Using machine learning techniques to improve earthquake focal mechanisms

Recent high-performance computing and machine learning approaches have improved our ability to detect earthquakes, but characterizing these earthquakes has proven to be more challenging. One desired characterization is determining earthquake focal mechanisms which inform us about fault structures, stress orientations, and rupture kinematics. However, these mechanisms typically can only be produced for well-recorded seismicity. I’ll talk about some machine learning techniques that we can use to increase the number of measurements, verify their results, and how we are developing software with these new workflows in mind.

Speaker: Rob Skoumal, Earthquake Science Center

Attend in person or register at weblink to attend online

Room 350/372