IST Lunch Bunch
Zachary Ross is an Assistant Professor of Geophysics at the California Institute of Technology. He received a Ph.D. in Geophysics from the University of Southern California in 2016. His research is focused on developing an improved understanding of earthquakes and faults through analysis of large seismic datasets. He is the recipient of the 2019 Keiiti Aki Early Career award from the American Geophysical Union.
The volume of seismic data recorded around the world is exploding. At the same time, standard techniques for earthquake detection still routinely miss the smallest earthquakes, which represent the vast majority of seismic activity that occurs naturally. These hidden events, however, are essential to advancing our understanding of earthquakes and faults because they fill in the gaps between larger events and provide a more complete picture of how earthquake sequences evolve in space and time. Reliable measurements of time and amplitude properties of seismic waves also enable tomographic images of Earth's interior, and delineate the earthquake rupture process below the surface. Earthquake early warning requires rapid identification that a large earthquake has occurred with only a tiny fraction of the total data available at that moment.
Seismology is a field that is rich in labeled data and has many difficult data-driven problems with some unique challenges. These aspects have led to a surge in recent applications of deep learning to seismology, resulting in state-of-the-art performance on numerous tasks. In this talk, I will discuss several important problems in seismology related to earthquake detection, localization, and earthquake early warning, and the development of deep learning algorithms to address them. Machine learning will play a prominent role in the future of seismology by improving real-time earthquake monitoring, as well as advancing earthquake science to the next level from analysis of large high-dimensional datasets.