Integration of machine learning and physical models for the reconstruction of mesoscale eddy sound speed profile

Li Hongchen; Li Ming; Wang Penghao; Mao Kefeng; Zhu Yuhang; Liu Yuhang

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Li Hongchen，Li Ming，Wang Penghao, et al. Integration of machine learning and physical models for the reconstruction of mesoscale eddy sound speed profile[J]. Haiyang Xuebao，2025, 47(x)：1–15

Citation:

Li Hongchen，Li Ming，Wang Penghao, et al. Integration of machine learning and physical models for the reconstruction of mesoscale eddy sound speed profile[J]. Haiyang Xuebao，2025, 47(x)：1–15

Citation:

Li Hongchen，Li Ming，Wang Penghao, et al. Integration of machine learning and physical models for the reconstruction of mesoscale eddy sound speed profile[J]. Haiyang Xuebao，2025, 47(x)：1–15

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Integration of machine learning and physical models for the reconstruction of mesoscale eddy sound speed profile

1.
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
2.
College of Frontier Interdisciplinary Sciences, National University of Defense Technology, Nanjing 211101, China
3.
College of Marine Science and Ecological Environment, Shanghai Ocean University, Shanghai 201306 , China
4.
College of Marine Technology and Surveying, Jiangsu Ocean University, Lianyungang 222005, China

Received Date: 2025-02-16
Rev Recd Date: 2025-04-18

Available Online: 2025-05-13

Abstract

Abstract

To address the issue of complex sound speed profile (SSP) structures and significantly large reconstruction errors within mesoscale eddies, this study proposes the PIRF-DEN model by integrating multi-source satellite remote sensing data and Argo profiles with a random forest algorithm and a unified mesoscale eddy structure model. By utilizing sea surface temperature, height anomaly, salinity, density, and other environmental parameters at the sea surface, along with Argo density data as inputs, the model establishes a "surface-to-underwater" sound speed mapping relationship. Additionally, it reconstructs the density field within eddies based on the unified mesoscale eddy structure model and incorporates both surface environmental parameters and reconstructed eddy densities into the mapping relationship to reconstruct the SSP within the eddies. The results demonstrate that the PIRF-DEN model markedly enhances the accuracy of SSP reconstruction, reducing the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to 0.8324 m/s and 1.3869 m/s, respectively. This represents an 87.3% and 83.7% reduction compared to the traditional sEOF-r method. Furthermore, the sound speed reconstruction accuracy and stability of the PIRF-DEN model surpass those of existing models.
- sound speed reconstruction,
- random forest,
- unified structure model of mesoscale eddy,
- vertical density

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References(37)

References

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