A Novel Method for Denoising Lunar Satellite Gravity Anomaly Data Based on Prior Knowledge Deep Learning

High-resolution lunar gravity anomaly data are of great significance for the study of the lunar crust and lithosphere structure, asymmetric thermal evolution, impact basin subsurface structure and mass tumor genesis, breccia, and magmatism. However, due to errors in satellite orbit and instrument observation, correlation error in high-order spherical harmonic coefficients, and other factors, satellite observation gravity anomaly data present evident aliasing phenomena of stripe noise and random noise in the spatial domain, resulting in difficulties in practical application analysis. In this paper, a lunar satellite gravity anomaly denoising method based on prior knowledge deep learning is proposed. In one instance, the prior knowledge is fused into the data set, the manual processing results are labeled, and the six label-superimposed directions of the simulated stripe noise are used as the sample input data. Conversely, because the gravity field is a harmonic field with smooth characteristics, the Laplace constraint is added to the loss function, and the deep learning results are optimized through Gaussian filtering. Synthetic and real data tests demonstrate the effectiveness of the proposed method in removing complex noise from lunar satellite gravity anomaly data.