Underwater Sound Source Depth Estimation Using Deep Learning and Vector Acoustic Features

Accurate estimation of underwater sound source depth plays a crucial role in ocean acoustic monitoring, underwater target localization, and marine environment exploration. This study exploits the capability of vector hydrophones to simultaneously and co-locally acquire both scalar and vector components of the underwater sound field. Based on the study of the line spectrum interference structure characteristics of the underwater sound field, the vertical sound intensity flux of the underwater sound source is extracted. Additionally, a parallel BiLSTM and ResNet network structure is proposed to train this feature and achieve depth estimation of underwater sound sources. Experimental results show that under ±10% and ±15% errors in the source–hydrophone distance, the proposed model maintains stable performance within a signal-to-noise ratio (SNR) range of −15 dB to +15 dB. Compared with the LSTM model, the ResNet model, and the matched-field processing (MFP) algorithm, the average RMSE of our model is reduced by 72.4%, 54.0%, and 64.1%, respectively. Furthermore, under 5% and 10% frequency estimation errors, the average RMSE of the proposed model within the same SNR range is reduced by 47.7%, 20.3%, and 79.3%, respectively. It effectively estimates the depth of underwater sound sources, with estimation errors below 5 m under non-extreme SNR conditions. These results fully demonstrate the robustness and effectiveness of the proposed method under practical uncertainties in the ocean environment.