Self-Supervised Learning for Seismic Data Reconstruction and Denoising

With their powerful feature extraction ability, convolutional neural network (CNN) models achieve excellent signal reconstruction and recovery performances compared with those of traditional methods. The CNN-based approaches mainly use supervised learning approaches; thus, they require large numbers of ground-truth labeled samples. However, in the seismic denoising field, collecting large numbers of labeled samples is impossible; thus, the main challenge to using deep learning methods is a lack of labeled data. Moreover, the data that are available contain noise. To resolve these shortcomings, this letter proposes a novel self-supervised learning framework to reconstruct and perform blind denoising of seismic data images; this approach requires no labeled training data. We utilize a masking procedure to modify an observation input to a CNN to create a <tex-math notation="LaTeX">J </tex-math> -invariant function and incorporate a specific CNN architecture known as U 2 Net, which implements a two-level nested autoencoder that extracts complex feature information from different scales. We modify the network to make it more suitable for seismic signal reconstruction. Finally, we use the self-supervised loss between the original observation and the net output to update the weights of U 2 Net through backpropagation. Tests on both synthetic and field data demonstrate the superior performance of our algorithm on low signal-to-noise ratio data.