gravity gradiometry based on deep learning． The dataset is established by using the measured data of the aerial test，and the neural network is built and trained to map the motion parameters of the gradiometer to the output noise of the gradiometer，and the network is used to predict the output noise of the gradiometer． It is used to compensate the errors of other measured data． The results show that the output noise of the gradiometer predicted by deep learning is very close to the actual output noise． After compensation，the output noise of the gradiometer decreases by more than an order of magnitude，and the error level of compensation is close to the target accuracy of the prototype of the gradiometer．

error   error compensation   deep learning