Empirical Ground Motion Prediction Equations Research Articles

Estimating ground motion intensities using simulation-based estimates of local crustal seismic response

Abstract. It is estimated that 2 billion people will move to cities in the next 30 years, many of which possess high seismic risk, underscoring the importance of reliable hazard assessments. Current ground motion models for these assessments typically rely on an extensive catalogue of events to derive empirical ground motion prediction equations (GMPEs), which are often unavailable in developing countries. Considering the challenge, we choose an alternative method utilizing physics-based (PB) ground motion simulations and develop a simplified decomposition of ground motion estimation by considering regional attenuation (Δ) and local site amplification (A), thereby exploring how much of the observed variability can be explained solely by wave propagation effects. We deterministically evaluate these parameters in a virtual city named Tomorrowville, located in a 3D-layered crustal velocity model containing sedimentary basins, using randomly oriented extended sources. Using these physics-based empirical parameters (Δ and A), we evaluate the intensities, particularly peak ground acceleration (PGA), of hypothetical future earthquakes. The results suggest that the estimation of PGA using the deterministic Δ−A decomposition exhibits a robust spatial correlation with the PGA obtained from simulations within Tomorrowville. This method exposes an order-of-magnitude spatial variability in PGA within Tomorrowville, primarily associated with the near-surface geology and largely independent of the seismic source. In conclusion, advances in PB simulations and improved crustal structure determination offer the potential to overcome the limitations of earthquake data availability to some extent, enabling prompt evaluation of ground motion intensities.

Physically Based and Empirical Ground Motion Prediction Equations for Multiple Intensity Measures (PGA, PGV, Ia, FIV3, CII, and Maximum Fourier Acceleration Spectra) on Sakhalin Island

In the present study, empirical attenuation relations for multiple ground motion intensity measures (PGA, PGV, Ia, FIV3, CII, and MFAS) were developed for Sakhalin Island (in the far east of Russia). A recorded strong motion dataset was used, making GMPEs applicable in active crustal regions with an earthquake magnitude range of 4–6 and a distance range of up to 150 km. The hypocentral distance was used as a basic distance metric. For the first time in the research, an analytical representation of Arias intensity (Ia) was obtained in the framework of a multi-asperity source model. Asperities are considered as sub-sources of high-frequency incoherent radiation. The physical representation of the attenuation model in our study was based on a stress drop on the asperities and the ratio of the total rupture area to the combined area of asperities. The average stress drop on asperities for the examined earthquakes was approximately 13.4 MPa, and the ratio of the total rupture area to the asperity area was 0.22, which is generally close to similar estimates for crustal earthquakes. The coefficients and statistical scattering of the attenuation models were also analyzed. Moreover, a magnitude scale based on a modified Arias intensity is proposed in the present study. The new magnitude scale has an explicit physical meaning and is characterized by its simplicity of measurement. It is associated with the acceleration source spectrum level and can be successfully used in early warning systems.

Ground-Motion Model for Hard-Rock Sites by Correction of Surface Recordings (Part 2): Correction, Mixed-Effects Regressions, and Results

ABSTRACT In the framework of site-specific seismic hazard assessment, the definition of reference motion is a crucial step. Reference motion is generally associated with hard-rock conditions, characterized by S-wave velocity exceeding 1500 m/s. However, ground motion recorded at sites with such conditions is poorly represented in existing strong-motion databases. Thus, the validity domains of most empirical ground-motion prediction equations (GMPEs) are not representative of reference rock conditions. To overcome this limitation and assess ground motion at reference conditions, the so-called “deconvolution approach” was proposed by Laurendeau et al. (2018) to correct surface recordings from theoretical 1DSH site response before GMPE developments. With the same purpose, in this article, we propose to apply the deconvolution approach using empirical site-response estimates as an alternative to theoretical ones. Using the Kiban–Kyoshin network (KiK-net) data, we estimate empirical site responses at KiK-net stations using generalized inversion techniques in addition to those from 1DSH numerical simulations, as presented in the companion article. Finally, a reference ground-motion model (RGMM) is determined based on empirically deconvolved ground motions. The advantage of using empirical rather than 1DSH site responses in the deconvolution approach is that in the former case the RGMM can be built based on records from an extensive set of sites, whereas the latter case is restricted to well-characterized sites with dominant 1D behavior. This makes the proposed approach easily exportable to different regions of the world, where precise site characterizations are not systematically available, and the knowledge of site behavior is limited.

Characterization of strong‐motion recording sites in the Himalayas and adjoining areas

AbstractIn this paper, an attempt has been made to arrive at sufficiently realistic estimates of the average shear‐wave velocity in the top 30 m of the ground () for 200 strong motion recording sites spread over the Himalayas, adjoining Indo‐Gangetic plains and northeast Indian regions. This is achieved by extending the application of the horizontal to vertical spectral ratio (HVSR) method for the strong motion recording sites with a limited number of records. The knowledge of surface geology at the recording sites and the estimates based on the topographic slope method has been utilized as supplementary information to arrive at a good first‐order approximation of for the sites with a single record also. A total of 669 three‐component acceleration records have been obtained at the 200 sites considered from 158 different earthquakes in the magnitude range of 2.3–7.9 during 1986–2015. It had not been possible to use this database fruitfully due to lack of the required site characterization. Compared to the costly and time‐taking field studies required for the estimation of exact site‐specific values, the proposed scheme can be considered to provide a convenient methodology for obtaining sufficiently reliable approximations of from the recorded strong motion data. The values obtained in this study are found to be in very good agreement with the site‐specific experimental values available for 29 of the sites, establishing the validity of the proposed scheme. An alternative site characterization is also provided in terms of the site soil and the local geological conditions, both defined in terms of three categories specified by indicator variables with values of 0, 1 and 2. Lee and Trifunac have illustrated that this characterization has stronger correlation with the ground motion amplitudes than the as well as the NEHRP site classes based on broad intervals of . The site characterization of the 200 strong motion recording sites in the present study will find useful applications in developing region‐specific empirical ground motion prediction equations with the site amplification effects modeled realistically and in selecting suitable equations from the published literature using score‐based data‐driven methods.

Real-time prediction of earthquake potential damage: A case study for the January 8, 2022 MS 6.9 Menyuan earthquake in Qinghai, China

It is critical to determine whether a site has potential damage in real-time after an earthquake occurs, which is a challenge in earthquake disaster reduction. Here, we propose a real-time Earthquake Potential Damage predictor (EPDor) based on predicting peak ground velocities (PGVs) of sites. The EPDor is composed of three parts: (1) predicting the magnitude of an earthquake and PGVs of triggered stations based on the machine learning prediction models; (2) predicting the PGVs at distant sites based on the empirical ground motion prediction equation; (3) generating the PGV map through predicting the PGV of each grid point based on an interpolation process of weighted average based on the predicted values in (1) and (2). We apply the EPDor to the 2022 MS 6.9 Menyuan earthquake in Qinghai Province, China to predict its potential damage. Within the initial few seconds after the first station is triggered, the EPDor can determine directly whether there is potential damage for some sites to a certain degree. Hence, we infer that the EPDor has potential application for future earthquakes. Meanwhile, it also has potential in Chinese earthquake early warning system.

A data-driven artificial neural network model for the prediction of ground motion from induced seismicity: The case of The Geysers geothermal field

Ground-motion models have gained foremost attention during recent years for being capable of predicting ground-motion intensity levels for future seismic scenarios. They are a key element for estimating seismic hazard and always demand timely refinement in order to improve the reliability of seismic hazard maps. In the present study, we propose a ground motion prediction model for induced earthquakes recorded in The Geysers geothermal area. We use a fully connected data-driven artificial neural network (ANN) model to fit ground motion parameters. Especially, we used data from 212 earthquakes recorded at 29 stations of the Berkeley–Geysers network between September 2009 and November 2010. The magnitude range is 1.3 and 3.3 moment magnitude (Mw), whereas the hypocentral distance range is between 0.5 and 20 km. The ground motions are predicted in terms of peak ground acceleration (PGA), peak ground velocity (PGV), and 5% damped spectral acceleration (SA) at T=0.2, 0.5, and 1 s. The predicted values from our deep learning model are compared with observed data and the predictions made by empirical ground motion prediction equations developed bySharma et al. (2013)for the same data set by using the nonlinear mixed-effect (NLME) regression technique. For validation of the approach, we compared the models on a separate data made of 25 earthquakes in the same region, with magnitudes ranging between 1.0 and 3.1 and hypocentral distances ranging between 1.2 and 15.5 km, with the ANN model providing a 3% improvement compared to the baseline GMM model. The results obtained in the present study show a moderate improvement in ground motion predictions and unravel modeling features that were not taken into account by the empirical model. The comparison is measured in terms of both theR2statistic and the total standard deviation, together with inter-event and intra-event components.

Ground motion prediction equations as a proxy for medium properties variation due to geothermal resources exploitation

Sub surface operations for energy production such as gas storage, fluid injection or hydraulic fracking modify the physical properties of the crust, in particular seismic velocity and anelastic attenuation. Continuously measuring these properties may be crucial to monitor the status of the reservoir. Here we propose a not usual use of the empirical ground-motion prediction equations (GMPEs) to monitor large-scale medium properties variations in a reservoir during fluid injection experiments. In practice, peak-ground velocities recorded during field operations are used to update the coefficients of a reference GMPE whose variation can be physically interpreted in terms of anelastic attenuation and seismic velocity. We apply the technique to earthquakes recorded at The Geysers geothermal field in Southern California and events occurred in the St. Gallen (Switzerland) geothermal field. Our results suggest that the GMPEs can be effectively used as a proxy for some reservoir properties variation by using induced earthquakes recorded at relatively dense networks.

GMPE-consistent hard-rock site adjustment factors for Western North America

Empirical ground-motion prediction equations (GMPEs) such as the Next Generation Attenuation-West2 (NGA-West2) GMPEs are limited in the number of recordings on hard-rock stations used to develop the models. Therefore, the site response scaling in the GMPEs cannot be reliably extrapolated to hard-rock conditions. The state of practice for the development of hard-rock adjustment factors involves the use of analytical methods that typically assign small values to the small-strain damping parameter ([Formula: see text]) for hard-rock sites resulting in large scaling factors at short periods. Alternatively, the hard-rock scaling factors developed in Ktenidou and Abrahamson (KA16) based on empirical ground-motion data are used. These empirical factors, developed for a broad rock site category, show that the average hard-rock scaling factors observed in ground-motion data are small in amplitude contrary to the large factors typically obtained from analytical studies. The empirically derived KA16 factors also suffer from limitations due to the relatively small number of rock sites in the data set and do not distinguish between different hard-rock conditions. To address the shortcomings in the current state of practice, we present a methodology to develop linear site adjustment factors to adjust the NGA-West2 GMPEs from V S30 of 760 m/s to target hard-rock site conditions with V S30 ranging from 1000 to 2200 m/s. These factors are analytically derived using the inverse random vibration theory (IRVT) approach of Al Atik et al. but with inputs constrained using the empirical KA16 factors and normalized to the scaling of the NGA-West2 GMPEs for V S30 of 1000 m/s. The proposed factors merge the results of the NGA-West2 site response scaling for V S30 ≤ 1000 m/s with the KA16 hard-rock category factors to produce a site factor model that is a continuous function of V S30. The epistemic uncertainty of these factors is evaluated.

A Methodological Approach to Update Ground Motion Prediction Models Using Bayesian Inference

In recent decades, prediction of ground motion at a specific site or a region is of primary interest in probabilistic seismic hazard assessment (PSHA). Historically, several ground motion prediction equation (GMPE) models with different functional forms have been published using strong ground motion records available from NGA-West and European databases. However, low-to-moderate seismicity regions, such as Central & Eastern United States and western Europe, is characterized by limited strong-motion records in the magnitude–distance range of interest for PSHA. In these regions, the available data for the development of empirical GMPEs is very scarce and limited to small magnitude events. For these regions, the general practice in PSHA is to consider a set of GMPEs developed from data sets collected in other regions with high seismicity. This practice generates an overestimation of the seismic hazard for the low seismicity regions. There are two potential solutions to overcome this problem: (1) a new GMPE model can be developed; however, development of such a model can require significant amount of data which is not usually available, and (2) the existing GMPE models can be recalibrated based on the data sets collected in the new region rather than developing a new GMPE model. In this paper, we propose a methodological approach to recalibrate the coefficients in a GMPE model using different algorithms to perform Bayesian inference. The coefficients are recalibrated for a subset of European Strong-Motion (ESM) database that corresponds to low-to-moderate seismicity records. In this study, different statistical models are compared based on the functional form given by the chosen GMPE, and the best model and algorithm are recommended using the concept of information criteria.

Checking the site categorization criteria and amplification factors of the 2021 draft of Eurocode 8 Part 1\u20131

In this paper the site categorization criteria and the corresponding site amplification factors proposed in the 2021 draft of Part 1 of Eurocode 8 (2021-draft, CEN/TC250/SC8 Working Draft N1017) are first introduced and compared with the current version of Eurocode 8, as well as with site amplification factors from recent empirical ground motion prediction equations. Afterwards, these values are checked by two approaches. First, a wide dataset of strong motion records is built, where recording stations are classified according to 2021-draft, and the spectral amplifications are empirically estimated computing the site-to-site residuals from regional and global ground motion models for reference rock conditions. Second, a comprehensive parametric numerical study of one-dimensional (1D) site amplification is carried out, based on randomly generated shear-wave velocity profiles, classified according to the new criteria. A reasonably good agreement is found by both approaches. The most relevant discrepancies occur for the shallow soft soil conditions (soil category E) that, owing to the complex interaction of shear wave velocity, soil deposit thickness and frequency range of the excitation, show the largest scatter both in terms of records and of 1D numerical simulations. Furthermore, 1D numerical simulations for soft soil conditions tend to provide lower site amplification factors than 2021-draft, as well as lower than the corresponding site-to-site residuals from records, because of higher impact of non-linear (NL) site effects in the simulations. A site-specific study on NL effects at three KiK-net stations with a significantly large amount of high-intensity recorded ground motions gives support to the 2021-draft NL reduction factors, although the very limited number of recording stations allowing such analysis prevents deriving more general implications. In the presence of such controversial arguments, it is reasonable that a standard should adopt a prudent solution, with a limited reduction of the site amplification factors to account for NL soil response, while leaving the possibility to carry out site-specific estimations of such factors when sufficient information is available to model the ground strain dependency of local soil properties.

Regional ground-motion prediction equations for western Saudi Arabia: merging stochastic and empirical estimates

Two regional ground-motion models are available at present for western Saudi Arabia, a region that is characterized by considerable seismic activity due to the spreading in the Red Sea and continental volcanism. The first model was created by Sokolov and Zahran (Arab J Geosci 11:38, 2018) using the stochastic simulation technique (Boore in Pure Appl Geoph 160(3):635–676, 2003) and it was reported in tabular form for moment magnitudes (M) from 5 to 7. The second model was developed by Kiuchi et al. (Bull Seismol Soc Am 109:2722–2737, 2019) as mathematical expression (empirical ground motion prediction equation, GMPE) on the basis of records from regional earthquakes with local magnitudes from ML 3 to 5.4. In this work, we first construct a GMPE using stochastic simulation data. This stochastic GMPE considers magnitude, distance and stress drop as input parameters. Second, we compare these two independently developed regional ground-motion models and employ a scheme for the modification of the empirical GMPE up to M 7.5 using the stochastic GMPE as the reference model. The revised GMPEs presented here provide regional ground motion models (PGA and spectral amplitudes at natural periods 0.2 s and 1.0 s) that may be used in logic tree scheme for assessment of seismic hazard in western Saudi Arabia.

Regional seismic ground‐motion simulation and observation with engineering applications

Regional seismic ground‐motion simulation and observation with engineering applications

Using ground motion prediction equations to monitor variations in quality factor due to induced seismicity: a feasibility study

Sub-surface operations for energy production such as gas storage, fluid reinjection or hydraulic fracking may modify the physical properties of the rocks, in particular the seismic velocity and the anelastic attenuation. The aim of the present study is to investigate, through a synthetic test, the possibility of using empirical ground-motion prediction equations (GMPEs) to observe the variations in the reservoir. In the synthetic test, we reproduce the expected seismic activity (in terms of rate, focal mechanisms, stress drop and the b value of the Gutenberg-Richter) and the variation of medium properties in terms of the quality factor Q induced by a fluid injection experiment. In practice, peak-ground velocity data of the simulated earthquakes during the field operations are used to update the coefficients of a reference GMPE in order to test whether the coefficients are able to capture the medium properties variation. The results of the test show that the coefficients of the GMPE vary during the simulated field operations revealing their sensitivity to the variation of the anelastic attenuation. The proposed approach is suggested as a promising tool that, if confirmed by real data analysis, could be used for monitoring and interpreting induced seismicity in addition to more conventional techniques.

Earthquake Stress Drops From Dynamic Rupture Simulations Constrained by Observed Ground Motions

AbstractStress drop characterizing dynamics of earthquake rupture propagation has various definitions and measures with a yet unclear mutual relationship. We aim to reconcile this discrepancy by analyzing synthetic dynamic rupture models generated by random sampling of the model space of spatially heterogeneous dynamic rupture parameters. An ensemble of ~1,600 dynamic models, with waveforms statistically fitting empirical Ground Motion Prediction Equations, is treated as a synthetic event database. The events exhibit various magnitudes, degrees of complexity, and realistic ω−2 spectral decay of their average moment rates. While the variability of the stress drop Δτe estimated from the moment rates agrees with real data studies ( , the true static stress drops Δτs evaluated directly from the dynamic models exhibit larger values and lower variability ( ). Our study suggests that the disagreement between various stress drop definitions is related to the source model approximation and not to any particular data processing.

A Selection of GMPEs for the United Kingdom Based on Instrumental and Macroseismic Datasets

AbstractIn countries with low‐to‐moderate seismicity, the selection of appropriate ground‐motion prediction equations (GMPEs) to be used in a probabilistic seismic hazard analysis (PSHA) is a challenging step. Empirical observations of ground motion are limited, and GMPEs, when available, are generally based on stochastic simulations or adjusted empirical GMPEs from elsewhere. This article investigates the suitability of recent GMPEs to the United Kingdom. To this end, the spectral accelerations obtained from available instrumental ground‐motion data in the United Kingdom with magnitude lower than 4.5 are compared with the GMPEs’ predictions through the analysis of residuals and the application of statistical tests. To compensate for the scarcity of data for the magnitude range of interest in the PSHA, a macroseismic dataset is also considered. Macroseismic intensities are converted to peak ground acceleration (PGA) and statistically compared with the PGA predicted by the GMPEs. The GMPEs are then compared in terms of median ground‐motion prediction through Sammon’s maps to evaluate their similarities. The analyses from both datasets led to six suitable GMPEs, of which three are from the Next Generation Attenuation‐West2 project, one is European, one is based mainly on a Japanese dataset, and one is a stochastic GMPE developed specifically for the United Kingdom.

Sensitivity of High-Frequency Ground Motion to Kinematic Source Parameters

Empirical ground motion prediction equations are calibrated from past earthquake seismic recordings. Although they are often used to predict Peak Ground Acceleration (PGA) and its variability, the use of these equations to predict near-fault PGA remains questionable due to the scarcity of near-fault recordings for large earthquakes (e.g. Mai Encyclopedia of complexity and systems science (pp. 4435–4474). New York: Springer. https://doi.org/10.1007/978-0-387-30440-3_263. 2009). The simulation of strong ground motion offers an attractive alternative for the assessment of near-fault seismic hazards, but the a priori choice of the source parameters used to describe the fault rupture process remains a complex issue. In order to better understand the effects of rupture parameters on surface ground motion and to capture the key source ingredients that impact ground motion variability, we simulated ground motions produced by various M7 strike-slip rupture earthquake scenarios on vertical faults. We computed ground motion up to 5 Hz using the far-field approximation as well as at the near-field stations located at 5 km, 25 km and 70 km from the fault (assuming a visco-elastic medium). The kinematic rupture parameters are modeled using a statistical rupture model generator as proposed by Song et al. Geophysical Journal International,196(3), 1770–1786 (2014). Our work demonstrates that PGA is mostly generated by abrupt changes in the rupture propagation (e.g. stopping phases at the fault boundaries or strong heterogeneities of rupture speed along the fault). We observed that PGA is mostly controlled by average rupture speed and average stress drop (in the far-field), and to a lesser extent by the standard deviation of the rupture speed. It is worth noting that for the set of stations in study, the correlation between source parameters and spatial correlation length does not affect average PGA and related variability significantly.

Comparison of Soil Nonlinearity (In SituStress–Strain Relation and G/Gmax Reduction) Observed in Strong‐Motion Databases and Modeled in Ground‐Motion Prediction Equations

Research Article| November 20, 2018 Comparison of Soil Nonlinearity (In Situ Stress–Strain Relation and G/Gmax Reduction) Observed in Strong‐Motion Databases and Modeled in Ground‐Motion Prediction Equations Philippe Guéguen; Philippe Guéguen aISTerre, Université Grenoble Alpes, CNRS/IFSTTAR, 1381 rue de la piscine ‐ CS40700, 38058 Grenoble, Cedex 9, France, philippe.gueguen@univ-grenoble-alpes.fr Search for other works by this author on: GSW Google Scholar Luis Fabian Bonilla; Luis Fabian Bonilla bDépartement GERS (Géotechnique, Environnement, Risques naturels et Sciences de la terre), Laboratoire Séismes et Vibrations, Université Paris Est ‐ IFSTTAR, 14‐20 Boulevard Newton, Cité Descartes, 77447 Marne‐la‐Vallée, Cedex 2, France Search for other works by this author on: GSW Google Scholar John Douglas John Douglas cDepartment of Civil and Environmental Engineering, University of Strathclyde, James Weir Building, 75 Montrose Street, Glasgow G1 1XJ, United Kingdom Search for other works by this author on: GSW Google Scholar Bulletin of the Seismological Society of America (2019) 109 (1): 178–186. https://doi.org/10.1785/0120180169 Article history first online: 20 Nov 2018 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Philippe Guéguen, Luis Fabian Bonilla, John Douglas; Comparison of Soil Nonlinearity (In Situ Stress–Strain Relation and G/Gmax Reduction) Observed in Strong‐Motion Databases and Modeled in Ground‐Motion Prediction Equations. Bulletin of the Seismological Society of America 2018;; 109 (1): 178–186. doi: https://doi.org/10.1785/0120180169 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyBulletin of the Seismological Society of America Search Advanced Search Abstract Earthquake ground motions are strongly affected by the upper tens of meters of the Earth’s crust and consequently local site effects need to be included in any ground‐motion prediction. In ground‐motion prediction equations (GMPEs), it is increasingly common to account for possible nonlinear behavior of near‐surface materials (soil). These nonlinear site terms adjust observations made on soft soil sites to the ground motion expected on bedrock and hence allow these abundant soil records to be used within the regression analysis for the derivation of empirical GMPEs. These nonlinear site terms also allow rapid predictions of the expected ground motions on soil rather than requiring a site‐response analysis to be conducted. In this study, we compare the signature on observed peak ground acceleration as a function of a strain proxy of nonlinear soil behavior within four large strong‐motion databases to the predicted signature from four recent GMPEs, three of which explicitly include nonlinear site terms. We find that observed nonlinearity in the databases, interpreted in terms of strain–stress relationships and reduction of shear modulus, is limited, but even this limited effect is underestimated by the nonlinear site terms of the considered GMPEs, which suggests that predictions from these GMPEs could be biased for soft soil sites but also on bedrock. Some of this mismatch could be explained by the use of the average shear‐wave velocity in the top 30 m (⁠VS30⁠) to characterize sites as well as errors in these values. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Hybrid GMPEs for Region-Specific PSHA in Southern Italy

This paper describes the main findings of the project HYPSTHER (HYbrid ground motion prediction equations for PSha purposes: the study case of souTHERn Italy; supported by the Italian Institute of Geophysics and Volcanology). The goal of the project is to develop a methodological approach to retrieve hybrid Ground Motion Prediction Equations (GMPEs) based on integration of recorded and synthetic data. This methodology was applied to the study area of southern Italy, focusing on the southern Calabria and Sicily regions. The target area was chosen due to the expected high seismic hazard levels, despite the low seismic activity in recent decades. In addition, along the coast of the study area, there are many critical infrastructures, such as chemical plants, refineries, and large ports, which strongly increase the risk of technological accidents induced by earthquakes. Through the synthetic data, the predictions of the hybrid GMPEs have been improved under near-field conditions, with respect to empirical models for moderate to large earthquakes. Attenuation at distances greater than 50 km is instead controlled by the empirical data, because attenuation is faster with distance. The aleatory variability of the hybrid models has strong impact on probabilistic seismic hazard assessment, as it is lower than the sigma of the empirical GMPEs. The use of the hybrid GMPEs specific for the study area can produce remarkable reductions in hazard levels for long-return periods, mainly due to changes in median predictions and reduction of the aleatory variability.

Modeling of a historical earthquake in Erzincan, Turkey (Ms~7.8, in 1939) using regional seismological information obtained from a recent event

Located within a basin structure, at the conjunction of North East Anatolian, North Anatolian and Ovacik Faults, Erzincan city center (Turkey) is one of the most hazardous regions in the world. Combination of the seismotectonic and geological settings of the region has resulted in series of significant seismic activities including the 1939 (Ms~7.8) as well as the 1992 (Mw = 6.6) earthquakes. The devastative 1939 earthquake occurred in the pre-instrumental era in the region with no available local seismograms. Thus, a limited number of studies exist on that earthquake. However, the 1992 event, despite the sparse local network at that time, has been studied extensively. This study aims to simulate the 1939 Erzincan earthquake using available regional seismic and geological parameters. Despite several uncertainties involved, such an effort to quantitatively model the 1939 earthquake is promising, given the historical reports of extensive damage and fatalities in the area. The results of this study are expressed in terms of anticipated acceleration time histories at certain locations, spatial distribution of selected ground motion parameters and felt intensity maps in the region. Simulated motions are first compared against empirical ground motion prediction equations derived with both local and global datasets. Next, anticipated intensity maps of the 1939 earthquake are obtained using local correlations between peak ground motion parameters and felt intensity values. Comparisons of the estimated intensity distributions with the corresponding observed intensities indicate a reasonable modeling of the 1939 earthquake.

Generation of stochastic earthquake ground motion in western Saudi Arabia as a first step in development of regional ground motion prediction model

Earthquake ground motion model is an essential part of seismic hazard assessment. The model consists in several empirical ground motion prediction equations (GMPEs) that are considered to be applicable to the given region. When the recorded ground motion data are scarce, numerical modeling of ground motion based on available seismological information is widely used. We describe results of stochastic simulation of ground motion acceleration records for western Saudi Arabia. The simulation was performed using the finite fault model and considering peak ground acceleration and amplitudes of spectral acceleration at natural frequencies 0.2 and 1.0 s. Based on the parameters of the input seismological model that were accepted in similar previous studies, we analyze influence of variations in the source factor (stress drop) and in the local attenuation and amplification factors (kappa value, crustal amplification). These characteristics of the model are considered as the major contributors to the ground motion variability. The results of our work show that distribution of simulated ground motion parameters versus magnitude and distance reveals an agreement with the GMPEs recently used in seismic hazard assessment for the region. Collection of credible information about seismic source, propagation path, and site attenuation parameters using the regional ground motion database would allow constraining the seismological model and developing regional GMPEs. The stochastic simulation based on regional seismological model may be applied for generation of ground motion time histories used for development of analytical fragility curves for typical constructions in the region.