Enhanced Deep-Learning Method for Marine Gravity Recovery From Altimetry and Bathymetry Data

The deep learning method based on multi-channel convolutional neural network(MCCNN) can be used for gravity recovery from altimetry and bathymetry data. Compared with the traditional inverse Vening Meinesz (IVM) method, the MCCNN method enhances gravity accuracy but introduces long-wavelength gravity errors in training data-scarce areas. The Enhanced MCCNN method (EMCCNN) is proposed, which treats geo-locations in the input layer as nodes rather than channels. Evaluated by independent ship-borne data, the EMCCNN method demonstrates a gravity accuracy improvement of 0.05-0.24 mGal compared to the IVM method, maintaining consistent gravity accuracy compared to the MCCNN method. Additionally, assessed by a recognized global marine gravity model, the EMCCNN method achieves the highest accuracy. Particularly in regions with limited training data, the EMCCNN method outperforms the MCCNN method in minimizing gravity differences relative to assessment data. In the wavelengths larger than 100 km, the gravity noise for EMCCNN method can be reduced by up to about 50% compared to MCCNN method. These findings highlight EMCCNN’s effectiveness in improving gravity recovery in training data-scarce areas.