3D Conditional Generative Adversarial Networks to enable large-scale seismic image enhancement
Abstract
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.
Cite
@article{arxiv.1911.06932,
title = {3D Conditional Generative Adversarial Networks to enable large-scale seismic image enhancement},
author = {Praneet Dutta and Bruce Power and Adam Halpert and Carlos Ezequiel and Aravind Subramanian and Chanchal Chatterjee and Sindhu Hari and Kenton Prindle and Vishal Vaddina and Andrew Leach and Raj Domala and Laura Bandura and Massimo Mascaro},
journal= {arXiv preprint arXiv:1911.06932},
year = {2019}
}
Comments
To be Presented at the NeurIPS 2019, Second Workshop on Machine Learning and the Physicial Sciences, Vancouver, Canada