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The 2014 Earthquake Model of the Middle East: ground motion model and uncertainties

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Laurentiu Danciu
Laurentiu Danciu
Laurentiu Danciu
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Özkan Kale at TED University
Özkan Kale
  • TED University
Sinan Akkar at T-Rupt Technology Inc.
Sinan Akkar
  • T-Rupt Technology Inc.
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Abstract and Figures

We summarize the main elements of a ground-motion model, as built in three-year effort within the Earthquake Model of the Middle East (EMME) project. Together with the earthquake source, the ground-motion models are used for a probabilistic seismic hazard assessment (PSHA) of a region covering eleven countries: Afghanistan, Armenia, Azerbaijan, Cyprus, Georgia, Iran, Jordan, Lebanon, Pakistan, Syria and Turkey. Given the wide variety of ground-motion predictive models, selecting the appropriate ones for modeling the intrinsic epistemic uncertainty can be challenging. In this respect, we provide a strategy for ground-motion model selection based on data-driven testing and sensitivity analysis. Our testing procedure highlights the models of good performance in terms of both data-driven and non-data-driven testing criteria. The former aims at measuring the match between the ground-motion data and the prediction of each model, whereas the latter aims at identification of discrepancies between the models. The selected set of ground models were directly used in the sensitivity analyses that eventually led to decisions on the final logic tree structure. The strategy described in great details hereafter was successfully applied to shallow active crustal regions, and the final logic tree consists of four models (Akkar and Çağnan in Bull Seismol Soc Am 100:2978–2995, 2010; Akkar et al. in Bull Earthquake Eng 12(1):359–387, 2014; Chiou and Youngs in Earthq Spectra 24:173–215, 2008; Zhao et al. in Bull Seismol Soc Am 96:898–913, 2006). For other tectonic provinces in the considered region (i.e., subduction), we adopted the predictive models selected within the 2013 Euro-Mediterranean Seismic Hazard Model (Woessner et al. in Bull Earthq Eng 13(12):3553–3596, 2015). Finally, we believe that the framework of selecting and building a regional ground-motion model represents a step forward in ground-motion modeling, particularly for large-scale PSHA models.
Seismotectonic regionalization of the Middle East and Caucasus region as delineated for use within the EMME14. The regions in red represent the active shallow crust comprising active shallow faults (normal, reverse, strike-slip and thrust), major plate boundaries, and accretionary wedges in top of subduction zones as well as shallow seismicity in top of deep non-subduction seismicity (blue-dashed polygons). Subduction zones, interface in red with white dots, and inslab in black dashed polygons, depict the Mediterranean and the Makran subduction zones. Stable continental regions are shown in green
Seismotectonic regionalization of the Middle East and Caucasus region as delineated for use within the EMME14. The regions in red represent the active shallow crust comprising active shallow faults (normal, reverse, strike-slip and thrust), major plate boundaries, and accretionary wedges in top of subduction zones as well as shallow seismicity in top of deep non-subduction seismicity (blue-dashed polygons). Subduction zones, interface in red with white dots, and inslab in black dashed polygons, depict the Mediterranean and the Makran subduction zones. Stable continental regions are shown in green
… 
Mw versus distance (REPI, RHYP, RJB and RRUP) scatter plots of the strong-motion databank as recorded per countries
Mw versus distance (REPI, RHYP, RJB and RRUP) scatter plots of the strong-motion databank as recorded per countries
… 
RJB versus Mw scatter plots of the strong-motion database in terms of style-of-faulting (left) and NEHRP site classes (right)
RJB versus Mw scatter plots of the strong-motion database in terms of style-of-faulting (left) and NEHRP site classes (right)
… 
Country-based hypocentral depth versus Mw scatter plots of the events in the strong-motion database (left panel) and hypocentral depth versus Mw histograms of the events (middle panel) and accelerograms (right panel) in the strong-motion database. The hypocentral depth intervals vary as 0–15 km, 15–33 km, 33–50 km, 50–70 km and 70–98 km whereas Mw bounds of the bins are 4.0–5.0, 5.0–6.0, 6.0–7.0 and 7.0–7.6
Country-based hypocentral depth versus Mw scatter plots of the events in the strong-motion database (left panel) and hypocentral depth versus Mw histograms of the events (middle panel) and accelerograms (right panel) in the strong-motion database. The hypocentral depth intervals vary as 0–15 km, 15–33 km, 33–50 km, 50–70 km and 70–98 km whereas Mw bounds of the bins are 4.0–5.0, 5.0–6.0, 6.0–7.0 and 7.0–7.6
… 
Data histograms of the generated Mw–RJB bins. Intervals of Mw and RJB are given next to each legend. Different magnitude intervals are represented by different colors while different patterns identify the distance intervals. For example, the 7th bin (i.e., Mw–RJB bin Id = 7) represents the data for 5 < Mw < 6 and 50 < RJB < 100 km
+12
Data histograms of the generated Mw–RJB bins. Intervals of Mw and RJB are given next to each legend. Different magnitude intervals are represented by different colors while different patterns identify the distance intervals. For example, the 7th bin (i.e., Mw–RJB bin Id = 7) represents the data for 5 < Mw < 6 and 50 < RJB < 100 km
… 
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ORIGINAL RESEARCH PAPER
The 2014 Earthquake Model of the Middle East: ground
motion model and uncertainties
Laurentiu Danciu
1
•O
¨zkan Kale
2
•Sinan Akkar
2
Received: 26 November 2015 / Accepted: 14 August 2016 / Published online: 30 August 2016
ÓSpringer Science+Business Media Dordrecht 2016
Abstract We summarize the main elements of a ground-motion model, as built in three-
year effort within the Earthquake Model of the Middle East (EMME) project. Together
with the earthquake source, the ground-motion models are used for a probabilistic seismic
hazard assessment (PSHA) of a region covering eleven countries: Afghanistan, Armenia,
Azerbaijan, Cyprus, Georgia, Iran, Jordan, Lebanon, Pakistan, Syria and Turkey. Given the
wide variety of ground-motion predictive models, selecting the appropriate ones for
modeling the intrinsic epistemic uncertainty can be challenging. In this respect, we provide
a strategy for ground-motion model selection based on data-driven testing and sensitivity
analysis. Our testing procedure highlights the models of good performance in terms of both
data-driven and non-data-driven testing criteria. The former aims at measuring the match
between the ground-motion data and the prediction of each model, whereas the latter aims
at identification of discrepancies between the models. The selected set of ground models
were directly used in the sensitivity analyses that eventually led to decisions on the final
logic tree structure. The strategy described in great details hereafter was successfully
applied to shallow active crustal regions, and the final logic tree consists of four models
(Akkar and C¸ag
˘nan in Bull Seismol Soc Am 100:2978–2995, 2010; Akkar et al. in Bull
Earthquake Eng 12(1):359–387, 2014; Chiou and Youngs in Earthq Spectra 24:173–215,
2008; Zhao et al. in Bull Seismol Soc Am 96:898–913, 2006). For other tectonic provinces
in the considered region (i.e., subduction), we adopted the predictive models selected
within the 2013 Euro-Mediterranean Seismic Hazard Model (Woessner et al. in Bull
Earthq Eng 13(12):3553–3596, 2015). Finally, we believe that the framework of selecting
and building a regional ground-motion model represents a step forward in ground-motion
modeling, particularly for large-scale PSHA models.
&Laurentiu Danciu
laurentiu.danciu@sed.ethz.ch
1
Swiss Seismological Service, ETH Zurich, Sonneggstrasse 5, 8092 Zurich, Switzerland
2
Earthquake Engineering Division, Kandilli Observatory and Earthquake Research Institute,
Bog
˘azic¸i University, 34684 C¸ engelko
¨y, I
˙stanbul, Turkey
123
Bull Earthquake Eng (2018) 16:3497–3533
https://doi.org/10.1007/s10518-016-9989-1
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... Ground motion prediction equations are mathematical models that are used to estimate the ground motion intensities based on factors such as magnitude, distance from the epicenter, source mechanism, earthquake propagation path, and local site conditions (Atkinson and Adams 2013;Danciu et al. 2018). Ground motion prediction equations are essential components of PSHA and earthquake risk assessment (Bommer et al. 2010;Atkinson and Adams 2013). ...
Seismic Risk Model for the Beijing–Tianjin–Hebei Region, China: Considering Epistemic Uncertainty from the Seismic Hazard Models
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This study presents a probabilistic seismic risk model for the Beijing–Tianjin–Hebei region in China. The model comprises a township-level residential building exposure model, a vulnerability model derived from the Chinese building taxonomy, and a regional probabilistic seismic hazard model. The three components are integrated by a stochastic event-based method of the OpenQuake engine to assess the regional seismic risk in terms of average annual loss and exceedance probability curve at the city, province, and regional levels. The novelty and uniqueness of this study are that a probabilistic seismic risk model for the Beijing–Tianjin–Hebei region in China is developed by considering the impact of site conditions and epistemic uncertainty from the seismic hazard model.
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... This project is performed by cooperation researches from 11 countries in last four years. Danciu et al. (2018) comprehensively describe the calculation procedure in the EMME project. This seismic source model in the EMME project composed of an area source model, a fault model and a background seismicity model. ...
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This study provides a comprehensive probabilistic seismic hazard assessment for Karaj, the capital of Alborz province. In the present study, two probabilistic approaches, including the classical and Monte Carlo methods are applied. In this regard, the most recent earthquake catalog of the region, as well as, the most appropriate GMPEs based on the statistical tests of the likelihood and the log-likelihood are used. The results indicated that there are differences between the results of two approaches, which is intensified in the longer return periods. This disparity mainly stemmed from the different concept of two methods for incorporating the aleatory uncertainty. In the classical PSHA, the aleatory uncertainty takes into account using the integration, which is truncated at a fixed number of the logarithmic standard deviation. While, in the Monte Carlo simulation approach, the aleatory uncertainty is considered in calculation using random sampling of GMPEs variability. In addition, the ground motion shaking map of the region for the dominant seismic scenarios, including the rupture of the North-Tehran and Eshtehard faults are developed. These seismic scenarios have the potential of producing the greatest acceleration; consequently, the most vulnerability. The outcomes of this study can be used for providing urban plan or estimating the probable economic and casualty losses of Karaj.
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... Notably, such features of the ground motion models have not been captured explicitly in earlier GMM logic trees (e.g. Delavaud et al., 2012;Danciu et al., 2018a; Pagani et al., 2020). This approach was further adapted to regions of limited data such as the stable cratonic region of northeastern Europe and the subduction and deep seismicity sources by capitalising on recent developments in regionalised ground motion 70 modelling worldwide. ...
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The 2020 update of the European Seismic Hazard Model (ESHM20) is the most recent and up-to-date assessment of seismic hazard for the Euro-Mediterranean region. The new model, publicly released in May 2022, incorporates refined and cross-border harmonised earthquake catalogues, homogeneous tectonic zonation, updated active faults datasets and geological information, complex subduction sources, updated area source models, a smoothed seismicity model with an adaptive kernel optimised within each tectonic region and a novel ground motion characteristic model. ESHM20 supersedes the 2013 European Seismic Hazard Model (ESHM13, Wössner et al 2015) and provides full sets of hazard outputs such as hazard curves, maps, and uniform hazard spectra for the Euro-Mediterranean region. The model provides two informative hazard maps that will serve as a reference for the forthcoming revision of the European Seismic Design Code (CEN EC8) and provides input to the first earthquake risk model for Europe (Crowley et al., 2021). ESHM20 will continue to evolve and act as a key resource for supporting earthquake preparedness and resilience throughout Euro-Mediterranean region under the umbrella of the European Facilities for Seismic Hazard and Risk Consortium (EFEHR Consortium).
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... An assumed site with latitude and longitude coordinates of 35.70° N and 51.20° E is selected to conduct the analyses. To this end, the source model proposed by Danciu et al. (2018) in the Earthquake Model of the Middle East (EMME) project was used in the analysis process. The GMPEs used in the analysis and their weights are shown in Table 1. ...
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Probabilistic seismic hazard analysis (PSHA) is the primary method for determining the earthquake forces as input to structural seismic evaluation and design. Epistemic uncertainty has been incorporated into the PSHA process using a logic tree. One of the main challenges in using logic trees is determining ground motion prediction equations (GMPEs) and their branches’ weights. In this paper, regarding the different definitions of probability, the philosophy of GMPE selection and logic tree branches’ weight allocation in the PSHA is investigated. The results show that the classical and frequency definitions of probability are not applicable in the selection and weight allocation process. We suggest that the best way to allocate weight can be obtained by combining the inter-subjective and propensity probability definitions. To evaluate the effect of weight allocation on the PSHA results, PSHA was performed for a site in Tehran using different selection and weighting approaches. The results of the numerical example show up to a 50% variation in the spectral acceleration in the range of common building periods. We show that the issue of GMPE selection and weight allocation has not been adequately addressed in the current procedures of PSHA. So, it is necessary to develop specific agendas in this field.
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... Because of that, M w lacks saturation effects, which makes it the most suitable scale for many practical applications. The moment magnitude is the standard practice when it comes to seismic hazard assessment studies, i.e. both the activity rates and the ground motion models (Danciu et al., 2016) should be defined in terms of M w . Unfortunately, M w has been routinely calculated for large earthquakes worldwide only since the beginning of the Global Centroid Moment Tensor (GCMT) catalog in 1976 (Dziewonski et al., 1981). ...
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... The target spectrum, as discussed previously, was obtained from the elastic response spectrum according to the EC8 guidelines [31]. For the ground motion selection, the main parameters affecting the ground motion shape (magnitude, distance from the fault, site-conditions, basin effect, directivity effects, amongst other) [81] were defined from the 2014 earthquake hazard model of the Middle East (EMME) [82], which also included northern Algeria. The parameters were derived from the disaggregation analysis for the 475-year return period and site-specific data. ...
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... Because of that, Mw lacks of saturation effects, which makes it 12 the most suitable scale for many practical applications. The moment magnitude is the standard practice when 13 it comes to seismic hazard assessment studies, i.e. both the activity rates and the ground motion models 14 (Danciu et al. 2016) should be defined in terms of Mw. Unfortunately, Mw has been routinely calculated for 15 large earthquakes worldwide only since the beginning of the Global Centroid Moment Tensor (GCMT) 16 catalog in 1976 (Dziewonski et al. 1981). ...
View
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Assessment of Seismic Hazard in the Middle East and Caucasus: EMME (Earthquake Model of Middle East) Project
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View
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Toward a New Probabilistic Framework to Score and Merge Ground- Motion Prediction Equations: The Case of the Italian Region
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The ground-motion prediction equation (GMPE) is a basic component for probabilistic seismic-hazard analysis. There is a wide variety of GMPEs that are usually obtained by means of inversion techniques of datasets containing ground motions recorded at different stations. However, to date there is not yet a commonly accepted procedure to select the best GMPE for a specific case; usually, a set of GMPEs is implemented (more or less arbitrarily) in a logic-tree structure, in which each GMPE is weighted by experts, mostly according to gut feeling. Here, we discuss a general probabilistic framework to numerically score and weight GMPEs, highlighting features, limitations, and approximations; finally, we put forward a numerical procedure to score GMPEs, taking into account their forecasting performances , and to merge them through an ensemble modeling. Then, we apply the procedure to the Italian territory; in addition to illustrating how the procedure works, we investigate other relevant aspects (such as the importance of the focal mechanism) of the GMPEs to different site conditions. Online Material: Figures showing regression analysis for peak ground acceleration (PGA) values and location map, and earthquake catalog and summary table of parameters corresponding to each ground-motion prediction equation (GMPE) implemented.
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Recent and future developments in earthquake ground motion estimation
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  • Jul 2016
  • EARTH-SCI REV
Seismic hazard analyses (SHA) are routinely carried out around the world to understand the hazard, and consequently the risk, posed by earthquake activity. Whether single scenario, deterministic analyses, or state-of-the art probabilistic approaches, considering all possible events, a founding pillar of SHA is the estimation of the ground-shaking field from potential future earthquakes. Early models accounted for simple observations, such that ground shaking from larger earthquakes is stronger and that ground motion tends to attenuate rapidly away from the earthquake source. The first ground motion prediction equations (GMPEs) were, therefore, developed with as few as two principal predictor variables: magnitude and distance. Despite the significant growth of computer power over the last few decades, and with it the possibility to compute kinematic or dynamic rupture models coupled with simulations of 3D wave propagation, the simple parametric GMPE has remained the tool of choice for hazard analysts. There are numerous reasons for this. First and foremost GMPEs are robust and reliable within the model space considered during their derivation, and many can be extrapolated to a degree beyond this space with some confidence. With ever expanding datasets and improved metadata the models are becoming more and more useful: a range of predictor variables are now used, describing the source, path and site effects in detail. GMPEs are also relatively easy to implement and computationally inexpensive. Despite this, probabilistic hazard calculations using GMPEs and accounting for uncertainties can still take several days to run. Full simulation-based approaches, therefore, clearly lie outside the computation budget afforded to most projects. As well as the ever expanding list of predictor variables, other recent developments have also significantly improved the predictive power of GMPEs. This has allowed them to maintain their advantage over more ‘physical’ simulation techniques. Possibly the biggest aspect of this is not related to the median ground-shaking field, but rather its variability (and correlation in space and with oscillator period). This is a major advantage of empirical as opposed to simulation approaches, which typically struggle to replicate the covariance of input variables and, consequently, the variance of the ground motion. In this article we summarize some of the recent advances in ground motion prediction equations, including their application in SHA. We begin with a summary of the current state-of-the-art, then introduce the main additional predictor variables now used. Region- and event-type (tectonic or induced) specific predictions and adjustments are then discussed. Additional topics include advances in estimating ground-motion variability (epistemic and aleatory) and expanding GMPEs to predict other intensity measures or waveform features. The article concludes with a discussion on the path forward in earthquake ground motion prediction.
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Region‐Specific Assessment, Adjustment, and Weighting of Ground‐Motion Prediction Models: Application to the 2015 Swiss Seismic‐Hazard Ma
Article
  • Aug 2016
  • B SEISMOL SOC AM
We present a strategy for the region‐specific assessment, adjustment, and weighting of ground‐motion prediction models, with application to the 2015 Swiss national seismic‐hazard maps. The models are provided within a logic‐tree framework adopted for the probabilistic seismic‐hazard analysis (PSHA). Through this framework, we consider both aleatory and epistemic uncertainties in ground‐motion prediction in Switzerland, a region of low‐to‐moderate seismicity and consequently data poor in terms of strong‐motion records. We use both empirical models developed using global strong‐motion data and stochastic simulation models calibrated to local seismicity, characteristic wave‐propagation effects, and site conditions. The empirical models were adjusted to account for (a) the selected hazard reference rock velocity model (using VS‐κ0 adjustments) and (b) the median instrumental ground‐motion data at low magnitudes. The use of a carefully calibrated simulation model and VS‐κ0 adjusted empirical ground‐motion prediction equations allowed us to precisely define the reference‐rock profile, upon which subsequent analyses, such as microzonation and site‐specific hazard, can be applied without uncertainty related to the reference condition. We implemented partially nonergodic aleatory uncertainties in ground‐motion prediction through the single‐station sigma approach. This strategy, complemented with the known reference rock condition, leads to significant reductions in the contribution of uncertainty in ground‐motion characterization to PSHA in Switzerland. However, the application of the methodological framework outlined herein extends to any region, particularly those of low‐to‐moderate seismicity.
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Toward a New Probabilistic Framework to Score and Merge Ground- Motion Prediction Equations: The Case of the Italian Region
Article
  • Apr 2016
The ground-motion prediction equation (GMPE) is a basic component for probabilistic seismic-hazard analysis. There is a wide variety of GMPEs that are usually obtained by means of inversion techniques of datasets containing ground motions recorded at different stations. However, to date there is not yet a commonly accepted procedure to select the best GMPE for a specific case; usually, a set of GMPEs is implemented (more or less arbitrarily) in a logic-tree structure, in which each GMPE is weighted by experts, mostly according to gut feeling. Here, we discuss a general probabilistic framework to numerically score and weight GMPEs, highlighting features, limitations, and approximations; finally, we put forward a numerical procedure to score GMPEs, taking into account their forecasting performances, and to merge them through an ensemble modeling. Then, we apply the procedure to the Italian territory; in addition to illustrating how the procedure works, we investigate other relevant aspects (such as the importance of the focal mechanism) of the GMPEs to different site conditions.
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