3D Conditional Generative Adversarial Networks to enable large-scale seismic image enhancement
This work addresses uncertainty reduction and decision improvement for energy exploration, but it is incremental as it builds on existing GAN methods with conditional information.
The paper tackled the problem of resolution limitations and noise contamination in seismic images by proposing GAN-based models for frequency enhancement and denoising, resulting in improved performance on PSNR and SSIM metrics when adding lithology class information.
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.