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

2019/11/16 by Praneet Dutta, Dutta, Praneet, Bruce Power +23 · 1 citation
Computer Science · Earth and Planetary Sciences · #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Signal Denoising Methods #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Seismic Imaging and Inversion Techniques #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1911.06932

openalex publication_date 2019/11/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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