A Novel Sound Speed Profile Prediction Method Based on the Convolutional Long-Short Term Memory Network

• Published in: Journal of marine science and engineering Vol. 10; no. 5; p. 572
• Main Authors: Li, Bingyang, Zhai, Jingsheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2022
• Subjects: Accuracy; Acoustic equipment; Acoustic velocity; Acoustics; Algorithms; Communication; Convolution; convolutional LSTM; Datasets; Deep learning; Deep water; Dictionaries; History; Long short-term memory; Machine learning; Marine environment; Mathematical models; Neural networks; Parameters; Predictions; Propagation; Recurrent neural networks; Salinity; Seawater; Sound; Sound propagation; sound speed profile; Sound velocity; spatial prediction; Surface layers; Surveying; Time series; Tomography; Underwater acoustics; Underwater communication; Velocity; Wireless communications
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse10050572

As an important marine environmental parameter, sound velocity greatly affects the sound propagation characteristics in the ocean. In marine surveying work, prompt and low-cost acquisition of accurate sound speed profiles (SSP) is of immense significance for improving the measurement and positioning accuracy of marine acoustic equipment and ensuring underwater wireless communication. To address the problem of not being able to glean the accurate SSP in real time, we propose a convolution long short-term memory neural network (Conv-LSTM) which combines the long short-term memory (LSTM) neural network and convolution operation to predict the complete sound speed profile based on historical data. Considering SSP is a typical time series and has strong spatial correlation, Conv-LSTM can grasp not only the temporal relevance of time series, but also the spatial characteristics. The Argo temperature and salinity grid data of the North Pacific from 2004 to 2019 is imported to establish the model’s SSP dataset, and the convolution of input data is performed before going through the neurons in this recurrent neural network to extract the spatial relevance of the data itself. In the meantime, in order to prove the advanced nature of this model, we compare it with the LSTM network under the same parameter settings. The experimental results show that predicting the SSP time series at a single coordinate position under the same parameter conditions, it is best to predict the future SSP next month through the historical data of 24 months, and the prediction effect of Conv-LSTM is much better than that of the LSTM network, and the relative error (RE) is 0.872 m/s, which is 1.817 m/s less than that of LSTM. Predictions in the selected area are also exceedingly accurate relative to the actual data; the prediction error of deep water is less than 0.3 m/s, while RE on the surface layer is larger, exceeding 1.6 m/s.