This paper proposes a two-dimensional underwater sound propagation model using the Discontinuous Galerkin Finite Element Method (DG-FEM) to investigate the influence of current on sound propagation. The acoustic field is calculated by the convected wave equation with the current speed parameter. Based on the current speed data from an assimilation model, a two-dimensional coupled acoustic propagation model in the Fram Strait water area is established to observe the variability in propagation loss under different seasonal velocities in the real ocean environment. The transmission loss and signal time structure are examined. The results obtained in different source frequencies are also compared. It appears that the current velocity, time and range variation all have an effect on underwater sound propagation.

Normal mode separation and information extraction are popular topics in marine acoustics. The extraction of modal wavenumbers and modal depth functions using the data of a vertical line array is beneficial to various applications. In this paper, we systematically introduce a normal mode separation method based on the consistency restraint of the horizontal wavenumber, obtain the modal depth functions using the singular value decomposition (SVD) method, determine the vertical distribution of the horizontal wavenumber, and determine the SVD accuracy by the consistency of modal wavenumbers on the vertical direction. Moreover, this method can be used to determine the SVD accuracy without true depth functions. When the degeneracy problem leads to a poor SVD effect, the linear relation between the degenerate and true modes is obtained, and the minimum standard deviation of the vertical distribution of the modal wavenumbers is taken as the loss function, thus optimizing the SVD result and improving the accuracy of modal wavenumber estimation. The proposed method is validated using simulated data. In addition, the modes are successfully extracted from ambient noise.

A local linear layered (LLL) model is proposed in this paper and the underwater sound channel is described by a segmental linear function. Taking advantage of the LLL model, cycle length of refracted rays can be derived as an explicit formula and the factors that could affect cycle length can be reduced to several parameters with intuitive physical meanings. Numerical simulations have shown that the results of the formula agree well with Bellhop.

This study focuses on the Significant Wave Height (SWH) retrieval from the Ka-band radar interferometer (KaRIn) on the Surface Water and Ocean Topography (SWOT) satellite by combining backscattering and interference characteristics. To this end, the backscattering-related and interference-related parameters were jointly used as inputs to develop a machine learning model. Here, the backscattering-related data include normalized radar cross-section (NRCS), incidence angle, and the image spectra parameters extracted from KaRIn Level 1B (L1B) data, while the interference-related data correspond to the Level 2 (L2) volumetric correlation, which characterizes the influence of ocean wave scattering on interferometric coherence. The machine learning model is built upon a Multi-Layer Perceptron (MLP), which serves as a nonlinear fitting tool. SWH retrievals from the proposed method and the existing L2 SWH product as a reference were validated by the collocated European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data, Haiyang2C (HY2C) and Haiyang2D (HY2D) altimeter data, and National Data Buoy Center (NDBC) buoy data. Validations show that both KaRIn SWH have a good agreement with collocations in terms of correlation coefficient (COR), BIAS and root mean square error (RMSE). Moreover, the retrieval accuracy from the proposed method (with an RMSE of about 0.29 m) is better than that of the L2 product (with an RMSE of about 0.46 m) when validated against the collocated ECMWF datasets. Ablation analysis further confirms that image spectra parameters and volumetric correlation are the dominant factors driving the retrieval accuracy improvement, with notable contribution differences among the sub-parameters of spectral features. This performance gain arises from the complementary physical mechanisms of backscattering and interferometric observables, which describe sea state information from independent dimensions. These accurate SWH retrievals can help correct sea state biases for collocated KaRIn sea surface height products and complement wave products from other satellite sensors.