Modelling Convergence Zone Propagation Under the Influence of Arctic Front

Xue, Ruyi; Yang, Yanming; Weng, Jin-bao; Wen, Hao; Chen, Hongxia; Lin, Lina

doi:10.1109/coa50123.2021.9520075

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…The propagation loss experienced notable variation as the internal wave traversed the CZ, and the uncertainty in the acoustic field grew with the CZ range. In a study based on simulations using in-situ observation data from the Eighth Scientific Expedition of China, Xue et al (2021) observed that Arctic fronts modify CZ characteristics, influencing their distance and width within a specific range. Additionally, Xiao et al (2021) developed a theoretical model of the acoustic field under the influence of ocean mesoscale eddies using finite element analysis.…”

Section: Introductionmentioning

confidence: 99%

Machine learning–based feature prediction of convergence zones in ocean front environments

Xu,

Zhang,

Wang

2024

Front. Mar. Sci.

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The convergence zone holds significant importance in deep-sea underwater acoustic propagation, playing a pivotal role in remote underwater acoustic detection and communication. Despite the adaptability and predictive power of machine learning, its practical application in predicting the convergence zone remains largely unexplored. This study aimed to address this gap by developing a high-resolution ocean front-based model for convergence zone prediction. Out of 24 machine learning algorithms tested through K-fold cross-validation, the multilayer perceptron–random forest hybrid demonstrated the highest accuracy, showing its superiority in predicting the convergence zone within a complex ocean front environment. The research findings emphasized the substantial impact of ocean fronts on the convergence zone’s location concerning the sound source. Specifically, they highlighted that in relatively cold (or warm) water, the intensity of the ocean front significantly influences the proximity (or distance) of the convergence zone to the sound source. Furthermore, among the input features, the turning depth emerged as a crucial determinant, contributing more than 25% to the model’s effectiveness in predicting the convergence zone’s distance. The model achieved an accuracy of 82.43% in predicting the convergence zone’s distance with an error of less than 1 km. Additionally, it attained a 77.1% accuracy in predicting the convergence zone’s width within a similar error range. Notably, this prediction model exhibits strong performance and generalizability, capable of discerning evolving trends in new datasets when cross-validated using in situ observation data and information from diverse sea areas.

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Section: Introductionmentioning

confidence: 99%

Machine learning–based feature prediction of convergence zones in ocean front environments

Xu,

Zhang,

Wang

2024

Front. Mar. Sci.

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Ocean Fronts and Their Acoustic Effects: A Review

Liu

Zhang²,

Chen³

et al. 2022

JMSE

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As one of the widespread physical phenomena in the global ocean system, the ocean front has a very important influence on underwater sound propagation. Firstly, this paper systematically reviews several methods for the detection of ocean fronts in the past decades, including traditional oceanographic methods, artificial intelligence methods, and acoustic methods, highlighting the advantages and disadvantages of each method. Next, some modeling studies of ocean fronts are reported in this paper. Based on the above research, we pay more attention to research progress on the acoustic effects of ocean fronts, including simulation analysis and experimental research, which has also been the focus of underwater acousticians for a long time. In addition, this paper looks forward to the future development direction of this field, which can provide good guidance for the study of ocean fronts and their acoustic effects in the future.

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Modelling Convergence Zone Propagation Under the Influence of Arctic Front

Cited by 2 publications

References 14 publications

Machine learning–based feature prediction of convergence zones in ocean front environments

Machine learning–based feature prediction of convergence zones in ocean front environments

Ocean Fronts and Their Acoustic Effects: A Review

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