Comparison of Seafloor Topography Prediction Using the Gravity-Geologic Method and Analytical Algorithm

Seafloor topography prediction plays a crucial role in filling data gaps in regions lacking ship sounding measurements. However, the reliance of prediction algorithms on ship sounding data varies significantly. This study evaluates the impact of ship sounding coverage and distribution on the prediction accuracy of two methods: the gravity–geologic method (GGM) and an analytical algorithm. Simulation experiments reveal that increasing the ship sounding coverage from 5.40% to 31.80% and achieving a more uniform distribution significantly enhance the accuracy of the GGM, reducing the RMS error from 238.68 m to 42.90 m (an improvement of 82.03%). In contrast, the analytical algorithm maintains a stable RMS error of 40.39 m, demonstrating independence from ship sounding data. Further analysis in a 1° × 1° sea area (134.8°–135.8°E, 30.0°–31.0°N) shows that higher ship sounding coverage (33.19%) reduces the GGM RMS error from 204.17 m to 126.95 m compared to lower coverage (8.19%). However, the analytical algorithm's RMS error remains consistent at 167.94 m. These results underscore the GGM's sensitivity to ship sounding data and the analytical algorithm's robustness. The findings highlight the importance of combining algorithms based on ship sounding coverage. For regions where coverage exceeds 30%, the GGM offers superior accuracy. Conversely, the analytical algorithm performs better in low-coverage scenarios. This study provides a basis for integrating multiple algorithms to enhance global seafloor topography models.