"Generative modeling is a game-changer. We can now capture high-dimensional statistics that we could never have captured in the past."

Felix Herrmann explains how digital twins and generative AI are reshaping subsurface geophysics. He highlights the importance of open-source tools, multimodal data, and uncertainty-aware models for better decision-making in energy and storage projects. By combining physics with AI, his work shows how geophysics can move beyond silos and create more reliable and efficient solutions.

KEY TAKEAWAYS

> Digital twins informed by multimodal data can reduce uncertainty and improve reservoir management.

> Open-source tools and agreed benchmarks are essential for accelerating innovation in geophysics.

> Combining physics-based models with generative AI creates robust, practical solutions for complex subsurface challenges.

Read Felix's article in The Leading Edge, "President's Page: Digital twins in the era of generative AI," at https://doi.org/10.1190/tle42110730.1.

GUEST BIO

Felix J. Herrmann earned his Ph.D. in engineering physics from Delft University of Technology in 1997, followed by research appointments at Stanford and MIT. He later joined the University of British Columbia faculty in 2002 and moved to the Georgia Institute of Technology in 2017, where he is the Georgia Research Alliance Scholar Chair in Energy with cross-appointments across multiple schools.

Dr. Herrmann leads a cross-disciplinary research program in computational imaging, spanning seismic and medical applications, and is recognized for innovations in machine learning, optimization, and high-performance computing that have reduced costs in seismic data acquisition and imaging. A past SEG Distinguished Lecturer and recipient of the SEG Reginald Fessenden Award, he directs the Seismic Laboratory for Imaging and Modeling and co-founded the Center for Machine Learning for Seismic (ML4Seismic) to advance AI-assisted seismic imaging and reservoir monitoring through industry collaboration.