TY  - JOUR
A1  - Cesca, Simone
A1  - Sen, Ali Tolga
A1  - Dahm, Torsten
T1  - Seismicity monitoring by cluster analysis of moment tensors
JF  - Geophysical journal international
N2  - 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.
KW  - Persistence
KW  - memory
KW  - correlations
KW  - clustering
KW  - Earthquake source observations
Y1  - 2014
U6  - https://doi.org/10.1093/gji/ggt492
SN  - 0956-540X
SN  - 1365-246X
VL  - 196
IS  - 3
SP  - 1813
EP  - 1826
PB  - Oxford Univ. Press
CY  - Oxford
ER  -