ALBERT

Description: An automatic procedure is presented to retrieve rupture parameters for large earthquakes along the Sunda arc subduction zone. The method is based on standard array analysis and broadband seismograms registered within 30°–100° epicentral distance. No assumptions on source mechanism are required. By means of semblance the coherency of P waveforms is analysed at separate large-aperture arrays. Waveforms are migrated to a 10°×10° wide source region to study the spatio-temporal evolution of earthquakes at each array. The multiplication of the semblance source maps resulting at each array increases resolution. Start, duration, extent, direction, and propagation velocity are obtained and published within 25 min after the onset of the event. First preliminary results can be obtained even within 16 min. Their rapid determination may improve the mitigation of the earthquake and tsunami hazard. Real-time application will provide rupture parameters to the GITEWS project (German Indonesian Tsunami Early Warning System). The method is applied to the two M8.0 Sumatra earthquakes on 12 September 2007, to the M7.4 Java earthquake on 2 September 2009, and to major subduction earthquakes that have occurred along Sumatra and Java since 2000. Obtained rupture parameters are most robust for the largest earthquakes with magnitudes M≥8. The results indicate that almost the entire seismogenic part of the subduction zone off the coast of Sumatra has been ruptured. Only the great Sumatra event in 2004 and the M7.7 Java event on 17 July 2006 could reach to or close to the surface at the trench. Otherwise, the rupturing was apparently confined to depths below 25 km. Major seismic gaps seem to remain off the coast of Padang and the southern tip of Sumatra.

Description: Earthquake rupture length and width estimates are in demand in many seismological applications. Earthquake magnitude estimates are often available, whereas the geometrical extensions of the rupture fault mostly are lacking. Therefore, scaling relations are needed to derive length and width from magnitude. Most frequently used are the relationships of Wells and Coppersmith (1994) derived on the basis of a large dataset including all slip types with the exception of thrust faulting events in subduction environments. However, there are many applications dealing with earthquakes in subduction zones because of their high seismic and tsunamigenic potential. There are no well-established scaling relations for moment magnitude and length/width for subduction events. Within this study, we compiled a large database of source parameter estimates of 283 earthquakes. All focal mechanisms are represented, but special focus is set on (large) subduction zone events, in particular. Scaling relations were fitted with linear least-square as well as orthogonal regression and analyzed regarding the difference between continental and subduction zone/oceanic relationships. Additionally, the effect of technical progress in earthquake parameter estimation on scaling relations was tested as well as the influence of different fault mechanisms. For a given moment magnitude we found shorter but wider rupture areas of thrust events compared to Wells and Coppersmith (1994). The thrust event relationships for pure continental and pure subduction zone rupture areas were found to be almost identical. The scaling relations differ significantly for slip types. The exclusion of events prior to 1964 when the worldwide standard seismic network was established resulted in a remarkable effect on strike-slip scaling relations: the data do not show any saturation of rupture width of strike-slip earthquakes. Generally, rupture area seems to scale with mean slip independent of magnitude. The aspect ratio L/W, however, depends on moment and differs for each slip type.

Description: 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.