An Attempt for Symbolic Regression of Earthquake Ground-Motion Prediction Equation Using AI Feynman

Abstract

In the field of earthquake engineering, an empirical equation called Ground Motion Prediction Equation (GMPE) is often used to predict the intensity of ground motions caused by earthquakes. In previous studies, the relationship between object variables and explanatory variables has been determined empirically and subjectively based on simplified physical models. Constructing GMPEs that better reproduce the data will not only improve the accuracy of earthquake motion prediction, but also lead to the acquisition of knowledge in seismology and earthquake engineering. Recently, AI Feynman, a physics-inspired method for symbolic regression that combines neural network fitting with a suite of physics-inspired techniques, has been proposed and is superior to conventional methods. In this study, we attempted to apply AI Feynman to the construction of GMPEs. Synthetic tests showed that the symbolic regression was successfully achieved by normalizing the values of explanatory variables, while the symbolic regression of GMPEs was difficult in the noisy case.