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Yan Y. Kagan, David D. Jackson Dept. Earth and Space Sciences, UCLA, Los Angeles,

Yan Y. Kagan, David D. Jackson Dept. Earth and Space Sciences, UCLA, Los Angeles, CA 90095-1567, ykagan@ucla.edu , djackson@ucla.edu http://scec.ess.ucla.edu/ykagan.html. SHORT- AND LONG-TERM EARTHQUAKE FORECASTING. http://moho.ess.ucla.edu/~kagan/ US012_Kagan_Jackson.ppt.

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Yan Y. Kagan, David D. Jackson Dept. Earth and Space Sciences, UCLA, Los Angeles,

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  1. Yan Y. Kagan, David D. Jackson Dept. Earth and Space Sciences, UCLA, Los Angeles, CA 90095-1567, ykagan@ucla.edu, djackson@ucla.edu http://scec.ess.ucla.edu/ykagan.html SHORT- AND LONG-TERM EARTHQUAKE FORECASTING http://moho.ess.ucla.edu/~kagan/US012_Kagan_Jackson.ppt

  2. Stochastic models of earthquake occurrence and forecasting • Long-term models for earthquake occurrence, optimization of smoothing procedure and its testing (Kagan and Jackson, 1994, 2000). • Empirical branching models (Kagan, 1973a,b; Kagan and Knopoff, 1987; Ogata, 1988, 1998; Kagan, 2006). • Physical branching models – propagation of earthquake fault is simulated (Kagan and Knopoff, 1981; Kagan, 1982).

  3. Kagan, Y. Y., and Knopoff, L., 1987. Statistical short-term earthquake prediction, Science, 236, 1563-1567.

  4. Kagan, Y. Y., and D. D. Jackson, 1994. Long-term probabilistic forecasting of earthquakes, J. Geophys. Res., 99, 13,685-13,700.

  5. CMT catalog: Shallow earthquakes, 1976-2005

  6. Long-term forecast: 1977-today Spatial smoothing kernel is optimized by using the first part of a catalog to forecast its second part. Kagan, Y. Y., and D. D. Jackson, 2000. Probabilistic forecasting of earthquakes, Geophys. J. Int., 143, 438-453.

  7. Time history of long-term and hybrid (short-term plus 0.8 * long-term) forecast for a point at latitude 39.47 N., 143.54 E. northwest of Honshu Island, Japan. Blue line is the long-term forecast; red line is the hybrid forecast.

  8. The table below and accompanying plots are calculated on 2007/ 4/19 at midnight Los Angeles time. The last earthquake with scalar seismic moment M>=10^17.7 Nm (Mw>=5.8) entered in the catalog occurred in the region 0.0 > LAT. > -60.0, -170.0 > LONG. > 110.0 on 2007/ 4/16 at latitude -57.89 and longitude 148.14, Mw = 6.42 ____________________________________________________________________ LONG-TERM FORECAST | SHORT-TERM Probability Focal mechanism | Probability Probability M>5.8 T-axis P-axis M>5.8 ratio eq/day*km^2 Pl Az Pl Az eq/day*km^2 Time- Longitude | | | Rotation Time- dependent/ | Latitude | | | angle dependent independent v v v v degree ……………………………………………………………………………………………………… 154.0 -7.5 1.49E-08 67 24 16 251 58.6 6.17E-11 4.13E-03 154.5 -7.5 2.02E-08 68 18 10 262 60.9 7.17E-11 3.54E-03 155.0 -7.5 2.60E-08 75 18 13 222 28.5 1.53E-09 5.88E-02 155.5 -7.5 3.51E-07 71 20 19 210 8.7 7.66E-07 2.2 156.0 -7.5 6.72E-08 76 20 14 216 20.0 8.49E-07 13. 156.5 -7.5 3.10E-08 76 28 13 231 41.4 5.12E-07 17. 157.0 -7.5 1.90E-08 1 327 16 236 44.7 1.37E-07 7.2 157.5 -7.5 8.92E-09 77 45 13 229 46.6 4.57E-09 0.51 158.0 -7.5 7.42E-09 79 60 11 228 45.3 9.06E-11 1.22E-02 158.5 -7.5 1.05E-08 49 147 4 52 54.2 1.66E-10 1.58E-02 159.0 -7.5 7.64E-09 47 147 4 242 58.8 1.07E-10 1.41E-02 ………………………………………………………………………………………………………

  9. Short-term forecast uses Omori's law to extrapolate present seismicity. Forecast one day before the recent (2006/11/15) M8.3 Kuril Islands earthquake.

  10. KURILE ISLANDS SEISMICITY 2005-PRESENT (2007/04/22) LATITUDE 40-50N, LONGITUDE 150-160E 1 2005 8 1 4 40 47.09 154.22 15.0 5.70 0.4 68 286 5 29 Thr 2 2005 10 15 10 6 46.85 154.33 46.0 6.17 1.8 64 289 1 22 Thr 3 2006 8 20 3 1 49.58 156.87 35.6 6.05 1.2 72 350 10 229 Thr 4 2006 9 28 1 36 46.45 153.66 12.0 6.00 1.0 71 278 18 122 Thr 5 2006 9 30 17 50 46.36 153.50 12.0 6.65 9.3 69 296 21 130 Thr 6 2006 9 30 17 56 46.19 153.35 16.4 6.03 1.1 72 291 18 122 Thr 7 2006 10 1 9 6 46.44 153.68 12.0 6.63 8.8 70 297 20 127 Thr 8 2006 10 13 13 47 46.15 153.73 12.0 5.90 0.7 70 287 19 129 Thr 9 2006 11 12 21 27 48.21 154.77 48.6 6.00 1.0 80 299 10 120 Thr 10 2006 11 15 11 15 46.75 154.32 13.4 8.35 3400.0 60 302 30 123 Thr 11 2006 11 16 6 20 46.41 154.68 12.0 6.00 1.0 4 314 82 190 Nor 12 2006 12 7 19 10 46.23 154.44 13.8 6.40 4.0 2 322 86 83 Nor 13 2006 12 26 15 19 48.18 155.20 38.0 6.00 1.0 77 340 11 133 Thr 14 2007 1 13 4 23 46.18 154.80 12.0 8.14 1650.0 10 150 67 264 Nor 15 2007 1 13 17 37 47.04 156.44 26.6 6.02 1.1 5 176 48 272 Nor 16 2007 4 9 10 18 48.21 155.17 33.0 5.83 0.6 73 318 16 124 Thr

  11. Forecast one day after the recent (2006/11/15) M8.3 Kuril Islands earthquake.

  12. Forecast one day before the recent (2007/01/13) M8.1 Kuril Islands earthquake.

  13. Forecast one day after the recent (2007/01/13) M8.1 Kuril Islands earthquake.

  14. Forecast one day before the recent (2007/4/1)M8.1 Solomon Islands earthquake.

  15. Forecast one day after the recent (2007/4/1) M8.1 Solomon Islands earthquake

  16. Long-term Forecast Efficiency Evaluation • We simulate synthetic catalogs using smoothed seismicity map. • Likelihood function for simulated catalogs and for real earthquakes in the time period of forecast is computed. • If the `real earthquakes’ likelihood value is within 2.5—97.5% of synthetic distribution, the forecast is considered successful. Kagan, Y. Y., and D. D. Jackson, 2000. Probabilistic forecasting of earthquakes, Geophys. J. Int., 143, 438-453.

  17. Here we demonstrate forecast effectiveness: displayed earthquakes occurred after smoothed seismicity forecast had been calculated.

  18. Kossobokov, 2006. Testing earthquake prediction methods: ``The West Pacific short-term forecast of earthquakes with magnitude MwHRV \ge 5.8", Tectonophysics, 413(1-2), 25-31. See also Kagan & Jackson, TECTO, 2006, pp. 33-38.

  19. . Likelihood ratio – information/eq Similarly we obtain likelihood function for the null hypothesismodel (Poisson process in time) Information content of a catalog : (bits/earthquake) characterizes uncertainty reduction by use of a particular model. Kagan and Knopoff, PEPI, 1977; Kagan, GJI, 1991; Kagan and Jackson, GJI, 2000; Helmstetter, Kagan and Jackson, BSSA, 2006

  20. Conclusions • We present an earthquake forecast program which quantitatively predicts both long- and short-term earthquake probabilities. • The program is numerically and rigorously testable. • It is ready to be implemented as a technological solution for earthquake hazard forecasting and early warning.

  21. END Thank you

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