@article{DiGiulioSavvaidisOhrnbergeretal.2012, author = {Di Giulio, Giuseppe and Savvaidis, Alexandros and Ohrnberger, Matthias and Wathelet, Marc and Cornou, Cecile and Knapmeyer-Endrun, Brigitte and Renalier, Florence and Theodoulidis, Nikos and Bard, Pierre-Yves}, title = {Exploring the model space and ranking a best class of models in surface-wave dispersion inversion application at European strong-motion sites}, series = {Geophysics}, volume = {77}, journal = {Geophysics}, number = {3}, publisher = {Society of Exploration Geophysicists}, address = {Tulsa}, issn = {0016-8033}, doi = {10.1190/GEO2011-0116.1}, pages = {B147 -- B166}, year = {2012}, abstract = {The inversion of surface-wave dispersion curve to derive shear-wave velocity profile is a very delicate process dealing with a nonunique problem, which is strongly dependent on the model space parameterization. When independent and reliable information is not available, the selection of most representative models within the ensemble produced. by the inversion is often difficult. We implemented a strategy in the inversion of dispersion curves able to investigate the influence of the parameterization of the model space and to select a "best" class of models. We analyzed surface-wave dispersion curves measured at 14 European strong..-motion sites within the NERIES EC-Project. We focused on the inversion task exploring the model space by means of four distinct pararneterization classes composed of layers progressively added over a half-space. The classes differ in the definition of the shear-wave velocity profile; we considered models with uniform velocity as well as models with increasing velocity with depth. At each site and for each model parameterization, we performed an extensive surface-wave inversion (200,100 models for five seeds) using the conditional neighborhood algorithm. We addressed the model evaluation following the corrected Akaike's information criterion (AlCc) that combines the concept of misfit to the number of degrees of freedom of the system. The misfit was computed as least-squares estimation between theoretical and observed dispersion curve. The model complexity was accounted in a penalty term by AlCc. By applying such inversion strategy on 14 strong-motion sites, we found that the best parameterization of the model space is mostly three to four layers over a half-space: where the shear-wave velocity of the uppermost layers can follow uniform or power-law dependence with depth. The shear-wave velocity profiles derived by inversion agree with shear-wave velocity profiles provided by borehole surveys at approximately 80\% of the sites.}, language = {en} } @article{HammerBeyreutherOhrnberger2012, author = {Hammer, Conny and Beyreuther, Moritz and Ohrnberger, Matthias}, title = {A seismic-event spotting system for volcano fast-response systems}, series = {Bulletin of the Seismological Society of America}, volume = {102}, journal = {Bulletin of the Seismological Society of America}, number = {3}, publisher = {Seismological Society of America}, address = {Albany}, issn = {0037-1106}, doi = {10.1785/0120110167}, pages = {948 -- 960}, year = {2012}, abstract = {Volcanic eruptions are often preceded by seismic activity that can be used to quantify the volcanic activity. In order to allow consistent inference of the volcanic activity state from the observed seismicity patterns, objective and time-invariant classification results achievable by automatic systems should be preferred. Most automatic classification approaches need a large preclassified data set for training the system. However, in case of a volcanic crisis, we are often confronted with a lack of training data due to insufficient prior observations. In the worst case (e. g., volcanic crisis related reconfiguration of stations), there are even no prior observations available. Finally, due to the imminent crisis there might be no time for the time-consuming process of preparing a training data set. For this reason, we have developed a novel seismic-event spotting technique in order to be less dependent on previously acquired data bases and classification schemes. We are using a learning-while-recording approach based on a minimum number of reference waveforms, thus allowing for the build-up of a classification scheme as early as interesting events have been identified. First, short-term wave-field parameters (here, polarization and spectral attributes) are extracted from a continuous seismic data stream. The sequence of multidimensional feature vectors is then used to identify a fixed number of clusters in the feature space. Based on this general description of the overall wave field by a mixture of multivariate Gaussians, we are able to learn particular event classifiers (here, hidden Markov models) from a single waveform example. To show the capabilities of this new approach we apply the algorithm to a data set recorded at Soufriere Hills volcano, Montserrat. Supported by very high classification rates, we conclude that the suggested approach provides a valuable tool for volcano monitoring systems.}, language = {en} } @article{BeyreutherHammerWassermannetal.2012, author = {Beyreuther, Moritz and Hammer, Conny and Wassermann, Joachim and Ohrnberger, Matthias and Megies, Tobias}, title = {Constructing a hidden Markov Model based earthquake detector: application to induced seismicity}, series = {Geophysical journal international}, volume = {189}, journal = {Geophysical journal international}, number = {1}, publisher = {Wiley-Blackwell}, address = {Malden}, issn = {0956-540X}, doi = {10.1111/j.1365-246X.2012.05361.x}, pages = {602 -- 610}, year = {2012}, abstract = {The triggering or detection of seismic events out of a continuous seismic data stream is one of the key issues of an automatic or semi-automatic seismic monitoring system. In the case of dense networks, either local or global, most of the implemented trigger algorithms are based on a large number of active stations. However, in the case of only few available stations or small events, for example, like in monitoring volcanoes or hydrothermal power plants, common triggers often show high false alarms. In such cases detection algorithms are of interest, which show reasonable performance when operating even on a single station. In this context, we apply Hidden Markov Models (HMM) which are algorithms borrowed from speech recognition. However, many pitfalls need to be avoided to apply speech recognition technology directly to earthquake detection. We show the fit of the model parameters in an innovative way. State clustering is introduced to refine the intrinsically assumed time dependency of the HMMs and we explain the effect coda has on the recognition results. The methodology is then used for the detection of anthropogenicly induced earthquakes for which we demonstrate for a period of 3.9 months of continuous data that the single station HMM earthquake detector can achieve similar detection rates as a common trigger in combination with coincidence sums over two stations. To show the general applicability of state clustering we apply the proposed method also to earthquake classification at Mt. Merapi volcano, Indonesia.}, language = {en} } @article{HammerOhrnberger2012, author = {Hammer, Conny and Ohrnberger, Matthias}, title = {Forecasting seismo-volcanic activity by using the dynamical behavior of volcanic earthquake rates}, series = {Journal of volcanology and geothermal research}, volume = {229}, journal = {Journal of volcanology and geothermal research}, number = {11}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0377-0273}, doi = {10.1016/j.jvolgeores.2012.01.016}, pages = {34 -- 43}, year = {2012}, abstract = {We present a novel approach for short-term forecasting of volcano seismic activity. Volcanic earthquakes can be seen as a response mechanism of the earth crust to stresses induced by magma injection. From this point of view the temporal evolution of seismicity can be represented as a diffusion process which compensates pressure differences. By means of this dynamical approach we are able to estimate the system behavior in the near future which in turn allows us to forecast the evolution of the earthquake rate for the next time span from actual and past observations. For this purpose we model the earthquake rate as a random walk process embedded in a moving and deforming potential function. The center of the potential function is given by a moving average of the random walk's trace. We successfully apply this procedure to estimate the next day seismicity at Soufriere Hills volcano, Montserrat, over a time period of six years. When comparing the dynamical approach to the well known method of material failure forecast we find much better predictions of the critical stages of volcanic activity using the new approach.}, language = {en} } @article{BlaserOhrnbergerKruegeretal.2012, author = {Blaser, Lilian and Ohrnberger, Matthias and Kr{\"u}ger, Frank and Scherbaum, Frank}, title = {Probabilistic tsunami threat assessment of 10 recent earthquakes offshore Sumatra}, series = {Geophysical journal international}, volume = {188}, journal = {Geophysical journal international}, number = {3}, publisher = {Wiley-Blackwell}, address = {Malden}, issn = {0956-540X}, doi = {10.1111/j.1365-246X.2011.05324.x}, pages = {1273 -- 1284}, year = {2012}, abstract = {Tsunami early warning (TEW) is a challenging task as a decision has to be made within few minutes on the basis of incomplete and error-prone data. Deterministic warning systems have difficulties in integrating and quantifying the intrinsic uncertainties. In contrast, probabilistic approaches provide a framework that handles uncertainties in a natural way. Recently, we have proposed a method using Bayesian networks (BNs) that takes into account the uncertainties of seismic source parameter estimates in TEW. In this follow-up study, the method is applied to 10 recent large earthquakes offshore Sumatra and tested for its performance. We have evaluated both the general model performance given the best knowledge we have today about the source parameters of the 10 events and the corresponding response on seismic source information evaluated in real-time. We find that the resulting site-specific warning level probabilities represent well the available tsunami wave measurements and observations. Difficulties occur in the real-time tsunami assessment if the moment magnitude estimate is severely over- or underestimated. In general, the probabilistic analysis reveals a considerably large range of uncertainties in the near-field TEW. By quantifying the uncertainties the BN analysis provides important additional information to a decision maker in a warning centre to deal with the complexity in TEW and to reason under uncertainty.}, language = {en} }