TY - JOUR A1 - Hammer, Conny A1 - Ohrnberger, Matthias A1 - Faeh, Donat T1 - Classifying seismic waveforms from scratch: a case study in the alpine environment JF - Geophysical journal international N2 - 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. KW - Time series analysis KW - Neural networks, fuzzy logic KW - Seismic monitoring and test-ban treaty verification KW - Early warning KW - Probability distributions Y1 - 2013 U6 - https://doi.org/10.1093/gji/ggs036 SN - 0956-540X SN - 1365-246X VL - 192 IS - 1 SP - 425 EP - 439 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Hobiger, M. A1 - Cornou, C. A1 - Wathelet, M. A1 - Di Giulio, G. A1 - Knapmeyer-Endrun, B. A1 - Renalier, F. A1 - Bard, Pierre-Yves A1 - Savvaidis, Alexandros A1 - Hailemikael, S. A1 - Le Bihan, N. A1 - Ohrnberger, Matthias A1 - Theodoulidis, N. T1 - Ground structure imaging by inversions of Rayleigh wave ellipticity sensitivity analysis and application to European strong-motion sites JF - Geophysical journal international N2 - The knowledge of the local soil structure is important for the assessment of seismic hazards. A widespread, but time-consuming technique to retrieve the parameters of the local underground is the drilling of boreholes. Another way to obtain the shear wave velocity profile at a given location is the inversion of surface wave dispersion curves. To ensure a good resolution for both superficial and deeper layers, the used dispersion curves need to cover a wide frequency range. This wide frequency range can be obtained using several arrays of seismic sensors or a single array comprising a large number of sensors. Consequently, these measurements are time-consuming. A simpler alternative is provided by the use of the ellipticity of Rayleigh waves. The frequency dependence of the ellipticity is tightly linked to the shear wave velocity profile. Furthermore, it can be measured using a single seismic sensor. As soil structures obtained by scaling of a given model exhibit the same ellipticity curve, any inversion of the ellipticity curve alone will be ambiguous. Therefore, additional measurements which fix the absolute value of the shear wave velocity profile at some points have to be included in the inversion process. Small-scale spatial autocorrelation measurements or MASW measurements can provide the needed data. Using a theoretical soil structure, we show which parts of the ellipticity curve have to be included in the inversion process to get a reliable result and which parts can be omitted. Furthermore, the use of autocorrelation or high-frequency dispersion curves will be highlighted. The resulting guidelines for inversions including ellipticity data are then applied to real data measurements collected at 14 different sites during the European NERIES project. It is found that the results are in good agreement with dispersion curve measurements. Furthermore, the method can help in identifying the mode of Rayleigh waves in dispersion curve measurements. KW - Inverse theory KW - Surface waves and free oscillations KW - Site effects KW - Computational seismology KW - Wave propagation Y1 - 2013 U6 - https://doi.org/10.1093/gji/ggs005 SN - 0956-540X VL - 192 IS - 1 SP - 207 EP - 229 PB - Oxford Univ. Press CY - Oxford ER - TY - GEN A1 - Dahm, Torsten A1 - Becker, Dirk A1 - Bischoff, Monika A1 - Cesca, Simone A1 - Dost, B. A1 - Fritschen, R. A1 - Hainzl, Sebastian A1 - Klose, C. D. A1 - Kuhn, D. A1 - Lasocki, S. A1 - Meier, Thomas A1 - Ohrnberger, Matthias A1 - Rivalta, Eleonora A1 - Wegler, Ulrich A1 - Husen, Stephan T1 - Recommendation for the discrimination of human-related and natural seismicity T2 - Journal of seismology N2 - Various techniques are utilized by the seismological community, extractive industries, energy and geoengineering companies to identify earthquake nucleation processes in close proximity to engineering operation points. These operations may comprise fluid extraction or injections, artificial water reservoir impoundments, open pit and deep mining, deep geothermal power generations or carbon sequestration. In this letter to the editor, we outline several lines of investigation that we suggest to follow to address the discrimination problem between natural seismicity and seismic events induced or triggered by geoengineering activities. These suggestions have been developed by a group of experts during several meetings and workshops, and we feel that their publication as a summary report is helpful for the geoscientific community. Specific investigation procedures and discrimination approaches, on which our recommendations are based, are also published in this Special Issue (SI) of Journal of Seismology. KW - Triggered seismicity KW - Induced seismicity Y1 - 2013 U6 - https://doi.org/10.1007/s10950-012-9295-6 SN - 1383-4649 VL - 17 IS - 1 SP - 197 EP - 202 PB - Springer CY - Dordrecht ER -