Ocean sound speed is an essential foundation for marine scientific research and marine engineering applications. In this article, a model based on a comprehensive optimal back propagation artificial neural network model is developed. The Levenberg–Marquardt algorithm is used to optimize the model, and the momentum term, normalization, and early termination method were used to predict the high precision marine sound speed profile. The sound speed profile was described by five indicators: date, time, latitude, longitude, and depth. The model used data from the CTD observation dataset of scientific investigation over the South China Sea (2009–2012) (108°–120°E, 6°–8°N), which includes comprehensive scientific investigation data from four voyages. The feasibility of modeling the sound speed field in the South China Sea is investigated. The proposed model uses the momentum term, normalization, and early termination in a traditional BP artificial neural network structure and mitigates issues with overtraining and difficulty when determining the BP neural network parameters. With the LM algorithm, a fast-modeling method for the sound field effectively achieves the precision requirement for sound speed prediction. Through the prediction and verification of the data from 2009 to 2012, the newly proposed optimized BP network model is shown to dramatically reduce the training time and improve precision compared to the traditional network model. Results showed that the root mean squared error decreased from 1.7903 m/s to 0.95732 m/s, and the training time decreased from 612.43 s to 4.231 s. Finally, the sound ray tracing simulations confirm that the model meets the accuracy requirements of acoustic sounding and verify the model’s feasibility for the real-time prediction of the vertical sound speed in saltwater bodies.
Development of a 2-D deep learning regional wave field forecast model based on convolutional neural network and the application in South China Sea
