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V áclav Vavry č uk 1 , Daniela Kühn 2 1 Institute of Geophysics, Prague 2 NORSAR, Kjeller

Determination of Source Parameters and Full Moment Tensor s in a Very Heterogeneous M ining Environment. V áclav Vavry č uk 1 , Daniela Kühn 2 1 Institute of Geophysics, Prague 2 NORSAR, Kjeller. Motivation. Motivation. Waveform modelling. Polaities and amplitudes. MTI strategy.

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V áclav Vavry č uk 1 , Daniela Kühn 2 1 Institute of Geophysics, Prague 2 NORSAR, Kjeller

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  1. Determination of Source Parameters and Full Moment Tensorsin a Very Heterogeneous Mining Environment Václav Vavryčuk1, Daniela Kühn2 1 Institute of Geophysics, Prague 2 NORSAR, Kjeller

  2. Motivation Motivation Waveform modelling Polaities and amplitudes MTI strategy Application to real data Summary

  3. Pyhäsalmi ore mine, Finland • microseismic monitoring: • since January 2003 • safety of the underground personnel • optimisation of mining process • network: • 12 1-C geophones • + 6 3-C geophones (ISS) • 3-D geometry • sampling rate: < 3000 Hz • events: • 1500 events /months (including blasting) • -2 < Mw < 1.5 Motivation Waveform modelling Polaities and amplitudes MTI strategy Application to real data Summary owned by Inmet Mining Co., installation of seismometer network by the ISS Int. Ltd.

  4. Velocity model • Strongly heterogeneous velocity model • ore body: vp = 6.3 km/s • host rock: vp = 6.0 km/s • excavation area: vp = 0.3 km/s Motivation Waveform modelling Polaities and amplitudes U MTI strategy W E D Application to real data Summary

  5. Motivation Waveform modelling Waveform modelling Polaities and amplitudes MTI strategy Application to real data Summary

  6. Waveform modelling: 2D • E3D: viscoelastic 3-D FD code (Larsen and Grieger, 1998) • strong interaction with mining cavities: reflection, scattering, conversion Motivation 620 m Waveform modelling Polaities and amplitudes MTI strategy Application to real data Summary

  7. Waveform modelling: 3D Motivation Waveform modelling Polaities and amplitudes MTI strategy Application to real data Summary

  8. Waveformmodelling synthetic seismograms Motivation • - complex waveforms • long, strong coda • complex secondary arrivals • difficult to interpret P-wave • polarities • difficult to identify S-wave • arrivals Waveform modelling Polaities and amplitudes MTI strategy Application to real data Summary observed seismograms

  9. Motivation Polarities and amplitudes of direct P waves Waveform modelling Polaities and amplitudes MTI strategy Application to real data Summary

  10. Geophonenetwork (artificial) Motivation Waveform modelling Polaities and amplitudes source location . MTI strategy source mechanisms Application to real data Summary

  11. Comparison 1-D/3-D Motivation Waveform modelling Polaities and amplitudes + polarity: red - polarity: blue MTI strategy Application to real data Summary

  12. Moment tensor inversionfor a homogeneous model Observed amplitudes Retrieved source mechanism Synthetic source mechanism Motivation Waveform modelling Polaities and amplitudes MTI strategy Application to real data Summary ISO = 0 % DC = 100 % CLVD = 0 % ISO = 23 % DC = 48 % CLVD = 52 %

  13. Source depth U Motivation W E Waveform modelling D . Polaities and amplitudes MTI strategy Application to real data Summary

  14. Motivation Waveform modelling Moment tensor inversion Polaities and amplitudes MTI strategy Application to real data Summary

  15. Moment tensor inversions Motivation Waveform modelling wave amplitudes (Vavryčuk et al. 2008; Fojtíková et al. 2010; Godano et al. 2011) amplitude ratios (Miller et al. 1998; Hardebeck & Shearer 2003; Jechumtálová & Šílený 2005) full waveforms (Šílený et al. 1992 Cesca et al. 2006; Cesca & Dahm 2008; Sokos & Zahradník 2009) Polaities and amplitudes MTI strategy Application to real data • applicable to simple • media • linear • fast • applicable to simple • media • insensitive to • amplifications • non-linear • applicable to complex • media • linear • more time consuming Summary

  16. Moment tensor inversions Motivation Waveform modelling wave amplitudes (Vavryčuk et al. 2008; Fojtíková et al. 2010; Godano et al. 2011) amplitude ratios (Miller et al. 1998; Hardebeck & Shearer 2003; Jechumtálová & Šílený 2005) full waveforms (Šílený et al. 1992 Cesca et al. 2006; Cesca & Dahm 2008; Sokos & Zahradník 2009) Polaities and amplitudes MTI strategy Application to real data • applicable to simple • media • linear • fast • applicable to simple • media • insensitive to sensor • amplifications • non-linear • applicable to complex • media • linear • more time consuming Summary

  17. Proposed MTI strategy • accurate locations using the eikonal solver • the first arrival need not correspomd to a direct wave • eikonal solver takes into account refractions and diffractions • Green’s functions computed using viscoelastic 3D FD code • detailed 3D velocity model with spatial sampling of 2 m • time sampling: 10 KHz • waveform inversion in time domain • source time function assumed as Dirac delta function • waveforms low-pass filtered (f < 80 Hz) • waveform alignment using cross-correlation of observed data and synthetics (maximum time shift: ± 0.01 s) • errors estimated using repeated inversions of waveforms contaminated by random noise and with random time shift Motivation Waveform modelling Polaities and amplitudes MTI strategy Application to real data Summary

  18. Application to real data Motivation Waveform modelling Polaities and amplitudes MTI strategy Application to real data Summary

  19. Motivation 5 Blasts Waveform modelling Polaities and amplitudes MTI strategy Application to real data Summary 5 Events

  20. Results: blasts blast 1 1E 12Z 7Z 1N 8Z 13N 13E 2Z 9E 4Z 9N 13Z 14Z 9Z 5N Motivation 10Z 15Z 5E 11Z 16Z 6Z Waveform modelling blast 2 1E 6Z 12Z 7Z 13N 1N Polaities and amplitudes 1Z 8Z 13E 2Z 13Z 9N 9Z 14Z 4Z MTI strategy 5N 10Z 15Z 5Z 11Z 16Z Application to real data blast 4 1E 12Z 7Z 8Z 13N 1N 9E 1Z 13E Summary 9N 3Z 13Z 9Z 14Z 4Z 10Z 15Z 5E 11Z 16Z 6Z

  21. Results: events 1E 11Z 5E event 2 5Z 12Z 1N 1Z 6Z 13N 7Z 2Z 13E 9E 13Z 3Z Motivation 9Z 14Z 4Z 10Z 15Z 5N Waveform modelling 1E 11Z 5Z event 3 6Z 12Z 1N Polaities and amplitudes 1Z 7Z 13E 9E 13Z 3Z 9N 14Z 4Z MTI strategy 9Z 15Z 5N 5E 10Z 16Z Application to real data 1E 11Z 5E event 4 5Z 12Z 1N 1Z 7Z 13N Summary 13E 2Z 8Z 13Z 9N 3Z 9Z 14Z 4Z 5N 10Z 16Z

  22. Blast 1: stability of ISO Mean value stable and positive isotropic components ISO > 65% Motivation Waveform modelling Polaities and amplitudes MTI strategy Standard deviation Application to real data Summary

  23. Results: overview • Blasts: • high and positive ISO % • DC is minor • DC may reflect minor shearing induced during blasting or errors Motivation Waveform modelling Polaities and amplitudes • Events: • CLVD and ISO are implosive • predominant mechanism is probably related to collapse of rock due to mining activity MTI strategy Application to real data Summary

  24. Summary I structural model in mines usually is very complex large and abrupt changes in velocity at cavities the model varies in time Motivation Waveform modelling Polaities and amplitudes MTI strategy earthquake source is complex (single forces, non-DC components, complex source history) Application to real data Summary

  25. Summary II radiated wave field is complex (reflected, converted, scattered waves, head waves) Motivation Waveform modelling Polaities and amplitudes MTI strategy inversion in a homogeneous model may lead to erroneous results Application to real data Summary

  26. Summary III • Amplitude inversion: • simple approach • limited applicability (simple Green’s functions are not adequate) • no control on frequency bands • amplitudes can be wrongly interpreted • Full waveform inversion: • accurate model and accurate location needed • eikonal solver for event location • complex Green’s functions can be calculated by 3-D FD codes • frequency band of inverted waves can be controlled • promising, but computationally demanding and laborious Motivation Waveform modelling Polaities and amplitudes MTI strategy Application to real data Summary

  27. Thank you!

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