Sjoerd de Ridder

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Sjoerd de Ridder
Sjoerd de Ridder 2017 headshot.png
PhD university Stanford University

SEG J. Clarence Karcher Award 2017

Sjoerd A. L. de Ridder received his PhD from Stanford and is presently a research fellow in the School of Mathematics at the University of Edinburgh. He has published 10 peer- reviewed papers with seven as first author. He was nominated primarily for his novel work on utilizing surface waves from ambient noise for exploration. He made a nice video for the EAGE YouTube series on this topic, which will give an idea of his communication skills. This video also illustrates some of the striking time-lapse images that he produced from repeated ambient noise surveys at the Valhall Field. His strong collection of a large number of letters illustrates exceptional technical contributions including and beyond the primary topic noted above. Additionally, he possesses an unusual intellectual curiosity and engaging style. He is likely to become a leader in our profession and is exemplary of the J. Clarence Karcher Award criteria.

SEG J. Clarence Karcher Award 2017 [1]

by Biondo Biondi and Andrew Curtis

Sjoerd A. L. de Ridder is the quintessential scientist. He tirelessly challenges received wisdom, and his advisor or indeed anyone else, until he fully understands the logic of an argument, or explains unexpected surprises that he encounters in theory or seismic data. He faces scientific problems with boundless energy and perseverance, and often arrives at creative solutions.

Sjoerd arrived for his PhD at Stanford knowing that he wanted to work on seismic interferometry. In the following years he demonstrated the power of interferometry for imaging and continuously monitoring near-surface seismic velocity without deploying active sources, using long recordings (days) of passive seismic data acquired by dense receiver arrays.

He applied interferometry to passive data from the large, dense 2D surface array above Valhall deployed by BP and partners. Both he and Aurelian Mordret, students at IPGP in Paris, worked in parallel on the same data and showed that surface waves synthesized from passive data can image seismic velocity in the subsurface to a depth of several hundred meters. Sjoerd validated his results by comparing them to images obtained by the use of full-waveform inversion on active-source data. In addition to the Valhall data, Sjoerd applied his method to passive data recorded by the Ekofisk permanent array deployed by ConocoPhillips and partners, obtaining similarly convincing results.

Sjoerd then devised a new method for applying an azimuthally anisotropic eikonal equation directly to the tomography of surface waves synthesized from passive data. The anisotropic velocity fields that he estimated in Valhall and Ekofisk correlate well with the expected anisotropy induced by the measured subsidence of the sea bottom in both fields. The potential to monitor the subsurface continuously in a cost-effective way is one of the exciting applications of passive seismic imaging from permanent arrays. For this application, Sjoerd studied the convergence rate of the interferometric crosscorrelations and the statistical stability of the corresponding estimated models for the Valhall data set recorded in 2004. He then applied the method to two data sets recorded in 2004 and 2010, and obtained qualitatively similar features to changes estimated by other authors from active seismic data sets.

During his fellowship in Edinburgh, Sjoerd envisioned moving beyond interferometry to estimate both the near-surface velocity structure and the spatially complete ambient seismic wavefield using very short periods of noise (minutes). This is a new way to image the subsurface using passive seismic data that may have a huge impact on subsurface real-time monitoring.

He calls this method “full wavefield inversion,” a significant extension of the methods first called “wave equation inversion” then “seismic gradiometry.” This powerful new way to estimate subsurface velocity from well-sampled passive data does not require the use of large-scale crosscorrelations as is required by interferometry. First tests on field data show that using the new method we need only 10 minutes of recording to estimate a velocity field roughly equivalent to the one obtained by using the correlation method on 40 hours of passive data, and recent results show that even the passive wavefield itself can be parameterized and estimated.

Finally, Sjoerd also has been active within SEG, serving as president of the Stanford local chapter and participating in the Student Leadership Program. He also has been a student member of the SEG Research Committee and a special section editor for the upcoming Geophysics special section: “Shared advances in exploration and fundamental geophysics.” In Edinburgh, he has been active in engaging academics in a crossdisciplinary research group in observation, imaging, and interpretation that spans geoscience, engineering, medical imaging, and mathematics. His dynamism and creativity always shine through in his actions, which will take him far. He thoroughly deserves this award.

References

  1. (2017). ”Honors and Awards.” The Leading Edge, 36(10), 806–819. http://dx.doi.org/10.1190/tle36100806.1