Early Warning for Earthquakes with Large Rupture Dimension

Abstract

Earthquake early warning systems have become popular these days, and many seismologists and engineers are making research efforts for their practical application. The existing earthquake early warning systems provide estimates of the location and size of earthquakes, and then ground motions at a site are estimated as a function of the epicentral distance and site soil properties. However, for large earthquakes, the energy is radiated from a large area surrounding the entire fault plane, and the epicenter indicates only where rupture starts. In this project, we focus on an earthquake early warning system considering fault finiteness. We provide a new methodology to estimate rupture geometry and slip size on a finite fault in real time for the purpose of earthquake early warning. We propose a new model to simulate high-frequency motions from earthquakes with large fault dimension: the envelope of high-frequency ground motion from a large earthquake can be expressed as a root-mean-squared combination of envelope functions from smaller earthquakes. We parameterize the fault geometry with an epicenter, a fault strike, and two along-strike rupture lengths, and find these parameters by minimizing the residual sum of squares of errors between ground motion models and observed ground motion envelopes. To provide the information on the spatial extent of rupture geometry, we present a methodology to estimate a fault dimension of an earthquake in real time by classifying seismic records into near-source or far-source records. We analyze peak ground motions and use Bayesian model class selection to find a function that best classifies near-source and far-source records based on these parameters. This discriminant function is useful to estimate the fault rupture dimension in real time, especially for large earthquakes. In order to characterize slip on the fault in real time, we construct an analytical function to estimate slip on the fault from near-source ground displacement observations. In real-time analysis, we back project the recorded displacement data onto the fault line to estimate the size of the slip on the fault. The simulation results show that the slip size estimation predicts the observed GPS static displacement on the fault quite well. This current slip size on the fault is used for a probabilistic prediction of additional rupture length in the near future. We characterize the distribution of additional rupture length conditioned on the current slip on the fault for the ongoing rupture from the simulation with a 1-D slip model. The probability density of additional rupture length can be approximated by a lognormal distribution conditioned on the current slip size.