A Time Series Prediction Method for Ocean Sound Speed Profiles Based on Improved TCN Neural Network and Its Application in Seafloor Geodetic Positioning

Abstract

Ocean sound speed profile (SSP) is a key parameter for underwater acoustic detection, remote sensing, and seafloor geodetic positioning, and its temporal prediction is essential for improving acoustic positioning accuracy. Conventional direct measurements are inefficient and spatially sparse, while statistical and acoustic inversion methods fail to capture the strong nonlinear evolution of the sound speed field. Among existing time series models, LSTM, a recurrent network for time series forecasting, lacks an explicit receptive field. In contrast, the original TCN, a temporal convolutional network with dilated convolutions, poorly captures local fine structures and relies heavily on empirical tuning. To overcome these limitations, we propose an improved TCN-based SSP prediction method and apply it to seafloor geodetic positioning. The approach first constructs a sound speed increment field via first-order time differencing to remove global trends and highlight local variations. It then employs Optuna (version 4.9.0), a Bayesian sampling-based automatic optimization framework, to automatically tune key TCN parameters within a predefined search space, reducing reliance on manual tuning. The predicted high-resolution sound speed time series is finally used for ray tracing positioning to enhance seafloor geodetic accuracy. Experiments on the GLORYS12V1 reanalysis dataset show that LSTM and the original TCN achieve root mean square error (RMSE) and mean absolute error (MAE) values of 0.414 and 0.299 m/s, as well as 0.360 and 0.258 m/s, respectively, whereas our improved TCN reduces these to 0.205 and 0.131 m/s, substantially outperforming both baselines. In simulated Global Navigation Satellite System–Acoustics (GNSS-A) seafloor positioning, the 3D positioning RMSE drops to about 0.075 m, with improved stability. The proposed method offers an effective solution for accurate SSP time series forecasting and high-precision seafloor geodesy.