Ground motion prediction equations for horizontal and vertical components of acceleration in Northern Iran

Recent studies have shown that the vertical component of ground motion can be quite destructive on a variety of structural systems. Development of response spectrum for design of buildings subjected to vertical component of earthquake needs ground motion prediction equations (GMPEs). The existing GMPEs for northern Iranian plateau are proposed for the horizontal component of earthquake, and there is not any specified GMPE for the vertical component of earthquake in this region. Determination of GMPEs is mostly based on regression analyses on earthquake parameters such as magnitude, site class, distance, and spectral amplitudes. In this study, 325 three-component records of 55 earthquakes with magnitude ranging from M _w 4.1 to M _w 7.3 are used for estimation on the regression coefficients. Records with distances less than 300 km are selected for analyses in the database. The regression analyses on earthquake parameters results in determination of GMPEs for peak ground acceleration and spectral acceleration for both horizontal and vertical components of the ground motion. The correlation between the models for vertical and horizontal GMPEs is studied in details. These models are later compared with some other available GMPEs. According to the result of this investigation, the proposed GMPEs are in agreement with the other relationships that were developed based on the local and regional data.     

Consistency of ground-motion predictions from the past four decades

Due to the limited observational datasets available for the derivation of ground-motion prediction equations (GMPEs) there is always epistemic uncertainty in the estimated median ground motion. Because of the increasing quality and quantity of strong-motion datasets it would be expected that the epistemic uncertainty in ground-motion prediction (related to lack of knowledge and data) is decreasing. In this study the predicted median ground motions from over 200 GMPEs for various scenarios are plotted against date of publication to examine whether the scatter in the predictions (a measure of epistemic uncertainty) is decreasing with time. It is found that there are still considerable differences in predicted ground motions from the various GMPEs and that the variation between estimates is not reducing although the ground motion estimated by averaging median predictions is roughly constant. For western North America predictions for moderate earthquakes have show a high level of consistency since the 1980s as do, but to a lesser extent, predictions for moderate earthquakes in Europe, the Mediterranean and the Middle East. A good match is observed between the predictions from GMPEs and the median ground motions based on observations from similar scenarios. Variations in median ground motion predictions for stable continental regions and subduction zones from different GMPEs are large, even for moderate earthquakes. The large scatter in predictions of the median ground motion shows that epistemic uncertainty in ground-motion prediction is still large and that it is vital that this is accounted for in seismic hazard assessments.     

Empirical correlation of PGA,spectral accelerations and spectrum intensities from active shallow crustal earthquakes

Empirical correlation equations between peak ground acceleration, spectral acceleration, spectrum intensity, and acceleration spectrum intensity are developed. The correlation equations are developed for shallow crustal earthquakes using the Next Generation Attenuation (NGA) ground motion database, and four of the NGA ground motion prediction equations (GMPEs). A particularly novel aspect of the present study is the explicit consideration of epistemic uncertainty in the correlation equations due to both the adopted ground motion database and GMPEs. The resulting correlation equations enable the joint consideration of these four ground motion intensity measures in ground motion selection using frameworks such as the generalized conditional intensity measure approach. Copyright © 2011 John Wiley & Sons, Ltd.     

Ground Motion Relations for Active Regions in India

In this article, a study on development of ground motion prediction equations (GMPEs) is undertaken for seismically active regions in India. To derive the equations, the seismically active regions are divided into four units based on seismotectonic setting and geology. Due to lack of strong motion data, a stochastic finite-fault simulation method is used for generating a complete synthetic database with respect to magnitude and distance. The input parameters in the stochastic seismological model, such as site amplification and stress drop, are first derived from the past strong-motion data. A total of 236 three-component records from 62 earthquakes with magnitudes ranging from M _w 3.4 to 7.8 are used to calibrate the seismological model. The obtained stress drops of these 62 events lie in between 60 and 165 bars. With the help of a large synthetic database generated from the calibrated seismological model, ground motion relations for 5 % damped spectral acceleration are obtained by regression analysis. The developed ground motion relations are compared with the existing GMPEs of the other active regions in the world. Although the proposed equations have trends similar to those of the existing relations, there are some differences attributed to stress drop and the quality factor of active regions in India. These relations will be useful to prepare spectral acceleration hazard maps of India for a given annual probability of exceedance.     

Fourier spectral- and duration models for the generation of response spectra adjustable to different source-, propagation-, and site conditions

One of the major challenges related with the current practice in seismic hazard studies is the adjustment of empirical ground motion prediction equations (GMPEs) to different seismological environments. We believe that the key to accommodating differences in regional seismological attributes of a ground motion model lies in the Fourier spectrum. In the present study, we attempt to explore a new approach for the development of response spectral GMPEs, which is fully consistent with linear system theory when it comes to adjustment issues. This approach consists of developing empirical prediction equations for Fourier spectra and for a particular duration estimate of ground motion which is tuned to optimize the fit between response spectra obtained through the random vibration theory framework and the classical way. The presented analysis for the development of GMPEs is performed on the recently compiled reference database for seismic ground motion in Europe (RESORCE-2012). Although, the main motivation for the presented approach is the adjustability and the use of the corresponding model to generate data driven host-to-target conversions, even as a standalone response spectral model it compares reasonably well with the GMPEs of Ambraseys et al. (Bull Earthq Eng 3:1–53, 2005), Akkar and Bommer (Seismol Res Lett 81(2):195–206, 2010) and Akkar and Cagnan (Bull Seismol Soc Am 100(6):2978–2995, 2010).     

Determination of GMPE functional form for an active region with limited strong motion data: application to the Himalayan region

Advancement in the seismic networks results in formulation of different functional forms for developing any new ground motion prediction equation (GMPE) for a region. Till date, various guidelines and tools are available for selecting a suitable GMPE for any seismic study area. However, these methods are efficient in quantifying the GMPE but not for determining a proper functional form and capturing the epistemic uncertainty associated with selection of GMPE. In this study, the compatibility of the recent available functional forms for the active region is tested for distance and magnitude scaling. Analysis is carried out by determining the residuals using the recorded and the predicted spectral acceleration values at different periods. Mixed effect regressions are performed on the calculated residuals for determining the intra- and interevent residuals. Additionally, spatial correlation is used in mixed effect regression by changing its likelihood function. Distance scaling and magnitude scaling are respectively examined by studying the trends of intraevent residuals with distance and the trend of the event term with magnitude. Further, these trends are statistically studied for a respective functional form of a ground motion. Additionally, genetic algorithm and Monte Carlo method are used respectively for calculating the hinge point and standard error for magnitude and distance scaling for a newly determined functional form. The whole procedure is applied and tested for the available strong motion data for the Himalayan region. The functional form used for testing are five Himalayan GMPEs, five GMPEs developed under NGA-West 2 project, two from Pan-European, and one from Japan region. It is observed that bilinear functional form with magnitude and distance hinged at 6.5 M _w and 300 km respectively is suitable for the Himalayan region. Finally, a new regression coefficient for peak ground acceleration for a suitable functional form that governs the attenuation characteristic of the Himalayan region is derived.     

Earthquake ground motion predictive equations for Garhwal Himalaya,India

Predictive equations based on the stochastic approach are developed for earthquake ground motions from Garhwal Himalayan earthquakes of 3.5≤M_w≤6.8 at a distance of 10≤R≤250 km. The predicted ground motion parameters are response spectral values at frequencies from 0.25 to 20 Hz, and peak ground acceleration (PGA). The ground motion prediction equations (GMPEs) are derived from an empirically based stochastic ground motion model. The GMPEs show a fair agreement with the empirically developed ground motion equations from Himalaya as well as the NGA equation. The proposed relations also reasonably predict the observed ground motion of two major Himalayan earthquakes from Garhwal Himalayan region. For high magnitudes, there is insufficient data to satisfactorily judge the relationship; however it reasonably predicts the 1991 Uttarkashi earthquake (M_w=6.8) and 1999 Chamoli earthquake (M_w=6.4) from Garhwal Himalaya region.     

Rapid earthquake hazard and loss assessment for Euro-Mediterranean region

The almost-real time estimation of ground shaking and losses after a major earthquake in the Euro-Mediterranean region was performed in the framework of the Joint Research Activity 3 (JRA-3) component of the EU FP6 Project entitled “Network of Research Infra-structures for European Seismology, NERIES”. This project consists of finding the most likely location of the earthquake source by estimating the fault rupture parameters on the basis of rapid inversion of data from on-line regional broadband stations. It also includes an estimation of the spatial distribution of selected site-specific ground motion parameters at engineering bedrock through region-specific ground motion prediction equations (GMPEs) or physical simulation of ground motion. By using the Earthquake Loss Estimation Routine (ELER) software, the multi-level methodology developed for real time estimation of losses is capable of incorporating regional variability and sources of uncertainty stemming from GMPEs, fault finiteness, site modifications, inventory of physical and social elements subjected to earthquake hazard and the associated vulnerability relationships.     

Comparing predicted and observed ground motions from subduction earthquakes in the Lesser Antilles

This brief article presents a quantitative analysis of the ability of eight published empirical ground-motion prediction equations (GMPEs) for subduction earthquakes (interface and intraslab) to estimate observed earthquake ground motions on the islands of the Lesser Antilles (specifically Guadeloupe, Martinique, Trinidad, and Dominica). In total, over 300 records from 22 earthquakes from various seismic networks are used within the analysis. It is found that most of the GMPEs tested perform poorly, which is mainly due to a larger variability in the observed ground motions than predicted by the GMPEs, although two recent GMPEs derived using Japanese strong-motion data provide reasonably good predictions. Analyzing separately the interface and intraslab events does not significant modify the results. Therefore, it is concluded that seismic hazard assessments for this region should use a variety of GMPEs in order to capture this large epistemic uncertainty in earthquake ground-motion prediction for the Lesser Antilles.     

Copula‐based joint probability function for PGA and CAV: a case study from Taiwan

This study aims to develop a joint probability function of peak ground acceleration (PGA) and cumulative absolute velocity (CAV) for the strong ground motion data from Taiwan. First, a total of 40,385 earthquake time histories are collected from the Taiwan Strong Motion Instrumentation Program. Then, the copula approach is introduced and applied to model the joint probability distribution of PGA and CAV. Finally, the correlation results using the PGA‐CAV empirical data and the normalized residuals are compared. The results indicate that there exists a strong positive correlation between PGA and CAV. For both the PGA and CAV empirical data and the normalized residuals, the multivariate lognormal distribution composed of two lognormal marginal distributions and the Gaussian copula provides adequate characterization of the PGA‐CAV joint distribution observed in Taiwan. This finding demonstrates the validity of the conventional two‐step approach for developing empirical ground motion prediction equations (GMPEs) of multiple ground motion parameters from the copula viewpoint. Copyright © 2016 John Wiley & Sons, Ltd.     

Characteristics of horizontal ground motion measures along principal directions

<正>Ground motion records are often used to develop ground motion prediction equations(GMPEs) for a randomly oriented horizontal component,and to assess the principal directions of ground motions based on the Arias intensity tensor or the orientation of the major response axis.The former is needed for seismic hazard assessment,whereas the latter can be important for assessing structural responses under multi-directional excitations.However,a comprehensive investigation of the pseudo-spectral acceleration(PSA) and of GMPEs conditioned on different axes is currently lacking.This study investigates the principal directions of strong ground motions and their relation to the orientation of the major response axis, statistics of the PSA along the principal directions on the horizontal plane,and correlation of the PSA along the principal directions on the horizontal plane.For these,three sets of strong ground motion records,including intraplate California earthquakes,inslab Mexican earthquakes,and interface Mexican earthquakes,are used.The results indicate that one of the principal directions could be considered as quasi-vertical.By focusing on seismic excitations on the horizontal plane,the statistics of the angles between the major response axis and the major principal axis are obtained;GMPEs along the principal axes are provided and compared with those obtained for a randomly oriented horizontal component;and statistical analysis of residuals associated with GMPEs along the principal directions is carried out.     

Ground motion prediction equations derived from the Italian strong motion database

We present a set of ground motion prediction equations (GMPEs) derived for the geometrical mean of the horizontal components and the vertical, considering the latest release of the strong motion database for Italy. The regressions are performed over the magnitude range 4?C6.9 and considering distances up to 200?km. The equations are derived for peak ground acceleration (PGA), peak ground velocity (PGV) and 5%-damped spectral acceleration at periods between 0.04 and 2?s. The total standard deviation (sigma) varies between 0.34 and 0.38?log₁₀ unit, confirming the large variability of ground shaking parameters when regional data sets containing small to moderate magnitude events (M?<?6) are used. The between-stations variability provides the largest values for periods shorter than 0.2?s while, for longer periods, the between-events and between-stations distributions of error provide similar contribution to the total variability.     

Peak ground accelerations from large (<Emphasis Type="Bold">M</Emphasis> ≥ 7.2) shallow crustal earthquakes: a comparison with predictions from eight recent ground-motion models

Strong-motion data from large (M ≥ 7.2) shallow crustal earthquakes invariably make up a small proportion of the records used to develop empirical ground motion prediction equations (GMPEs). Consequently GMPEs are more poorly constrained for large earthquakes than for small events. In this article peak ground accelerations (PGAs) observed in 38 earthquakes worldwide with M ≥ 7.2 are compared with those predicted by eight recent GMPEs. Well over half of the 38 earthquakes were not considered when deriving these GMPEs but the data were identified by a thorough literature review of strong-motion reports from the past 60 years. These data are provided in an electronic supplement for future investigations on ground motions from large earthquakes. The addition of these data provides better constraint of the between-event ground-motion variability in large earthquakes. It is found that the eight models generally provide good predictions for PGAs from these earthquakes, although there is evidence for slight under- or over-prediction of motions by some models (particularly for M > 7.6). The between-event variabilities predicted by most models match the observed variability, if data from two events (2001 Bhuj and 2005 Crescent City) that are likely atypical of earthquakes in active regions are excluded. For some GMPEs there is evidence that they are over-predicting PGAs in the near-source region of large earthquakes as well as over-predicting motions on hard rock. Overall, however, all the considered models, despite having been derived using limited data, provide reliable predictions of PGAs in the largest crustal earthquakes.     

Ground motion prediction equations based on shallow crustal earthquakes in Georgia and the surrounding Caucasus

Strong ground motions caused by earthquakes with magnitudes ranging from 3.5 to 6.9 and hypocentral distances of up to 300 km were recorded by local broadband stations and three-component accelerograms within Georgia’s enhanced digital seismic network. Such data mixing is particularly effective in areas where strong ground motion data are lacking. The data were used to produce models based on ground-motion prediction equations (GMPEs), one benefit of which is that they take into consideration information from waveforms across a wide range of frequencies. In this study, models were developed to predict ground motions for peak ground acceleration and 5%-damped pseudo-absolute-acceleration spectra for periods between 0.01 and 10 s. Short-period ground motions decayed faster than long-period motions, though decay was still in the order of approximately 1/r. Faulting mechanisms and local soil conditions greatly influence GMPEs. The spectral acceleration (SA) of thrust faults was higher than that for either strike-slip or normal faults but the influence of strike-slip faulting on SA was slightly greater than that for normal faults. Soft soils also caused significantly more amplification than rocky sites.     

Horizontal and vertical ground motion prediction equations derived from the Italian Accelerometric Archive (ITACA)

A set of Ground Motion Prediction Equations (GMPEs) for the Italian territory is proposed, exploiting a new strong-motion data set become available since July 2007 through the Italian Accelerometric Archive (ITACA). The data set is composed by 561 three-component waveforms from 107 earthquakes with moment magnitude in the range 4.0–6.9, occurred in Italy from 1972 to 2007 and recorded by 206 stations at distances up to 100 km. The functional form used to derive GMPEs in Italy (Sabetta and Pugliese in Bull Seismol Soc Am 86(2):337–352, 1996) has been modified introducing a quadratic term for magnitude and a magnitude-dependent geometrical spreading. The coefficients for the prediction of horizontal and vertical peak ground acceleration, peak ground velocity and 5% damped acceleration response spectra are evaluated. This paper illustrates the new data set, the regression analysis and the comparisons with recently derived GMPEs in Europe and in the Next Generation Attenuation of Ground Motions (NGA) Project.     

Latin hypercube approach to estimate uncertainty in ground water vulnerability

A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regression predictions of ground water vulnerability. Central to the proposed method is the assumption that prediction uncertainty in ground water vulnerability models is a function of input error propagation from uncertainty in the estimated logistic regression model coefficients (model error) and the values of explanatory variables represented in the GIS (data error). Input probability distributions that represent both model and data error sources of uncertainty were simultaneously sampled using a Latin hypercube approach with logistic regression calculations of probability of elevated nonpoint source contaminants in ground water. The resulting probability distribution represents the prediction intervals and associated uncertainty of the ground water vulnerability predictions. The method is illustrated through a ground water vulnerability assessment of the High Plains regional aquifer. Results of the LHS simulations reveal significant prediction uncertainties that vary spatially across the regional aquifer. Additionally, the proposed method enables a spatial deconstruction of the prediction uncertainty that can lead to improved prediction of ground water vulnerability.     

Seismic response prediction of reinforced concrete buildings through nonlinear combinations of intensity measures

A widespread approach for the prediction of the structural response as function of the ground motion intensity is based on the Cloud Analysis: once a set of points representing the engineering demand parameter (EDP) values is obtained as function of the selected seismic intensity measure (IM) for a collection of unscaled earthquake records, a regression analysis is performed by assuming a specific functional form to correlate these variables. Within this framework, many studies have been devoted so far to evaluate the effectiveness of several IMs in estimating the EDPs through intrinsically linear functional forms, but it is still unknown to what extent the use of the linear regression analysis affects the quality of the final results. This paper is intended to provide an answer to such question by means of the calibration of suitable nonlinear combinations of scalar IMs, whose statistical performances are compared with those obtained by using the functional form usually adopted for linear regression-based calibrations. Specifically, the Evolutionary Polynomial Regression technique is adopted to calibrate nonlinear regression models for the prediction of maximum inter-story drift ratio and maximum floor acceleration. The comparative analysis is performed for fixed-base and base-isolated reinforced concrete buildings subjected to ordinary or pulse-like ground motion taking into account accuracy, complexity, efficiency and sufficiency. Final results demonstrate that the linear regression analysis is suitable for fixed-base reinforced concrete buildings, but nonlinear regression models provide better estimates. On the other hand, the linear regression analysis can introduce a significant bias in the seismic response prediction of base-isolated buildings, and nonlinear regression models are deemed more appropriate.     

On the selection of ground motion prediction equations during the assessment of seismic hazard in stable continental regions

In the last decade, the number of ground motion prediction equations (GMPEs) significantly increased due to the higher quality and expansion of networks recording strong ground motions throughout the world. Therefore, the key point in seismic hazard assessment is the selection of a suitable ground motion prediction equation. This work presents the review of the modern state of the problem, the discussion of the criteria for selecting the models to include in the logic tree. The models chosen as part of the big international projects for stable continental regions are described in more detail. Several models have been proposed for use in the stable regions of the central part of the Russian Federation and have been compared. Despite numerous attempts to develop the formal criteria for the selection of a certain model, the construction of the logic tree for each particular problem is based on the expert opinions.     

Assessment of ground motion variability and its effects on seismic hazard analysis: a case study for iceland

Probabilistic seismic hazard analysis (PSHA) generally relies on the basic assumption that ground motion prediction equations (GMPEs) developed for other similar tectonic regions can be adopted in the considered area. This implies that observed ground motion and its variability at considered sites could be modelled by the selected GMPEs. Until now ground-motion variability has been taken into account in PSHA by integrating over the standard deviation reported in GMPEs, which significantly affects estimated ground motions, especially at very low probabilities of exceedance. To provide insight on this issue, ground-motion variability in the South Iceland Seismic Zone (SISZ), where many ground-motion records are available, is assessed. Three statistical methods are applied to separate the aleatory variability into source (inter-event), site (inter-site) and residual (intra-event and intra-site) components. Furthermore, the current PSHA procedure that makes the ergodic assumption of equality between spatially and temporal variability is examined. In contrast to the ergodic assumption, several recent studies show that the observed ground-motion variability at an individual location is lower than that implied by the standard deviation of a GMPE. This could imply a mishandling of aleatory uncertainty in PSHA by ignoring spatial variability and by mixing aleatory and epistemic uncertainties in the computation of sigma. Station correction coefficients are introduced in order to capture site effects at different stations. The introduction of the non-ergodic assumption in PSHA leads to larger epistemic uncertainty, although this is not the same as traditional epistemic uncertainty modelled using different GMPEs. The epistemic uncertainty due to the site correction coefficients (i.e. mean residuals) could be better constrained for future events if more information regarding the characteristics of these seismic sources and path dependence could be obtained.