A Novel Reconstruction Model for the Underwater Sound Speed Field Utilizing Ocean Remote Sensing Observations and Argo Profiles

The sound speed in the ocean has a considerable impact on the characteristics of underwater acoustic propagation. The swift gathering of the underwater three-dimensional (3D) sound speed field is essential for target detection, underwater acoustic communication, and navigation. Currently, the reconstruction of the underwater sound speed utilizing satellite remote sensing data of the sea surface has emerged as a significant area of research. However, dynamic activities within the ocean result in varying degrees of perturbation in the sound speed structure. Relying solely on sea surface information will restrict the accuracy of sound speed reconstruction. In response to this issue, by utilizing multi-source satellite remote sensing data alongside Argo profiles, we first implemented the random forest (RF) algorithm to establish the statistical mapping relationship from the sea surface temperature (SST), sea level anomaly (SLA), and absolute dynamic topography (ADT) to the density, and thus, reconstructed a 3D density field. Subsequently, based on the sea surface environmental information, we introduced the underwater vertical density as a novel input for sound speed calculations and proposed a new model for 3D sound speed field reconstruction (RF-SDR). The experimental results indicate that utilizing both the sea surface environmental variables and underwater density as inputs yielded an average root-mean-square error (RMSE) of 1.51 m/s for the reconstructed sound speed, along with an average mean absolute error (MAE) of 0.85 m/s. Following the incorporation of density into the reconstruction inputs, the two error metrics exhibited reductions of 31% and 35%, respectively. And the proposed RF-SDR model demonstrated a reduction in the RMSE by 36% and in the MAE by 43% when compared with the commonly utilized single Empirical Orthogonal Function regression (sEOF-r) method. Furthermore, simulations of the sound propagation with both the reconstructed sound speed and Argo sound speed demonstrated a high degree of consistency in the computed acoustic propagation losses. The correlation coefficients consistently exceeded 0.7, thereby reinforcing the validity of the reconstructed sound speed.