Arnab Dhara discusses his paper, "Physics-guided deep autoencoder to overcome the need for a starting model in full-waveform inversion," in the June issue of The Leading Edge. Arnab proposes employing deep learning as a regularization in full-waveform inversion. He explains why physics-based solutions with machine learning are challenging to develop, how he made it possible to train the network without known answers, and why he tested his approach with the Marmousi and SEAM models. Arnab also shares why this research took over 20 years to build on the initial idea and how he used full-waveform inversion without a starting model. This is a cutting-edge conversation that may represent the future of FWI.
Denes Vigh discusses the current developments and future growth areas for full-waveform inversion. In this informative conversation, Denes shares why it's necessary for full-waveform inversion (FWI) to utilize the full acquired wavefield, how ocean-bottom node surveys have impacted FWI, the next frontier for FWI, and his favorite feature of utilizing FWI in his work.
Dr. Michal Malinowski, special section lead editor for Interpretation and Jyoti Behura, special section lead editor for The Leading Edge join Andrew Geary to discuss full-waveform inversion.