  We propose GAN-based image enhancement models for frequency enhancement of 2D
and 3D seismic images. Seismic imagery is used to understand and characterize
the Earth's subsurface for energy exploration. Because these images often
suffer from resolution limitations and noise contamination, our proposed method
performs large-scale seismic volume frequency enhancement and denoising. The
enhanced images reduce uncertainty and improve decisions about issues, such as
optimal well placement, that often rely on low signal-to-noise ratio (SNR)
seismic volumes. We explored the impact of adding lithology class information
to the models, resulting in improved performance on PSNR and SSIM metrics over
a baseline model with no conditional information.
