Work

Prediction of Full-Frequency Deep-Sea Noise Based on Sea Surface Wind Speed and Real-Time Noise Data

Year: 2024

Type: article

Abstract: 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.

Source: Remote Sensing

DOI: 10.3390/rs17010101

Authors: Bo Yuan, Licheng Lu, Zhenzhu Wang, Guoli Song, Li Ma, Wenbo Wang

Institutions: Chinese Academy of Sciences, Institute of Acoustics, University of Chinese Academy of Sciences, China State Shipbuilding (China)

Language: English

Topic: Underwater Acoustics Research

Subfield: Oceanography

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDGs: Climate action, Life below water

Open Access status: gold

Estimated APC paid: $3,069