@misc{ZimmermannEckardtHornConradetal.2015, author = {Zimmermann, Matthias and Eckardt, Barbara and Horn-Conrad, Antje and J{\"a}ger, Heidi and Kampe, Heike and Scholz, Jana and G{\"o}rlich, Petra and S{\"u}tterlin, Sabine and J{\"a}ger, Sophie and Scherbaum, Frank}, title = {Portal Wissen = Wege}, number = {01/2015}, organization = {Universit{\"a}t Potsdam, Referat f{\"u}r Presse- und {\"O}ffentlichkeitsarbeit}, issn = {2194-4237}, doi = {10.25932/publishup-44085}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-440851}, pages = {99}, year = {2015}, abstract = {Wie Merkmale von Generation zu Generation weitervererbt werden, wie sich die Erbinformation dabei durch Mutationen ver{\"a}ndert und somit zur Auspr{\"a}gung neuer Eigenschaften und der Entstehung neuer Arten beitr{\"a}gt, sind spannende Fragen der Biologie. Genetische Differenzierung f{\"u}hrte im Laufe von Jahrmillionen zur Ausbildung einer schier unglaublichen Artenvielfalt. Die Evolution hat viele Wege beschritten. Sie hat zu großartiger nat{\"u}rlicher Biodiversit{\"a}t gef{\"u}hrt - zu Organismen, die an sehr unterschiedliche Umwelten angepasst sind und zum Teil eine ulkige Gestalt haben oder ein merkw{\"u}rdiges Verhalten zeigen. Aber auch die von Menschenhand gemachte Biodiversit{\"a}t ist {\"u}berw{\"a}ltigend - man denke nur an die 10.000 verschiedenen Rosensorten, die uns entz{\"u}cken, oder die Myriaden unterschiedlicher Weizen-, Gerste- oder Maisvarianten; Pflanzen, die allesamt fr{\"u}her einmal einfache Gr{\"a}ser waren, uns heute aber ern{\"a}hren. Wir Menschen schaffen eine eigene Biodiversit{\"a}t, eine, die die Natur selbst nicht kennt. Und wir „fahren" gut damit. Dank der Genomforschung k{\"o}nnen wir heute die gesamte Erbinformation von Organismen in wenigen Stunden bis Tagen aus- lesen. Sehr viel l{\"a}nger dauert es aber, die zahlreichen Abschnitte eines Genoms funktionell zu kartieren. Die Wissenschaftler bedienen sich dazu vielf{\"a}ltiger Methoden: Dabei geh{\"o}rt es heute weltweit zum Standardrepertoire, Gene gezielt zu inaktivieren oder zu aktivieren, ihren Code zu modifizieren oder Erbinformationen zwischen Organismen auszutauschen. Dennoch sind die Wege, die zur Erkenntnis f{\"u}hren, oft verschlungen. Nicht selten m{\"u}ssen ausgekl{\"u}gelte experimentelle Ans{\"a}tze gew{\"a}hlt werden, um neue Einsichten in biologische Prozesse zu gewinnen. Mit den Methoden der Genomforschung k{\"o}nnen wir nicht nur das erkunden, was sich in der Natur „da draußen" findet. Wir k{\"o}nnen auch fragen: „Wie verh{\"a}lt sich ein Lebewesen, beispielsweise ein Moos, eigentlich, wenn wir es zur International Space Station (ISS) schicken? Und k{\"o}nnen wir daraus Kenntnisse gewinnen {\"u}ber die Anpassungsstrategien von Lebewesen an harsche Umweltbedingungen oder gar f{\"u}r eine sp{\"a}tere Besiedlung des Mondes oder des Mars´?" Oder k{\"o}nnen wir mithilfe der synthetischen Biologie Mikroorganismen pr{\"a}zise, quasi am Reißbrett geplant, so ver{\"a}ndern, dass neue Optionen f{\"u}r die Behandlung von Krankheiten und f{\"u}r die Herstellung innovativer biobasierter Produkte entstehen? Die Antwort auf beide Fragen lautet eindeutig: Ja! (Wenngleich ein Umzug auf andere Planeten derzeit nat{\"u}rlich nicht vornan steht.). Landnutzung durch den Menschen bestimmt die Biodiversit{\"a}t. Andererseits tragen Organismen zur landschaftlichen Formenbildung bei und beeinflussen {\"u}ber kurz oder lang die Zusammensetzung unserer Atmosph{\"a}re. Auch hier gibt es spannende Fragen, mit denen sich die Forschung besch{\"a}ftigt. Um neue Erkenntnisse zu gewinnen, m{\"u}ssen Forscher immer wieder neue Wege einschlagen. Oft kreuzen sich auch Pfade. So war es beispielsweise vor wenigen Jahren noch kaum absehbar, wir stark die {\"o}kologische Forschung beispielsweise von den schnellen DNA-Sequenziermethoden profitieren w{\"u}rde, und die Genomforscher unter uns konnten kaum erahnen, wie die gleichen Techniken uns neue M{\"o}glichkeiten an die Hand geben sollten, die hochkomplexe Regulation in Zellen zu untersuchen und f{\"u}r die Optimierung biotechnologischer Prozesse zu nutzen. Beispiele aus den vielf{\"a}ltigen Facetten der biologischen Forschung finden Sie - neben anderen interessanten Beitr{\"a}gen - in der aktuellen Ausgabe von „Portal Wissen". Ich w{\"u}nsche Ihnen eine anregende Lekt{\"u}re! Prof. Dr. Bernd M{\"u}ller-R{\"o}ber Professor f{\"u}r Molekularbiologie}, language = {de} } @article{MolkenthinScherbaumGriewanketal.2015, author = {Molkenthin, Christian and Scherbaum, Frank and Griewank, Andreas and K{\"u}hn, Nicolas and Stafford, Peter J. and Leovey, Hernan}, title = {Sensitivity of Probabilistic Seismic Hazard Obtained by Algorithmic Differentiation: A Feasibility Study}, series = {Bulletin of the Seismological Society of America}, volume = {105}, journal = {Bulletin of the Seismological Society of America}, number = {3}, publisher = {Seismological Society of America}, address = {Albany}, issn = {0037-1106}, doi = {10.1785/0120140294}, pages = {1810 -- 1822}, year = {2015}, abstract = {Probabilistic seismic-hazard analysis (PSHA) is the current tool of the trade used to estimate the future seismic demands at a site of interest. A modern PSHA represents a complex framework that combines different models with numerous inputs. It is important to understand and assess the impact of these inputs on the model output in a quantitative way. Sensitivity analysis is a valuable tool for quantifying changes of a model output as inputs are perturbed, identifying critical input parameters, and obtaining insight about the model behavior. Differential sensitivity analysis relies on calculating first-order partial derivatives of the model output with respect to its inputs; however, obtaining the derivatives of complex models can be challenging. In this study, we show how differential sensitivity analysis of a complex framework such as PSHA can be carried out using algorithmic/automatic differentiation (AD). AD has already been successfully applied for sensitivity analyses in various domains such as oceanography and aerodynamics. First, we demonstrate the feasibility of the AD methodology by comparing AD-derived sensitivities with analytically derived sensitivities for a basic case of PSHA using a simple ground-motion prediction equation. Second, we derive sensitivities via AD for a more complex PSHA study using a stochastic simulation approach for the prediction of ground motions. The presented approach is general enough to accommodate more advanced PSHA studies of greater complexity.}, language = {en} } @article{KuehnScherbaum2015, author = {K{\"u}hn, Nico M. and Scherbaum, Frank}, title = {Ground-motion prediction model building: a multilevel approach}, series = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering}, volume = {13}, journal = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering}, number = {9}, publisher = {Springer}, address = {Dordrecht}, issn = {1570-761X}, doi = {10.1007/s10518-015-9732-3}, pages = {2481 -- 2491}, year = {2015}, abstract = {A Bayesian ground-motion model is presented that directly estimates the coefficients of the model and the correlation between different ground-motion parameters of interest. The model is developed as a multi-level model with levels for earthquake, station and record terms. This separation allows to estimate residuals for each level and thus the estimation of the associated aleatory variability. In particular, the usually estimated within-event variability is split into a between-station and between-record variability. In addition, the covariance structure between different ground-motion parameters of interest is estimated for each level, i.e. directly the between-event, between-station and between-record correlation coefficients are available. All parameters of the model are estimated via Bayesian inference, which allows to assess their epistemic uncertainty in a principled way. The model is developed using a recently compiled European strong-motion database. The target variables are peak ground velocity, peak ground acceleration and spectral acceleration at eight oscillator periods. The model performs well with respect to its residuals, and is similar to other ground-motion models using the same underlying database. The correlation coefficients are similar to those estimated for other parts of the world, with nearby periods having a high correlation. The between-station, between-event and between-record correlations follow generally a similar trend.}, language = {en} } @article{HaendelvonSpechtKuehnetal.2015, author = {H{\"a}ndel, Annabel and von Specht, Sebastian and Kuehn, Nicolas M. and Scherbaum, Frank}, title = {Mixtures of ground-motion prediction equations as backbone models for a logic tree: an application to the subduction zone in Northern Chile}, series = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering}, volume = {13}, journal = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering}, number = {2}, publisher = {Springer}, address = {Dordrecht}, issn = {1570-761X}, doi = {10.1007/s10518-014-9636-7}, pages = {483 -- 501}, year = {2015}, abstract = {In probabilistic seismic hazard analysis, different ground-motion prediction equations (GMPEs) are commonly combined within a logic tree framework. The selection of appropriate GMPEs, however, is a non-trivial task, especially for regions where strong motion data are sparse and where no indigenous GMPE exists because the set of models needs to capture the whole range of ground-motion uncertainty. In this study we investigate the aggregation of GMPEs into a mixture model with the aim to infer a backbone model that is able to represent the center of the ground-motion distribution in a logic tree analysis. This central model can be scaled up and down to obtain the full range of ground-motion uncertainty. The combination of models into a mixture is inferred from observed ground-motion data. We tested the new approach for Northern Chile, a region for which no indigenous GMPE exists. Mixture models were calculated for interface and intraslab type events individually. For each source type we aggregated eight subduction zone GMPEs using mainly new strong-motion data that were recorded within the Plate Boundary Observatory Chile project and that were processed within this study. We can show that the mixture performs better than any of its component GMPEs, and that it performs comparable to a regression model that was derived for the same dataset. The mixture model seems to represent the median ground motions in that region fairly well. It is thus able to serve as a backbone model for the logic tree.}, language = {en} } @article{BoraScherbaumKuehnetal.2015, author = {Bora, Sanjay Singh and Scherbaum, Frank and K{\"u}hn, Nicolas and Stafford, Peter and Edwards, Benjamin}, title = {Development of a Response Spectral Ground-Motion Prediction Equation (GMPE) for Seismic-Hazard Analysis from Empirical Fourier Spectral and Duration Models}, series = {Bulletin of the Seismological Society of America}, volume = {105}, journal = {Bulletin of the Seismological Society of America}, number = {4}, publisher = {Seismological Society of America}, address = {Albany}, issn = {0037-1106}, doi = {10.1785/0120140297}, pages = {2192 -- 2218}, year = {2015}, abstract = {Empirical ground-motion prediction equations (GMPEs) require adjustment to make them appropriate for site-specific scenarios. However, the process of making such adjustments remains a challenge. This article presents a holistic framework for the development of a response spectral GMPE that is easily adjustable to different seismological conditions and does not suffer from the practical problems associated with adjustments in the response spectral domain. The approach for developing a response spectral GMPE is unique, because it combines the predictions of empirical models for the two model components that characterize the spectral and temporal behavior of the ground motion. Essentially, as described in its initial form by Bora et al. (2014), the approach consists of an empirical model for the Fourier amplitude spectrum (FAS) and a model for the ground-motion duration. These two components are combined within the random vibration theory framework to obtain predictions of response spectral ordinates. In addition, FAS corresponding to individual acceleration records are extrapolated beyond the useable frequencies using the stochastic FAS model, obtained by inversion as described in Edwards and Fah (2013a). To that end, a (oscillator) frequency-dependent duration model, consistent with the empirical FAS model, is also derived. This makes it possible to generate a response spectral model that is easily adjustable to different sets of seismological parameters, such as the stress parameter Delta sigma, quality factor Q, and kappa kappa(0). The dataset used in Bora et al. (2014), a subset of the RESORCE-2012 database, is considered for the present analysis. Based upon the range of the predictor variables in the selected dataset, the present response spectral GMPE should be considered applicable over the magnitude range of 4 <= M-w <= 7.6 at distances <= 200 km.}, language = {en} } @article{BommerCoppersmithCoppersmithetal.2015, author = {Bommer, Julian J. and Coppersmith, Kevin J. and Coppersmith, Ryan T. and Hanson, Kathryn L. and Mangongolo, Azangi and Neveling, Johann and Rathje, Ellen M. and Rodriguez-Marek, Adrian and Scherbaum, Frank and Shelembe, Refilwe and Stafford, Peter J. and Strasser, Fleur O.}, title = {A SSHAC Level 3 Probabilistic Seismic Hazard Analysis for a New-Build Nuclear Site in South Africa}, series = {Earthquake spectra : the professional journal of the Earthquake Engineering Research Institute}, volume = {31}, journal = {Earthquake spectra : the professional journal of the Earthquake Engineering Research Institute}, number = {2}, publisher = {Earthquake Engineering Research Institute}, address = {Oakland}, issn = {8755-2930}, doi = {10.1193/060913EQS145M}, pages = {661 -- 698}, year = {2015}, abstract = {A probabilistic seismic hazard analysis has been conducted for a potential nuclear power plant site on the coast of South Africa, a country of low-to-moderate seismicity. The hazard study was conducted as a SSHAC Level 3 process, the first application of this approach outside North America. Extensive geological investigations identified five fault sources with a non-zero probability of being seismogenic. Five area sources were defined for distributed seismicity, the least active being the host zone for which the low recurrence rates for earthquakes were substantiated through investigations of historical seismicity. Empirical ground-motion prediction equations were adjusted to a horizon within the bedrock at the site using kappa values inferred from weak-motion analyses. These adjusted models were then scaled to create new equations capturing the range of epistemic uncertainty in this region with no strong motion recordings. Surface motions were obtained by convolving the bedrock motions with site amplification functions calculated using measured shear-wave velocity profiles.}, language = {en} }