The Surface Water and Ocean Topography (SWOT) wide-swath altimetry satellite was launched in December 2022. The performance of novel wide-swath altimetry in seafloor topography modeling needs to be evaluated. This study utilized 15 cycles of SWOT Level-3 product to construct seafloor topography model of the South China Sea by linear regression analysis. The root mean square error of the difference between the model and shipborne bathymetry at checkpoints is about 120 m, which is 20 m better than topo_27.1 and DTU18BAT, and 40 m better than ETOPO1. First, the effects of the shipborne bathymetry at control points and priori bathymetry model in different topography-gravity scaling factor estimation strategies (A: using robust least squares (RBLSQ) to estimate regional scaling factor; B: using ratio method to calculate scaling factors at control points; C: using the moving window method and RBLSQ to obtain scaling factor grids.) on SWOT seafloor topography modeling are explored. We find that the control point number barely affects strategy A/C but significantly affects strategy B, while the priori bathymetry model mainly affects strategy C. Then, the three strategies are applied to the traditional radar altimetry gravity anomaly, and the results are compared with the SWOT-derived seafloor topography. The results show that incorporating SWOT data can improve the accuracy of seafloor topography estimation by about 7 m, and improve the power spectral density in the wavelength range about 10∼20 km, which can help to reveal more detailed topography information.

This paper proposes a method for constructing a seafloor topography model based on a Transformer-enhanced U-Net network (Trans-UNet) to improve the accuracy of bathymetry predictions. The method incorporates gravity data (gravity anomalies, vertical deflections, and vertical gravity gradients) and geological data (the sea surface to Moho, sediment thickness, mean dynamic topography, and seafloor age). The proposed method is tested in the South China Sea (112°E-119°E, 11°N-20°N), and a seafloor topography model (Trans-UNet model) with a resolution of 1′×1′ is constructed for the region. By comparison, a seafloor topography model (U-Net model) for this region was constructed using U-Net. Compared to the inspect points, the standard deviation of the difference between the Trans-UNet model and the measured bathymetry at those points is 124.11 m. Compared to the U-Net model, topo_25.1, DTU18BAT and GEBCO_2024 models, the accuracy of Trans-UNet model shows improvements of 7.64 m, 21.49 m, 76.91 m, and 4.94 m, respectively. These results effectively demonstrate the validity of the proposed method and verify the accuracy of the Trans-UNet model's performance. By comparing the accuracy of various seafloor topography models across different depth ranges, the Trans-UNet model shows superior performance in deeper water regions, with improvements of 7.37m and 7.28m compared to the GEBCO_2024 model in the depth ranges of 2000-4000m and greater than 4000m, respectively.

Determining the detailed Moho topography is crucial for understanding geodynamic processes. Inversion of the Moho depth in the frequency domain from gravity observations is an effective tool for this purpose. However, existing inversion methods such as the Parker-Oldenburg (P-O) and Bott-Parker (B-P) methods face challenges including nonconvergence and noise amplification. We develop a modified B-P (mB-P) method to estimate the Moho depth with variable density contrast from gravity data. Our method casts exponential density-based Parker’s formula into the iterative continuation theoretical framework, allowing the use of an adaptive filter that has been proven effective in iterative continuation studies. This modification retains the efficiency of the B-P method while offering improved control over solution stability. Gauss-fast Fourier transform is adopted to improve the precision of the forward and inverse Fourier transforms in the inversion process. Furthermore, the mathematical connection between the P-O and B-P methods is established. Specifically, the B-P method acts as a low-pass filter to reduce the downward continuation effect in the P-O method and recovers the nonlinear topography through the nonlinearity of the fitting function. Synthetic inversion tests demonstrate that our method achieves superior inversion accuracy compared with the P-O and B-P methods under 3% gravity observation noise and significantly outperforms the P-O method in terms of computational efficiency. We apply the mB-P method to estimate the Moho depth beneath the Tibetan Plateau using satellite gravity data and seismic data, achieving reasonable agreement between the gravimetric model and seismic data.