Set Of Ground Motion Prediction Equations Research Articles

Ground-Motion Prediction Equations for Shallow-Crustal and Upper-Mantle Earthquakes in Japan Using Site Class and Segmented Geometric Attenuation Functions for the Horizontal Component

ABSTRACT We present a set of ground-motion prediction equations for the horizontal components of the strong-motion records from shallow-crustal and upper-mantle earthquakes in Japan. We used a reasonably large dataset from earthquakes with a moment magnitude (Mw) over 4.9 and a reliable earthquake category determined by the tectonic location of earthquakes up to the end of 2012. The modeling of source and site effects in this study is similar to that by Zhao, Zhou, et al. (2016); a two-segmented model for the magnitude scaling function hinged at Mw 7.1 and site-period-based site classes were used as the site effect proxy that accounts for the effect of the nonlinear soil response. We used two types of multisegmented geometric attenuation functions with the distance break points based on the distributions of within-event residuals versus source distance. We found that the geometric attenuation functions with three distance break points (70, 130, and 220 km) for the shallow-crustal earthquakes and with two distance break points (90 and 150 km) for the upper-mantle events improved the model performance significantly. The first two break points for the shallow-crustal events are similar to those for the attenuation of the Fourier spectrum due to Moho reflection, but the corresponding coefficients between these spectra differ significantly. For the upper-mantle events, the geometric attenuation coefficient is somewhat surprisingly close to −1.0 for the first segment, 0.0 for the second segment, and −0.5 for the third segment at many periods, because these values are close to the classic attenuation rates of the Fourier spectrum for shallow-crustal events but having different distance break points. Moho reflection is not usually present in the ground motions from deep events, and we can only provide a plausible explanation; a future study is warranted for a theoretical explanation.

Vine Copula-Based Dependence Modeling of Multivariate Ground-Motion Intensity Measures and the Impact on Probabilistic Seismic Slope Displacement Hazard Analysis

ABSTRACT Multivariate normality of logarithmic intensity measures (IMs) is conventionally assumed in earthquake engineering applications. This article introduces a vine copula approach as a useful tool for multivariate modeling of IMs. This approach provides a flexible way to decompose a joint distribution into individual marginal distributions and multiple dependences characterized by a cascade of bivariate copulas (pair-copulas), whereas the conventional multivariate normality can be considered as a special case of the vine copula model. Based on the Next Generation Attenuation-West1 database and various combinations of ground-motion prediction equations (GMPEs), the optimal dependence structures among peak ground acceleration, peak ground velocity, and Arias intensity, as well as that for spectral accelerations at four periods, are identified. The joint normality assumption for the two vector sets of logarithmic IMs is examined from the perspective of copula theory. The results illustrate that the normality assumption is generally adequate for bivariate IMs but may not be optimal for multivariate IMs. Using the same set of GMPEs (developed by the same researchers) may improve the joint normality for logarithmic IMs. Furthermore, the impact of dependence structures among IMs on probabilistic seismic slope displacement hazard analysis is explored. The results indicate that using the same Pearson correlation coefficients but different dependence structures for IMs produces different hazard results and this difference is generally enlarged with increasing hazard levels. As hazard difference from different dependence structures is generally not significant, the multivariate normality distribution for logarithmic IMs is judged to be an acceptable assumption in engineering practice. Alternatively, engineers may make a choice between the joint normal distribution and the vine copula tool depending on the specific situation because of the better generality of the latter.

Erratum to: Pan-European ground-motion prediction equations for the average horizontal component of PGA, PGV, and 5 %-damped PSA at spectral periods up to 3.0 s using the RESORCE dataset

This article presents a set of Ground-Motion Prediction Equations (GMPEs) for Europe and the Middle East, derived from the RESORCE strong motion data bank, following a standard regression approach. The parametric GMPEs are derived for the peak ground acceleration, peak ground velocity, and 5 %-damped pseudo-absolute acceleration response spectra computed over 23 periods between 0.02 and 3 s, considering the average horizontal-component ground-motions. The GMPEs are valid for distances less than 300 km, hypocentral depth up to 35 km and over the magnitude range 4–7.6. Two metrics for the source-to-station distance (i.e. Joyner-Boore and hypocentral) are considered. The selected dataset is composed by 2,126 recordings (at a period of 0.1 s) related to 365 earthquakes, that includes strong-motion data from 697 stations.The EC8 soil classification (four classes from A to D) discriminates recording sites and four classes (normal, reverse, strike-slip, and unspecified) describe the style of faulting. A subset which contains only stations with measured Vs30 and earthquakes with specified focal mechanism (1,224 records from 345 stations and 255 earthquakes) is used to test of the accuracy of the median prediction and the variability associated to the broader data set. A random effect regression scheme is applied and bootstrap analyses are performed to estimate the 95 % confidence levels for the parameters. The total standard deviation sigma is decomposed into between-events and within-event components, and the site-to-site component is evaluated as well. The results show that the largest contribution to the total sigma is coming from the within-event component. When analyzing the residual distributions, no significant trends are observed that can be ascribed to the earthquake type (mainshock-aftershock classification) or to the non-linear site effects. The proposed GMPEs have lower median values than global models at short periods and large distances, while are consistent with global models at long periods $$(\hbox {T} > 1)$$ s. Consistency is found with two regional models developed for Turkey and Italy, as the considered dataset is dominated by waveforms recorded in these regions.

A Local Ground-Motion Predictive Model for Turkey, and Its Comparison with Other Regional and Global Ground-Motion Models

We examined the differences between the ground-motion estimations of local and global prediction equations and explored some seismological parameters that may explain these differences. To achieve this objective, we first derived a set of ground-motion prediction equations (GMPEs) for estimating peak horizontal accel- eration, velocity, and pseudospectral acceleration using the recently compiled Turkish ground-motion database. The new GMPEs are comparable with the recent global GMPEs in terms of model sophistication, and they are based on a well-studied national dataset. Using global GMPEs from the Next Generation Attenuation of Ground Motions project (Power et al., 2008) and the pan-European Akkar and Bommer (2010) model, we observed that the discrepancy between local and global GMPEs is more prominent at small magnitudes provided that the GMPEs possess similar mag- nitude limits. Our more detailed comparisons with the pan-European Akkar and Bom- mer (2010) predictive model, as well as with the estimations from a combined Italian and Turkish accelerometric dataset, indicate that depth can be of importance for delineating the differences between local and global GMPEs.