1. HIGH FREQUENCY GROUND MOTION SCALING IN THE YUNNAN REGION W. Winston Chan, Multimax, Inc., Largo, MD W. Winston Chan, Multimax, Inc., Largo, MD Robert B. Herrmann, Saint Louis University, St. Louis, MO Robert B. Herrmann, Saint Louis University, St. Louis, MO Wenjie Jiao, Multimax, Inc., Largo, MD Wenjie Jiao, Multimax, Inc., Largo, MD

2. Purpose Pilot study of integrated approach to high frequency ground motion scaling Pilot study of integrated approach to high frequency ground motion scaling  Absolute ground motion scaling  Moment magnitudes Source inversionSource inversion –Crustal models

3. Outline High frequency ground motion processing High frequency ground motion processing Source parameter determination Source parameter determination High frequency ground motion modeling High frequency ground motion modeling Future directions Future directions

4. Background Processing of seismic network data in Yunnan, China Processing of seismic network data in Yunnan, China 325 events recorded at 23 stations yield over 5,000 regional S phases 325 events recorded at 23 stations yield over 5,000 regional S phases 2 Data sets merged 2 Data sets merged  regional broadband network  portable deployment

5. Station – Event Distribution

6. Distribution of Observations STATIONSSTATIONS

7. Data Processing Process raw seismograms Pick P, S Reduce to ground velocity (m/s) Compute tables consisting of - Peak motion Duration Spectra Energy RMS signal shape Apply coda normalization Regression Peak filtered velocities Fourier velocities Model using Random Vibration Theory or Bandpass filtered stochastic white noise

8. Regression Model Use filtered vertical component velocity at 0.33, 0.5, 1, 2, 3, 4, 6, 8, 10 and 12 Hz (DT = 0.02 s) Use Coda normalization as independent test of distance term Perform regression on Peak Velocities and Fourier Velocity Spectra at each frequency for the model log A = D(r) + E(r ref ) + S where A is observed motion, D(r) is distance term, E(r ref ) is the excitation term at the reference distance, and S is a site term Regression Specifics:  Sum S i =  0, D(40 km) =  0; D(r) is a piecewise linear continuous function defined by distance nodes

9. Regressions Results - 1.0 Hz

10. Frequency Dependence of D(r)

11. Distance Dependence Duration - 2 HZ

12. Distance Dependent Duration Duration decreases at high frequency Low frequency dominated by direct and scattered surface waves High frequency dominated by simple body wave arrivals

13. Modeling The predicted Fourier velocity spectra for a frequency f and a distance r is a(r, f ) = s( f,M W )g(r)e -3.14fr/Q( f ) V( f )e -3.14f kappa where a(r, f) is the Fourier velocity spectra s( f,M W ) is the source excitation as a function of moment-magnitude g(r) is the geometrical spreading function Q( f) is the frequency dependent quality factor which equals Q  ( f /1. 0) eta, Q  is the quality factor at 1.0 Hz V( f) is a frequency dependent site amplification, and kappa controls site dependent attenuation of high frequency

15. Modeling Procedure Model Fourier Velocity initially Start with g(r), Q 0 and eta Next look at small event E terms to get kappa 0 Include duration to model time domain D(r) Compare observed and predicted D(r)’s Compare observed and predicted E(r ref, f )’s Use events with known M W to constrain g(r) from the source to the first observation distance (about 40 km for this study)

16. Time Domain D( r ) Comparison of observed (color) and predicted (black) distance dependence (top figure) for frequencies of 0.33 - 12 HZ. The lower panel shows that the simple model fit is good to about 0.15 log units to a distance of 300 km

17. Propagation Model RVTDCAL1.0 COMMENT TEST OF SOURCE SPECTRUM KAPPA 0.050 QETA 2000.49 QVELOCITY 3.5 SHEAR 3.5 DENSITY 2.8 DISTANCE 4 1.0 -1.0 40.0 -1.2 90.0 0.0 150 -0.5 SITE 2 0.0 1.0 1000.0 1.0 FMAX 100 RADIATION 0.55 FREE 2.0 PARTITION 0.707 SIGMA 100 DURATION 14 0.000 0.000 10.000 0.250 20.000 1.250 40.000 2.750 60.000 6.250 80.000 5.250 100.000 5.750 120.000 5.250 150.000 5.250 200.000 6.250 250.000 8.250 300.000 8.250. 400.000 15.750 900.000 15.750 Input file to stochastic processing programs of Computer Programs in Seismology - 3.30

18. Excitation Modeling Motion at 40 km depends on propagation, Source spectra shape Source radiation Unknown geometrical spreading from source to first observation distance Can be resolved using independently determined moment magnitudes, especially for Mw <= 4!

19. Source Mechanism Use surface-wave spectral amplitudes   Robust - does not require perfect crustal model Validate using waveform fit

20. 2000/01/23 Stations (red) and epicenter (star)

21. Depth

22. Surface-Wave Radiation Love Rayleigh

23. Waveform fit Observed (red) and predicted (blue) ground velocities (m/sec) in 0.02 - 0.10 Hz band Each trace pair is plotted with the same scaling. Note Z R and T have different scales. Peak amplitudes are indicated This mechanism is well determined

24. 2000/01/23 Summary NODAL PLANES NODAL PLANES STK= 207.37 STK= 207.37 DIP= 80.34 DIP= 80.34 RAKE= -15.22 RAKE= -15.22 OR OR STK= 299.98 STK= 299.98 DIP= 75.00 DIP= 75.00 RAKE= -169.99 RAKE= -169.99 DEPTH = 8.0 km DEPTH = 8.0 km Mw = 4.24 Mw = 4.24

25. Excitation Use propagation model Assume 100 bar stress drop Compare predictions to events with known moment

26. Agreement at 1 Hz for Mw < 4.25 indicates that g( r ) and source radiation pattern are OK. Disagreement at high frequency could be due to kappa or stress drop. 000127 east of Yunnan is anomalous. Mw=5.5 is predicted very well

27. Discussion Data from Yunnan Regional Seismic Network stations are excellent for source and propagation studies Data from Yunnan Regional Seismic Network stations are excellent for source and propagation studies We have a forward model to describe the amplitude- distance relationship of 0.33-12 Hz ground motion in the distance range of 30 to 600 km in Yunnan We have a forward model to describe the amplitude- distance relationship of 0.33-12 Hz ground motion in the distance range of 30 to 600 km in Yunnan Source modeling of small events is possible and validates the propagation and radiation from the source Source modeling of small events is possible and validates the propagation and radiation from the source The question is source scaling must be addressed not only to understand the current lack of fit but also to constrain model predictions of large earthquake motions The question is source scaling must be addressed not only to understand the current lack of fit but also to constrain model predictions of large earthquake motions

28. Future Directions Automate source parameter determination by direct waveform modeling 2003/ 7/21 Mw=5.9 Yunnan event as test of predictions from current model Include local recordings of 2003/ 7/21 Mw=5.9 Yunnan event as test of predictions from current model Ultimately used 3-D modeling to understand effect of structure on high frequency motions. Ultimately used 3-D modeling to understand effect of structure on high frequency motions. Crustal structure and the source process control ground motions Crustal structure and the source process control ground motions