Bulletin of Earthquake Engineering

, Volume 16, Issue 8, pp 3497–3533 | Cite as

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

  • Laurentiu Danciu
  • Özkan Kale
  • Sinan Akkar
Original Research Paper


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.


Ground motion prediction equations (GMPEs) Ground motion modeling Ground motion uncertainties Regional seismic hazard assessment Earthquake Model of the Middle East Region (EMME) project 



The work presented in this article has been developed within the Earthquake Model of the Middle East Region (EMME) project sponsored by Japan Tobacco International. The authors would like to acknowledge the contribution of all regional experts that provided feedback during the Project workshops and meetings. Equally important is the support through years of the Global Earthquake Model (GEM) both scientific and IT teams. Finally, we thank Fabrice Cotton and an anonymous reviewer for their constructive comments and review of the manuscript.


  1. Abrahamson NA, Bommer JJ (2005) Probability and uncertainty in seismic hazard analysis. Earthq Spectra 21(2):603–607CrossRefGoogle Scholar
  2. Abrahamson NA, Silva WJ (2008) Summary of the Abrahamson and Silva NGA ground-motion relations. Earthq Spectra 24:67–97CrossRefGoogle Scholar
  3. Abrahamson NA, Silva WJ, Kamai R (2014) Summary of the ASK14 ground motion relation for active crustal regions. Earthq Spectra 30(3):1025–1055CrossRefGoogle Scholar
  4. Akkar S, Bommer JJ (2006) Influence of long-period filter cut-off on elastic spectral displacements. Earthq Eng Struct Dyn 35:1145–1165CrossRefGoogle Scholar
  5. Akkar S, Bommer JJ (2010) Empirical equations for the prediction of PGA, PGV and spectral accelerations in Europe, the Mediterranean and the Middle East. Seismol Res Lett 81:195–206CrossRefGoogle Scholar
  6. Akkar S, Çağnan Z (2010) A local ground-motion predictive model for Turkey, and its comparison with other regional and global ground-motion. Bull Seismol Soc Am 100:2978–2995CrossRefGoogle Scholar
  7. Akkar S, Kale Ö, Ansari A, Durgaryan R, Askan Gündoğan A, Hamzehloo H, Harmandar E, Tsereteli N, Waseem M, Yazjeen T, Yilmaz MT (2014a) EMME strong-motion database serving for predictive model selection to EMME ground-motion logic-tree applications. Second European conference on earthquake engineering and seismology, İstanbul, Turkey, Abstract No. 3220Google Scholar
  8. Akkar S, Sandikkaya MA, Bommer JJ (2014b) Empirical ground-motion models for point- and extended-source crustal earthquake scenarios in Europe and the Middle East. Bull Earthq Eng 12(1):359–387CrossRefGoogle Scholar
  9. Ambraseys NN, Douglas J, Sarma SK, Smit PM (2005) Equations for the estimation of strong ground motion from shallow crustal earthquakes using data from Europe and the Middle East: horizontal peak ground acceleration and spectral acceleration. Bull Earthq Eng 3:1–53CrossRefGoogle Scholar
  10. Ancheta TD, Darragh RB, Stewart JP, Seyhan E, Silva WJ, Chiou BSJ, Wooddell KE, Graves RW, Kottke AR, Boore DM, Kishida T, Donahue JL (2014) NGA-West2 database. Earthq Spectra 30:989–1005CrossRefGoogle Scholar
  11. Atkinson GM (2006) Single-station sigma. Bull Seismol Soc Am 96(2):446–455CrossRefGoogle Scholar
  12. Atkinson GM, Adams J (2013) Ground motion prediction equations for application to the 2015 Canadian national seismic hazard maps. Can J Civ Eng 40(10):988–998CrossRefGoogle Scholar
  13. Atkinson GM, Boore DM (2003) Empirical ground motion relations for subduction zone earthquakes and their application to Cascadia and other regions. Bull Seismol Soc Am 93:1703–1729CrossRefGoogle Scholar
  14. Atkinson GM, Boore DM (2006) Earthquake ground-motion prediction equations for eastern North America. Bull Seismol Soc Am 96(6):2181–2205CrossRefGoogle Scholar
  15. Atkinson GM, Boore DM (2011) Modifications to existing ground-motion prediction equations in light of new data. Bull Seismol Soc Am 101(3):1121–1135CrossRefGoogle Scholar
  16. Atkinson GM, Morrison M (2009) Regional variability in ground motion amplitudes along the west coast of North America. Bull Seismol Soc Am 99:2393–2409CrossRefGoogle Scholar
  17. Atkinson GM, Bommer JJ, Abrahamson NA (2014) Alternative approaches to modeling epistemic uncertainty in ground motions in probabilistic seismic-hazard analysis. Seismol Res Lett 85(6):1141–1144CrossRefGoogle Scholar
  18. Beyer K, Bommer JJ (2006) Relationships between median values and between aleatory variabilities for different definitions of the horizontal component of motion. Bull Seismol Soc Am 96(4A):1512–1522. doi: 10.1785/0120050210 CrossRefGoogle Scholar
  19. Bindi D, Luzi L, Massa M, Pacor F (2010) Horizontal and vertical ground motion prediction equations derived from the Italian Accelerometric Archive (ITACA). Bull Earthq Eng 8:1209–1230CrossRefGoogle Scholar
  20. Bindi D, Massa M, Luzi L, Ameri G, Pacor F, Puglia R, Augliera P (2014) Pan-European ground-motion prediction equations for the average horizontal component of PGA, PGV, and 5 %-damped PSA at spectral periods up to 30 s using the RESORCE dataset. Bull Earthq Eng 12:391–430CrossRefGoogle Scholar
  21. Bommer JJ (2005) On the use of logic trees for ground-motion prediction equations in seismic-hazard analysis. Bull Seismol Soc Am 95(2):377–389CrossRefGoogle Scholar
  22. Bommer JJ, Abrahamson NA (2006) Why do modern probabilistic seismic-hazard analyses often lead to increased hazard estimates? Bull Seismol Soc Am 96(6):1967–1977CrossRefGoogle Scholar
  23. Bommer JJ, Douglas J, Scherbaum F, Cotton F, Bungum H, Fäh D (2010) On the selection of ground-motion prediction equations for seismic hazard analysis. Seismol Res Lett 81:783–793CrossRefGoogle Scholar
  24. Bommer JJ, Akkar S, Drouet S (2012) Extending ground-motion prediction equations for spectral ordinates to higher response frequencies. Bull Earthq Eng 10:379–399CrossRefGoogle Scholar
  25. Boore DM, Atkinson GM (2008) Ground-motion prediction equations for the average horizontal component of PGA, PGV, and 5 %-damped PSA at spectral periods between 0.01 s and 10.0 s. Earthq Spectra 24(1):99–138. doi: 10.1193/1.2830434 CrossRefGoogle Scholar
  26. Boore DM, Watson-Lamprey J, Abrahamson NA (2006) Orientation-independent measures of ground motion. Bull Seismol Soc Am 96:1502–1511CrossRefGoogle Scholar
  27. Boore DM, Stewart JP, Seyhan E, Atkinson GM (2014) NGA-West2 equations for predicting PGA, PGV, and 5 % damped PSA for shallow crustal earthquakes. Earthq Spectra 30(3):1057–1085CrossRefGoogle Scholar
  28. Bozorgnia Y, Abrahamson NA, Atik LA, Ancheta TD, Atkinson GM, Baker JW, Darragh R (2014) NGA-West2 research project. Earthq Spectra 30(3):973–987CrossRefGoogle Scholar
  29. Campbell KW (2003) Prediction of strong ground motion using the hybrid empirical method and its use in the development of ground–motion (attenuation) relations in eastern North America. Bull Seismol Soc Am 93(3):1012–1033CrossRefGoogle Scholar
  30. Campbell KW, Bozorgnia Y (2008) NGA ground motion model for the geometric mean horizontal component of PGA, PGV, PGD and 5 %-damped linear elastic response spectra at periods ranging from 0.1 s to 10.0 s. Earthq Spectra 24:139–171CrossRefGoogle Scholar
  31. Campbell KW, Bozorgnia Y (2014) NGA-West2 ground motion model for the average horizontal components of PGA, PGV, and 5 % damped linear acceleration response spectra. Earthq Spectra 30(3):1087–1115CrossRefGoogle Scholar
  32. Cauzzi C, Faccioli E (2008) Broadband (0.05 to 20 s) prediction of displacement response spectra based on worldwide digital records. J Seismol 12:453–475CrossRefGoogle Scholar
  33. Cauzzi C, Faccioli E, Vanini M, Bianchini A (2015) Updated predictive equations for broadband (001–10 s) horizontal response spectra and peak ground motions, based on a global dataset of digital acceleration records. Bull Earthq Eng 13(6):1587–1612CrossRefGoogle Scholar
  34. Chiou BSJ, Youngs RR (2008) An NGA model for the average horizontal component of peak ground motion and response spectra. Earthq Spectra 24:173–215CrossRefGoogle Scholar
  35. Chiou BSJ, Youngs RR (2014) Update of the Chiou and Youngs NGA model for the average horizontal component of peak ground motion and response spectra. Earthq Spectra 30(3):1117–1153CrossRefGoogle Scholar
  36. Cotton F, Scherbaum F, Bommer JJ, Bungum H (2006) Criteria for selecting and adjusting ground-motion models for specific target regions: application to central Europe and rock sites. J Seismol 10:137–156CrossRefGoogle Scholar
  37. Cotton F, Pousse G, Bonilla F, Scherbaum F (2008) On the discrepancy of recent European ground-motion observations and predictions from empirical models: analysis of KiK-net accelerometric data and point-sources stochastic simulations. Bull Seismol Soc Am 98(5):2244–2261. doi: 10.1785/0120060084 CrossRefGoogle Scholar
  38. Danciu L, Woessner J (2014) Pseudo-probabilistic seismic hazard sources for vrancea deep seismicity second European conference on earthquake engineering and seismology, 2ECEES, 24–29 August 2014, Istanbul, Turkey, Abstract id 3269Google Scholar
  39. Danciu L, Sesetyan K, Demircioglu M, Elias A, Gulent L, Zare M et al (2016) The 2014 earthquake model of the middle east: seismogenic sources. Bull Earthq Eng (current issue)Google Scholar
  40. Delavaud E, Scherbaum F, Kuehn N, Riggelsen C (2009) Information-theoretic selection of ground-motion prediction equations for seismic hazard analysis: an applicability study using Californian data. Bull Seismol Soc Am 99(6):3248–3263CrossRefGoogle Scholar
  41. Delavaud E, Cotton F, Akkar S, Scherbaum F, Danciu L, Beauval C, Drouet S, Douglas J, Basili R, Sandıkkaya MA, Segou M, Faccioli E, Theodoulidis N (2012) Toward a ground-motion logic tree for probabilistic seismic hazard assessment in Europe. J Seismol 16:451–473CrossRefGoogle Scholar
  42. Douglas J (2004) An investigation of analysis of variance as a tool for exploring regional differences in strong ground motions. J Seismol 8:485–496CrossRefGoogle Scholar
  43. Douglas J (2010) Assessing the epistemic uncertainty of ground-motion predictions. In: Proceedings of the Ninth US National and 10th Canadian Conference on Earthquake Engineering, Toronto, Canada Paper No 219Google Scholar
  44. Douglas J, Edwards B (2016) Recent and future developments in earthquake ground motion estimation. Earth Sci Rev 160:203–219CrossRefGoogle Scholar
  45. Douglas J, Cotton F, Abrahamson NA, Akkar S, Boore DM, Di Alessandro C (2013) Pre-selection of ground motion prediction equations, report produced in context of GEM GMPE project.
  46. Edwards B, Cauzzi C, Danciu L, Fäh D (2016) Assessment, adjustment and weighting of ground motion prediction models for the 2015 Swiss seismic hazard maps. Bull Seismol Soc Am. doi: 10.1785/0120150367 Google Scholar
  47. Erdik M, Sestyan K, Demircioglu MB, Tuzun C,  Giardini D, Gulen L,  Akkar S, Zare M (2012). Assessment of seismic hazard in the Middle East and Caucasus: EMME (Earthquake Model of Middle East) project. In Proceedings of 15th world conference on earthquake engineering, Lisbon, Portugal, 24–28 September 2012, Paper Number 2100, 10 ppGoogle Scholar
  48. Faccioli E, Villani M, Vanini M, Cauzzi C (2010) Mapping seismic hazard for the needs of displacement-based design: the case of Italy. Adv Performance-Based Earthq Eng 13:3–14CrossRefGoogle Scholar
  49. Fukushima Y, Berge-Thierry C, Volant P, Griot-Pommera DA, Cotton F (2003) Attenuation relation for western Eurasia determined with recent near-fault records from California, Japan and Turkey. J Earthq Eng 7:573–598Google Scholar
  50. Ghasemi H, Zare M, Fukushima Y, Koketsu K (2009) An empirical spectral ground-motion model for Iran. J Seismol 13:499–515CrossRefGoogle Scholar
  51. Giardini D (1999) The global seismic hazard assessment program (GSHAP)-1992/1999. Ann Geophys 42(6) ISSN 2037-416XGoogle Scholar
  52. Giardini D, Woessner J, Danciu L (2014) Mapping Europe’s seismic hazard. EOS 95:261–262CrossRefGoogle Scholar
  53. Hintersberger E, Scherbaum F, Hainzl S (2007) Update of likelihood-based ground-motion model selection for seismic hazard analysis in western central Europe. Bull Earthq Eng 5:1–16CrossRefGoogle Scholar
  54. Idriss IM (2014) An NGA-West2 empirical model for estimating the horizontal spectral values generated by shallow crustal earthquakes. Earthq Spectra 30(3):1155–1177CrossRefGoogle Scholar
  55. Kaklamanos J, Baise LG (2011) Model validations and comparisons of the Next Generation Attenuation of Ground Motions (NGA-West) Project. Bull Seismol Soc Am 101(1):160–175. doi: 10.1785/0120100038 CrossRefGoogle Scholar
  56. Kaklamanos J, Boore DM, Thompson EM, Campbell KW (2010) Implementation of the next generation attenuation (NGA) ground-motion prediction equations in Fortran and R, U.S. Geol. Surv. Open-File Rept. 2010–1296, 43pGoogle Scholar
  57. Kaklamanos J, Baise LG, Boore DM (2011) Estimating unknown input parameters when implementing the NGA ground-motion prediction equations in engineering practice. Earthq Spectra 27:1219–1235CrossRefGoogle Scholar
  58. Kale Ö (2014) Practical tools for ranking and selection of ground-motion prediction equations (GMPEs) for probabilistic seismic hazard assessment and development of a regional GMPE. PhD Thesis, Civil Engineering Department, Middle East Technical University, Ankara, TurkeyGoogle Scholar
  59. Kale Ö, Akkar S (2013a) A new perspective for selecting and ranking ground-motion prediction equations (GMPEs): the euclidian distance-based ranking method. Bull Seismol Soc Am 103(2A):1069–1084CrossRefGoogle Scholar
  60. Kale Ö, Akkar S (2013b) Türkiye için Geliştirilen Yeni Bir Yer Hareketi Tahmin Denklemi ve Bu Denklemin Orta Doğu Bölgesinde Yapılacak Sismik Tehlike Çalışmaları için Uygunluğunun Test Edilmesi. 2. Türkiye Deprem Mühendisliği ve Sismoloji Konferansı, MKU, Hatay, Paper No. 172Google Scholar
  61. Kale Ö, Akkar S (2015) An auxiliary tool to build ground-motion logic-tree framework for probabilistic seismic hazard assessment, 3. Türkiye Deprem Mühendisliği ve Sismoloji Konferansı, DEÜ, İzmir, Paper No. 068Google Scholar
  62. Kale Ö, Akkar S, Ansari A, Hamzehloo H (2015) A ground-motion predictive model for Iran and Turkey for horizontal PGA, PGV and 5 %-damped response spectrum: investigation of possible regional effects. Bull Seismol Soc Am 105:963–980CrossRefGoogle Scholar
  63. Kalkan E, Gülkan P (2004) Site-dependent spectra derived from ground-motion records in Turkey. Earthq Spectra 20:1111–1138CrossRefGoogle Scholar
  64. Kotha SR, Bindi D, Cotton F (2016) Partially non-ergodic region specific GMPE for Europe and Middle-East. Bull Earthq Eng 4(4):1245–1263CrossRefGoogle Scholar
  65. Kulkarni RB, Youngs RR, Coppersmith KJ (1984) Assessment of confidence intervals for results of seismic hazard analysis. Eighth World Conference on Earthquake Engineering, vol 1, pp 263–270Google Scholar
  66. Lin P-S, Lee C-T (2008) Ground-motion attenuation relationships for subduction zone earthquakes in northeastern Taiwan. Bull Seismol Soc Am 98:220–240CrossRefGoogle Scholar
  67. McGuire RK, Cornell CA, Toro GR (2005) The case for using mean seismic hazard. Earthq Spectra 21(3):879–886CrossRefGoogle Scholar
  68. Mousavi M, Ansari A, Zafarani H, Azarbakht A (2012) Selection of ground motion prediction models for seismic hazard analysis in the Zagros region. Iran J Earthq Eng 16:1184–1207CrossRefGoogle Scholar
  69. Musson RMW (2005) Against fractiles. Earthq Spectra 21(3):887–891CrossRefGoogle Scholar
  70. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models: part I—A discussion of principles. J Hydrol 10:282–290CrossRefGoogle Scholar
  71. NUREG/CR-5411 (1990) Elicitation and use of expert judgment in performance assessment for high-level radioactive waste repositories, SAND89-1821, May 1990Google Scholar
  72. Özbey C, Sari A, Manuel L, Erdik M, Fahjan Y (2004) An empirical attenuation relationship for northwestern Turkey ground motion using a random effects approach. Soil Dyn Earthq Eng 20:853–882Google Scholar
  73. Pagani M, Monelli D, Weatherill G, Danciu L, Crowley H, Silva V, Henshaw P, Butler L, Nastasi M, Panzeri L, Simionato M, Vigano D (2014) OpenQuake Engine: an Open Hazard (and Risk) Software for the Global Earthquake Model. Seismol Res Lett 85:692–702CrossRefGoogle Scholar
  74. Pagani M, Weatherill G, Garcia J (2015) Seismic hazard models: a view on reproducibility, coherence and quality assurance. In: Proceedings of the international workshop on ground motion prediction equation and seismic hazard assessment, March 12, 2015Google Scholar
  75. Petersen MD, Moschetti MP, Powers PM, Mueller CS, Haller KM, Frankel AD, Field N, Chen R, Rukstales KS, Luco N, Wheeler RL, Williams RA, Olsen AH (2015) The 2014 United States National Seismic Hazard Model. Earthq Spectra 31(S1):S1–S30. doi: 10.1193/120814EQS210M CrossRefGoogle Scholar
  76. Renault P (2014) Approach and challenges for the seismic hazard assessment of nuclear power plants: the Swiss Experience. Bollettino di Geosicia Teorica ed Applicata 55(1):149–164. doi: 10.4430/bgta0089 Google Scholar
  77. Rodriguez-Marek A, Montalva GA, Cotton F, Bonilla F (2011) Analysis of single-station standard deviation using the KiK-net data. Bull Seismol Soc Am 101:1242–1258CrossRefGoogle Scholar
  78. Roselli P, Marzocchi W, Faenza L (2016) Toward a new probabilistic framework to score and merge ground-motion prediction equations: the case of the Italian region. Bull Seismol Soc Am 106(2):720–733CrossRefGoogle Scholar
  79. Runge AK, Scherbaum F, Curtis A, Riggelsen C (2013) An interactive tool for the elicitation of subjective probabilities in probabilistic seismic-hazard analysis. Bull Seismol Soc Am 103(5):2862–2874CrossRefGoogle Scholar
  80. Sabetta F, Lucantoni A, Bungum H, Bommer JJ (2005) Sensitivity of PSHA results to ground motion prediction relations and logic-tree weights. Soil Dyn Earthq Eng 25:317–329CrossRefGoogle Scholar
  81. Scherbaum F, Kuehn NM (2011) Logic tree branch weights and probabilities: summing up to one is not enough. Earthq Spectra 27:1237–1251CrossRefGoogle Scholar
  82. Scherbaum F, Cotton F, Smit P (2004) On the use of response spectral-reference data for the selection and ranking of ground-motion models for seismic-hazard analysis in regions of moderate seismicity: the case of rock motion. Bull Seismol Soc Am 94(6):2164–2185CrossRefGoogle Scholar
  83. Scherbaum F, Bommer JJ, Bungum H, Cotton F, Abrahamson NA (2005) Composite ground-motion models and logic-trees: methodology, sensitivities and uncertainties. Bull Seismol Soc Am 95:1575–1593CrossRefGoogle Scholar
  84. Scherbaum F, Delavaud E, Riggelsen C (2009) Model selection in seismic hazard analysis: an informationtheoretic perspective. Bull Seismol Soc Am 99(6):3234–3247CrossRefGoogle Scholar
  85. Stafford PJ, Strasser FO, Bommer JJ (2008) An evaluation of the applicability of the NGA models to ground-motion prediction in the Euro-Mediterranean region. Bull Earthq Eng 6:149–177CrossRefGoogle Scholar
  86. Stewart JP, Douglas J, Javanbarg M, Abrahamson NA, Bozorgnia Y, Boore DM, Campbell KW, Delavaud E, Erdik M, Stafford PJ (2015) Selection of ground motion prediction equations for the global earthquake model. Earthq Spectra 31:19–45CrossRefGoogle Scholar
  87. Strasser FO, Abrahamson NA, Bommer JJ (2009) Sigma: issues, insights, and challenges. Seismol Res Lett 80(1):40–56CrossRefGoogle Scholar
  88. Toro GR (2002) Modification of the Toro et al. (1997) Attenuation equations for large magnitudes and short distances. Risk Engineering, Inc, 4-1 to 4-10Google Scholar
  89. Toro GR, Abrahamson NA, Schneider JF (1997) Model of strong ground motions from earthquake in central and eastern North America: best estimates and uncertainties. Seismol Res Lett 68(1):41–57CrossRefGoogle Scholar
  90. Weatherill GA, Pagani M, Garcia J (2014) OpenQuake ground motion toolkit user guide, global earthquake model (GEM) Technical ReportGoogle Scholar
  91. Wells DL, Coppersmith KJ (1994) New empirical relationships among magnitude, rupture length, rupture width, rupture area, and surface displacement. Bull Seismol Soc Am 84:974–1002Google Scholar
  92. Woessner J, Danciu L, Giardini D, Crowley H, Cotton F, Grünthal G, SHARE Consortium (2015) The 2013 European seismic hazard model: key components and results. Bull of Earthq Eng 13(12):3553–3596CrossRefGoogle Scholar
  93. Woo G (1992) Calibrated expert judgement in seismic hazard analysis. In proceedings of the 10th world conference on earthquake engineering, pp 333–338Google Scholar
  94. Youngs RR, Chiou BSJ, Silva WJ, Humphrey JR (1997) Strong ground motion attenuation relationships for subduction zone earthquakes. Seismol Res Lett 68:58–73CrossRefGoogle Scholar
  95. Zafarani H, Mousavi M (2014) Applicability of different ground-motion prediction models for northern Iran. Nat Hazards 73(3):1199–1228CrossRefGoogle Scholar
  96. Zhao JX, Zhang J, Asano A, Ohno Y, Oouchi T, Takahashi T, Ogawa H, Irikura K, Thio HK, Somerville PG, Fukushima Y (2006) Attenuation relations of strong ground motion in Japan using site classifications based on predominant period. Bull Seismol Soc Am 96:898–913CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Swiss Seismological ServiceETH ZurichZurichSwitzerland
  2. 2.Earthquake Engineering Division, Kandilli Observatory and Earthquake Research InstituteBoğaziçi UniversityÇengelköy, İstanbulTurkey

Personalised recommendations