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Ming Li, Yuhang Liu, Yiyuan Sun, Kefeng Liu. Quantitative analysis and prediction of the sound field convergence zone in mesoscale eddy environment based on data mining methods[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-024-2328-5
Citation: Ming Li, Yuhang Liu, Yiyuan Sun, Kefeng Liu. Quantitative analysis and prediction of the sound field convergence zone in mesoscale eddy environment based on data mining methods[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-024-2328-5

Quantitative analysis and prediction of the sound field convergence zone in mesoscale eddy environment based on data mining methods

doi: 10.1007/s13131-024-2328-5
Funds:  The National Natural Science Foundation of China under contract Nos 41875061 and 41775165.
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  • Corresponding author: E-mail: mingli152@163.com
  • Received Date: 2023-12-08
  • Accepted Date: 2024-04-03
  • Available Online: 2024-05-13
  • The mesoscale eddy (ME) has a significant influence on the convergence effect in deep-sea acoustic propagation. This paper use statistical approaches to express quantitative relationships between the ME conditions and convergence zone (CZ) characteristics. Based on the Gaussian vortex model, we construct various sound propagation scenarios under different eddy conditions, and carry out sound propagation experiments to obtain simulation samples. With a large number of samples, we first adopt the unified regression to set up analytic relationships between eddy conditions and CZ parameters. The sensitivity of eddy indicators to the CZ is quantitatively analyzed. Then, we adopt the machine learning (ML) algorithms to establish prediction models of CZ parameters by exploring the nonlinear relationships between multiple ME indicators and CZ parameters. Through the research, we can express the influence of ME on the CZ quantitatively, and achieve the rapid prediction of CZ parameters in ocean eddies. The prediction accuracy (R) of the CZ distance (mean R: 0.9815) is obviously better than that of the CZ width (mean R: 0.8728). Among the three ML algorithms, Gradient Boosting Decision Tree has the best prediction ability (root mean square error (RMSE): 0.136), followed by Random Forest (RMSE: 0.441) and Extreme Learning Machine (RMSE: 0.518).
  • These authors contributed equally to this work.
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