Polarity-Constrained Dual-Cycle Generative Adversarial Networks for Irregularly Sampled Seismic Data Reconstruction

With the rise of deep learning (DL), there are kinds of DL-based models proposed for addressing seismic data reconstruction, especially convolutional neural network (CNN)-based methods. However, most of these models are supervised, which require a large amount of training data and ground-truth labels that are difficult to obtain in real applications. We propose the polarity-constrained dual-cycle generative adversarial networks (PCDC-GANs) for reconstructing the irregularly sampled seismic data. We first adopt a traditional generative adversarial networks (GANs) framework as the base model, integrating with a dual-cycle mechanism. Instead of the conventional generator network, we suggest a self-prior generator that uses the prior information extracted by the generator in the first stage to enhance the reconstruction performance, i.e., the mask data of the irregularly sampled seismic data. In addition, we introduce polarity constraints to promote the recovery of sampled seismic traces, amplifying the model's sensitivity to the locations of missing traces. Furthermore, our model incorporates various loss functions to promote its convergence. Numerical experiments on synthetic and field data show that our PCDC-GANs can reconstruct irregularly sampled seismic data more accurately than the SuperstarGAN and CycleGANs, and achieve comparable performance to the supervised U-Net.