Gravity Inversion using Convolutional Neural Networks

Small bodies such as asteroids, comets and moons are primary targets for space exploration. To test guidance, navigation and control algorithms an accurate model of the target’s gravity field is crucial for mission success. However, creating an accurate model near the body’s surface is a challenging task due to the often highly irregular shape and the limited information about the internal mass distribution.

This study presents a Convolutional Neural Network (CNN) to determine the density distribution from accelerations at the surface using gravity inversion. We used our voxel-based mascon simulation environment VMC to generate over 100k realistic density distributions (labels) for a cube and calculated the corresponding acceleration (features) for a fixed grid of positions. We selected this simplistic toy problem due to the perfect shape reconstruction and optimal data representation for the deep learning architectures. To investigate the effect of the shape mismatch between a voxel-reconstructed object and the real object we trained an additional Neural Network (NN) to extract the mass distribution for a triaxial ellipsoid using a similar amount of data.

We used a train/test ratio of 80/20 and trained both models using multiple hyperparameter sets for a maximum of 200 epochs using the Adam optimizer. After convergence, all models conserve the total mass. The best-performing architectures are able to determine the general trend of the mass distribution. For the ellipsoid, it can be observed that the model’s prediction is strongly influenced by the contribution of the body’s shape to the gravitational field.

Our results show that NNs are a promising candidate to extract the density distribution using gravity inversion.