Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (12)

Search Parameters:
Keywords = ocean acoustic tomography

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
23 pages, 1302 KiB  
Article
Deep Learning-Enhanced Ocean Acoustic Tomography: A Latent Feature Fusion Framework for Hydrographic Inversion with Source Characteristic Embedding
by Jiawen Zhou, Zikang Chen, Yongxin Zhu and Xiaoying Zheng
Information 2025, 16(8), 665; https://doi.org/10.3390/info16080665 - 4 Aug 2025
Viewed by 3
Abstract
Ocean Acoustic Tomography (OAT) is an important marine remote sensing technique used for inverting large-scale ocean environmental parameters, but traditional methods face challenges in computational complexity and environmental interference. This paper proposes a causal analysis-driven AI FOR SCIENCE method for high-precision and rapid [...] Read more.
Ocean Acoustic Tomography (OAT) is an important marine remote sensing technique used for inverting large-scale ocean environmental parameters, but traditional methods face challenges in computational complexity and environmental interference. This paper proposes a causal analysis-driven AI FOR SCIENCE method for high-precision and rapid inversion of oceanic hydrological parameters in complex underwater environments. Based on the open-source VTUAD (Vessel Type Underwater Acoustic Data) dataset, the method first utilizes a fine-tuned Paraformer (a fast and accurate parallel transformer) model for precise classification of sound source targets. Then, using structural causal models (SCM) and potential outcome frameworks, causal embedding vectors with physical significance are constructed. Finally, a cross-modal Transformer network is employed to fuse acoustic features, sound source priors, and environmental variables, enabling inversion of temperature and salinity in the Georgia Strait of Canada. Experimental results show that the method achieves accuracies of 97.77% and 95.52% for temperature and salinity inversion tasks, respectively, significantly outperforming traditional methods. Additionally, with GPU acceleration, the inference speed is improved by over sixfold, aimed at enabling real-time Ocean Acoustic Tomography (OAT) on edge computing platforms as smart hardware, thereby validating the method’s practicality. By incorporating causal inference and cross-modal data fusion, this study not only enhances inversion accuracy and model interpretability but also provides new insights for real-time applications of OAT. Full article
(This article belongs to the Special Issue Advances in Intelligent Hardware, Systems and Applications)
Show Figures

Figure 1

12 pages, 5578 KiB  
Article
Transformation of Hydroacoustic Energy into Seismoacoustic Energy at 22 Hz in Medium Depth- and Deep-Sea Conditions
by Grigory Dolgikh, Mikhail Bolsunovskii, Sergey Budrin, Stanislav Dolgikh, Mikhail Ivanov, Vladimir Ovcharenko, Aleksandr Pivovarov, Aleksandr Samchenko, Vladimir Chupin and Igor Yaroshchuk
Appl. Sci. 2025, 15(1), 267; https://doi.org/10.3390/app15010267 - 30 Dec 2024
Viewed by 759
Abstract
This work is devoted to an experiment studying the regularities of the propagation of hydroacoustic low-frequency signals in the conditions of the sea at intermediate depth and deep in terms of their transformation into vibrations in the upper layer of the Earth’s crust. [...] Read more.
This work is devoted to an experiment studying the regularities of the propagation of hydroacoustic low-frequency signals in the conditions of the sea at intermediate depth and deep in terms of their transformation into vibrations in the upper layer of the Earth’s crust. This experiment belongs to the field of acoustic tomography and is aimed at solving the problems of non-contact methods for studying the geological structure of the shelf areas of the World Ocean. The novelty and uniqueness of the work lies in the use of a harmonic low-frequency hydroacoustic signal with a frequency of 22 Hz of high power, capable of creating Rayleigh surface waves at the “water–bottom” interface. The surface waves propagating at the bottom are registered by a coastal laser-interference measuring system capable of recording deformations in the upper crustal layer with an accuracy of 0.01 nm. The experimental results showed that the radiated hydroacoustic energy is not localized in the liquid half-space and propagates predominantly according to the law close to spherical divergence, even when the shelf depth is comparable to the wavelength of the radiated signal. Full article
(This article belongs to the Section Marine Science and Engineering)
Show Figures

Figure 1

16 pages, 955 KiB  
Article
Automatically Differentiable Higher-Order Parabolic Equation for Real-Time Underwater Sound Speed Profile Sensing
by Mikhail Lytaev
J. Mar. Sci. Eng. 2024, 12(11), 1925; https://doi.org/10.3390/jmse12111925 - 28 Oct 2024
Cited by 2 | Viewed by 1032
Abstract
This paper is dedicated to the acoustic inversion of the vertical sound speed profiles (SSPs) in the underwater marine environment. The method of automatic differentiation is applied for the first time in this context. Representing the finite-difference Padé approximation of the propagation operator [...] Read more.
This paper is dedicated to the acoustic inversion of the vertical sound speed profiles (SSPs) in the underwater marine environment. The method of automatic differentiation is applied for the first time in this context. Representing the finite-difference Padé approximation of the propagation operator as a computational graph allows for the analytical computation of the gradient with respect to the SSP directly within the numerical scheme. The availability of the gradient, along with the high computational efficiency of the numerical method used, enables rapid inversion of the SSP based on acoustic measurements from a hydrophone array. It is demonstrated that local optimization methods can be effectively used for real-time sound speed inversion. Comparative analysis with existing methods shows the significant superiority of the proposed method in terms of computation speed. Full article
(This article belongs to the Section Ocean Engineering)
Show Figures

Figure 1

17 pages, 4560 KiB  
Article
Monitoring Thermal Exchange of Hot Water Mass via Underwater Acoustic Tomography with Inversion and Optimization Method
by Shijie Xu, Fengyuan Yu, Xiaofei Zhang, Yiwen Diao, Guangming Li and Haocai Huang
Remote Sens. 2024, 16(6), 1105; https://doi.org/10.3390/rs16061105 - 21 Mar 2024
Cited by 2 | Viewed by 1444
Abstract
Thermal exchange of underwater water mass caused by marine heat wave is a hot point of research recently. In particular, because the water temperature observation along hot water mass transportation is hard work. Acoustic tomography is an advanced method to measure water temperature [...] Read more.
Thermal exchange of underwater water mass caused by marine heat wave is a hot point of research recently. In particular, because the water temperature observation along hot water mass transportation is hard work. Acoustic tomography is an advanced method to measure water temperature variations via sound signal transmission with multi-station network sensing. The 5 kHz frequency acoustic tomography used for observing water temperature variations caused by ocean heat waves is interesting work. In this paper, the numerical simulation of hot water mass is completed first, then floatation and diffusion of hot water mass in a simulation are monitored by acoustic tomography. A new inversion optimization method is proposed to obtain hot water mass transportation variations at two-dimensional temperature vertical profile. The proposed inversion method adds a regularized mode matrix and the optimization method adds the model correlation matrix to improve the results quality. The accuracy of inversion optimization results is compared and discussed, where the mean temperature error is less than 0.4 °C. Sensing water temperature variation of marine heat waves is verified via acoustic signal transmission and improved inversion optimization method. The water dynamical process observation is an application of acoustic tomography, which can be further used observe underwater environmental characteristics. Full article
Show Figures

Graphical abstract

16 pages, 8323 KiB  
Technical Note
Prediction of Water Temperature Based on Graph Neural Network in a Small-Scale Observation via Coastal Acoustic Tomography
by Pan Xu, Shijie Xu, Kequan Shi, Mingyu Ou, Hongna Zhu, Guojun Xu, Dongbao Gao, Guangming Li and Yun Zhao
Remote Sens. 2024, 16(4), 646; https://doi.org/10.3390/rs16040646 - 9 Feb 2024
Cited by 2 | Viewed by 1698
Abstract
Coastal acoustic tomography (CAT) is a remote sensing technique that utilizes acoustic methodologies to measure the dynamic characteristics of the ocean in expansive marine domains. This approach leverages the speed of sound propagation to derive vital ocean parameters such as temperature and salinity [...] Read more.
Coastal acoustic tomography (CAT) is a remote sensing technique that utilizes acoustic methodologies to measure the dynamic characteristics of the ocean in expansive marine domains. This approach leverages the speed of sound propagation to derive vital ocean parameters such as temperature and salinity by inversely estimating the acoustic ray speed during its traversal through the aquatic medium. Concurrently, analyzing the speed of different acoustic waves in their round-trip propagation enables the inverse estimation of dynamic hydrographic features, including flow velocity and directional attributes. An accurate forecasting of inversion answers in CAT rapidly contributes to a comprehensive analysis of the evolving ocean environment and its inherent characteristics. Graph neural network (GNN) is a new network architecture with strong spatial modeling and extraordinary generalization. We proposed a novel method: employing GraphSAGE to predict inversion answers in OAT, using experimental datasets collected at the Huangcai Reservoir for prediction. The results show an average error 0.01% for sound speed prediction and 0.29% for temperature predictions along each station pairwise. This adequately fulfills the real-time and exigent requirements for practical deployment. Full article
(This article belongs to the Special Issue Recent Advances in Underwater and Terrestrial Remote Sensing)
Show Figures

Figure 1

18 pages, 6502 KiB  
Article
A Comparative Study of the Temperature Change in a Warm Eddy Using Multisource Data
by Xiaohong Yang, Yanming Yang and Jinbao Weng
Remote Sens. 2023, 15(6), 1650; https://doi.org/10.3390/rs15061650 - 18 Mar 2023
Cited by 1 | Viewed by 2099
Abstract
An ocean acoustic tomography (OAT) experiment conducted in the northern South China Sea in 2021 measured a month-long record of acoustic travel times along paths of over one hundred kilometers in range. A mesoscale eddy passed through the experimental region during the deployment [...] Read more.
An ocean acoustic tomography (OAT) experiment conducted in the northern South China Sea in 2021 measured a month-long record of acoustic travel times along paths of over one hundred kilometers in range. A mesoscale eddy passed through the experimental region during the deployment of four acoustic moorings, providing unique OAT data for examining the deep temperature change in the eddy and for comparison with the Hybrid Coordinate Ocean Model (HYCOM) data. The existence of the eddy is first confirmed by the merged sea level anomaly (MSLA) image and HYCOM data and it can exceed the depth of the sound channel axis. The temperature changes measured by temperature and depth (TD)/conductivity–temperature–depth (CTD) loggers and by the OAT sound speed are in accordance with those reflected on the MSLA image during the movement of the eddy. However, the eddy movement prompted by temperature changes in the HYCOM data is different from that measured by TD/CTD. The modeled eddy intensity is at least two times less than the measured eddy intensity. At the sound channel axis depth, a factor of approximately 4.17 ms−1 °C−1 can be used to scale between sound speed and temperature. The transmission/reception path-averaged temperature of the eddy derived from the OAT-computed sound speed at the depth of the sound channel axis is five times greater than those in the HYCOM data. OAT is feasible as a tool to study mesoscale eddy properties in the deep ocean, while HYCOM data are not accurate enough for this mesoscale eddy at the sound channel axis depth. It is suggested that the model be refined by the OAT path-averaged temperature as constraints when the HYCOM data capture the mesoscale eddies. Full article
Show Figures

Graphical abstract

20 pages, 6682 KiB  
Article
Physics-Guided Reduced-Order Representation of Three-Dimensional Sound Speed Fields with Ocean Mesoscale Eddies
by Xingyu Ji, Lei Cheng and Hangfang Zhao
Remote Sens. 2022, 14(22), 5860; https://doi.org/10.3390/rs14225860 - 19 Nov 2022
Cited by 4 | Viewed by 2231
Abstract
Ocean mesoscale eddies have an important role in the ocean and affect the underwater sound speed field (SSF). Many physical models have been proposed for mesoscale eddy three-dimensional (3D) structure analysis and construction. Here, we propose a model for the reduced-order representation of [...] Read more.
Ocean mesoscale eddies have an important role in the ocean and affect the underwater sound speed field (SSF). Many physical models have been proposed for mesoscale eddy three-dimensional (3D) structure analysis and construction. Here, we propose a model for the reduced-order representation of 3D SSF with ocean mesoscale eddies. Particularly, the radial basis functions (RBFs), which are guided by the universal physics model of mesoscale eddy in horizontal dimensions, are employed. RBF and empirical orthogonal function (EOF) are used as basis functions for 3D representation. The proposed method is an approximation of the classical Gaussian eddy model in the first-order form. Simulation results confirm the reduced-order representation performance and effectiveness in reconstruction using 136 days of HYCOM data in the northwestward of the South China Sea with a warm eddy and a cold eddy. The proposed RBF + EOF method roughly halves the number of coefficients for mesoscale eddy representation, compared with classical methods. The reduced-order representation method can be utilized in ocean acoustic tomography and acoustic remote sensing in a mesoscale area. Full article
(This article belongs to the Special Issue Recent Advancements in Remote Sensing for Ocean Current)
Show Figures

Graphical abstract

19 pages, 4984 KiB  
Article
Synchronous Assimilation of Tidal Current-Related Data Obtained Using Coastal Acoustic Tomography and High-Frequency Radar in the Xiangshan Bay, China
by Ze-Nan Zhu, Xiao-Hua Zhu, Weibing Guan, Chuanzheng Zhang, Minmo Chen, Zhao-Jun Liu, Min Wang, Hua Zheng, Juntian Chen, Longhao Dai, Zhenyi Cao, Qi Chen and Arata Kaneko
Remote Sens. 2022, 14(13), 3235; https://doi.org/10.3390/rs14133235 - 5 Jul 2022
Cited by 3 | Viewed by 2994
Abstract
To accurately reconstruct large-area three-dimensional current fields in coastal regions, simultaneous observations with ten coastal acoustic tomography (CAT) stations and two high-frequency radar (HFR) stations were performed in the Xiangshan Bay (XSB) on 4–5 December 2020. The section-averaged velocity that was observed by [...] Read more.
To accurately reconstruct large-area three-dimensional current fields in coastal regions, simultaneous observations with ten coastal acoustic tomography (CAT) stations and two high-frequency radar (HFR) stations were performed in the Xiangshan Bay (XSB) on 4–5 December 2020. The section-averaged velocity that was observed by CAT and the radial velocity that was observed by HFR were, for the first time, synchronously assimilated into a three-dimensional barotropic ocean model. Compared with acoustic Doppler current profile data, the velocities of the model assimilating both CAT and HFR data had the highest accuracy according to root mean square differences (RMSDs), ranging from 0.05 to 0.08 m/s for all the vertical layers. For the models individually assimilating CAT and HFR, the values in the vertical layers ranged from 0.07 to 0.12 m/s and 0.08 to 0.13 m/s, respectively. A harmonic analysis of the model grid data showed that the spatial mean amplitudes of the M2, M4, and residual currents were 0.66, 0.14, and 0.09 m/s, respectively. Furthermore, the standing wave characteristics of the M2 current and M4 associated-oscillation in the inner XSB, mouth of the Xiangshan fjord, were better captured by the model assimilating both CAT and HFR. Our study demonstrates the advances in three-dimensional tidal current analysis using a model that assimilates both CAT and HFR data, especially in regions with complex coastal geography. Full article
Show Figures

Figure 1

22 pages, 14881 KiB  
Article
Layer-Averaged Water Temperature Sensing in a Lake by Acoustic Tomography with a Focus on the Inversion Stratification Mechanism
by Shijie Xu, Zhao Xue, Xinyi Xie, Haocai Huang and Guangming Li
Sensors 2021, 21(22), 7448; https://doi.org/10.3390/s21227448 - 9 Nov 2021
Cited by 8 | Viewed by 2262
Abstract
Continuous sensing of water parameters is of great importance to fluid dynamic progress study in oceans, coastal areas and inland waters. The acoustic tomography technique can perform water temperature field measurements horizontally and vertically using sound wave travel information. The layer-averaged water temperature [...] Read more.
Continuous sensing of water parameters is of great importance to fluid dynamic progress study in oceans, coastal areas and inland waters. The acoustic tomography technique can perform water temperature field measurements horizontally and vertically using sound wave travel information. The layer-averaged water temperature can also be measured with the acoustic tomography method. However, investigations focusing on the stratified mechanism, which consists of stratification form and its influence on inversion error, are seldom performed. In this study, an acoustic tomography experiment was carried out in a reservoir along two vertical slices to observe the layer-averaged water temperature. Specifically, multi-path sound travel information is identified through ray tracing using high-precision topography data obtained via a ship-mounted ADCP during the experiment. Vertical slices between sound stations are divided into different layers to study layer division inversion methods in different preset types. The inversion method is used to calculate the average water temperature and inversion temperature error of every layer. Different layer methods are studied with a comparison of results. The layer division principle studied in this paper can be used for layer-averaged water temperature sensing with multi-path sound transmission information. Full article
(This article belongs to the Special Issue Sensors and Sensor Systems for Hydrodynamics)
Show Figures

Figure 1

15 pages, 2764 KiB  
Article
Deep Learning Convolutional Neural Network Applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement
by Kangkang Jin, Jian Xu, Zichen Wang, Can Lu, Long Fan, Zhongzheng Li and Jiaxin Zhou
J. Mar. Sci. Eng. 2021, 9(7), 755; https://doi.org/10.3390/jmse9070755 - 8 Jul 2021
Cited by 3 | Viewed by 3154
Abstract
Warm current has a strong impact on the melting of sea ice, so clarifying the current features plays a very important role in the Arctic sea ice coverage forecasting study field. Currently, Arctic acoustic tomography is the only feasible method for the large-range [...] Read more.
Warm current has a strong impact on the melting of sea ice, so clarifying the current features plays a very important role in the Arctic sea ice coverage forecasting study field. Currently, Arctic acoustic tomography is the only feasible method for the large-range current measurement under the Arctic sea ice. Furthermore, affected by the high latitudes Coriolis force, small-scale variability greatly affects the accuracy of Arctic acoustic tomography. However, small-scale variability could not be measured by empirical parameters and resolved by Regularized Least Squares (RLS) in the inverse problem of Arctic acoustic tomography. In this paper, the convolutional neural network (CNN) is proposed to enhance the prediction accuracy in the Arctic, and especially, Gaussian noise is added to reflect the disturbance of the Arctic environment. First, we use the finite element method to build the background ocean model. Then, the deep learning CNN method constructs the non-linear mapping relationship between the acoustic data and the corresponding flow velocity. Finally, the simulation result shows that the deep learning convolutional neural network method being applied to Arctic acoustic tomography could achieve 45.87% accurate improvement than the common RLS method in the current inversion. Full article
(This article belongs to the Section Physical Oceanography)
Show Figures

Figure 1

20 pages, 14653 KiB  
Article
Continuous Sensing of Water Temperature in a Reservoir with Grid Inversion Method Based on Acoustic Tomography System
by Haocai Huang, Shijie Xu, Xinyi Xie, Yong Guo, Luwen Meng and Guangming Li
Remote Sens. 2021, 13(13), 2633; https://doi.org/10.3390/rs13132633 - 5 Jul 2021
Cited by 16 | Viewed by 3430
Abstract
The continuous sensing of water parameters is of great importance to the study of dynamic processes in the ocean, coastal areas, and inland waters. Conventional fixed-point and ship-based observing systems cannot provide sufficient sampling of rapidly varying processes, especially for small-scale phenomena. Acoustic [...] Read more.
The continuous sensing of water parameters is of great importance to the study of dynamic processes in the ocean, coastal areas, and inland waters. Conventional fixed-point and ship-based observing systems cannot provide sufficient sampling of rapidly varying processes, especially for small-scale phenomena. Acoustic tomography can achieve the sensing of water parameter variations over time by continuously using sound wave propagation information. A multi-station acoustic tomography experiment was carried out in a reservoir with three sound stations for water temperature observation. Specifically, multi-path propagation sound waves were identified with ray tracing using high-precision topography data obtained with ship-mounted ADCP. A new grid inverse method is proposed in this paper for water temperature profiling along a vertical slice. The progression of water temperature variation in three vertical slices between acoustic stations was mapped by solving an inverse problem. The reliability and adaptability of the grid method developed in this research are verified by comparison with layer-averaged water temperature results. The grid method can be further developed for the 3D mapping of water parameters over time, especially in small-scale water areas, where sufficient multi-path propagation sound waves can be obtained. Full article
Show Figures

Figure 1

16 pages, 5028 KiB  
Article
Mapping Small-Scale Horizontal Velocity Field in Panzhinan Waterway by Coastal Acoustic Tomography
by Haocai Huang, Xinyi Xie, Yong Guo and Hangzhou Wang
Sensors 2020, 20(19), 5717; https://doi.org/10.3390/s20195717 - 8 Oct 2020
Cited by 3 | Viewed by 3162
Abstract
Mapping small-scale high-precision velocity fields is of great significance to oceanic environment research. Coastal acoustic tomography (CAT) is a frontier technology used to observe large-scale velocity field in the horizontal slice. Nonetheless, it is difficult to observe the velocity field using the CAT [...] Read more.
Mapping small-scale high-precision velocity fields is of great significance to oceanic environment research. Coastal acoustic tomography (CAT) is a frontier technology used to observe large-scale velocity field in the horizontal slice. Nonetheless, it is difficult to observe the velocity field using the CAT in small-scale areas, specifically where the flow field is complex such as ocean ranch and artificial upwelling areas. This paper conducted a sound transmission experiment using four 50 kHz CAT systems in the Panzhinan waterway. Notably, sound transmission based on the round-robin method was recommended for small-scale CAT observation. The travel time between stations, obtained by correlation of raw data, was applied to reconstruct the horizontal velocity fields using Tapered Least Square inversion. The minimum net volume transport was 8.7 m3/s at 12:32, 1.63% of the total inflow volume transport indicating that the observational errors were acceptable. The relative errors of the range-average velocity calculated by differential travel time were 1.54% (path 2) and 0.92% (path 6), respectively. Moreover, the inversion velocity root-mean-square errors (RMSEs) were 0.5163, 0.1494, 0.2103, 0.2804 and 0.2817 m/s for paths 1, 2, 3, 4 and 6, respectively. The feasibility and acceptable accuracy of the CAT method in the small-scale velocity profiling measurement were validated. Furthermore, a three-dimensional (3-D) velocity field mapping should be performed with combined analysis in horizontal and vertical slices. Full article
(This article belongs to the Special Issue Intelligent Sound Measurement Sensor and System)
Show Figures

Figure 1

Back to TopTop