Abstract
Abstract. In this paper, we employed polynomial chaos (PC) expansions to understand earthquake rupture model responses to random fault plane properties. A sensitivity analysis based on our PC surrogate model suggests that the hypocenter location plays a dominant role in peak ground velocity (PGV) responses, while elliptical patch properties only show secondary impact. In addition, the PC surrogate model is utilized for Bayesian inference of the most likely underlying fault plane configuration in light of a set of PGV observations from a ground-motion prediction equation (GMPE). A restricted sampling approach is also developed to incorporate additional physical constraints on the fault plane configuration and to increase the sampling efficiency.
Highlights
One of the most important challenges seismologists and earthquake engineers face in designing large civil structures and response plans, especially in highly populated cities prone to large damaging earthquakes, is the reliable estimation of groundmotion characteristics at a given location
As mentioned before, introducing the auxiliary parameter ζ leads to significant efficiency improvement in the MCMC sampling process. 4.4 Comparing peak ground velocity (PGV) We summarize the Bayesian analysis by comparing PCpredicted PGV responses to the three inferred fault plane configurations discussed above with the reference ground-motion prediction equation (GMPE) curve
An earthquake rupture model was adopted to explore the stochastic dependence of ground motion on random fault plane configurations
Summary
One of the most important challenges seismologists and earthquake engineers face in designing large civil structures (e.g., buildings, dams, bridges, power plants) and response plans, especially in highly populated cities prone to large damaging earthquakes, is the reliable estimation of groundmotion characteristics at a given location. We investigate the level of complexity needed in kinematic rupture models of magnitude 6.5 strike-slip events to produce ground motion similar to a reference GMPE. To this end, we utilize the polynomial chaos (PC) approach (Ghanem and Spanos, 1991; Xiu and Karniadakis, 2002; Le Maître and Knio, 2010) to build functional representations of PGV responses of an original source model. The ranking considers uncertainties in both the GMPE and model parameters This provides useful insight on the level of complexity needed in kinematic source models for ground-motion simulations to satisfy both observational constraints and engineering/design requirements for seismic safety. ∗ denotes parameters whose feasible ranges are dependent on others
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