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Near Real Time Estimation of Seismic Event Magnitude and Moment via P and Lg phases

  • C. Deniz Mendi
  • Eystein S. Husebye
Conference paper
Part of the NATO ASI Series book series (ASEN, volume 4)

Summary

Local magnitude and moment of a seismic event can be estimated in near real time by means of signal detector parameters being defined as rms trace estimates. The advantage of such a signal detector is accurate prediction of maximum signal amplitude via the Random Vibration Theory (RVT) which implies a relationship between the maximum amplitude and the rms value for stationary signals. Although seismic signals are non-stationary, the RVT validity was tested on many seismic signals with excellent results. For moment and magnitude estimation, the geometrical spreading and attenuation effects are accounted for by using empirical correction curves which are often unavailable for P g and P n -phases and sometimes for L g -waves. Using source theory, we have computed such correction curves for different frequency ranges using P and L g propagation parameters as published by Sereno et al. (1988). This novel approach was used for automatic maximum phase amplitude measurements and subsequently event magnitude and moment estimation using 50 local events recorded by NSN (Norwegian Seismological Network). The results here are in very good agreement with reference magnitudes from NORSAR. L g based magnitudes appear more stable than those tied to P g and P n amplitudes. Also, at distances below 100 km P magnitudes are consistently below the corresponding L g magnitudes. This may be explained by too small corrections at short distances as proper spreading and attenuation terms remain problematic at such ranges.

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References

  1. Alsaker, A., L.B. Kvamme, R.A. Hansen, A. Dahle and H. Bungum (1991). The M L scale in Norway, Bull. Seism. Soc. Am. 81, 379–398.Google Scholar
  2. Boore, D.M. (1983). Stochastic simulation of high-frequency ground motions based on seis-mological models of the radiated spectra, Bull. Seism. Soc. Am. 73, 1865–1894.Google Scholar
  3. Boore, D.M. and W.B. Joyner (1984). A Note on the use of random vibration theory to predict peak amplitudes of transient signals, Bull. Seism. Soc. Am. 74, 2035–2039.Google Scholar
  4. Ba’ th, M. (1981). Earthquake magnitude: recent research and current trends, Earth Sci. Rev. 17, 315–398.CrossRefGoogle Scholar
  5. Cartwright, D.E. and M.S. Longuet-Higgins (1956). The statistical distribution of the maxima of a random function, Proc. Roy. Soc. London, Ser. A237, 212–223.CrossRefGoogle Scholar
  6. Hansen, R.A., F. Ringdal and P.G. Richards (1990). The stability of rms L g measurements and their potential for accurate estimation of the yields of Soviet underground nuclear explosions, Bull. Seism. Soc. Am., 80, 2106–2126.Google Scholar
  7. Kanamori H. (1993). Locating earthquakes with amplitude: Application to real-time seismology, Bull. Seism. Soc. Am., 83, 264–268.Google Scholar
  8. Leach, R.R., F.U. Dowla and E.S. Vergino (1993). Yield estimation using bandpass-filtered seismograms: preliminary results using neural networks with m b (P n ), short-time, longtime, and coda energy measurements, Bull. Seism. Soc. Am., 83, 488–508.Google Scholar
  9. Mendi, C.D. and E.S. Husebye (1994). Near real time estimation of magnitudes and moments for local seismic events, Annali di Geofisica, 37, 365–382.Google Scholar
  10. Nuttli, O.W. (1973). Seismic wave attenuation and magnitude relations for eastern North America, J. Geophys. Res. 78, 876–885.CrossRefGoogle Scholar
  11. Richter, C.F. (1935). An instrumental earthquake magnitude scale, Bull. Seism. Soc. Am. 25, 1–32.Google Scholar
  12. Ringdal, F. and T. Kvwrna (1992). Continuous seismic threshold monitoring, Geophys. J. Int. 111, 505–514.CrossRefGoogle Scholar
  13. Ruud, B.O. and E.S. Husebye (1992). A new three-component detector and automatic single station bulletin production, Bull. Seism. Soc. Am. 82, 221–237.Google Scholar
  14. Ruud, B.O., C.D. Lindholm and E.S. Husebye (1993). An exercise in automating seismic record analysis and network bulletin production, Bull. Seism. Soc. Am. 83, 660–679.Google Scholar
  15. Sereno, T.J., S.R. Bratt and T.C. Bache, (1988). Simultaneous inversion of regional wave spectra for attenuation and seismic moment in Scandinavia, J. Geophys. Res. 93, 2019–2035.CrossRefGoogle Scholar
  16. Sereno, T.J. and S.R. Bratt (1989). Seismic detection capability at NORESS and implications for the detection threshold of a hypothetical network in the Soviet Union, J. Geophys. Res. 94, 10397–10414.CrossRefGoogle Scholar
  17. Stevens, J. and S. Day (1985). The physical basis of rab: M s and variable frequency magnitude methods for earthquake/explosion discrimination, J. Geophys. Res, 90, 3009–3020.CrossRefGoogle Scholar
  18. Street, R., R. Herrmann and O. Nuttli (1975). Spectral Characteristics of the L g wave generated by central United States earthquakes, Geophys. J. R. Astron. Soc., 41, 51–63.CrossRefGoogle Scholar
  19. Xie, J. (1993). Simultaneous inversion for source spectrum and path Q using L 9 with appli-cation to three semipalatinsk explosions, Bull. Seism. Soc. Am. 83, 1547–1562.Google Scholar

Copyright information

© Springer-VerlagBerlin Heidelberg 1995

Authors and Affiliations

  • C. Deniz Mendi
    • 1
  • Eystein S. Husebye
    • 1
  1. 1.Institute of Solid Earth PhysicsUniversity of BergenBergenNorway

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