@article{SenCescaBischoffetal.2013,
  author    = {Sen, Ali Tolga and Cesca, Simone and Bischoff, Monika and Meier, Thomas and Dahm, Torsten},
  title     = {Automated full moment tensor inversion of coal mining-induced seismicity},
  series = {Geophysical journal international},
  volume    = {195},
  journal   = {Geophysical journal international},
  number    = {2},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {0956-540X},
  doi       = {10.1093/gji/ggt300},
  pages     = {1267 -- 1281},
  year      = {2013},
  abstract  = {Seismicity induced by coal mining in the Ruhr region, Germany, has been monitored continuously over the last 25 yr. In 2006, a dense temporary network (HAMNET) was deployed to locally monitor seismicity induced by longwall mining close to the town of Hamm. Between 2006 July and 2007 July, more than 7000 events with magnitudes M-L from -1.7 to 2.0 were detected. The spatiotemporal distribution of seismicity shows high correlation with the mining activity. In order to monitor rupture processes, we set up an automated source inversion routine and successfully perform double couple and full moment tensor (MT) inversions for more than 1000 events with magnitudes above M-L -0.5. The source inversion is based on a full waveform approach, both in the frequency and in the time domain, providing information about the centroid location, focal mechanism, scalar moment and full MT. Inversion results indicate a strong dominance of normal faulting focal mechanisms, with a steeper plane and a subhorizontal one. Fault planes are oriented parallel to the mining stopes. We classify the focal mechanisms based on their orientation and observe different frequency-magnitude distributions for families of events with different focal mechanisms; the overall frequency-magnitude distribution is not fitting the Gutenberg-Richter relation. Full MTs indicate that non-negligible opening tensile components accompanied normal faulting source mechanisms. Finally, extended source models are investigated for largest events. Results suggest that the rupture processes mostly occurred along the subvertical planes.},
  language  = {en}
}
@article{CescaSenDahm2014,
  author    = {Cesca, Simone and Sen, Ali Tolga and Dahm, Torsten},
  title     = {Seismicity monitoring by cluster analysis of moment tensors},
  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/ggt492},
  pages     = {1813 -- 1826},
  year      = {2014},
  abstract  = {We suggest a new clustering approach to classify focal mechanisms from large moment tensor catalogues, with the purpose of automatically identify families of earthquakes with similar source geometry, recognize the orientation of most active faults, and detect temporal variations of the rupture processes. The approach differs in comparison to waveform similarity methods since clusters are detected even if they occur in large spatial distances. This approach is particularly helpful to analyse large moment tensor catalogues, as in microseismicity applications, where a manual analysis and classification is not feasible. A flexible algorithm is here proposed: it can handle different metrics, norms, and focal mechanism representations. In particular, the method can handle full moment tensor or constrained source model catalogues, for which different metrics are suggested. The method can account for variable uncertainties of different moment tensor components. We verify the method with synthetic catalogues. An application to real data from mining induced seismicity illustrates possible applications of the method and demonstrate the cluster detection and event classification performance with different moment tensor catalogues. Results proof that main earthquake source types occur on spatially separated faults, and that temporal changes in the number and characterization of focal mechanism clusters are detected. We suggest that moment tensor clustering can help assessing time dependent hazard in mines.},
  language  = {en}
}