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<p>Temporal variation errors in ocean sound speed constitute a critical factor affecting underwater navigation and positioning accuracy. However, real-time and large-scale measurement of ocean sound speed remains challenging. Therefore, the inversion of the sound speed profile (SSP) is essential for obtaining ocean sound speed field data at high spatiotemporal resolution. Because the ocean SSP inversion function is relatively complex, neural network-based inversion models demonstrate clear advantages over conventional SSP inversion methods. This study presents a multi-parameter, single-layer residual long short-term memory network (Res-LSTM) for SSP prediction. The proposed model enhances prediction accuracy and generalization capability in complex marine environments. It introduces residual connections within the traditional long short-term memory (LSTM) hidden layer, utilizing skip connections to form identity mappings. This architecture learns residuals between the inputs and outputs while integrating key influencing factors, such as temperature, salinity, and depth. The approach achieves high-precision SSP prediction through these technical innovations. To evaluate the performance of the Res-LSTM model, this study utilizes Copernicus Marine reanalysis data. The proposed model is systematically compared against benchmark methods, including generalized regression neural networks (GRNN), radial basis function networks (RBF), and standard LSTM architectures. Experimental results indicate that at four representative points, the root mean square error (RMSE) of the Res-LSTM decreased by 62.4%, 46.0%, and 32.9% compared with GRNN, RBF, and LSTM, respectively. Furthermore, in tests conducted across different time scales, the RMSE of Res-LSTM consistently remained the lowest. Ablation experiments further indicate that the introduction of residual connections is a key factor in the model’s performance improvement. Therefore, the Res-LSTM is an effective method for predicting SSP and can further improve prediction accuracy and stability.</p>

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Sound speed profile prediction using a multi-parameter, single-layer residual LSTM approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月27日

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  • Zetao Yang,
  • Tianhe Xu,
  • Junting Wang,
  • Yangfan Liu,
  • Wenlong Yang,
  • Zhen Xiao,
  • Yijie Zhao

摘要

Temporal variation errors in ocean sound speed constitute a critical factor affecting underwater navigation and positioning accuracy. However, real-time and large-scale measurement of ocean sound speed remains challenging. Therefore, the inversion of the sound speed profile (SSP) is essential for obtaining ocean sound speed field data at high spatiotemporal resolution. Because the ocean SSP inversion function is relatively complex, neural network-based inversion models demonstrate clear advantages over conventional SSP inversion methods. This study presents a multi-parameter, single-layer residual long short-term memory network (Res-LSTM) for SSP prediction. The proposed model enhances prediction accuracy and generalization capability in complex marine environments. It introduces residual connections within the traditional long short-term memory (LSTM) hidden layer, utilizing skip connections to form identity mappings. This architecture learns residuals between the inputs and outputs while integrating key influencing factors, such as temperature, salinity, and depth. The approach achieves high-precision SSP prediction through these technical innovations. To evaluate the performance of the Res-LSTM model, this study utilizes Copernicus Marine reanalysis data. The proposed model is systematically compared against benchmark methods, including generalized regression neural networks (GRNN), radial basis function networks (RBF), and standard LSTM architectures. Experimental results indicate that at four representative points, the root mean square error (RMSE) of the Res-LSTM decreased by 62.4%, 46.0%, and 32.9% compared with GRNN, RBF, and LSTM, respectively. Furthermore, in tests conducted across different time scales, the RMSE of Res-LSTM consistently remained the lowest. Ablation experiments further indicate that the introduction of residual connections is a key factor in the model’s performance improvement. Therefore, the Res-LSTM is an effective method for predicting SSP and can further improve prediction accuracy and stability.