Felix J. Herrmann graduated from Delft University of Technology in 1992 and received in 1997 a PhD in Engineering Physics (DELPHI Consortium) from that same institution. After research positions at Stanford University and the Massachusetts Institute of Technology (Earth Resources Laboratory), he joined the faculty of the University of British Columbia in 2002 where he is now affiliate professor. Since 2017, he is cross-appointed at the Schools of Earth & Atmospheric Sciences, Computational Science & Engineering, and Electrical & Computer Engineering of the Georgia Institute of Technology. His research program spans several areas of computational exploration seismology including economic and low-environmental impact (time-lapse) acquisition with compressive sensing, data processing, and wave-equation-based imaging and inversion. He was among the first to recognize the importance of curvelet transforms, compressive sensing, and large-scale (convex) optimization addressing problems involving simultaneously acquired/blended (time-lapse) data with surface-related multiples. He developed curvelet-based denoising and matched filtering methods that are now widely used by industry. He also made several contributions to full-waveform inversion and (least-squares) reverse-time migration by introducing concepts from stochastic and constrained optimization designed to produce high-fidelity results at lower costs. More recently, he has been involved in developing rank minimization techniques for seismic data acquisition, in the development of a domain-specific language for finite differences called Devito, and in the application of deep convolutional neural nets to seismic data processing and inversion. To drive innovations within industry, he started in 2004 SINBAD, a research consortium responsible for several major breakthroughs resulting in tangible efficiency improvements in industrial data acquisition and full-waveform inversion. At Georgia Tech, he vows to continue these activities by setting up a new research consortium with a focus on machine learning. He serves as deputy editor for Geophysical Prospecting and is a Georgia Research Alliance eminent scholar.
In the spring of 2019, he served as the SEG Distinguished Lecturer. Felix Herrmann with Charles C. Mosher were the 2020 recipients of the SEG Reginald Fessenden Award for their work in randomized survey design and wavefield reconstruction via sparse inversion.
2019 1Q/2Q SEG Distinguished Lecturer
Sometimes it pays to be cheap – Compressive time-lapse seismic data acquisition
During these times of sustained low oil prices, it is essential to look for new innovative ways to collect (time-lapse) seismic data at reduced costs and preferably also at reduced environmental impact. By now, there is an increasing body of corroborating evidence — whether these are simulated case studies or actual acquisitions on land and marine — that seismic acquisition based on the principles of compressive sensing delivers on this premise by removing the need to acquire replicated dense surveys. Up to ten-fold increases in acquisition efficiency have been reported by industry while there are indications that this breakthrough is only the beginning of a paradigm shift where full-azimuth time-lapse processing will become a reality. To familiarize the audience with this new technology, I will first describe the basics of compressive sensing, how it relates to missing-trace interpolation and simultaneous source acquisition, followed by how this technology is driving innovations in full-azimuth (time-lapse) acquisition, yielding high-fidelity data with a high degree of repeatability and at a fraction of the costs.
The 2020 SEG Reginald Fessenden Award 
by Doug Foster, Yu Zhang, and Nick Moldoveanu
Felix Herrmann has focused his research and consortium on compressive sensing and sparse inversion for many years, and he and his students have suggested applications involving seismic acquisition, processing, and imaging. Motivated by rendering harmful sub-sampling related aliases into incoherent noise through randomized sampling, Moser derisked this technology and improved acquisition productivity substantially via randomized survey design and wavefield reconstruction via sparse inversion. This breakthrough is a great example of how academic research can help drive innovations in our industry.
Biography Citation for the 2020 SEG Reginald Fessenden Award
It is our great pleasure to introduce Charles (Chuck) Mosher of ConocoPhillips and Felix Herrmann of Georgia Institute of Technology as the 2020 SEG Reginald Fessenden Award winners, recognizing their pioneering work in the development and application of compressive sensing (CS) in seismology. Borrowing from electrical engineering and mathematics, they have shown how new theories can be utilized to efficiently acquire higher-quality seismic surveys at costs much lower than that afforded by traditional methods. These two award winners did not directly work together but both benefitted from each other’s contributions and set an exemplary example of how technical success can be achieved by the interaction between academia and industry. Their efforts are establishing the new paradigm for seismic acquisition, and their innovations are deserving of this prestigious award.
Such concepts as sampling interval and aliasing have been well established, but these concepts are based on regularly discretizing a continuous signal. Irregular sampling allows CS to avoid the traditional Nyquist criteria of sampling two points per wavelength to eliminate aliasing. This opens the possibility for sparser sampling while maintaining or enhancing bandwidth and managing incoherently aliased energy. This is the basic premise of CS, but there are significant hurdles in implementing any new approach for effective use in the field. Questions such as how to acquire irregularly sampled field data, represent it in a compressed form, deblend simultaneous sources, and perform a sparse inversion to reconstruct the desired output data are among the key challenges Chuck and Felix have addressed successfully.
While a professor at the University of British Columbia, Felix led the industry-supported SINBAD consortium from 2005 to 2017. The focus of this consortium was on applications of CS for cost reduction of seismic acquisition, seismic processing, and seismic imaging. Felix and his colleagues addressed sampling- related cost of seismic acquisition by using CS wavefield reconstruction methods based on randomized sampling techniques and simultaneous shooting in land and marine acquisitions. Through several publications, he and his team demonstrated that a signal can be represented sparsely, interference (aliasing) can be rendered into incoherent noise by random sampling, and a nontraditional optimization algorithm can recover the desired signal from the sparse representation. Key areas in which Felix has contributed are: seismic data processing, wave equation imaging, and full-waveform inversion (FWI). In seismic processing, he has shown that multidimensional data can either be sparsely represented using a curvelet transform or in low-rank factored form. Given these structured representations, Felix demonstrated how seismic wavefields can be reconstructed from severe undersamplings by promoting structure via optimization. He showed how to represent primary reflections with a sparse spike inversion, which also draws on new techniques from modern convex optimization. In wave equation imaging, he has shown how statistical sampling of shots, in combination with curvelet-domain sparsity promotion, can yield impressive cost reductions of reverse time migration and FWI. He and his team also were responsible for the development of wavefield reconstruction inversion, a new technique designed to mitigate the impact of local minima. Finally, he was selected as the SEG 2019 first-quarter/second- quarter Distinguished Lecturer to present “Sometimes it pays to be cheap — Compressive time-lapse seismic data acquisition,” which focuses on obtaining repeatable time- lapse data without insisting on replication in the field.
Chuck and his team at ConocoPhillips have also made significant advances that are currently realizing the potential of CS in acquisition and processing. Chuck extends the windowed Fourier transform to a fast generalized windowed transform by introducing fractional decimation concepts to overcome sub-band aliasing artifacts, and this provides a sparse transform to represent data with fewer samples. Chuck et al. developed nonuniform optimal sampling for choosing nonuniform sensor locations for seismic survey planning and prove that the new sampling strategy makes it possible to recover significantly broader spatial bandwidth than could be obtained using uniform sampling. CS data reconstruction is an important step, and Chuck et al. developed an effective seismic data reconstruction workflow. They also introduced a novel optimization algorithm for data reconstruction, which adapts the alternating direction method with a variable- splitting technique to recover a sparse representation of the seismic data. Source deblending is an important step, and they have demonstrated how this can improve seismic data quality with reduced acquisition time and cost. To date, ConocoPhillips and its business partners have acquired 17 CS data sets globally, including ocean- bottom node/cable, narrow-azimuth marine streamer, and land vibroseis surveys. For all the finished processing projects, the imaging results from the CS surveys exceeds the quality of legacy or neighboring surveys with traditional designs. The paradoxical result is that CS theory produces higher data quality at lower cost and in shorter time frames than would be achieved with equivalent traditionally sampled survey designs. To date, global deployments of CS technology in production have led to direct acquisition cost savings of more than US$165 million and indirect cost savings of US$180 million from optimized drilling decisions.
Both of the recipients have dedicated their entire careers to geophysical research, and they have always been active, enthusiastic, and visionary. Their reputations as distinguished geophysical innovators are well recognized in our community, earning them the distinction of the 2020 Reginald Fessenden Award.
- ↑ Honors and Awards Program, SEG 2020, 13 Oct 2020, Houston