Removing elastic effects in FWI using supervised cycled generative adversarial networks - Yao,J; Guasch,L; Warner,M; Davies,D; Wild,A (2019)
© 81st EAGE Conference and Exhibition 2019 Workshop Programme. All rights reserved. We use a CycleGAN to map acoustic synthetic data to elastic data, and to map elastic field data to acoustic data, and use the resulting data to perform acoustic FWI on a 3D field dataset that shows strong elastic effects at top chalk. Using machine learning to change the effective physics of field data has many other potential applications.