Mercury, the enigmatic innermost planet in the solar system, is one of the most important targets of space exploration. High-quality gravity field data are significant to refine the physical characterization of Mercury in planetary exploration missions. However, Mercury’s gravity model is limited by relatively low spatial resolution and stripe noises, preventing fine-scale analysis and applications. By analyzing Mercury’s gravity data and topography data in the 2D spatial field, we find they have fairly high spatial structure similarity. Based on this, in this paper, a novel convolution neural network (CNN) approach is proposed to improve the quality of Mercury’s gravity field data. CNN can extract the spatial structure features of gravity data and construct a nonlinear mapping between low- and high-degree data directly. From a low-degree gravity input, the corresponding initial high-degree result can be obtained. Meanwhile, the structure characteristics of the high-resolution digital elevation model (DEM) are extracted and fused to the initial data, to get the final stripe-free result with improved resolution. Given the paucity of Mercury’s data, high-quality lunar datasets are employed as pretraining data after verifying the spatial similarity between gravity and terrain data of the Moon. The HgM007 gravity field obtained by the MErcury Surface, Space ENvironment, GEochemistry and Ranging (MESSENGER) mission at Mercury is selected for experiments to test the ability of the proposed algorithm to remove the stripes caused by quality differences of the highly eccentric orbit data. Experimental results show that our network can directly obtain stripe-free and higher-degree data via inputting low-degree data and implicitly assuming a lunar-like relation between crustal density and porosity. Albeit the CNN-based method cannot be sensitive to subsurface features not present in the initial dataset, it still provides a new perspective for the gravity field refinement.
A Global Gravity Reconstruction Method for Mercury Employing Deep Convolutional Neural Network