Abstract
Seismic data interpolation is a meaningful research topic in the field of seismic data processing. In this paper, we propose deep internal learning for interpolating regularly sampled aliased seismic data, to improve the upsampling accuracy of regularly sampled aliased seismic data. The proposed algorithm, contrary to previous deep external learning-based seismic interpolation relying on prior training for vast external seismic data, exploits the characteristics of the field data itself, based on the feature similarity between the regularly missing and remaining samples. Internal learning generates training samples solely from the currently remaining regularly undersampled seismic data, and then trains a simple convolutional neural network using the training set. Finally, the trained model is used to upsample the current seismic traces regularly with high accuracy, and can adapt itself intelligently to different field data for the upsampling requirement. This enables seismic data antialiasing interpolation on regularly sampled seismic data with a small sample in the case of insufficient data. The performance of the proposed deep internal learning is assessed using synthetic and field data, respectively. Moreover, the comparison of the proposed deep internal learning with a classic prediction-based interpolation method and deep external learning-based seismic interpolation validates the effectiveness of the proposed algorithm.
Published Version
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