Will be broadcast live on ERL's Youtube channel: https://www.youtube.com/channel/UCj-nizf-7D28iIfFreLS1Xw

Dr. Daniel Trugman, Feynman Postdoctoral Fellow at Los Alamos National Laboratory, presents "Characterizing Earthquake Hazards and Source Dynamics Using Machine Learning" at the MIT Earth Resources Laboratory.

"Observational seismology is an increasingly data-rich field in which data-driven machine learning techniques show significant potential. In this seminar, I focus on three specific examples from my research where simple algorithms and concepts from machine learning prove useful in characterizing earthquake source properties and hazard. First, I discuss how clustering and graph theoretical techniques can effectively be applied to large-scale differential travel time datasets to provide precise earthquake hypocentral relocations. The open-source software package GrowClust that incorporates these concepts is currently being used for large-scale relocation problems in study areas ranging from California, Nevada, and Kansas to Japan and Costa Rica. Next, I describe a new framework for empirical Ground Motion Prediction Equations (GMPEs) based on a supervised learning algorithm known as a Random Forest. I then use the Random Forest GMPE to measure the influence of dynamic stress drop on the measured peak ground accelerations of moderate earthquakes in the San Francisco Bay Area. Finally, I discuss ongoing collaborative efforts to systematically analyze the P-waveform features of large magnitude earthquakes for potential signatures of nucleation and rupture onset characteristics that correlate with event size. Results from this study may yield insight into the physics of earthquake rupture and have practical implications for real-time earthquake early warning algorithms."