Using Machine Learning to Improve Ground-Motion Prediction Equations
- 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.