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The spatial-temporal distribution of underwater sound velocity affects thepropagation mode of underwater acoustic signals. Therefore, rapid estimationand prediction of underwater sound velocity distribution is crucial forproviding underwater positioning, navigation and timing (PNT) services.Currently, sound speed profile (SSP) inversion methods have a faster timeresponse rate compared to direct measurement methods, however, most SSPinversion methods focus on constructing spatial dimensional sound velocityfields and are highly dependent on sonar observation data, thus highrequirements have been placed on observation data sources. To explore thedistribution pattern of sound velocity in the time dimension and achieve futureSSP prediction without sonar observation data, we propose a hierarchical longshort-term memory (H-LSTM) neural network for SSP prediction. By our SSPprediction method, the sound speed distribution could be estimated without anyon-site data measurement process, so that the time efficiency could be greatlyimproved. Through comparing with other state-of-the-art methods, H-LSTM hasbetter accuracy performance on prediction of monthly average sound velocitydistribution, which is less than 1 m/s in different depth layers.

Future Full-Ocean Deep SSPs Prediction based on Hierarchical Long Short-Term Memory Neural Networks