Seismic data interpolation with conditional generative adversarial networks (cGANs)

Abstract

In seismic acquisition and processing, several factors may cause missing data or data issues. Primarily, the physical constraints of the method, such as limitation on the available length of the streamer or receiver cable, instrumental and recording problems, and target illumination, e.g., when a geo body shadows the waves, are some of the significant sources of issues in the survey. Many works have tackled this problem using pre-stack data and can be classified into three main categories: wave-equation, domain transform, and prediction-error-filter methods. In this work, we assess the performance of a cGAN (Conditional Generative Adversarial Network) for the interpolation problem in post-stack seismic datasets. To the best of our knowledge, this is the first work to evaluate a deep learning approach in this context. Quantitative and qualitative evaluations of our experiments indicate that deep-networks may present a compelling alternative to classical methods.