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

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

  • Slide 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.htmlykagan@ucla.edu Regional and Global Earthquake Forecasts http://moho.ess.ucla.edu/~kagan/Kagan_Jackson_Seismo.ppt
  • Slide 2
  • Earthquake forecasting in diverse tectonic zones of the Globe Since 1999 we have been forecasting long- and short-term rate of M>=5.8 earthquakes in two western Pacific regions using the CMT catalog (Jackson and Kagan, SRL, 1999; Kagan and Jackson, GJI, 2000). These forecasts are now available for testing at the SCEC CSEP (Collaboratory for the Study of Earthquake Predictability). However, the present method based on the CMT catalog can be extended only for subduction zones. Moreover, the high magnitude threshold of the catalog makes it unsuitable for forecasts in the areas with a relatively low seismicity level. The PDE (USGS) catalog has a lower magnitude threshold (4.8 vs 5.8 for CMT) and higher location accuracy. With moderate modifications the present methodology can be used for global or regional forecasts. We carried out a likelihood analysis of the PDE global catalog as well as subcatalogs covering different tectonic zones to determine the values of parameters for the short-term earthquake forecast. As an illustration of the forecast capability we applied it to California/Nevada, Greece, and Italy with surrounding territories. We discuss how these forecasts can be tested both retrospectively and prospectively. In principle a program can be designed to issue long- and short-term forecasts within a few minutes with the PDE catalog at the arbitrary spatial window: all it needs is some human capital, and rapid provision of the PDE catalog.
  • Slide 3
  • Literature (available at WEB) Kagan, Y. Y., and D. D. Jackson, 1994. Long-term probabilistic forecasting of earthquakes, J. Geophys. Res., 99, 13,685-13,700. Jackson, D. D., and Y. Y. Kagan, 1999. Testable earthquake forecasts for 1999, Seism. Res. Lett., 70, 393-403. Kagan, Y. Y., and D. D. Jackson, 2000. Probabilistic forecasting of earthquakes, Geophys. J. Int., 143, 438-453. Kagan, Y. Y., P. Bird, and D. D. Jackson, 2008. Earthquake Patterns in Diverse Tectonic Zones of the Globe, submitted to Evison PAGEOPH issue. Kagan, Y. Y. and D. D. Jackson, 2008. Earthquake forecasting in diverse tectonic zones of the Globe, submitted to Evison PAGEOPH issue.
  • Slide 4
  • 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).
  • Slide 5
  • GOALS 1. Forecasts must be falsifiable (testable) within a reasonable time period -- all methods. 2. Forecasts should produce in principle a stochastic ensemble of seismograms to simulate shaking of an object (statistical scenarios) -- CMT. 3. Forecast should be extended to all global seismogenic areas PDE. 4. Both global and regional earthquake catalogs should be used for the forecast PDE, ANSS.
  • Slide 6
  • CMT catalog: Shallow earthquakes, 1976-2005
  • Slide 7
  • We used the CMT catalog because it employs relatively consistent methods and reports tensor focal mechanisms. The focal mechanisms allow us to estimate the fault plane orientation for past earthquakes, through which we can identify a preferred direction for future events. Using the forecasted tensor focal mechanism, it is possible in principle to calculate an ensemble of seismograms for each point of interest on the earth's surface. CMT catalog
  • Slide 8
  • Long-term forecast: 1977-today, CMT 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.
  • Slide 9
  • Slide 10
  • 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.
  • Slide 11
  • Here we demonstrate forecast effectiveness: displayed earthquakes occurred after smoothed seismicity forecast had been calculated.
  • Slide 12
  • Color tones show the rate density of earthquake occurrence calculated using the CMT 1977- 2003 catalog; 1700 simulated earthquakes for 2004-2006 are shown in white.
  • Slide 13
  • Slide 14
  • Slide 15
  • I_2 the likelihood score for earthquakes. I_3 the likelihood score for forecasts.
  • Slide 16
  • World seismicity: 1990 2000 (PDE)
  • Slide 17
  • The PDE catalog has significant advantages over the CMT one. The PDE has a longer observation period (the surface wave magnitude M_S was determined starting from the middle of 1968), and a lower magnitude threshold (m_t). Depending on time period and region, the threshold is of the order 4.5 to 4.7 (Kagan, 2003), i.e., much lower than the CMT catalog threshold (around 5.4 to 5.8). This means that the forecast estimates can be practically obtained for all global seismic areas. The PDE reports earthquake hypocenters, which can be estimated much more precisely than the moment centroid locations reported by the CMT catalog. PDE catalog
  • Slide 18
  • Drawbacks when compared to the CMT dataset: The PDE catalog lacks the focal mechanism solutions. Also, the PDE reports a somewhat inconsistent mix of different magnitudes (local-, body wave-, surface wave-, moment-, etc.) with less accuracy than the moment magnitude inferred from the CMT catalog. Moreover, the PDE magnitudes are influenced by strong systematic effects and biases. Another drawback is that the hypocenter, which the PDE catalog uses for representing location, could be at the edge of the rupture zone for a large earthquake. The moment centroid, reported by the CMT, more meaningfully describes the location even though the centroid is generally more uncertain than the hypocenter. PDE catalog
  • Slide 19
  • Long-term forecast: 1977-today, CMT catalog, Mw>=5.8
  • Slide 20
  • Long-term forecast: 1977-today, PDE catalog, M>=5.8
  • Slide 21
  • Long-term forecast: 1969-today, PDE catalog, M>=5.0
  • Slide 22
  • http://bemlar.ism.ac.jp/wiki/index.php/Bird%27s_Zones
  • Slide 23
  • N -- is the earthquake number, M>=4.7, m_max maximum magnitude in a subcatalog, I/N -- information score per event in bits/eq, lambda T/N -- ratio of spontaneous events to total, m -- branching ratio b -- b-value, a -- parent productivity exponent theta -- temporal exponent, s_r -- focal size for M4 earthquake in km, epsilon_rho -- horizontal error in km epsilon_h -- vertical error in km.
  • Slide 24
  • N -- is the earthquake number, M>=4.7, m_max maximum magnitude in a subcatalog, I/N -- information score per event in bits/eq, lambda T/N -- ratio of spontaneous events to total, m -- branching ratio b -- b-value, a -- parent productivity exponent theta -- temporal exponent, s_r -- focal size for M4 earthquake in km, epsilon_rho horiz. error (km) epsilon_h -- vertical error (km).
  • Slide 25
  • 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)
  • Slide 26
  • ETAS model parameterization Omori s law: n = 1/(t + c)^p. c is not scaled with magnitude M, thus it is dependent on the magnitude threshold (Ogata, 1988, 1998). Spatial effects are not differentiated: location errors and dimension of aftershock zone need to be separated in parameterization. No determination of the likelihood score per event, thus the results are not comparable.
  • Slide 27
  • Kagan, Y. Y., and Knopoff, L., 1987. Statistical short- term earthquake prediction, Science, 236, 1563-1567.
  • Slide 28
  • 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.
  • Slide 29
  • Short-term forecast: 1977-today, CMT catalog, M>=5.8 Omoris law is used to extrapolate earthquake record. Parameters are determined by the maximum likelihood search.
  • Slide 30
  • 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