194: Improving integration in machine learning workflows

Felix J. Herrmann highlights the July 2023 special section in The Leading Edge - digitalization in energy. In this episode with host Andrew Geary, Felix discusses his open-access article, "Learned multiphysics inversion with differentiable programming and machine learning." He shares why the future of the oil and gas industry depends on the democratization of technology design. He provides insights into why modernizing wave-equation inversion frameworks is important to geophysics and shares the implications for the results of his study. This episode provides a glimpse into the future capabilities of machine learning to help provide the path for the next great discoveries in geophysics. Listen to the full archive at https://seg.org/podcast. SPONSOR This episode is sponsored by Katalyst Data Management®. Katalyst Data Management® provides the only integrated, end-to-end subsurface data management solution for the oil and gas industry. Over 215 employees operate in North America, Europe, Asia-Pacific, and South America, dedicated to enabling digital transformation and optimizing the value of geotechnical information for exploration, production, and M&A activity. Learn more at http://www.katalystdm.com/. BIOGRAPHY Felix J. Herrmann graduated from Delft University of Technology in 1992 and received his Ph.D. in engineering physics from that same institution in 1997. After research positions at Stanford University and the Massachusetts Institute of Technology, he returned in 2002 as faculty at the University of British Columbia. In 2017, he joined the Georgia Institute of Technology, now a Georgia Research Alliance Scholar Chair in Energy. He was cross-appointed between the Schools of Earth & Atmospheric Sciences, Computational Science & Engineering, and Electrical & Computer Engineering. His cross-disciplinary research program spans several areas of computational imaging, including seismic and, more recently, medical imaging. Dr. Herrmann is widely known for tackling challenging problems in the imaging sciences by adapting techniques from randomized linear algebra, PDE-constrained and convex optimization, high-performance computing, machine learning, and uncertainty quantification. Over his career, he has been responsible for several cost-saving innovations in industrial time-lapse seismic data acquisition and wave-equation-based imaging. RELATED LINKS * Join Software Underground - The place for scientists and engineers that love rocks and computers. (https://softwareunderground.org/) * Mathias Louboutin, Ziyi Yin, Rafael Orozco, Thomas J. Grady II, Ali Siahkoohi, Gabrio Rizzuti, Philipp A. Witte, Olav Møyner, Gerard J. Gorman, and Felix J. Herrmann, (2023), "Learned multiphysics inversion with differentiable programming and machine learning," The Leading Edge 42: 474–486. (https://doi.org/10.1190/tle42070474.1 - open access) * Vladimir Kazei and Mita Sengupta, (2023), "Introduction to this special section: Digitalization in energy," The Leading Edge 42: 456–456. (https://doi.org/10.1190/tle42070456.1) * Read the July 2023 special section: Digitalization in energy. (https://library.seg.org/toc/leedff/42/7) Subscribers can read the full articles at https://library.seg.org/; abstracts are always free. CREDITS Seismic Soundoff explores the depth and usefulness of geophysics for the scientific community and the public. If you want to be the first to know about the next episode, please follow or subscribe to the podcast wherever you listen to podcasts. Two of our favorites are Apple Podcasts and"Spotify. If you have episode ideas, feedback for the show, or want to sponsor a future episode, find the "Contact Seismic Soundoff" box at https://seg.org/podcast. Zach Bridges created original music for this show. Andrew Geary hosted, edited, and produced this episode at TreasureMint. The SEG podcast team is Jennifer Cobb, Kathy Gamble, and Ally McGinnis.