Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/9958
AuthorsDouglas, J.* 
Akkar, S.* 
Ameri, G.* 
Bard, P.* 
Bindi, D.* 
Bommer, J.* 
Bora, S. S.* 
Cotton, F.* 
Derras, B.* 
Hermkes, M.* 
Kuehn, N. M.* 
Luzi, L.* 
Massa, M.* 
Pacor, F.* 
Riggelsen, C.* 
Sandikkaya, M. A.* 
Scherbaum, F.* 
Stafford, P.* 
Traversa, P.* 
TitleComparisons among the five ground-motion models developed using RESORCE for the prediction of response spectral accelerations due to earthquakes in Europe and the Middle East
Issue DateFeb-2014
Series/Report no./12 (2014)
DOI10.1007/s10518-013-9522-8
URIhttp://hdl.handle.net/2122/9958
Keywordsground motion prediction equation
europe
Subject Classification04. Solid Earth::04.06. Seismology::04.06.04. Ground motion 
AbstractThis article presents comparisons among the five ground-motion models described in other articles within this special issue, in terms of data selection criteria, characteristics of the models and predicted peak ground and response spectral accelerations. Comparisons are also made with predictions from the Next Generation Attenuation (NGA) models to which the models presented here have similarities (e.g. a common master database has been used) but also differences (e.g. some models in this issue are nonparametric). As a result of the differing data selection criteria and derivation techniques the predicted median ground motions show considerable differences (up to a factor of two for certain scenarios), particularly for magnitudes and distances close to or beyond the range of the available observations. The predicted influence of style-of-faulting shows much variation among models whereas site amplification factors are more similar, with peak amplification at around 1s. These differences are greater than those among predictions from the NGA models. The models for aleatory variability (sigma), however, are similar and suggest that ground-motion variability from this region is slightly higher than that predicted by the NGA models, based primarily on data from California and Taiwan.
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