Sergey Fomel is Wallace E. Pratt Professor of Geophysics at The University of Texas at Austin and the Director of the Texas Consortium for Computational Seismology (TCCS). At UT Austin, he is affiliated with the Bureau of Economic Geology, the Department of Geological Sciences, and the Oden Institute for Computational Engineering and Sciences. Sergey received a PhD in Geophysics from Stanford University in 2001. Previously, he worked at the Institute of Geophysics in Russia (currently Trofimuk Institute of Petroleum Geology and Geophysics), Schlumberger Geco-Prakla, and the Lawrence Berkeley National Laboratory.
For his contributions to exploration geophysics, he has been recognized with a number of professional awards, including the J. Clarence Karcher Award from SEG in 2001, Best SEG Poster Presentation Awards in 2007 and 2011, and the Conrad Schlumberger Award from EAGE in 2011. He has served SEG in different roles, most recently as the Vice President, Publications. He devotes part of his time to developing Madagascar, an open-source software package for geophysical data analysis.
SEG Honorary Membership 2022
Sergey Fomel and Aria Abubakar are the 2022 recipients of Honorary Membership, awarded to those who have made distinguished contributions to exploration geophysics or a related field or to the advancement of the profession of exploration geophysics through service to the Society. Fomel served as SEG Distinguished Lecturer in 2001 and was SEG Vice President, Publications from 2017 to 2019. He received the J. Clarence Karcher Award in 2001 and won the Best Paper in Interpretation Award in 2017. He developed the Madagascar and SEGTeX software packages, has graduated numerous MS and PhD students, and is a prolific writer with 172 peer-reviewed papers. He has been associate editor of GEOPHYSICS, along with numerous guest editorships for GEOPHYSICS, IEEE, and other journals.
Biography Citation for SEG Honorary Membership 2022
Contributed by Raymond Abma
It is an honor and a pleasure to write this award citation for Professor Sergey Fomel for SEG Honorary Membership. Sergey earned his PhD at Stanford University and was mentored by Jon Claerbout, working with the Stanford Exploration Project. Before this, he attended Novosibirsk State University where he graduated with honors, after which he worked at the Institute of Geophysics at Novosibirsk in Russia. After graduating from Stanford, he was a postdoctoral fellow at the Lawrence Berkeley National Laboratory. He is now the Wallace E. Pratt Professor of Geophysics at the University of Texas at Austin and is the director of the Texas Consortium for Computational Seismology (TCCS). At UT Austin, he is affiliated with the Bureau of Economic Geology, the Department of Geological Sciences, and the Oden Institute for Computational Engineering and Sciences.
Sergey served as SEG vice president, publications from 2017 to 2019. He has been an associate editor for Geophysics and IEEE Transactions on Geoscience and Remote Sensing. He presently has hundreds of published journal articles and SEG Annual Meeting expanded abstracts. His most cited articles are on plane-wave destruction, shaping regularization, local seismic attributes, and the seislet transform. Additionally, Sergey collaborated with Jon Claerbout on Jon's book Geophysical Image Estimation by Example.
In 2001, Sergey received the SEG J. Clarence Karcher Award and, in 2011, the European Association of Geoscientists and Engineers (EAGE) Conrad Schlumberger Award. In 2020, he presented the SEG Distinguished Lecture Automating seismic data analysis and interpretation. He won the Best Paper in Interpretation Award in 2017 (with coauthor Rui Zhang), the Best Poster Paper Presented at the Annual Meeting Award in 2006 and 2010 (with coauthors Lexing Ying and Xiaolei Song), and the Honorable Mention Award (Geophysics) in 2003 with coauthor Paul Sava for their paper Angle-domain common-image gathers by wavefield continuation methods. He won the Loránd Eötvös Award from EAGE with Evgeny Landa and Tijmen Jan Moser in 2007 for their Geophysical Prospecting paper Path-integral seismic imaging.
Sergey is a champion of reproduceable research. As one aspect of this, he started the development of the Madagascar open-source software project for geophysical data analysis that attracted many developers from around the world and now has more than 1000 data analysis programs and 300 reproducible papers. He also developed and maintains SEGTeX, which is used by about half of the authors of Geophysics papers and expanded abstracts.
Sergey is one of the most brilliant and hardworking geophysicists I have known. We shared an office at Stanford, and I was always impressed with his productivity and the range of his work. At Stanford, he tried to read one new technical book every month, while most of us students worked hard just to keep up with our studies. Originally, Sergey was a little shy about speaking. After a couple of years at Stanford, he was much more comfortable with English. Still, when he started teaching at Berkeley, he asked an actor friend of his to teach him a stage voice. At the end of the semester, his student's only complaint was that he spoke too loudly.
Sergey has made substantial contributions to geophysics. He has mentored many students at the University of Texas, where he established his consortium, TCCS. He has supervised 27 students and is a popular mentor for his students. TCCS has produced works including elastic full-waveform inversion, velocity analysis, seismic imaging, anisotropy, least-squares migration, diffraction imaging, seismic data processing, and simultaneous sourcing. A recent focus of TCCS's work is the geophysical applications of machine learning, of which there have been several important advances developed by Sergey's group.
In short, Sergey's contributions to SEG and to geophysics make this recognition well deserved.
2020 1Q/2Q SEG Distinguished Lecturer
Automating seismic data analysis and interpretation
Recent developments in artificial intelligence and machine learning can automate different tasks in data analysis. I will discuss the quest for automation by tracking the development of automatic picking algorithms, from velocity picking in seismic processing to horizon picking in seismic interpretation. We will search for the limits of automation to discover the distinguishing qualities that separate human geophysicists from machines.
The automatic picking algorithm follows the analogy between picking trajectories in images with variable intensities and tracking seismic rays in the subsurface with variable velocities. Picking trajectories from local similarity panels generated from time shifts provides an effective means for measuring local shifts between images, with practical applications in time-lapse and multicomponent image registration, matching seismic with well logs, and data compression using the seislet transform. In seismic interpretation, automatic picking finds additional application for tracking fault surfaces, salt boundaries, and other geologic features.
The power of automatic picking is further enhanced by novel deep learning algorithms. The deep learning approach can use a convolutional neural network trained on synthetically generated images to detect geologic features in real images with an unmatched level of performance in both efficiency and accuracy. The lessons to learn from these developments include not only the potential for automation, harvested through artificial neural networks and modern computing resources, but also the potential for human ingenuity, harvested through professional networks.
Figure from X. Wu, S. Fomel, and M. Hudec, 2018, Fast salt boundary interpretation with optimal path picking , Geophysics, v. 83, O45–O53.
A recording of the lecture is available.
Listen to Sergey discuss his lecture Modern seismic interpretation & what separates humans from machines with Sergey Fomel in Episode 76 of Seismic Soundoff, in-depth conversations in applied geophysics.
- Sava, P. and S. Fomel (2003) Angle-domain common-image gathers by wavefield continuation methods, GEOPHYSICS 68(3):1065.