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  -