Time series prediction of shallow water sound speed profile in the presence of internal solitary wave trains

The internal waves, especially the internal solitary wave (ISW) trains, cause violent perturbations of sound speed. Sound speed profile (SSPs) facilitates the pre-understanding of the sound field distribution in the experimental sea, therefore, the real-time prediction of SSPs in the presence of ISW trains are of great significance. In this paper, an orthogonal representation of SSPs that considers the background field (background SSP) variation is proposed. Based on the statistical characteristics of time-series SSPs, high-precision SSP prediction is realized by the long short-term memory recurrent neural network (LSTM). The prediction accuracy is demonstrated with the SSP data from an experiment in the South China Sea, and the mean RMSE of SSP prediction is reduced to about 1 m/s.

Automated summary

This paper proposes an orthogonal representation of sound speed profiles that considers background field variation. High-precision sound speed profile prediction is achieved using the long short-term memory recurrent neural network. The prediction accuracy is demonstrated with real data, reducing the mean RMSE of sound speed profile prediction to about 1 m/s.