Improving Seismic Data Resolution with Deep Generative Networks

Abstract

Noisy traces, gaps in coverage, or irregular/inadequate trace spacing are common problems in both land and marine surveys, possibly hindering the geological interpretation of an area of interest. This problem has been typically addressed in the literature using prestack data; however, prestack data are not always available. As an alternative, poststack interpolations may aid the geological interpretation by increasing the spatial density of a seismic section and can also be used to reconstruct entire sections by interpolating neighboring traces, reducing field costs. In this letter, we evaluate the performance of conditional Generative Adversarial Networks (cGANs) as an interpolation tool for improving seismic data resolution on a public poststack seismic data set and compare our results with the traditional cubic interpolation. To perform the comparisons, we used structural similarity (SSIM), mean squared error (mse), and local binary patterns (LBPs) texture descriptor. The results show that cGANs outperform traditional algorithms by up to 72% and that the texture descriptor was able to better capture image similarities, producing results more coherent with the visual perception.