A predictive model for Arias intensity at multiple sites and consideration of spatial correlations

Abstract

SUMMARYArias intensity, I a , has been identified as an efficient intensity measure for the estimation of earthquakeinduced losses. In this paper, a new model for the prediction of Arias intensity, which incorporates nonlinear site response through the use of the average shear-wave velocity and a heteroskedastic variance structure, is proposed. In order to estimate the effects of ground motions on spatially-distributed systems, it is important to take into account the spatial correlation of the intensity measure. However, existing lossestimation models, which use I a as an input, do not take this aspect of the ground motion into account. Therefore, the potential to model the spatial correlation of Arias intensity is also investigated. The empirical predictive model is developed using recordings from the Pacific Earthquake Engineering Research Center Next Generation of Attenuation database whereas the model for spatial correlation makes use of the wellrecorded events from this database, that is the Northridge and Chi-Chi earthquakes.

Interfrequency Correlations among Fourier Spectral Ordinates and Implications for Stochastic Ground‐Motion Simulation

“…It is implicitly assumed in this comparison that the empirical model represents the distribution of naturally occurring motions. Figure 10 compares the empirical cumulative distribution functions from 200 stochastic simulations for three different magnitude values with and without consideration of interfrequency correlations and compares these with the predictions of Foulser-Piggott and Stafford (2011). In the left panel where predictions from a magnitude 5.0 event are shown, there is a significant disagreement between the sets of results.…”

“…In the left panel where predictions from a magnitude 5.0 event are shown, there is a significant disagreement between the sets of results. Part of this may be related to the Foulser-Piggott and Stafford (2011) model overpredicting slightly at the lower end of its applicability (Bommer et al, 2007), but the differences are large. The empirical model predicts median Arias intensities that are more than an order of magnitude greater than the stochastic results without correlations and more than three times greater than the case considering correlations.…”

Ground‐Motion Prediction Models for Arias Intensity and Cumulative Absolute Velocity for Japanese Earthquakes Considering Single‐Station Sigma and Within‐Event Spatial Correlation

“…I A and CAV have also been found to correlate with seismic intensity measures such as modified Mercalli intensity (Campbell and Bozorgnia, 2012a) and spectral accelerations (Bradley, 2012). Modern predictive equations for I A and CAV adopt complex functional forms for median and logarithmic standard deviation based on the state-of-the-art seismological findings (Campbell and Bozorgnia, 2010;Foulser-Piggott and Stafford, 2012). Moreover, they can be used to estimate I A and CAV values simultaneously at multiple sites for a given earthquake scenario (Foulser-Piggott and Stafford, 2012;Du and Wang, 2013a).…”

“…Modern predictive equations for I A and CAV adopt complex functional forms for median and logarithmic standard deviation based on the state-of-the-art seismological findings (Campbell and Bozorgnia, 2010;Foulser-Piggott and Stafford, 2012). Moreover, they can be used to estimate I A and CAV values simultaneously at multiple sites for a given earthquake scenario (Foulser-Piggott and Stafford, 2012;Du and Wang, 2013a). The latter extension is important to assess earthquake hazard and risk of spatially distributed infrastructure systems (Du and Wang, 2013b).…”

“…This facilitates the spatial correlation analysis for individual events, rather than combining heterogeneous events, as was done in previous studies (excluding Goda, 2011). We adopt an extended version of the functional form based on Foulser-Piggott and Stafford (2012), which takes into account a wider magnitude range (up to M 9) and different earthquake types (i.e., crustal versus interface versus inslab events). A nonlinear random-effect regression analysis is carried out to determine final sets of model coefficients for both median and logarithmic standard deviations of regression residuals.…”