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We derive a set of regional ground-motion prediction equations (GMPEs) in the Fourier amplitude spectra (FAS-GMPE) and in the spectral acceleration (SA-GMPE) domains for the purpose of interpreting the between-event residuals in terms of source parameter variability. We analyze a dataset of about 65,000 recordings generated by 1400 earthquakes (moment magnitude 2: 5 <= M-w <= 6: 5, hypocentral distance R-hypo <= 150 km) that occurred in central Italy between January 2008 and October 2017. In a companion article (Bindi, Spallarossa, et al., 2018), the nonparametric acceleration source spectra were interpreted in terms of omega-square models modified to account for deviations from a high-frequency flat plateau through a parameter named k(source). Here, the GMPEs are derived considering the moment (M-w), the local (M-L), and the energy (M-e) magnitude scales, and the between-event residuals are computed as random effects. We show that the between-event residuals for the FAS-GMPE implementing M-w are correlated with stress drop, with correlation coefficients increasing with increasing frequency up to about 10 Hz. Contrariwise, the correlation is weak for the FAS-GMPEs implementing M-L and M-e, in particular between 2 and 5 Hz, where most of the corner frequencies lie. At higher frequencies, all models show a strong correlation with k(source). The correlation with the source parameters reflects in a different behavior of the standard deviation tau of the between-event residuals with frequency. Although tau is smaller for the FAS-GMPE using M-w below 1.5 Hz, at higher frequencies, the model implementing either M-L or M-e shows smaller values, with a reduction of about 30% at 3 Hz (i.e., from 0.3 for M-w to 0.1 for M-L). We conclude that considering magnitude scales informative for the stress-drop variability allows to reduce the between-event variability with a significant impact on the hazard assessment, in particular for studies in which the ergodic assumption on site is removed.
Shear-waves are the most energetic body-waves radiated from an earthquake, and are responsible for the destruction of engineered structures. In both short-term emergency response and long-term risk forecasting of disaster-resilient built environment, it is critical to predict spatially accurate distribution of shear-wave amplitudes. Although decades’ old theory proposes a deterministic, highly anisotropic, four-lobed shear-wave radiation pattern, from lack of convincing evidence, most empirical ground-shaking prediction models settled for an oversimplified stochastic radiation pattern that is isotropic on average. Today, using the large datasets of uniformly processed seismograms from several strike, normal, reverse, and oblique-slip earthquakes across the globe, compiled specifically for engineering applications, we could reveal, quantify, and calibrate the frequency-, distance-, and style-of-faulting dependent transition of shear-wave radiation between a stochastic-isotropic and a deterministic-anisotropic phenomenon. Consequent recalibration of empirical ground-shaking models dramatically improved their predictions: with isodistant anisotropic variations of ±40%, and 8% reduction in uncertainty. The outcomes presented here can potentially trigger a reappraisal of several practical issues in engineering seismology, particularly in seismic ground-shaking studies and seismic hazard and risk assessment.