In deep-sea source localization, some of the existing methods only estimate the source range, while the others produce large errors in distance estimation when estimating both the range and depth. Here, a convolutional neural network-based method with high accuracy is introduced, in which the source localization problem is solved as a regression problem. The proposed neural network is trained by a normalized acoustic matrix and used to predict the source position. Experimental data from the western Pacific indicate that this method performs satisfactorily: the mean absolute percentage error of the range is 2.10%, while that of the depth is 3.08%.

The characteristics of the reliable acoustic path (RAP) have been widely used to estimate source depth in the deep ocean. Here, a passive broadband source localization method that estimates both depth and range using the interference of the broadband signal with a non-synchronous vertical array in the RAP is demonstrated. The acoustic intensity distribution versus frequency and depth, which includes interference characteristics, is calculated from non-synchronous vertical array data and used to locate the source. Simulations and an experiment conducted in the western Pacific show that this method can successfully locate a broadband submerged source in the range of 10–30 km.

Underwater operational turbine noise emitted by China's first offshore wind farm in the East China Sea Bridge of Shanghai was measured and analyzed in this study. Two sensors were used in the measurement: a hydrophone recording the underwater sound and an accelerometer placed in the turbine tower detecting the tower vibrations. Measurements were performed at two different types of wind turbines: a Sinovel 3 MW SL3000 turbine and a Shanghai Electric 3.6 MW W3600 turbine. The two turbines show similar tower vibration characteristics, characterized by a number of tonal components, mainly in the low-frequency domain (30–500 Hz). The peak vibration frequencies changed with the wind speed until the turbine approached its nominal power rating. Spectral analysis of the underwater acoustic data showed that the amplitude spectra had a strong correlation with the spectra of the turbine vibration intensity level, indicating that the measured underwater noise was generated by the tower mechanical vibration.

Typhoon induced sediment warming has been proven important to coastal sound-field change in this study. It was observed that the temperature of the bottom waters off the coast of Qingdao increased by 5 °C after the passage of typhoon Damrey in 2012. Sediment warming due to bottom-water temperature increase was calculated and the change in sediment sound speed was estimated. Transmission loss (TL) was simulated both with and without consideration of the change in sediment sound speed induced by temperature variation and results showed TL change could be >10 dB within a distance of 16 km.

To localize a moving source in shallow water with a single hydrophone, a passive range localization method based on synthetic aperture beamforming is proposed. First, the horizontal wavenumber spectrum excited by the source is obtained by synthetic aperture beamforming. Then, according to the theoretical derivation (when the integration time is short, the maximum value of the horizontal wavenumber spectrum is related to the average horizontal wavenumber and the radial velocity of the source), the radial velocity can be obtained after obtaining the average horizontal wavenumber. Finally, in the case where there is a closest point of approach (CPA), the range can be recovered from estimation of the range and time of CPA, and from the constant source speed alone the linear track by fitting the source velocity with the model of radial velocity. The only a priori information required is the sound velocity in water. The processing results using simulated data and SWellEx-96 experimental data show that the proposed method can effectively estimate the range of a moving source in shallow sea.