A regionalized partially nonergodic ground-motion model for subduction earthquakes using the NGA-Sub database

Abstract

In this study, we derived a regionalized partially nonergodic empirical ground-motion model (GMM) for subduction interface and intraslab earthquakes using an extensive global database compiled as part of the NGA-Subduction project. The model can be used to estimate peak ground acceleration (PGA), peak ground velocity (PGV), and ordinates of 5%-damped pseudo-spectral acceleration (PSA) at periods ranging from 0.01 to 10 s for M ≥ 5.0, M≤ 8.5 for intraslab events, M≤ 9.5 for interface events, ZTOR≤ 50 km for interface events, ZTOR≤ 200 km for intraslab events, 10 ≤RRUP≤ 800 km, and 100 ≤VS30≤ 1000 m/s. Besides a global version of the model, the GMM accounts for regional differences in the overall amplitude (constant), anelastic attenuation, linear site response, and basin response for seven subduction-zone regions: Alaska (AK), Central America and Mexico (CAM), Cascadia (CASC), Japan (JP), New Zealand (NZ), South America (SA), and Taiwan (TW). The functional form of the model is structured such that the breakpoint magnitude, the magnitude at which the magnitude-scaling rate (MSR) transitions from a steeper to a shallower slope, is an adjustable parameter in the model. This makes it possible to take epistemic uncertainty in this parameter into account or adjust it based on other empirical or physical information, such as when the model is applied to a subduction zone not considered in the GMM. Besides the traditional mixed-effects aleatory between-event standard deviations and within-event standard deviations, within-model epistemic standard deviations in the median prediction for each region is quantified from a posterior distribution of model coefficients, standard deviations, and coefficient correlations using a Bayesian regression approach. Our full 800-sample posterior distribution can be used to account for epistemic uncertainty in the model coefficients, standard deviations, and predicted values. We also provide a simplified epistemic model using magnitude- and distance-dependent within-model standard deviations that can be used to facilitate the inclusion of within-model epistemic uncertainty directly in a probabilistic seismic hazard analysis. The within-model standard deviations can also be used to scale the GMM using a backbone modeling approach.