Self-Supervised Deep Learning to Reconstruct Seismic Data With Consecutively Missing Traces

Abstract

Seismic data processing requires careful interpolation or reconstruction to restore the regularly or irregularly missing traces. In practice, seismic data with consecutively missing traces are quite common, which will lead to a great challenge for conventional interpolation or reconstruction methods. To effectively reconstruct the successively blank traces in seismic data, we proposed a self-supervised deep learning approach, with which the convolutional neural network is trained in a supervised manner with pseudolabels obtained from unlabeled observed data. The pseudolabels are automatically generated by randomly masking the observed data to simulate the consecutively missing scenario. We train a nested U-Net structure (UNet++) with a hybrid loss function so that the local and global structural information can be captured to ensure the quality of reconstruction. A two-step reconstruction workflow is designed to recover the missing recordings with respect to both the receivers and sources. Synthetic and field data examples demonstrate that the proposed self-supervised learning can effectively reconstruct the corrupted seismic data.