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. • The SSP can be expressed as a random process consisting of the background field and the sound speed disturbance. • The background-field coefficient is approximately linearly correlated with the first EOF coefficient, and roughly parabolically correlated with the second EOF coefficient in the presence of internal solitary wave trains. • Compared with the direct method (predicting each EOF coefficient respectively), the indirect method (combining with the statistical characteristics of EOF coefficients) has higher accuracy in the SSP time-series prediction. [ABSTRACT FROM AUTHOR]