eprintid: 157 rev_number: 15 eprint_status: archive userid: 2 dir: disk0/00/00/01/57 datestamp: 2015-02-20 13:42:04 lastmod: 2020-03-24 08:08:12 status_changed: 2015-04-27 07:26:46 type: article metadata_visibility: show creators_name: Cesca, Simone creators_name: Sen, Ali Tolga creators_name: Dahm, Torsten creators_id: simone.cesca@gfz-potsdam.de creators_id: creators_id: corp_creators: GFZ German Research Centre for Geosciences, Telegrafenberg, D-14473 Potsdam, Germany corp_creators: Institute of Earth and Environmental Sciences, University of Potsdam, Potsdam, Germany corp_creators: GFZ German Research Centre for Geosciences, Telegrafenberg, D-14473 Potsdam, Germany title: Seismicity monitoring by cluster analysis of moment tensors ispublished: pub subjects: SS divisions: EPOS-P full_text_status: none keywords: Persistence; memory; correlations; clustering; Earthquake source observations. 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 date: 2013-12-30 date_type: published publication: Geophysical Journal International volume: 196 number: 3 publisher: Oxford University Press pagerange: 1813-1826 id_number: doi:10.1093/gji/ggt492 refereed: TRUE issn: 0956-540X official_url: http://dx.doi.org/10.1093/gji/ggt492 access_IS-EPOS: unlimited owner: Publisher citation: Cesca, Simone and Sen, Ali Tolga and Dahm, Torsten (2013) Seismicity monitoring by cluster analysis of moment tensors. Geophysical Journal International, 196 (3). pp. 1813-1826. DOI: https://doi.org/10.1093/gji/ggt492