@article{GrigoliCescaAmorosoetal.2014, author = {Grigoli, Francesco and Cesca, Simone and Amoroso, Ortensia and Emolo, Antonio and Zollo, Aldo and Dahm, Torsten}, title = {Automated seismic event location by waveform coherence analysis}, series = {Geophysical journal international}, volume = {196}, journal = {Geophysical journal international}, number = {3}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {0956-540X}, doi = {10.1093/gji/ggt477}, pages = {1742 -- 1753}, year = {2014}, abstract = {Automated location of seismic events is a very important task in microseismic monitoring operations as well for local and regional seismic monitoring. Since microseismic records are generally characterized by low signal-to-noise ratio, automated location methods are requested to be noise robust and sufficiently accurate. Most of the standard automated location routines are based on the automated picking, identification and association of the first arrivals of P and S waves and on the minimization of the residuals between theoretical and observed arrival times of the considered seismic phases. Although current methods can accurately pick P onsets, the automatic picking of the S onset is still problematic, especially when the P coda overlaps the S wave onset. In this paper, we propose a picking free earthquake location method based on the use of the short-term-average/long-term-average (STA/LTA) traces at different stations as observed data. For the P phases, we use the STA/LTA traces of the vertical energy function, whereas for the S phases, we use the STA/LTA traces of a second characteristic function, which is obtained using the principal component analysis technique. In order to locate the seismic event, we scan the space of possible hypocentral locations and origin times, and stack the STA/LTA traces along the theoretical arrival time surface for both P and S phases. Iterating this procedure on a 3-D grid, we retrieve a multidimensional matrix whose absolute maximum corresponds to the spatial coordinates of the seismic event. A pilot application was performed in the Campania-Lucania region (southern Italy) using a seismic network (Irpinia Seismic Network) with an aperture of about 150 km. We located 196 crustal earthquakes (depth < 20 km) with magnitude range 1.1 < M-L < 2.7. A subset of these locations were compared with accurate manual locations refined by using a double-difference technique. Our results indicate a good agreement with manual locations. Moreover, our method is noise robust and performs better than classical location methods based on the automatic picking of the P and S waves first arrivals.}, 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} } @article{HammerOhrnbergerFaeh2013, author = {Hammer, Conny and Ohrnberger, Matthias and Faeh, Donat}, title = {Classifying seismic waveforms from scratch: a case study in the alpine environment}, series = {Geophysical journal international}, volume = {192}, journal = {Geophysical journal international}, number = {1}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {0956-540X}, doi = {10.1093/gji/ggs036}, pages = {425 -- 439}, year = {2013}, abstract = {Nowadays, an increasing amount of seismic data is collected by daily observatory routines. The basic step for successfully analyzing those data is the correct detection of various event types. However, the visually scanning process is a time-consuming task. Applying standard techniques for detection like the STA/LTAtrigger still requires the manual control for classification. Here, we present a useful alternative. The incoming data stream is scanned automatically for events of interest. A stochastic classifier, called hidden Markov model, is learned for each class of interest enabling the recognition of highly variable waveforms. In contrast to other automatic techniques as neural networks or support vector machines the algorithm allows to start the classification from scratch as soon as interesting events are identified. Neither the tedious process of collecting training samples nor a time-consuming configuration of the classifier is required. An approach originally introduced for the volcanic task force action allows to learn classifier properties from a single waveform example and some hours of background recording. Besides a reduction of required workload this also enables to detect very rare events. Especially the latter feature provides a milestone point for the use of seismic devices in alpine warning systems. Furthermore, the system offers the opportunity to flag new signal classes that have not been defined before. We demonstrate the application of the classification system using a data set from the Swiss Seismological Survey achieving very high recognition rates. In detail we document all refinements of the classifier providing a step-by-step guide for the fast set up of a well-working classification system.}, language = {en} }