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.     

Criteria for Selecting and Adjusting Ground-Motion Models for Specific Target Regions: Application to Central Europe and Rock Sites

A vital component of any seismic hazard analysis is a model for predicting the expected distribution of ground motions at a site due to possible earthquake scenarios. The limited nature of the datasets from which such models are derived gives rise to epistemic uncertainty in both the median estimates and the associated aleatory variability of these predictive equations. In order to capture this epistemic uncertainty in a seismic hazard analysis, more than one ground-motion prediction equation must be used, and the tool that is currently employed to combine multiple models is the logic tree. Candidate ground-motion models for a logic tree should be selected in order to obtain the smallest possible suite of equations that can capture the expected range of possible ground motions in the target region. This is achieved by starting from a comprehensive list of available equations and then applying criteria for rejecting those considered inappropriate in terms of quality, derivation or applicability. Once the final list of candidate models is established, adjustments must be applied to achieve parameter compatibility. Additional adjustments can also be applied to remove the effect of systematic differences between host and target regions. These procedures are applied to select and adjust ground-motion models for the analysis of seismic hazard at rock sites in West Central Europe. This region is chosen for illustrative purposes particularly because it highlights the issue of using ground-motion models derived from small magnitude earthquakes in the analysis of hazard due to much larger events. Some of the pitfalls of extrapolating ground-motion models from small to large magnitude earthquakes in low seismicity regions are discussed for the selected target region.     

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.     

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.     

Quantifying the epistemic uncertainty in ground motion models and prediction

The aim of this paper is to compute the ground-motion prediction equation (GMPE)-specific components of epistemic uncertainty, so that they may be better understood and the model standard deviation potentially reduced. The reduced estimate of the model standard deviation may also be more representative of the true aleatory uncertainty in the ground-motion predictions.The epistemic uncertainty due to input variable uncertainty and uncertainty in the estimation of the GMPE coefficients are examined. An enhanced methodology is presented that may be used to analyse their impacts on GMPEs and GMPE predictions. The impacts of accounting for the input variable uncertainty in GMPEs are demonstrated using example values from the literature and by applying the methodology to the GMPE for Arias Intensity. This uncertainty is found to have a significant effect on the estimated coefficients of the model and a small effect on the value of the model standard deviation.The impacts of uncertainty in the GMPE coefficients are demonstrated by quantifying the uncertainty in hazard maps. This paper provides a consistent approach to quantifying the epistemic uncertainty in hazard maps using Monte Carlo simulations and a logic tree framework. The ability to quantify this component of epistemic uncertainty offers significant enhancements over methods currently used in the creation of hazard maps as it is both theoretically consistent and can be used for any magnitude–distance scenario.     

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.     

Sensitivity analysis study of the source parameter uncertainty factors for predicting near-field strong ground motion

Uncertainty factors have substantial influences on the numerical simulations of earthquakes. However, most simulation methods are deterministic and do not sufficiently consider those uncertainty factors. A good approach for predicting future destructive earthquakes that is also applied to probabilistic hazard analysis is studying those uncertainty factors, which is very significant for improving the reliability and accuracy of ground-motion predictions. In this paper, we investigated several uncertainty factors, namely the initial rupture point, stress drop, and number of sub-faults, all of which display substantial influences on ground-motion predictions, via sensitivity analysis. The associated uncertainties are derived by considering the uncertainties in the parameter values, as those uncertainties are associated with the ground motion itself. A sensitivity analysis confirms which uncertainty factors have large influences on ground motion predictions, based upon which we can allocate appropriate weights to those uncertainty factors during the prediction process. We employ the empirical Green function method as a numerical simulation tool. The effectiveness of this method has been previously validated, especially in areas with sufficient earthquake record data such as Japan, Southwest China, and Taiwan, China. Accordingly, we analyse the sensitivities of the uncertainty factors during a prediction of strong ground motion using the empirical Green function method. We consequently draw the following conclusions. (1) The stress drop has the largest influence on ground-motion predictions. The discrepancy between the maximum and minimum PGA among three different stations is very large. In addition, the PGV and PGD also change drastically. The Arias intensity increases exponentially with an increase in the stress drop ratio of two earthquakes. (2) The number of sub-faults also has a large influence on various ground-motion parameters but a small influence on the Fourier spectrum and response spectrum. (3) The initial rupture point largely influences the PGA and Arias intensity. We will accordingly pay additional attention to these uncertainty factors when we conduct ground-motion predictions in the future.     

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.     

基于数值模拟的设定地震场地谱时程拟合方法

本文将确定性数值模拟方法与地震动预测方程相结合,提出了一种重大水电工程场址设定地震的地震动时程生成方法。该方法基于场址设定地震,首先采用地震动预测方程确定场址的场地相关反应谱;其次建立包含震源和场址的场地模型,通过确定性数值模拟方法生成场址地震动时程;最后对生成的场址地震动时程进行调整,使其反应谱与设计谱相一致,用于工程抗震分析。这一方法生成的地震动时程既考虑了震源机制、传播路径以及局部场地效应等物理背景,又与场地相关的设计地震反应谱保持一致,为重大工程抗震分析与评价提供了一种新的思路。     

Assessing the impact of ground-motion variability and uncertainty on empirical fragility curves

Empirical fragility curves, constructed from databases of thousands of building-damage observations, are commonly used for earthquake risk assessments, particularly in Europe and Japan, where building stocks are often difficult to model analytically (e.g. old masonry structures or timber dwellings). Curves from different studies, however, display considerable differences, which lead to high uncertainty in the assessed seismic risk. One potential reason for this dispersion is the almost universal neglect of the spatial variability in ground motions and the epistemic uncertainty in ground-motion prediction. In this paper, databases of building damage are simulated using ground-motion fields that take account of spatial variability and a known fragility curve. These databases are then inverted, applying a standard approach for the derivation of empirical fragility curves, and the difference with the known curve is studied. A parametric analysis is conducted to investigate the impact of various assumptions on the results. By this approach, it is concluded that ground-motion variability leads to flatter fragility curves and that the epistemic uncertainty in the ground-motion prediction equation used can have a dramatic impact on the derived curves. Without dense ground-motion recording networks in the epicentral area empirical curves will remain highly uncertain. Moreover, the use of aggregated damage observations appears to substantially increase uncertainty in the empirical fragility assessment. In contrast, the use of limited randomly-chosen un-aggregated samples in the affected area can result in good predictions of fragility.     

Ground-motion characterization for the probabilistic seismic hazard assessment in Turkey

This study describes the methodology implemented to establish the ground-motion logic-tree for national probabilistic seismic hazard map of Turkey for shallow active crustal regions. The presented procedure provides quantitative information to guide the hazard experts while establishing the logic tree to capture the epistemic uncertainty in ground-motion characterization. It uses non-data-driven and data-driven testing methods to identify and rank candidate ground-motion prediction equations (GMPEs) under a specific ground-motion database. The candidate GMPEs are subjected to visual inspection and are classified into center, body and range (CBR) spectral estimates for a proper consideration of epistemic uncertainty. The GMPEs classified into CBR are then used in a suite of seismic hazard sensitivity analysis to establish the most suitable GMPE logic-tree whose spectral estimates are not biased by any one of the GMPEs in the logic-tree structure. The sensitivity analysis considers normalized spectral ordinates and is not manipulated by the spectral amplitudes. The proposed procedure is inherited from the relevant studies of the Earthquake Model of the Middle East (EMME; www.efehr.org:8080/jetspeed/portal/emme.psml) regional seismic hazard project. This paper also highlights the similarities and differences in ground-motion characterization between EMME and our approach.     

Toward a ground-motion logic tree for probabilistic seismic hazard assessment in Europe

The Seismic Hazard Harmonization in Europe (SHARE) project, which began in June 2009, aims at establishing new standards for probabilistic seismic hazard assessment in the Euro-Mediterranean region. In this context, a logic tree for ground-motion prediction in Europe has been constructed. Ground-motion prediction equations (GMPEs) and weights have been determined so that the logic tree captures epistemic uncertainty in ground-motion prediction for six different tectonic regimes in Europe. Here we present the strategy that we adopted to build such a logic tree. This strategy has the particularity of combining two complementary and independent approaches: expert judgment and data testing. A set of six experts was asked to weight pre-selected GMPEs while the ability of these GMPEs to predict available data was evaluated with the method of Scherbaum et al. (Bull Seismol Soc Am 99:3234?C3247, 2009). Results of both approaches were taken into account to commonly select the smallest set of GMPEs to capture the uncertainty in ground-motion prediction in Europe. For stable continental regions, two models, both from eastern North America, have been selected for shields, and three GMPEs from active shallow crustal regions have been added for continental crust. For subduction zones, four models, all non-European, have been chosen. Finally, for active shallow crustal regions, we selected four models, each of them from a different host region but only two of them were kept for long periods. In most cases, a common agreement has been also reached for the weights. In case of divergence, a sensitivity analysis of the weights on the seismic hazard has been conducted, showing that once the GMPEs have been selected, the associated set of weights has a smaller influence on the hazard.     

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.     

Sensitivity of ground-motion scenarios to earthquake source parameters in the Tehran metropolitan area,Iran

The northern Tehran fault (NTF) is a principal active fault of the Alborz mountain belt in the northern Iran. The fault is located north of the highly populated Metropolitan Area of Tehran. Historical records and paleoseismological studies have shown that the NTF poses a high seismic risk for the Tehran region and the surrounding cities (e.g. Karaj). A series of ground-motion simulations are carried out using a hybrid kinematic-stochastic model to calculate broadband (0.1–20 Hz) ground-motion time histories for deterministic earthquake scenarios (M7.2) on the NTF. We will describe the source characteristics of the target event to develop a list of scenario earthquakes that are probably similar to a large earthquake on the NTF. The effect of varying different rupture parameters such as rupture velocity and rise time on the resulting broadband strong motions has been investigated to evaluate the range of uncertainty in seismic scenarios. The most significant parameters in terms of ground-shaking level are the rise time and the value of the rupture velocity. For the worst-case scenario, the maximum expected horizontal acceleration, and velocity at rock sites in Tehran range between 128 and 1315 cm/s/s and 11–191 cm/s, respectively. For the lowest scenario, the corresponding values range between 102 and 776 cm/s/s and 12 to 81 cm/s. Nonlinear soil effects may change these results but are not accounted for in this study. The largest variability of ground motion is observed in neighborhood of asperity and also in the direction of rupture propagation. The calculated standard deviation of all ground-motion scenarios is less than 30% of the mean. The capability of the simulation method to synthesize expected ground motions and the appropriateness of the key parameters used in the simulations are confirmed by comparing the synthetic peak ground motions (PGA, PGV and response spectra) with empirical ground-motion prediction equations.     

A partially non-ergodic ground-motion prediction equation for Europe and the Middle East

A partially non-ergodic ground-motion prediction equation is estimated for Europe and the Middle East. Therefore, a hierarchical model is presented that accounts for regional differences. For this purpose, the scaling of ground-motion intensity measures is assumed to be similar, but not identical in different regions. This is achieved by assuming a hierarchical model, where some coefficients are treated as random variables which are sampled from an underlying global distribution. The coefficients are estimated by Bayesian inference. This allows one to estimate the epistemic uncertainty in the coefficients, and consequently in model predictions, in a rigorous way. The model is estimated based on peak ground acceleration data from nine different European/Middle Eastern regions. There are large differences in the amount of earthquakes and records in the different regions. However, due to the hierarchical nature of the model, regions with only few data points borrow strength from other regions with more data. This makes it possible to estimate a separate set of coefficients for all regions. Different regionalized models are compared, for which different coefficients are assumed to be regionally dependent. Results show that regionalizing the coefficients for magnitude and distance scaling leads to better performance of the models. The models for all regions are physically sound, even if only very few earthquakes comprise one region.     

Sensitivity of PSHA results to ground motion prediction relations and logic-tree weights

Epistemic uncertainty in ground motion prediction relations is recognized as an important factor to be considered in probabilistic seismic hazard analysis (PSHA), together with the aleatory variability that is incorporated directly into the hazard calculations through integration across the log-normal scatter in the ground motion relations. The epistemic uncertainty, which is revealed by the differences in median values of ground motion parameters obtained from relations derived for different regions, is accounted for by the inclusion of two or more ground motion prediction relations in a logic-tree formalism. The sensitivity of the hazard results to the relative weights assigned to the branches of the logic-tree, is explored through hazard analyses for two sites in Europe, in areas of high and moderate seismicity, respectively. The analyses reveal a strong influence of the ground motion models on the results of PSHA, particularly for low annual exceedance frequencies (long return periods) and higher confidence levels. The results also show, however, that as soon as four or more relations are included in the logic-tree, the relative weights, unless strongly biased towards one or two relations, do not significantly affect the hazard. The selection of appropriate prediction relations to include in the analysis, therefore, has a greater impact than the expert judgment applied in assigning relative weights to the branches of the logic-tree.     

New predictive equations for Arias intensity from crustal earthquakes in New Zealand

Arias Intensity (Arias, MIT Press, Cambridge MA, pp 438–483, 1970) is an important measure of the strength of a ground motion, as it is able to simultaneously reflect multiple characteristics of the motion in question. Recently, the effectiveness of Arias Intensity as a predictor of the likelihood of damage to short-period structures has been demonstrated, reinforcing the utility of Arias Intensity for use in both structural and geotechnical applications. In light of this utility, Arias Intensity has begun to be considered as a ground-motion measure suitable for use in probabilistic seismic hazard analysis (PSHA) and earthquake loss estimation. It is therefore timely to develop predictive equations for this ground-motion measure. In this study, a suite of four predictive equations, each using a different functional form, is derived for the prediction of Arias Intensity from crustal earthquakes in New Zealand. The provision of a suite of models is included to allow for epistemic uncertainty to be considered within a PSHA framework. Coefficients are presented for four different horizontal-component definitions for each of the four models. The ground-motion dataset for which the equations are derived include records from New Zealand crustal earthquakes as well as near-field records from worldwide crustal earthquakes. The predictive equations may be used to estimate Arias Intensity for moment magnitudes between 5.1 and 7.5 and for distances (both r_jb and r_rup) up to 300 km.     

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.