Using Machine Learning to Improve Ground-Motion Prediction Equations

Brad Aagaard


Date & Time
Yosemite Conference Room, Building 19, Moffett Field

I assess the potential for artificial neural networks to capture complex relationships among input parameters in ground-motion prediction equations (GMPEs) that may be difficult to describe in traditional empirical regressions. I construct a suite of GMPEs for California using an artificial neural network model with data from the PEER NGA West2 ground-motion database. I consider different parameterizations for capturing the spatial variations in ground motion associated with the earthquake mechanism, site characterization, and distance from the rupture. For the inputs associated with each effect, I assess how the neural network weights the combination of parameters, the effect on the uncertainty in the resulting GMPE, and compare the functional form to traditional empirical regression-based GMPEs. For example, complex variations in amplitude associated with hanging wall/footwall effects can be captured using a combination of Joyner-Boore distance and mean rupture distance. The artificial neural network provides a generalized form that remains the same for all of these different parameterizations, thereby facilitating exploration of how to best represent complex spatial variations of ground-motion amplitude in GMPEs.

Closed captions are typically available a few days after the seminar. To turn them on, press the ‘CC’ button on the video player. For older seminars that don’t have closed captions, please email us, and we will do our best to accommodate your request.

Video Podcast