A Novel Method for Inverting Deep-Sea Sound-Speed Profiles Based on Hybrid Data Fusion Combined with Surface Sound Speed
Sound speed profiles (SSPs) must be detected simultaneously to perform a multibeam depth survey. Accurate real-time sound speed profile (SSP) acquisition remains a critical challenge in deep-sea multibeam bathymetry due to the limitations regarding direct measurements under harsh operational conditi...
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MDPI AG
2025-04-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/4/787 |
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| author | Qiang Yuan Weiming Xu Shaohua Jin Xiaohan Yu Xiaodong Ma Tong Sun |
| author_facet | Qiang Yuan Weiming Xu Shaohua Jin Xiaohan Yu Xiaodong Ma Tong Sun |
| author_sort | Qiang Yuan |
| collection | DOAJ |
| description | Sound speed profiles (SSPs) must be detected simultaneously to perform a multibeam depth survey. Accurate real-time sound speed profile (SSP) acquisition remains a critical challenge in deep-sea multibeam bathymetry due to the limitations regarding direct measurements under harsh operational conditions. To address the issue, we propose a joint inversion framework integrating World Ocean Atlas 2023 (WOA23) temperature–salinity model data, historical in situ SSPs, and surface sound speed measurements. By constructing a high-resolution regional sound speed field through WOA23 and historical SSP fusion, this method effectively mitigates spatiotemporal heterogeneity and seasonal variability. The artificial lemming algorithm (ALA) is introduced to optimize the inversion of empirical orthogonal function (EOF) coefficients, enhancing global search efficiency while avoiding local optimization. An experimental validation in the northwest Pacific Ocean demonstrated that the proposed method has a better performance than that of conventional substitution, interpolation, and WOA23-only approaches. The results indicate that the mean absolute error (MAE), root mean square error (RMSE), and maximum error (ME) of SSP reconstruction are reduced by 41.5%, 46.0%, and 49.4%, respectively. When the reconstructed SSPs are applied to multibeam bathymetric correction, depth errors are further reduced to 0.193 m (MAE), 0.213 m (RMSE), and 0.394 m (ME), effectively suppressing the “smiley face” distortion caused by sound speed gradient anomalies. The dynamic selection of the first six EOF modes balances computational efficiency and reconstruction fidelity. This study provides a robust solution for real-time SSP estimation in data-scarce deep-sea environments, particularly for underwater autonomous vehicles. This method effectively mitigates the seabed distortion caused by missing real-time SSPs, significantly enhancing the accuracy and efficiency of deep-sea multibeam surveys. |
| format | Article |
| id | doaj-art-225f755f7a20435ba632bd8a8b897569 |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-225f755f7a20435ba632bd8a8b8975692025-08-20T03:13:55ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-04-0113478710.3390/jmse13040787A Novel Method for Inverting Deep-Sea Sound-Speed Profiles Based on Hybrid Data Fusion Combined with Surface Sound SpeedQiang Yuan0Weiming Xu1Shaohua Jin2Xiaohan Yu3Xiaodong Ma4Tong Sun5Department of Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, ChinaDepartment of Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, ChinaDepartment of Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, ChinaDepartment of Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, ChinaDepartment of Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, ChinaDepartment of Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, ChinaSound speed profiles (SSPs) must be detected simultaneously to perform a multibeam depth survey. Accurate real-time sound speed profile (SSP) acquisition remains a critical challenge in deep-sea multibeam bathymetry due to the limitations regarding direct measurements under harsh operational conditions. To address the issue, we propose a joint inversion framework integrating World Ocean Atlas 2023 (WOA23) temperature–salinity model data, historical in situ SSPs, and surface sound speed measurements. By constructing a high-resolution regional sound speed field through WOA23 and historical SSP fusion, this method effectively mitigates spatiotemporal heterogeneity and seasonal variability. The artificial lemming algorithm (ALA) is introduced to optimize the inversion of empirical orthogonal function (EOF) coefficients, enhancing global search efficiency while avoiding local optimization. An experimental validation in the northwest Pacific Ocean demonstrated that the proposed method has a better performance than that of conventional substitution, interpolation, and WOA23-only approaches. The results indicate that the mean absolute error (MAE), root mean square error (RMSE), and maximum error (ME) of SSP reconstruction are reduced by 41.5%, 46.0%, and 49.4%, respectively. When the reconstructed SSPs are applied to multibeam bathymetric correction, depth errors are further reduced to 0.193 m (MAE), 0.213 m (RMSE), and 0.394 m (ME), effectively suppressing the “smiley face” distortion caused by sound speed gradient anomalies. The dynamic selection of the first six EOF modes balances computational efficiency and reconstruction fidelity. This study provides a robust solution for real-time SSP estimation in data-scarce deep-sea environments, particularly for underwater autonomous vehicles. This method effectively mitigates the seabed distortion caused by missing real-time SSPs, significantly enhancing the accuracy and efficiency of deep-sea multibeam surveys.https://www.mdpi.com/2077-1312/13/4/787deep seamultibeam bathymetrysound-speed profile inversionWOA23empirical orthogonal functionartificial lemming algorithm |
| spellingShingle | Qiang Yuan Weiming Xu Shaohua Jin Xiaohan Yu Xiaodong Ma Tong Sun A Novel Method for Inverting Deep-Sea Sound-Speed Profiles Based on Hybrid Data Fusion Combined with Surface Sound Speed Journal of Marine Science and Engineering deep sea multibeam bathymetry sound-speed profile inversion WOA23 empirical orthogonal function artificial lemming algorithm |
| title | A Novel Method for Inverting Deep-Sea Sound-Speed Profiles Based on Hybrid Data Fusion Combined with Surface Sound Speed |
| title_full | A Novel Method for Inverting Deep-Sea Sound-Speed Profiles Based on Hybrid Data Fusion Combined with Surface Sound Speed |
| title_fullStr | A Novel Method for Inverting Deep-Sea Sound-Speed Profiles Based on Hybrid Data Fusion Combined with Surface Sound Speed |
| title_full_unstemmed | A Novel Method for Inverting Deep-Sea Sound-Speed Profiles Based on Hybrid Data Fusion Combined with Surface Sound Speed |
| title_short | A Novel Method for Inverting Deep-Sea Sound-Speed Profiles Based on Hybrid Data Fusion Combined with Surface Sound Speed |
| title_sort | novel method for inverting deep sea sound speed profiles based on hybrid data fusion combined with surface sound speed |
| topic | deep sea multibeam bathymetry sound-speed profile inversion WOA23 empirical orthogonal function artificial lemming algorithm |
| url | https://www.mdpi.com/2077-1312/13/4/787 |
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