@article{HermkesKuehnRiggelsen2014,
  author    = {Hermkes, Marcel and K{\"u}hn, Nicolas M. and Riggelsen, Carsten},
  title     = {Simultaneous quantification of epistemic and aleatory uncertainty in GMPEs using Gaussian process regression},
  series = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering},
  volume    = {12},
  journal   = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering},
  number    = {1},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {1570-761X},
  doi       = {10.1007/s10518-013-9507-7},
  pages     = {449 -- 466},
  year      = {2014},
  abstract  = {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.},
  language  = {en}
}
@article{DouglasAkkarAmerietal.2014,
  author    = {Douglas, John and Akkar, Sinan and Ameri, Gabriele and Bard, Pierre-Yves and Bindi, Dino and Bommer, Julian J. and Bora, Sanjay Singh and Cotton, Fabrice Pierre and Derras, Boumediene and Hermkes, Marcel and Kuehn, Nicolas Martin and Luzi, Lucia and Massa, Marco and Pacor, Francesca and Riggelsen, Carsten and Sandikkaya, M. Abdullah and Scherbaum, Frank and Stafford, Peter J. and Traversa, Paola},
  title     = {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},
  series = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering},
  volume    = {12},
  journal   = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering},
  number    = {1},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {1570-761X},
  doi       = {10.1007/s10518-013-9522-8},
  pages     = {341 -- 358},
  year      = {2014},
  abstract  = {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.},
  language  = {en}
}