Yan Y. Kagan, David D. Jackson Dept. Earth and Space Sciences, UCLA, Los Angeles, CA 90095-1567,

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Presentation transcript:

Yan Y. Kagan, David D. Jackson Dept. Earth and Space Sciences, UCLA, Los Angeles, CA , SHORT- AND LONG-TERM EARTHQUAKE FORECASTING

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).

Kagan, Y. Y., and Knopoff, L., Statistical short- term earthquake prediction, Science, 236,

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

CMT catalog: Shallow earthquakes,

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, Probabilistic forecasting of earthquakes, Geophys. J. Int., 143,

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

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, > LONG. > on 2007/ 4/16 at latitude and longitude , 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 ……………………………………………………………………………………………………… E E E E E E E E E E E E E E E E E E E E E E E E E E E E-02 ………………………………………………………………………………………………………

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.

KURILE ISLANDS SEISMICITY 2005-PRESENT (2007/04/22) LATITUDE 40-50N, LONGITUDE E Thr Thr Thr Thr Thr Thr Thr Thr Thr Thr Nor Nor Thr Nor Nor Thr

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

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

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

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

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

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, Probabilistic forecasting of earthquakes, Geophys. J. Int., 143,

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

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

Likelihood ratio – information/eq Similarly we obtain likelihood function for the null hypothesis model (Poisson process in time). Information content of a catalog : 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 (bits/earthquake)

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.

END Thank you