High-resolution (HR) marine gravity data are critical for geophysical modeling, seafloor mapping, and tectonic analysis. However, acquiring such data remains challenging due to the inherent trade-offs between distinct measurement sources. While shipborne gravity surveys offer high accuracy and resolution, they are spatially sparse and geographically restricted; conversely, satellite altimetry provides global coverage but comes at the expense of reduced resolution and increased noise. To address this challenge, we propose a semi-supervised dual regression learning (SDRL) framework for gravity field super-resolution (SR) that synergizes the strengths of both data types. By jointly training on a limited number of paired shipborne-satellite samples and a large set of unpaired satellite observations, SDRL leverages cycle-consistent learning to preserve cross-domain structural integrity and enhance generalization. Extensive experiments under varying data conditions—including noisy, ideal, and label-scarce scenarios—demonstrate that SDRL consistently outperforms purely supervised models in terms of structural similarity and error reduction. Moreover, SDRL exhibits strong robustness against data imperfections and generalizes effectively to geophysically distinct test regions. These results highlight the practical advantages of semi-supervised learning for global marine gravity field reconstruction, particularly in real-world settings where high-quality labeled data are scarce.
Jia et al. (Sun,) studied this question.