An Attention Assisted LSTM Model for Underwater Sound Speed Profile Prediction

The variation of underwater sound velocity greatly influences the distribution of acoustic fields for underwater sound signals, since the irregular distribution of sound velocity dictates the route that signals take as they propagate. To achieve efficient underwater communication and accurate positioning, it is essential to accurately and real-time obtain the distribution of sound velocity. For the method of obtaining sound velocity distribution, sound speed profile (SSP) inversion methodologies provide more rapid response times in comparison to direct measurement techniques. Nevertheless, these approaches predominantly concentrate on the development of spatial sound velocity fields and are significantly dependent on sonar observation data. Consequently, they impose stringent requirements on the implementation of data acquisition systems. In order to effectively model the variation patterns of SSPs and facilitate SSP prediction independent of sonar observation data, we propose a long short-term memory (LSTM) neural network enhanced by an attention mechanism (Att-LSTM). The proposed methodology facilitates the estimation of SSPs without the necessity for on-site data collection, which consequently leads to a reduction in time expenditure. The Att-LSTM demonstrates a root mean square error (RMSE) of less than 0.6 m/s in forecasting the monthly average SSP, which exhibits an average accuracy improvement of 23.5% and 33.0% when compared to LSTM and gated recurrent unit (GRU) neural networks, respectively. These results substantiate the efficacy of the proposed approach in predicting sound velocity distribution.