Sound intensity fluctuation plays a significant role in underwater acoustic studies. In this article, an experiment was conducted in the South China Sea using a buoy system that autonomously emitted and received sound signals to obtain data on sound fluctuations under different wind conditions (in windy weather) for about 179 h. Results showed that the fluctuation of the transmission loss in a fixed position reached up to 15–20 dB. Different potential reasons for this transmission loss fluctuation are analyzed. Using the wind speed data provided by the Earth system, the sea surface wave spectrum model, and the bubble layer model, the fluctuation of the transmission loss was simulated based on the modified Ramsurf sound propagation model. The simulation and experimental results were in good agreement with each other. Therefore, the fluctuation of the transmission loss due to wind-generated waves in high sea states should be considered while studying underwater communication, particularly in shallow water with a positive gradient sound velocity profile.

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.

When the signal frequency surpasses the Nyquist sampling rate, aliasing arises, leading to a loss of amplitude and phase information in the spectrum. This study introduces a technique that effectively mitigates the aliasing effect by employing a complex Fourier transform, which supplements the imaginary component information based on the 90° phase relationship between sound pressure and acceleration. Simulation and experimental outcomes substantiate that this approach not only extends the range of analyzable spectral frequencies to match the sampling rate but also offers the advantages of a straightforward principle and convenient implementation. According to the Nyquist theorem, the minimum sampling rate for uniform sampling should be twice the highest frequency of the signal. Insufficient sampling frequency results in aliasing, causing distortion in the received signal. Consequently, the resulting spectrum fails to accurately represent the true amplitude and phase information of the signal. Scholars both domestically and internationally have proposed various methods to tackle the analysis of aliased signals, including multi-channel signal reconstruction, variable sampling rate sampling, filter design, and the application of deep learning techniques. However, these methods often entail a high computational complexity. In contrast, the method of jointly processing sound pressure and acceleration offers a relatively simple calculation approach [1- 10]. This paper presents an analysis of aliased spectra utilizing the Fourier transformation of complex numbers. Exploiting the inherent 90-degree phase relationship between sound pressure and acceleration, we employ the phase difference to build complex signals. Our approach introduces a fresh perspective for the analysis of aliased spectra.

In this study, single- and double-parameter empirical equations were produced, and the influences of physical properties on sound speed were assessed, based on the measured data of seafloor sediments obtained in an area near southeastern Hainan Island. The correlation coefficients of the single-parameter empirical equations were found to be greater than 0.65, while those of the double-parameter empirical equations were found to be greater than 0.72, indicating that the double-parameter empirical equations have an advantage in predicting sound speed. According to the sensitivity analysis, mean grain size is the best predictor for sediment sound speed. Furthermore, the empirical equations constructed in this study are a significant addition to sound speed prediction equations and have important application potential in predicting sound speed in the studied area near southeastern Hainan Island.