Prediction of Full-Frequency Deep-Sea Noise Based on Sea Surface Wind Speed and Real-Time Noise Data
The prediction of ocean ambient noise is crucial for protecting the marine ecosystem and ensuring communication and navigation safety, especially under extreme weather conditions such as typhoons and strong winds. Ocean ambient noise is primarily caused by ship activities, wind waves, and other fact...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/101 |
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| author | Bo Yuan Licheng Lu Zhenzhu Wang Guoli Song Li Ma Wenbo Wang |
| author_facet | Bo Yuan Licheng Lu Zhenzhu Wang Guoli Song Li Ma Wenbo Wang |
| author_sort | Bo Yuan |
| collection | DOAJ |
| description | The prediction of ocean ambient noise is crucial for protecting the marine ecosystem and ensuring communication and navigation safety, especially under extreme weather conditions such as typhoons and strong winds. Ocean ambient noise is primarily caused by ship activities, wind waves, and other factors, and its complexity makes it a significant challenge to effectively utilize limited data to observe future changes in noise energy. To address this issue, we have designed a multi-modal linear model based on a “decomposition-prediction-modal trend fusion-total fusion” framework. This model simultaneously decomposes wind speed data and ocean ambient noise data into trend and residual components, enabling the wind speed information to effectively extract key trend features of ocean ambient noise. Compared to polynomial fitting methods, single-modal models, and LSTM multi-modal models, the average error of the relative sound pressure level was reduced by 1.3 dB, 0.5 dB, and 0.3 dB, respectively. Our approach demonstrates significant improvements in predicting future trends and detailed fittings of the data. |
| format | Article |
| id | doaj-art-a4c55d77bdc74d3f8ebf05faa7aeeb81 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-a4c55d77bdc74d3f8ebf05faa7aeeb812025-01-10T13:20:13ZengMDPI AGRemote Sensing2072-42922024-12-0117110110.3390/rs17010101Prediction of Full-Frequency Deep-Sea Noise Based on Sea Surface Wind Speed and Real-Time Noise DataBo Yuan0Licheng Lu1Zhenzhu Wang2Guoli Song3Li Ma4Wenbo Wang5Key Laboratory of Underwater Acoustic Environment, Chinese Academy of Sciences, No. 21 North 4th Ring Road, Haidian District, Beijing 100084, ChinaKey Laboratory of Underwater Acoustic Environment, Chinese Academy of Sciences, No. 21 North 4th Ring Road, Haidian District, Beijing 100084, ChinaChina State Shipbuilding Corporation Systems Engineering Research Institute, 1st Fengxian East Road, Haidian District, Beijing 100094, ChinaKey Laboratory of Underwater Acoustic Environment, Chinese Academy of Sciences, No. 21 North 4th Ring Road, Haidian District, Beijing 100084, ChinaKey Laboratory of Underwater Acoustic Environment, Chinese Academy of Sciences, No. 21 North 4th Ring Road, Haidian District, Beijing 100084, ChinaKey Laboratory of Underwater Acoustic Environment, Chinese Academy of Sciences, No. 21 North 4th Ring Road, Haidian District, Beijing 100084, ChinaThe prediction of ocean ambient noise is crucial for protecting the marine ecosystem and ensuring communication and navigation safety, especially under extreme weather conditions such as typhoons and strong winds. Ocean ambient noise is primarily caused by ship activities, wind waves, and other factors, and its complexity makes it a significant challenge to effectively utilize limited data to observe future changes in noise energy. To address this issue, we have designed a multi-modal linear model based on a “decomposition-prediction-modal trend fusion-total fusion” framework. This model simultaneously decomposes wind speed data and ocean ambient noise data into trend and residual components, enabling the wind speed information to effectively extract key trend features of ocean ambient noise. Compared to polynomial fitting methods, single-modal models, and LSTM multi-modal models, the average error of the relative sound pressure level was reduced by 1.3 dB, 0.5 dB, and 0.3 dB, respectively. Our approach demonstrates significant improvements in predicting future trends and detailed fittings of the data.https://www.mdpi.com/2072-4292/17/1/101ocean ambient noisepredictionsingle-modalmulti-modal |
| spellingShingle | Bo Yuan Licheng Lu Zhenzhu Wang Guoli Song Li Ma Wenbo Wang Prediction of Full-Frequency Deep-Sea Noise Based on Sea Surface Wind Speed and Real-Time Noise Data Remote Sensing ocean ambient noise prediction single-modal multi-modal |
| title | Prediction of Full-Frequency Deep-Sea Noise Based on Sea Surface Wind Speed and Real-Time Noise Data |
| title_full | Prediction of Full-Frequency Deep-Sea Noise Based on Sea Surface Wind Speed and Real-Time Noise Data |
| title_fullStr | Prediction of Full-Frequency Deep-Sea Noise Based on Sea Surface Wind Speed and Real-Time Noise Data |
| title_full_unstemmed | Prediction of Full-Frequency Deep-Sea Noise Based on Sea Surface Wind Speed and Real-Time Noise Data |
| title_short | Prediction of Full-Frequency Deep-Sea Noise Based on Sea Surface Wind Speed and Real-Time Noise Data |
| title_sort | prediction of full frequency deep sea noise based on sea surface wind speed and real time noise data |
| topic | ocean ambient noise prediction single-modal multi-modal |
| url | https://www.mdpi.com/2072-4292/17/1/101 |
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