  Seismic data interpolation of irregularly missing traces plays a crucial role
in subsurface imaging, enabling accurate analysis and interpretation throughout
the seismic processing workflow. Despite the widespread exploration of deep
supervised learning methods for seismic data reconstruction, several challenges
still remain open. Particularly, the requirement of extensive training data and
poor domain generalization due to the seismic survey's variability poses
significant issues. To overcome these limitations, this paper introduces a
deep-learning-based seismic data reconstruction approach that leverages data
redundancy. This method involves a two-stage training process. First, an
adversarial generative network (GAN) is trained using synthetic seismic data,
enabling the extraction and learning of their primary and local seismic
characteristics. Second, a reconstruction network is trained with synthetic
data generated by the GAN, which dynamically adjusts the noise and distortion
level at each epoch to promote feature diversity. This approach enhances the
generalization capabilities of the reconstruction network by allowing control
over the generation of seismic patterns from the latent space of the GAN,
thereby reducing the dependency on large seismic databases. Experimental
results on field and synthetic seismic datasets both pre-stack and post-stack
show that the proposed method outperforms the baseline supervised learning and
unsupervised approaches such as deep seismic prior and internal learning, by up
to 8 dB of PSNR.
