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Seismic data reconstruction is an inverse problem in the geophysical community. Deep learning-based methods directly learn the projection between undersampled and complete data from large training datasets. However, when the feature difference between the test and training datasets increases, the recovery performance is degraded. Fine-tuning the pretrained network on the undersampled test field data has been introduced to adapt the trained network to new data. However, the pseudo-labels of the fine-tuning process need to be obtained by some traditional method in advance. In this paper, we explore a strategy for interpolating regularly sampled aliased seismic data by incorporating deep external and internal learning, i.e. by combing external pretraining with internal unsupervised fine-tuning. The pretrained network is obtained from an external synthetic dataset. Then, the pretrained network is fine-tuned on the internal dataset to obtain the final trained network adapting the test data. The inputs and labels of the internal dataset are generated solely from the currently regularly undersampled test data. As such, the fine-tuning process is totally unsupervised. Finally, the interpolated result is acquired by feeding the test data into the trained network. The proposed algorithm learns the external priors from large external datasets and it learns the internal features from undersampled test data. Thus, the network has an enhanced capacity to adapt to new data. Poststack and prestack field data are provided to verify the performance of the proposed algorithm.

Reconstructing seismic data by incorporating deep external and internal learning