Evaluation of Image Comparison Methods for Complex Textures
Rodrigo S. Ferreira, Julia Noce, et al.
SEG 2019
High-resolution seismic data enable us to characterize the reservoirs with higher accuracy and/or detect smaller targets. Enhancing the seismic bandwidth can be achieved with broadband acquisition, various processing algorithms or a combination of both. In contrast to classic spectral matching type processes, we propose to take a different approach by using deep Generative Adversarial Networks (GANs). In theory, they can reconstruct the seismic data both temporally and spatially. This is inherent by design given the convolutional architecture of the GANs. That means GANs allow recovering the frequency content or the missing traces in seismic data. We propose amplitude encoding and histogram equalization to stabilize the performance of GANs on seismic data and show promising preliminary results for typical seismic processing and interpretation applications.
Rodrigo S. Ferreira, Julia Noce, et al.
SEG 2019
Byungchul Tak, Shu Tao, et al.
IC2E 2016
Kevin Gu, Eva Tuecke, et al.
ICML 2024
Kristjan Greenewald, Yuancheng Yu, et al.
NeurIPS 2024