Probabilistic Approach to Prepare Input Models for Physics-based Ground Motion Simulation

Abstract

Accurate prediction of ground motion intensity and its variability is an important element in seismic hazard assessment. Physics-based ground motion prediction has become popular recently, given increasingly complex earthquake rupture and wave propagation modeling techniques and fastgrowing computing capability. Physics-based ground motion prediction may contribute significantly to complementing empirical ground motion prediction equations (GMPEs), especially in near-source regions for large events, where we suffer from a lack of recorded ground motion data. However, it is also true that most input parameters required for modeling are not well constrained by observed geophysical data, and more importantly, the variability of the input parameters is not well quantified. It is essential to prepare reasonable input models and quantify their epistemic and aleatory uncertainty for successful application of the physics-based modeling to advanced seismic hazard assessment. I previously proposed to quantify the aleatory uncertainty of finite earthquake rupture processes in the framework of 1-point and 2-point statistics of key kinematic source parameters, such as slip, slip velocity, and rupture velocity. I developed a generalized source statistics model for the magnitude range of Mw 6.5–7.0 by analyzing a number (~150) of spontaneous dynamic rupture models and tested simulated ground motions against empirical GMPEs, following the broadband platform project of the Southern California Earthquake Center. This paper discusses various aspects of the potentials and challenges in the proposed earthquake source characterization, and subsequent earthquake source and ground motion modeling. When we combine realistic input models, whose epistemic and aleatory uncertainties are well quantified, with physics-based modeling, simulated ground motions may significantly contribute to advanced seismic hazard assessment.