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To solve the problem of array detection performance in environments with mesoscale eddies, this study utilizes the Gaussian eddy model to describe the sound speed structure disturbed by eddies. Through numerical simulations, the corresponding sound field is obtained, and the transmission loss influenced by the eddy is analyzed. Furthermore, to investigate the relation between the array gain and spatial correlation in the eddy environments, the differences in vertical correlation at different positions and their effects on the vertical array gain of conventional beamforming (CBF) are studied. When the source is around the eddy center, the conclusions drawn are as follows: (1) The presence of an eddy changes the turning-point depth and the sound field distribution, significantly affecting the direct sound region and the first convergence zone, while having a minor impact on the first shadow zone. (2) In different eddy-induced environments, the first convergence zone maintains a high vertical correlation, but the vertical correlation of the direct sound region is greatly influenced by the eddy. (3) The array gain of CBF is consistent with the vertical correlation. When the correlation between each element of the sound field is great, the array gain increases with the number of array elements. [ABSTRACT FROM AUTHOR]

Attenuation is the most difficult seafloor acoustic property to get, particularly at low to mid frequencies. For low velocity bottoms (LVB), it becomes even more challenging, due to its small attenuation and lower velocity (relative to the velocity of the adjacent water). The latter one causes a fatal "seafloor velocity-attenuation couplings" in geo-acoustic inversions. Thus, attenuation inversions for the LVB require an accurate seafloor velocity profile, especially the velocity in the LVB layer. The propagation of explosive sound in the Yellow Sea with a strong thermocline and a top LVB layer exhibits many prominent characteristics: modal dispersion (the ground wave, water wave, Airy phase), two groups of water waves at high frequencies, and the siphon effect which causes abnormally large sound transmission loss at selected frequencies, etc. These observations are used to precisely measure the critical frequency, the Airy frequency, Airy wave velocity, 1st mode group velocity, and to derive the velocities in the LVB layer and in the basement. Using inverted seafloor parameters, the source level-normalized transmission loss and the first mode decay rate in ranges up to 27.66 km, the sound attenuations in the LVB are derived for a frequency range of 13–5000 Hz. [ABSTRACT FROM AUTHOR]

A feature matching method based on the convolutional neural network (named FM-CNN), inspired from matched-field processing (MFP), is proposed to estimate source depth in shallow water. The FM-CNN, trained on the acoustic field replicas of a single source generated by an acoustic propagation model in a range-independent environment, is used to estimate single and multiple source depths in range-independent and mildly range-dependent environments. The performance of the FM-CNN is compared to the conventional MFP method. Sensitivity analysis for the two methods is performed to study the impact of different environmental mismatches (i.e., bottom parameters, water column sound speed profile, and topography) on depth estimation performance in the East China Sea environment. Simulation results demonstrate that the FM-CNN is more robust to the environmental mismatch in both single and multiple source depth estimation than the conventional MFP. The proposed FM-CNN is validated by real data collected from four tracks in the East China Sea experiment. Experimental results demonstrate that the FM-CNN is capable of reliably estimating single and multiple source depths in complex environments, while MFP has a large failure probability due to the presence of strong sidelobes and wide mainlobes. [ABSTRACT FROM AUTHOR]

A multi-range vertical array data processing (MRP) method based on a convolutional neural network (CNN) is proposed to estimate geoacoustic parameters in shallow water. The network input is the normalized sample covariance matrices of the broadband multi-range data received by a vertical line array. Since the geoacoustic parameters (e.g., bottom sound speed, density, and attenuation) have different scales, the multi-task learning is used to estimate these parameters simultaneously. To reduce the influence of the uncertainty of the source position, the training and validation data are composed of the simulation data of different source depths. Simulation results demonstrate that compared with the conventional matched-field inversion (MFI), the CNN with MRP alleviates the coupling between the geoacoustic parameters and is more robust to different source depths in the shallow water environment. Based on the inversion results, better localization performance is achieved when the range-dependent environment is assumed to be a range-independent model. Real data from the East China Sea experiment are used to validate the MRP method. The results show that, compared with the MFI and the CNN with single-range vertical array data processing, the use of geoacoustic parameters from MRP achieves better localization performance. [ABSTRACT FROM AUTHOR]

The structure of a sound speed profile (SSP) in deep water causes refraction of sound rays and Convergence Zones (CZs) of high intensity where the rays focus at shallow depth. Study of sound field characteristics in the CZs has always been the focus of deep-water acoustics research. Many studies have been conducted on sound propagation in different parts of the oceans with different environments and, in this paper, the range and width of CZ is analyzed in the East Indian Ocean (EIO) and the South China Sea (SCS). Through the experimental data collected in different seasons with the propagation conditions change in the EIO and the SCS, we observe that the SSPs in different marine environments have a significant impact on the CZs of deep water. The sound channel mixing layer and isothermal layer have great effect on the CZ ranges. The water depths in the two experimental areas are similar, the range of the first CZ in the EIO is 7–8 km farther than that in the SCS, and the width of the CZs in the EIO is about 2–3 km narrower than that in the SCS. The surface mixed layer and the thermocline affect the CZ width but has little effect on the CZ range when the sound speed at the source and the bottom are practically the same. As the propagation conditions change along the seasons in the EIO, the range of the first CZ is almost the same, but the width of the CZs in the summer is about 2 km narrower than that in the spring. The water depth affects the CZ width but has little effect on the CZ range if the CZs can be formed. The different CZ characteristics between EIO and SCS are explained by both theoretical calculation and numerical simulation. The influence of the SSP structure and water depth on the CZ range are analyzed and the corresponding mechanism is explained. The research results are of great significance for underwater acoustic detection in deep sea. [ABSTRACT FROM AUTHOR]

As sea waves break, a bubble layer forms beneath the sea surface. The bubble scattering affects sound propagation, thus influencing the accuracy of sound field prediction. This paper aims to investigate the effects of bubble scattering on the statistical characteristics of the sound field, the distribution of transmission loss (TL), and the average scattering attenuation in shallow water. A bubble layer model based on the bubble spectrum and a parallel Parabolic Equation (PE) model are combined to calculate and analyse the sound field in the marine environment with bubbles. The effects of the bubble layer are then compared with those of the fluctuant sea surface. The results show that the bubble scattering causes additional energy loss and random fluctuations of the sound field. The TL distribution properties and the average scattering attenuation are related to the wind speed, range, frequency, and source position relative to the negative gradient sound speed layer in shallow water. The comparison demonstrates that the random variation caused by the fluctuation of the sea surface is more significant than that caused by bubbles, and the energy loss caused by bubble scattering is more significant than the fluctuant sea surface under strong wind conditions. [ABSTRACT FROM AUTHOR]

• We used an automatic detection method to locate and track a warm mesoscale eddy from satellite data. • We deployed hydrophone and temperature-sensor arrays in the center and outside the eddy. • We recorded acoustic signals from explosive sources and chirped signals from a transceiver. • We combined the measured hydrological data to create a spatially and temporally dependent empirical model of the eddy. • We compared the simulations with and without an eddy to investigate the effects of the eddy on sound propagation. • We utilized the measured data to analyze the effects of the eddy on transmission losses to the two hydrographic arrays. • We employed recordings of the chirped signals to study the effects of the eddy on sound-energy fluctuations. The spatial and temporal redistribution of sound energy caused by a warm eddy was measured via a multiday experiment conducted in the South China Sea, utilizing two vertical line arrays, a mooring source, and some wide-band explosive sources. In the experiment area, a warm eddy was identified and tracked using an automatic detection method combined with the empirically reconstructed data. During the experimental period, the warm eddy diminished in size and kinetic energy until it vanished. The influence of the warm eddy on sound propagation during its lifespan is comprehensively analyzed and explained. The experimental acoustic results and simulations based on dynamic hydrographic data demonstrate that energy is redistributed both spatially and temporally due to the presence of the warm eddy. In comparison to the environment without an eddy, the first convergence zone disappears and the high-energy region appears in the typical position of the first shadow zone. The oscillating disturbance in energy is mainly affected by the fluctuation of sound speed caused by the warm eddy. [ABSTRACT FROM AUTHOR]

The three-dimensional (3-D) sound speed structure of the ocean is a fundamental environmental element in studying underwater sound propagation modeling and forecasting. The accurate classification of the sound speed fields (SSFs) provides a comprehensive understanding and analysis of the sound propagation pattern. To take advantage of the 3-D structure of the ocean SSFs, this paper presents a quick method based on tensor decomposition for classifying the ocean 3-D SSFs. Utilizing the WOA18 dataset, High Order Iterative Orthogonal (HOOI) decomposition of the 3-D SSFs is executed so as to accurately extract the characteristic information of the SSFs. The Fuzzy C-Means clustering (FCM) method is applied to classify the feature tensors, partitioning of regional categories in different seasons and revealing the typical SSFs structures. By combining the BELLHOP model with analysis of the characteristics of the first convergence zone of each category, it is concluded that there are six categories of SSFs in the Western Pacific Ocean. The SSFs across all categories are primarily latitudinally distributed, featuring distinct sound channel axes and surface sound speed variations. • Effective extraction of tensor decomposition in sound speed fields. • Automatic tensor-based classification of sound speed fields. • Impact of typical sound speed vertical structure on sound transmission in the western Pacific Ocean. [ABSTRACT FROM AUTHOR]

The Arctic Ocean was modeled as an ice–seawater–sediment system, where the ice cover and seawater were assumed to be inhomogeneous solid and liquid, respectively, while the sediment was assumed to be homogeneous liquid. Transfer matrixes relating the displacements and stresses at the lower surface and those at the upper surface for a thin solid layer, and a thin liquid layer were derived. Furthermore, a dispersion equation for waves propagating in the ice-covered Arctic Ocean was derived using the transfer matrix technique. The phase- and group-velocity dispersion curves were obtained by solving the dispersion equation numerically. The results show that the dispersion curves for the Arctic Ocean with ice cover are much more complex than those without ice cover. Except for the new mode, the phase-velocity curve for the n -th (n > 2) mode exhibited a slight distortion, which caused a sharp peak in the group-velocity curve. These peak values, which depend on the order of the mode, may be significantly higher than the speed of sound in seawater. The variation of the ice cover thickness had significant influence on the dispersion curves of the first and second modes. Moreover, the influence of the seawater depth on the dispersion curves were investigated. • The dispersion equation of wave propagation in the ice-covered Arctic Ocean was derived. • The dispersion characteristics of the waves propagating in the Arctic Ocean with and without ice cover were compared with each other. • The influence of the ice cover thickness on the phase- and group velocities of the waves propagating in the Arctic Ocean was analyzed. [ABSTRACT FROM AUTHOR]

The internal waves, especially the internal solitary wave (ISW) trains, cause violent perturbations of sound speed. Sound speed profile (SSPs) facilitates the pre-understanding of the sound field distribution in the experimental sea, therefore, the real-time prediction of SSPs in the presence of ISW trains are of great significance. In this paper, an orthogonal representation of SSPs that considers the background field (background SSP) variation is proposed. Based on the statistical characteristics of time-series SSPs, high-precision SSP prediction is realized by the long short-term memory recurrent neural network (LSTM). The prediction accuracy is demonstrated with the SSP data from an experiment in the South China Sea, and the mean RMSE of SSP prediction is reduced to about 1 m/s. • The SSP can be expressed as a random process consisting of the background field and the sound speed disturbance. • The background-field coefficient is approximately linearly correlated with the first EOF coefficient, and roughly parabolically correlated with the second EOF coefficient in the presence of internal solitary wave trains. • Compared with the direct method (predicting each EOF coefficient respectively), the indirect method (combining with the statistical characteristics of EOF coefficients) has higher accuracy in the SSP time-series prediction. [ABSTRACT FROM AUTHOR]

• A feature-compressed multi-task learning U-Net with CBAM (MTL-UNET-CBAM) for shallow-water source localization with the presence of internal waves is constructed. • CBAM improves range localization performance of MTL-UNET, but has no significant improvement on depth estimation. • MTL-UNET-CBAM has higher localization robustness and computation speed then CMFP. • Applying DTL to MTL-UNET-CBAM significantly improve the localization performance of MTL-UNET-CBAM on both range and depth estimation. The spatiotemporal variation of sound speed profile caused by internal waves usually causes the mismatch in the Matched-Field Processing (MFP) source localization. A feature-compressed multi-task learning U-Net with Convolutional Block Attention Module (MTL-UNET-CBAM) is proposed to estimate the range and depth of underwater sources in the South China Sea environment with the presence of internal waves. To handle the mismatch caused by internal waves, the temperature sensor chain data are used to reconstruct the two-dimensional sound speed profiles (2D-SSPs) based on the 2D advection model. Then, 2D-SSPs are used to generate the training set with the parabolic equation method. Sensitivity analysis is investigated to examine the effects of sound speed profile mismatch on the source localization performance. The simulation result shows the higher robustness of MTL-UNET-CBAM to the sound speed profile mismatch compared with the conventional Matched-Field Processing (CMFP) method. Experiment data in the South China Sea also used to validate the source localization performance of MTL-UNET-CBAM. [ABSTRACT FROM AUTHOR]