The Identification of Impact Craters from GRAIL-Acquired Gravity Data by U-Net Architecture

Abstract

The identification of impact craters on the Moon and other planetary bodies is of great significance to studying and constraining the dynamical process and evolution of the Solar System. Traditionally, this has been performed through the visual examination of images. Due to the effect of overburden, some structural features cannot be effectively identified from optical images, resulting in limitations in the scope, efficiency and accuracy of identification. In this paper, we investigate the viability of convolutional neural networks (CNNs) to perform the detection of impact craters from GRAIL-acquired gravity data. The ideal values of each hyperparameter in U-net architecture are determined after dozens of iterations of model training, testing and evaluation. The final model was evaluated by the Loss function with the low value of 0.04, indicating that the predicted output of the model reached a relatively high fitting degree with the prior labelled output. The comparative results with different methods show that the proposed method has a clear detection of the target features, with an accuracy of more than 80%. In addition, the detection results of the whole image account for 83% of the number of manually delineated gravity anomalies. The proposed method can still achieve the same quality for the identification of the gravity anomalies caused by impact craters under the condition that the resolution of GRAIL gravity data are not superior. Our results demonstrate that the U-net architecture can be a very effective tool for the rapid and automatic identification of impact craters from gravity map on the Moon, as well as other Solar System bodies.