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Potential field data are of great significance to the study of geological characteristics. Downward continuation of the potential field converts potential field data from a high plane to a low plane. Since this method is mathematically an inverse problem solution, it is unstable. The Tikhonov regularization strategy is an effective means of the downward continuation of the potential field. However, achieving high-precision requirements in the stage of precise geophysical exploration is still challenging. Deep learning can effectively solve unstable problems with excellent nonlinear mapping capabilities. Inspired by this, for the downward continuation of the potential field, we propose a new neural network architecture for downward continuation named D-Unet. This study uses the potential field data of a high horizontal plane and the initial model as the network’s input, with the corresponding low-level data serving as the output for supervised learning. Moreover, we add noise to 10% of the data in the training dataset. Model testing shows that our D-Unet has higher accuracy, validity, and stability. In addition, adding noise to the training data can further improve the robustness of the model. Finally, we use the actual potential data of a particular place in northeast China to test our model and satisfactory results have been obtained.

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