband striation structures of direct and surface-reflected arrivals after propagating to receivers near the ocean bottom are applied. A tracking algorithm for the striation structures is proposed based on the extended Kalman filter. A cost function for source depth estimation is presented by matching the traces of the measured striations with a library of model-based traces under different source depths. The method is demonstrated on array data collected during an acoustic research experiment in the South China Sea in 2016.