TY  - JOUR
A1  - Hermkes, Marcel
A1  - Kühn, Nicolas M.
A1  - Riggelsen, Carsten
T1  - Simultaneous quantification of epistemic and aleatory uncertainty in GMPEs using Gaussian process regression
JF  - Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering
N2  - This paper presents a Bayesian non-parametric method based on Gaussian Process (GP) regression to derive ground-motion models for peak-ground parameters and response spectral ordinates. Due to its non-parametric nature there is no need to specify any fixed functional form as in parametric regression models. A GP defines a distribution over functions, which implicitly expresses the uncertainty over the underlying data generating process. An advantage of GP regression is that it is possible to capture the whole uncertainty involved in ground-motion modeling, both in terms of aleatory variability as well as epistemic uncertainty associated with the underlying functional form and data coverage. The distribution over functions is updated in a Bayesian way by computing the posterior distribution of the GP after observing ground-motion data, which in turn can be used to make predictions. The proposed GP regression models is evaluated on a subset of the RESORCE data base for the SIGMA project. The experiments show that GP models have a better generalization error than a simple parametric regression model. A visual assessment of different scenarios demonstrates that the inferred GP models are physically plausible.
KW  - Gaussian Process regression
KW  - Epistemic uncertainty
KW  - Aleatory variability
KW  - Empirical ground-motion models
KW  - Bayesian non-parametrics
KW  - GMPE
KW  - Generalization error
Y1  - 2014
U6  - https://doi.org/10.1007/s10518-013-9507-7
SN  - 1570-761X
SN  - 1573-1456
VL  - 12
IS  - 1
SP  - 449
EP  - 466
PB  - Springer
CY  - Dordrecht
ER  - 
TY  - JOUR
A1  - Douglas, John
A1  - Akkar, Sinan
A1  - Ameri, Gabriele
A1  - Bard, Pierre-Yves
A1  - Bindi, Dino
A1  - Bommer, Julian J.
A1  - Bora, Sanjay Singh
A1  - Cotton, Fabrice Pierre
A1  - Derras, Boumediene
A1  - Hermkes, Marcel
A1  - Kuehn, Nicolas Martin
A1  - Luzi, Lucia
A1  - Massa, Marco
A1  - Pacor, Francesca
A1  - Riggelsen, Carsten
A1  - Sandikkaya, M. Abdullah
A1  - Scherbaum, Frank
A1  - Stafford, Peter J.
A1  - Traversa, Paola
T1  - Comparisons 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
JF  - Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering
N2  - This 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.
KW  - Strong-motion data
KW  - Ground-motion models
KW  - Ground-motion prediction equations
KW  - Style of faulting
KW  - Site amplification
KW  - Aleatory variability
KW  - Epistemic uncertainty
KW  - Europe
KW  - Middle East
Y1  - 2014
U6  - https://doi.org/10.1007/s10518-013-9522-8
SN  - 1570-761X
SN  - 1573-1456
VL  - 12
IS  - 1
SP  - 341
EP  - 358
PB  - Springer
CY  - Dordrecht
ER  -