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MICRO-SEISMICITY AND FOCAL MECHANISM S IN THE MAL É KARPATY MTS., SLOVAKIA - New Outcomes

MICRO-SEISMICITY AND FOCAL MECHANISM S IN THE MAL É KARPATY MTS., SLOVAKIA - New Outcomes. Lucia Fojt íková, Miriam Kristekov á, Václav Vavryčuk. Geological Map. Tectonic Sketch. Data. magnitudes: 1.1 – 3.4 depth of hypo: 1km – 15km. Focal Mechanisms and Moment Tensors.

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MICRO-SEISMICITY AND FOCAL MECHANISM S IN THE MAL É KARPATY MTS., SLOVAKIA - New Outcomes

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  1. MICRO-SEISMICITY AND FOCAL MECHANISMS IN THE MALÉ KARPATY MTS., SLOVAKIA - New Outcomes Lucia Fojtíková, Miriam Kristeková, Václav Vavryčuk

  2. Geological Map

  3. Tectonic Sketch

  4. Data magnitudes: 1.1 – 3.4 depth of hypo: 1km – 15km

  5. Focal Mechanisms and Moment Tensors FOCMEC (Snoke, 2003):inversion from P-wave polarities local, regional stations gradient medium model take-off angles calculated by ray tracing AMT (Vavryčuk, 2008):inversion from P-wave amplitudes local stations smooth gradient medium model Green’s functions calculated using the ray method ISOLA (Sokos & Zahradník, 2009): inversion from seismograms waveforms from 3 to 5 nearest local seismic stations layered medium model Green’s functions calculated using the DWN method band-pass filter in the frequency range of 0.8 – 1.6 Hz

  6. FOCAL MECHANISMS – All Events ISOLA AMT FOCMEC N = 41 N = 35 N = 16

  7. ISOLA Method is based on the best fitting synthetic and real seismograms W01 Lat=48.478 Lon=17.329 h=3.0 Ml=1.9

  8. ISOLA W01 Lat=48.478 Lon=17.329 h=3.0 Ml=1.9

  9. Inversion from 3-C seismograms The most disputable method of the inversion for weak events • Disadvantages • - very sensitive to data quality • very sensitive to the model • => using S and surface waves Advantages - capability to obtain MT from records of small amount of stations We have used Time-Frequency analysis for finding low frequency limits in filtering data in the inversion Continuous Wavelet Transform (program RCWT, Kristekova et al 2006) time-frequency representations (TFRs) for each earthquake record used for waveform inversion computed using Continuous Wavelet Transform (CWT) with Morlet wavelet (w0=6)

  10. Time-Frequency Analysis HRA-Z V14 Lat=48.516 Lon=17.680 h=5.23 Ml=2.2

  11. Time-Frequency Analysis HRA-N V14 Lat=48.516 Lon=17.680 h=5.23 Ml=2.2

  12. Time-Frequency Analysis HRA-E V14 Lat=48.516 Lon=17.680 h=5.23 Ml=2.2

  13. T P T P Waveform inversion from records of 3 stations: KAT, DVO and HRA band-pass filter 1.2 -1.8 Hz band-pass filter 1.2 -3.0 Hz V14 Lat=48.516 Lon=17.680 h=5.23 Ml=2.2

  14. Time-Frequency Analysis SMO-Z V14 Lat=48.516 Lon=17.680 h=5.23 Ml=2.2

  15. Time-Frequency Analysis SMO-N V14 Lat=48.516 Lon=17.680 h=5.23 Ml=2.2

  16. Time-Frequency Analysis SMO-E V14 Lat=48.516 Lon=17.680 h=5.23 Ml=2.2

  17. T P Waveform inversion from records of 1 station: SMO band-pass filter 1.2 -1.8 Hz band-pass filter 1.2 -3.0 Hz band-pass filter 1.2 -5.0 Hz V14 Lat=48.516 Lon=17.680 h=5.23 Ml=2.2

  18. Conclusions • The majority of mechanisms have the P axes clustered in the NE direction • Focal mechanisms inverted from waveforms of one station and of three stations are similar • Different suitably chosen frequency ranges of inversion provided consistent results. Including higher frequencies, however, decreased degree of similarity of the synthetic and observed waveforms (e.g. for the range up to 5 Hz: often too low) • Different degree of similarity for the same event and various stations also in their TFRs indicates significant lateral variations in the velocity model in this region. • This emphasizes the importance of the sufficient knowledge about the velocity model

  19. Thank you for your attention September 30, 2010, Bratislava

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