Zachary Ross received his B.S. in physics at the University of California, Davis; his M.S. in civil engineering at California Polytechnic State University, San Luis Obispo; and his Ph.D. at the University of Southern California. He then joined the California Institute of Technology Seismological Laboratory, first as a postdoc and now as a faculty member. Zach has emerged as an outstanding early-career seismologist who is taking earthquake science in important new directions. He is an exceptionally worthy recipient of the 2019 Keiiti Aki Early Career Award from the Seismology section of AGU.
The volume of seismological data is growing rapidly, fueled by the now standard practice of retaining continuous data and by the development and deployment of relatively inexpensive sensors. That growth is likely to accelerate for the foreseeable future—perhaps dramatically with the advent of fiber-optic seismology. Seismology needs new approaches to extract as much information as possible from these massive data volumes. Zach is playing a key role in developing those approaches and has made exceptional contributions to seismology in the process.
Zach took the powerful technique of template matching to a new level, matching digital earthquake waveforms from all known earthquakes against all available continuous seismic data in Southern California. To do this required a deep dive into graphics processing units (GPU) supercomputing and substantial adaptation of data storage and earthquake location algorithms. Through such efforts, Zach uncovered over a factor of 10 more earthquakes than appear in the SCSN catalog, and locally up to 50 times more—all of this in an already very well studied area. His paper will stand as a landmark contribution to earthquake seismology and is certain to be emulated by others around the world. The sort of data-intensive computing this contribution represents is an important part of seismology’s future. Speaking of which, Zach is among a handful of young scientists who are rapidly forging new pathways in observational seismology through the methods of machine learning.
In his short career, Zach has already made important contributions in diverse areas including fault zone imaging, earthquake triggering, the dynamics of earthquake sequences, and the study of foreshocks. The Aki Award properly recognizes the significance of these accomplishments, but also anticipates further outstanding contributions in the future.
—Gregory C. Beroza, Department of Geophysics, Stanford University, Stanford, Calif.
It is quite an honor to receive the Keiiti Aki Early Career Award from AGU. My career would not be what it is today without the many wonderful mentors and collaborators I have had the good fortune of working with. In particular, I would like to acknowledge Yehuda Ben-Zion, who among many things, taught me to embrace the complexity of earthquakes and faults. I am grateful to Jean-Philippe Avouac, Egill Hauksson, Greg Beroza, Hiroo Kanamori, Peter Shearer, Elizabeth Cochran, Daniel Trugman, and the many others who have contributed to my growth as a scientist. I also thank my family and friends for their support over the years.
Earthquake science is entering a remarkable period. Today we are acquiring vast amounts of high-quality data, yet we lack the capability to analyze more than a tiny fraction of them simultaneously. Seismologists routinely face barriers to addressing important science questions because basic seismic data processing, such as building a seismicity catalog from scratch, is an astonishingly difficult task. The trajectory of my own research program was heavily influenced by my frustration arising from these limitations. Our data sets are inherently high dimensional, and their richness is far from being fully understood. I am convinced that being able to better navigate and find structure in these data sets is the key to a better understanding of earthquakes and faults.
—Zachary E. Ross, California Institute of Technology, Pasadena
(2020), Ross receives 2019 Keiiti Aki Early Career Award, Eos, 101, https://doi.org/10.1029/2020EO149101. Published on 16 September 2020.
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