# 3D Conditional Generative Adversarial Networks to enable large-scale seismic image enhancement

arXiv: https://arxiv.org/abs/1911.06932 · HTML: https://mlsanity.com/paper/1911.06932/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Using Convolutional Neural Networks to Detect Compression Algorithms](https://mlsanity.com/paper/2111.09034/) (2021)
- [DGL-GAN: Discriminator Guided Learning for GAN Compression](https://mlsanity.com/paper/2112.06502/) (2021)

## Abstract (arXiv)

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.