TY - JOUR A1 - Bora, Sanjay Singh A1 - Scherbaum, Frank A1 - Kühn, Nicolas A1 - Stafford, Peter T1 - Fourier spectral- and duration models for the generation of response spectra adjustable to different source-, propagation-, and site conditions JF - Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering N2 - One of the major challenges related with the current practice in seismic hazard studies is the adjustment of empirical ground motion prediction equations (GMPEs) to different seismological environments. We believe that the key to accommodating differences in regional seismological attributes of a ground motion model lies in the Fourier spectrum. In the present study, we attempt to explore a new approach for the development of response spectral GMPEs, which is fully consistent with linear system theory when it comes to adjustment issues. This approach consists of developing empirical prediction equations for Fourier spectra and for a particular duration estimate of ground motion which is tuned to optimize the fit between response spectra obtained through the random vibration theory framework and the classical way. The presented analysis for the development of GMPEs is performed on the recently compiled reference database for seismic ground motion in Europe (RESORCE-2012). Although, the main motivation for the presented approach is the adjustability and the use of the corresponding model to generate data driven host-to-target conversions, even as a standalone response spectral model it compares reasonably well with the GMPEs of Ambraseys et al. (Bull Earthq Eng 3:1-53, 2005), Akkar and Bommer (Seismol Res Lett 81(2):195-206, 2010) and Akkar and Cagnan (Bull Seismol Soc Am 100(6):2978-2995, 2010). KW - Ground motion prediction equation KW - Fourier amplitude spectrum KW - Duration KW - Random vibration theory KW - Response Spectrum Y1 - 2014 U6 - https://doi.org/10.1007/s10518-013-9482-z SN - 1570-761X SN - 1573-1456 VL - 12 IS - 1 SP - 467 EP - 493 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 - TY - JOUR A1 - Gianniotis, Nikolaos A1 - Kuehn, Nicolas A1 - Scherbaum, Frank T1 - Manifold aligned ground motion prediction equations for regional datasets JF - Computers & geosciences : an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets ; an official journal of the International Association for Mathematical Geology N2 - Inferring a ground-motion prediction equation (GMPE) for a region in which only a small number of seismic events has been observed is a challenging task. A response to this data scarcity is to utilise data from other regions in the hope that there exist common patterns in the generation of ground motion that can contribute to the development of a GMPE for the region in question. This is not an unreasonable course of action since we expect regional GMPEs to be related to each other. In this work we model this relatedness by assuming that the regional GMPEs occupy a common low-dimensional manifold in the space of all possible GMPEs. As a consequence, the GMPEs are fitted in a joint manner and not independent of each other, borrowing predictive strength from each other's regional datasets. Experimentation on a real dataset shows that the manifold assumption displays better predictive performance over fitting regional GMPEs independent of each other. (C) 2014 Elsevier Ltd. All rights reserved. KW - Ground-motion-model KW - Bagging KW - Ensembles KW - Manifold KW - Regional-dependence Y1 - 2014 U6 - https://doi.org/10.1016/j.cageo.2014.04.014 SN - 0098-3004 SN - 1873-7803 VL - 69 SP - 72 EP - 77 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Krüger, Frank A1 - Scherbaum, Frank T1 - The 29 September 1969, Ceres, South Africa, Earthquake: full waveform moment tensor inversion for point source and kinematic source parameters JF - Bulletin of the Seismological Society of America N2 - The Ceres earthquake of 29 September 1969 is the largest known earthquake in southern Africa. Digitized analog recordings from Worldwide Standardized Seismographic Network stations (Powell and Fries, 1964) are used to retrieve the point source moment tensor and the most likely centroid depth of the event using full waveform modeling. A scalar seismic moment of 2.2-2.4 x 10(18) N center dot m corresponding to a moment magnitude of 6.2-6.3 is found. The analysis confirms the pure strike-slip mechanism previously determined from onset polarities by Green and Bloch (1971). Overall good agreement with the fault orientation previously estimated from local aftershock recordings is found. The centroid depth can be constrained to be less than 15 km. In a second analysis step, we use a higher order moment tensor based inversion scheme for simple extended rupture models to constrain the lateral fault dimensions. We find rupture propagated unilaterally for 4.7 s from east-southwest to west-northwest for about 17 km ( average rupture velocity of about 3: 1 km/s). Y1 - 2014 U6 - https://doi.org/10.1785/0120130209 SN - 0037-1106 SN - 1943-3573 VL - 104 IS - 1 SP - 576 EP - 581 PB - Seismological Society of America CY - Albany ER - TY - JOUR A1 - Molkenthin, Christian A1 - Scherbaum, Frank A1 - Griewank, Andreas A1 - Kuehn, Nicolas A1 - Stafford, Peter T1 - A Study of the sensitivity of response spectral amplitudes on seismological parameters using algorithmic differentiation JF - Bulletin of the Seismological Society of America N2 - Response spectra are of fundamental importance in earthquake engineering and represent a standard measure in seismic design for the assessment of structural performance. However, unlike Fourier spectral amplitudes, the relationship of response spectral amplitudes to seismological source, path, and site characteristics is not immediately obvious and might even be considered counterintuitive for high oscillator frequencies. The understanding of this relationship is nevertheless important for seismic-hazard analysis. The purpose of the present study is to comprehensively characterize the variation of response spectral amplitudes due to perturbations of the causative seismological parameters. This is done by calculating the absolute parameter sensitivities (sensitivity coefficients) defined as the partial derivatives of the model output with respect to its input parameters. To derive sensitivities, we apply algorithmic differentiation (AD). This powerful approach is extensively used for sensitivity analysis of complex models in meteorology or aerodynamics. To the best of our knowledge, AD has not been explored yet in the seismic-hazard context. Within the present study, AD was successfully implemented for a proven and extensively applied simulation program for response spectra (Stochastic Method SIMulation [SMSIM]) using the TAPENADE AD tool. We assess the effects and importance of input parameter perturbations on the shape of response spectra for different regional stochastic models in a quantitative way. Additionally, we perform sensitivity analysis regarding adjustment issues of groundmotion prediction equations. Y1 - 2014 U6 - https://doi.org/10.1785/0120140022 SN - 0037-1106 SN - 1943-3573 VL - 104 IS - 5 SP - 2240 EP - 2252 PB - Seismological Society of America CY - Albany ER - TY - JOUR A1 - Rodriguez-Marek, A. A1 - Rathje, E. M. A1 - Bommer, Julian J. A1 - Scherbaum, Frank A1 - Stafford, P. J. T1 - Application of single-station sigma and site-response characterization in a probabilistic Seismic-Hazard analysis for new uclear site JF - Bulletin of the Seismological Society of America N2 - Aleatory variability in ground-motion prediction, represented by the standard deviation (sigma) of a ground-motion prediction equation, exerts a very strong influence on the results of probabilistic seismic-hazard analysis (PSHA). This is especially so at the low annual exceedance frequencies considered for nuclear facilities; in these cases, even small reductions in sigma can have a marked effect on the hazard estimates. Proper separation and quantification of aleatory variability and epistemic uncertainty can lead to defensible reductions in sigma. One such approach is the single-station sigma concept, which removes that part of sigma corresponding to repeatable site-specific effects. However, the site-to-site component must then be constrained by site-specific measurements or else modeled as epistemic uncertainty and incorporated into the modeling of site effects. The practical application of the single-station sigma concept, including the characterization of the dynamic properties of the site and the incorporation of site-response effects into the hazard calculations, is illustrated for a PSHA conducted at a rock site under consideration for the potential construction of a nuclear power plant. Y1 - 2014 U6 - https://doi.org/10.1785/0120130196 SN - 0037-1106 SN - 1943-3573 VL - 104 IS - 4 SP - 1601 EP - 1619 PB - Seismological Society of America CY - Albany ER - TY - JOUR A1 - Schroeter, Kai A1 - Kreibich, Heidi A1 - Vogel, Kristin A1 - Riggelsen, Carsten A1 - Scherbaum, Frank A1 - Merz, Bruno T1 - How useful are complex flood damage models? JF - Water resources research N2 - We investigate the usefulness of complex flood damage models for predicting relative damage to residential buildings in a spatial and temporal transfer context. We apply eight different flood damage models to predict relative building damage for five historic flood events in two different regions of Germany. Model complexity is measured in terms of the number of explanatory variables which varies from 1 variable up to 10 variables which are singled out from 28 candidate variables. Model validation is based on empirical damage data, whereas observation uncertainty is taken into consideration. The comparison of model predictive performance shows that additional explanatory variables besides the water depth improve the predictive capability in a spatial and temporal transfer context, i.e., when the models are transferred to different regions and different flood events. Concerning the trade-off between predictive capability and reliability the model structure seem more important than the number of explanatory variables. Among the models considered, the reliability of Bayesian network-based predictions in space-time transfer is larger than for the remaining models, and the uncertainties associated with damage predictions are reflected more completely. KW - floods KW - damage KW - model validation KW - Bayesian networks KW - regression tree Y1 - 2014 U6 - https://doi.org/10.1002/2013WR014396 SN - 0043-1397 SN - 1944-7973 VL - 50 IS - 4 SP - 3378 EP - 3395 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Vogel, Kristin A1 - Riggelsen, Carsten A1 - Korup, Oliver A1 - Scherbaum, Frank T1 - Bayesian network learning for natural hazard analyses JF - Natural hazards and earth system sciences N2 - Modern natural hazards research requires dealing with several uncertainties that arise from limited process knowledge, measurement errors, censored and incomplete observations, and the intrinsic randomness of the governing processes. Nevertheless, deterministic analyses are still widely used in quantitative hazard assessments despite the pitfall of misestimating the hazard and any ensuing risks. In this paper we show that Bayesian networks offer a flexible framework for capturing and expressing a broad range of uncertainties encountered in natural hazard assessments. Although Bayesian networks are well studied in theory, their application to real-world data is far from straightforward, and requires specific tailoring and adaptation of existing algorithms. We offer suggestions as how to tackle frequently arising problems in this context and mainly concentrate on the handling of continuous variables, incomplete data sets, and the interaction of both. By way of three case studies from earthquake, flood, and landslide research, we demonstrate the method of data-driven Bayesian network learning, and showcase the flexibility, applicability, and benefits of this approach. Our results offer fresh and partly counterintuitive insights into well-studied multivariate problems of earthquake-induced ground motion prediction, accurate flood damage quantification, and spatially explicit landslide prediction at the regional scale. In particular, we highlight how Bayesian networks help to express information flow and independence assumptions between candidate predictors. Such knowledge is pivotal in providing scientists and decision makers with well-informed strategies for selecting adequate predictor variables for quantitative natural hazard assessments. Y1 - 2014 U6 - https://doi.org/10.5194/nhess-14-2605-2014 SN - 1561-8633 VL - 14 IS - 9 SP - 2605 EP - 2626 PB - Copernicus CY - Göttingen ER -