Full-Depth Inversion of the Sound Speed Profile Using Remote Sensing Parameters via a Physics-Informed Neural Network

Due to the limited number of deep sound speed profile (SSP) samples, the existing wide-area SSP inversion methods cannot estimate the full-depth SSP. In this paper, the full-depth SSP inversion is achieved by adding physical mechanism constraints to the neural network inversion algorithm. A dimensionality reduction approach for SSP perturbation, based on the hydrodynamic mechanism of seawater, is proposed. Constrained by the characteristics of ocean stratification, a self-organizing map is employed to invert the depth of the sound channel axis and reconstruct the SSP from the sea surface to the sound channel axis. The SSP from the sound channel axis to the seabed is reconstructed by integrating the characteristics of the sound channel axis and the sound speed gradient characteristics of the deep sea isothermal layer. The efficacy of the method was validated by the Argo data from the South China Sea. The average root mean square error of the reconstructed full-depth SSP is 2.85 m/s. Additionally, the average error of transmission loss prediction within 50 km is 2.50 dB. The proposed method is capable of furnishing effective full-depth SSP information without the necessity of any in situ measurements, thereby meeting the requirements of certain underwater acoustic applications.