Data-Driven Analysis of Ocean Fronts’ Impact on Acoustic Propagation: Process Understanding and Machine Learning Applications, Focusing on the Kuroshio Extension Front
Ocean fronts, widespread across the global ocean, cause abrupt shifts in physical properties such as temperature, salinity, and sound speed, significantly affecting underwater acoustic communication and detection. While past research has concentrated on qualitative analysis and small-scale research...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/11/2010 |
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| author | Weishuai Xu Lei Zhang Ming Li Xiaodong Ma Maolin Li |
| author_facet | Weishuai Xu Lei Zhang Ming Li Xiaodong Ma Maolin Li |
| author_sort | Weishuai Xu |
| collection | DOAJ |
| description | Ocean fronts, widespread across the global ocean, cause abrupt shifts in physical properties such as temperature, salinity, and sound speed, significantly affecting underwater acoustic communication and detection. While past research has concentrated on qualitative analysis and small-scale research on ocean front sections, a comprehensive analysis of ocean fronts’ characteristics and their impact on underwater acoustics is lacking. This study employs high-resolution reanalysis data and in situ observations to accurately identify ocean fronts, sound speed structures, and acoustic propagation features from over six hundred thousand Kuroshio Extension Front (KEF) sections. Utilizing marine big data statistics and machine learning evaluation metrics such as out-of-bag (OOB) error and Shapley values, this study quantitatively assesses the variations in sound speed structures across the KEF and their effects on acoustic propagation shifts. This study’s key findings reveal that differences in sound speed structure are significantly correlated with KEF strength, with the channel axis depth and conjugate depth increasing with front strength, while the thermocline intensity and depth excess decrease. Acoustic propagation features in the KEF environment exhibit notable seasonal variations. |
| format | Article |
| id | doaj-art-43f5dddfc17a4d4396c8e7cfd9a4cb8f |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-43f5dddfc17a4d4396c8e7cfd9a4cb8f2025-08-20T02:48:02ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-11-011211201010.3390/jmse12112010Data-Driven Analysis of Ocean Fronts’ Impact on Acoustic Propagation: Process Understanding and Machine Learning Applications, Focusing on the Kuroshio Extension FrontWeishuai Xu0Lei Zhang1Ming Li2Xiaodong Ma3Maolin Li4Department of Military Oceanography and Hydrography and Cartography, Dalian Naval Academy, Dalian 116018, ChinaDepartment of Military Oceanography and Hydrography and Cartography, Dalian Naval Academy, Dalian 116018, ChinaCollege of Advanced Interdisciplinary Studies, National University of Defense Technology, Nanjing 211101, ChinaDepartment of Military Oceanography and Hydrography and Cartography, Dalian Naval Academy, Dalian 116018, ChinaDepartment of Military Oceanography and Hydrography and Cartography, Dalian Naval Academy, Dalian 116018, ChinaOcean fronts, widespread across the global ocean, cause abrupt shifts in physical properties such as temperature, salinity, and sound speed, significantly affecting underwater acoustic communication and detection. While past research has concentrated on qualitative analysis and small-scale research on ocean front sections, a comprehensive analysis of ocean fronts’ characteristics and their impact on underwater acoustics is lacking. This study employs high-resolution reanalysis data and in situ observations to accurately identify ocean fronts, sound speed structures, and acoustic propagation features from over six hundred thousand Kuroshio Extension Front (KEF) sections. Utilizing marine big data statistics and machine learning evaluation metrics such as out-of-bag (OOB) error and Shapley values, this study quantitatively assesses the variations in sound speed structures across the KEF and their effects on acoustic propagation shifts. This study’s key findings reveal that differences in sound speed structure are significantly correlated with KEF strength, with the channel axis depth and conjugate depth increasing with front strength, while the thermocline intensity and depth excess decrease. Acoustic propagation features in the KEF environment exhibit notable seasonal variations.https://www.mdpi.com/2077-1312/12/11/2010Kuroshio Extension Frontvertical sound speed structureacoustic propagationmarine big datamachine learningShapley value |
| spellingShingle | Weishuai Xu Lei Zhang Ming Li Xiaodong Ma Maolin Li Data-Driven Analysis of Ocean Fronts’ Impact on Acoustic Propagation: Process Understanding and Machine Learning Applications, Focusing on the Kuroshio Extension Front Journal of Marine Science and Engineering Kuroshio Extension Front vertical sound speed structure acoustic propagation marine big data machine learning Shapley value |
| title | Data-Driven Analysis of Ocean Fronts’ Impact on Acoustic Propagation: Process Understanding and Machine Learning Applications, Focusing on the Kuroshio Extension Front |
| title_full | Data-Driven Analysis of Ocean Fronts’ Impact on Acoustic Propagation: Process Understanding and Machine Learning Applications, Focusing on the Kuroshio Extension Front |
| title_fullStr | Data-Driven Analysis of Ocean Fronts’ Impact on Acoustic Propagation: Process Understanding and Machine Learning Applications, Focusing on the Kuroshio Extension Front |
| title_full_unstemmed | Data-Driven Analysis of Ocean Fronts’ Impact on Acoustic Propagation: Process Understanding and Machine Learning Applications, Focusing on the Kuroshio Extension Front |
| title_short | Data-Driven Analysis of Ocean Fronts’ Impact on Acoustic Propagation: Process Understanding and Machine Learning Applications, Focusing on the Kuroshio Extension Front |
| title_sort | data driven analysis of ocean fronts impact on acoustic propagation process understanding and machine learning applications focusing on the kuroshio extension front |
| topic | Kuroshio Extension Front vertical sound speed structure acoustic propagation marine big data machine learning Shapley value |
| url | https://www.mdpi.com/2077-1312/12/11/2010 |
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