Influence of mesoscale eddies and frontal zones on sound propagation at the Northwest Pacific Ocean

Akulichev, V. A.; Bugaeva, L. K.; Morgunov, Yu. N.; Solovjev, A. A.

doi:10.1121/1.4708575

Cited by 3 publications

(2 citation statements)

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“…They have explored the relationship between eddies and acoustic propagation through observations and experiments. Akulichev, Bugaeva (Akulichev et al, 2012) reported the results of an acoustic survey in the KE region of the NW Pacific Ocean. They found that a mesoscale eddy significantly affected the horizontal propagation of acoustic signals through a towed source at a depth of 100 m. Liu, Piao (Liu et al, 2021) detected and tracked a mesoscale eddy in the western North Pacific Ocean, studying the eddy strength, oceanographic, and acoustic characteristics.…”

Section: Introductionmentioning

confidence: 99%

A physics-informed machine learning approach for predicting acoustic convergence zone features from limited mesoscale eddy data

Xu,

Zhang,

et al. 2024

Front. Mar. Sci.

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Mesoscale eddies are prevalent mesoscale phenomena in the oceans that alter the thermohaline structure of the ocean, significantly impacting acoustic propagation patterns. Accurately predicting acoustic convergence zone features has become an urgent task, especially when data are limited in deep-sea mesoscale eddy environments. This study utilizes physics-informed machine learning to identify and predict the acoustic convergence zone features of mesoscale eddies under limited data conditions. Initially, a method based on convex hull ratio was utilized to identify mesoscale eddies from the JCOPE2M reanalysis dataset and AVISO data in the Kuroshio‐Oyashio Extension. Subsequently, by integrating physical models and ray acoustics, relevant features of mesoscale eddies and convergence zones are extracted. Then, K-fold cross-validation and sparrow search algorithms are employed to select the optimal machine learning algorithm, ensuring high model accuracy. The resulting model requires only a thermohaline profile near the eddy center and sea surface height to predict convergence zone features within the mesoscale eddy environment, achieving a MAE of approximately 1.00 km and an accuracy (within 3 km) exceeding 95%. Additionally, leveraging physics-informed machine learning methods contributes to a maximum reduction of 0.82 km in MAE and an improvement in accuracy by 2.80% to 11.92% compared to models without physical information input. Finally, the model’s validity and reliability in the actual ocean environment are verified by cross-validating it with data from various sea regions" in bright yellow and Argo profiling float data. The findings provide novel insights into acoustic propagation in mesoscale eddy environments and subsequent ocean acoustic research.

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Section: Introductionmentioning

confidence: 99%

A physics-informed machine learning approach for predicting acoustic convergence zone features from limited mesoscale eddy data

Xu,

Zhang,

et al. 2024

Front. Mar. Sci.

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“…The 3D structures of eddies can be hardly determined, but are generally acquired by constructing ideal eddy structure models [1][2][3][4], the effects of eddies on sound propagation are analyzed together with sound propagation models. Also a few experiments have been conducted to investigate the effects of mesoscale eddies on sound propagation [5][6][7]. In all, the existing studies about the effects of mesoscale eddies on sound propagation are mainly based on construction of eddy structure models, but the accuracy of models should be further validated.…”

Section: Introductionmentioning

confidence: 99%

Analysis the Influence of a Mesoscale Cold Eddy on Underwater Sound Propagation in the East of Luzon Strait

Ruan¹,

Yan-ming²,

Wen³

et al. 2019

Recent Developments on Information and Communication Technology (ICT) Engineering

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1Based on the data of Argo float, satellite altimeter merged data, and HYCOM model output, we analyzed the temporal-spatial variations of a mesoscale cold eddy in the east of Luzon Strait, and used FOR3D model to simulate the effects of this mesoscale cold eddy on sound propagation, Calculated out the transmission loss(TL) with or without the cold eddy. Result show that the presence of cold eddy led to significant variation of sound propagation. Typically, the distances of convergence zones were narrowed several kilometers, and the gain of convergence zones were inhibited about 2 dB.

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A mesoscale eddy reconstruction method based on generative adversarial networks

Ma,

Zhang,

et al. 2024

Front. Mar. Sci.

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Mesoscale eddies are phenomena that widely exist in the ocean and have a significant impact on the ocean’s temperature and salt structure, as well as on acoustic propagation effects. Currently, utilizing the limited data on mesoscale eddy environments for refined acoustic field reconstruction in offshore conditions at mid-to-far-ocean distances is an urgent problem that needs to be addressed. In this paper, we propose a mesoscale eddy reconstruction method (EddyGAN) based on the generative adversarial network (GAN) model which is inspired by the concept of global localization. We adopt a hybrid algorithm for eddy identification using JCOPE2M high-resolution reanalysis data and Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) satellite altimeter data to extract mesoscale eddy sound speed profile (SSP) sample data, and then apply the EddyGAN model to train this dataset and perform mesoscale eddy acoustic field reconstruction. We also propose an evaluation method for mesoscale eddy acoustic field reconstruction that uses RMSE, SSIM, and convergence zone (CZ) accuracy based on World Ocean Atlas (WOA) climate state data completion as indicators. The reconstruction result of this model achieves an RMSE of 1.7 m/s, an SSIM of 0.77, and an average CZ accuracy of over 70%. This method better characterizes the mesoscale eddy sound field than the native GAN and other reconstruction methods, improves the accuracy of mesoscale eddy acoustic field reconstruction, and provides superior performance, offering significant reference value for mesoscale eddy reconstruction technology and subsequent ocean acoustic research.

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Influence of mesoscale eddies and frontal zones on sound propagation at the Northwest Pacific Ocean

Cited by 3 publications

References 0 publications

A physics-informed machine learning approach for predicting acoustic convergence zone features from limited mesoscale eddy data

A physics-informed machine learning approach for predicting acoustic convergence zone features from limited mesoscale eddy data

Analysis the Influence of a Mesoscale Cold Eddy on Underwater Sound Propagation in the East of Luzon Strait

A mesoscale eddy reconstruction method based on generative adversarial networks

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