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Short-term foreshocks and their predictive value

Short-term foreshocks and their predictive value. G. A. Papadopoulos (1) M. Avlonitis (2), B. Di Fiore (1) & G. Minadakis (1) 1. Institute of Geodynamics National Observatory of Athens, Greece papadop@noa.gr 2. Dept. of Informatics, Ionian University, Greece. EARTHWARN.

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Short-term foreshocks and their predictive value

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  1. Short-term foreshocks and their predictive value G. A. Papadopoulos (1) M. Avlonitis (2),B. Di Fiore (1) & G. Minadakis (1) 1. Institute of Geodynamics National Observatory of Athens, Greece papadop@noa.gr 2. Dept. of Informatics, Ionian University, Greece EARTHWARN

  2. Definitions of short-term foreshocks • No standard definitions….but • Literature Consensus for foreshocks: Spatio-temporal seismicity clusters that exhibit a power-law rise in seismic moment release in the area where a larger mainshock is under preparation, and occurring up to a few months before the mainshock occurrence. • Swarms (Yamashita, 1998): Spatio-temporal seismicity clusters that exhibit a gradual rise and fall in seismic moment release, lacking a mainshock-aftershocks pattern.

  3. First evidence • Power-law increase, b-value decrease - Laboratory experiments (Mogi, 1962, Scholtz, 1968) - Seismic sequences (e.g. Jones & Molnar, 1979) • However, only very few examples were available

  4. Characteristic patterns of short-term foreshocks • Time: mode of power-law increase • Space: move towards mainshock epicenter • Magnitude: b-value drops • Foreshock rate? • Why some mainshocks have foreshocks and others do not?

  5. Method of analysis • Seismicity is a 3D process: space-time-size domains • Basic method: in-houseFORMA algorithm for the detection of significant seismicity changes - space: select target area, repeat tests by changing - perform completeness analysis - time: seismicity rate changes (z-test, t-test) - Size: b-valuechanges (Utsu-test)

  6. Good examples of foreshocks: L’Aquila, 6 Apr. 2009, M6.3

  7. Chile, 1 Apr. 2014, M8.1

  8. Tohoku, 11 March 2011, M9.0

  9. S. California, 4 Apr 2010, M7.20

  10. S. California 26 Apr 1981, M5.75

  11. South Greece, 14.8.2011, M4.5

  12. South Greece, 14.8.2011, M4.5

  13. Basilicata (Italy), M5.0, 25.10.2012

  14. Predictive value: time • Time: power-law mode • Short-term: up to about 6 months at maximum however, 80% in the last 10 days P (t) =A – B (log t)

  15. Alternative: Poisson Hidden Markov Models Orfanogiannaki et al. PAGEOPH (2011) Research in Geophys. (2014) Recognizing changes in the states of seismicity, e.g. Sumatra 2004

  16. Predictive value: space • Space: move towards mainshock epicenter • Topological metrics based on Network Theory : e.g. Betweeness Centrality e.g. Daskalaki et al., J. of Seismology (2013)

  17. Application in L’ Aquila, 2009

  18. Evolution of Betweeness Centrality L’ Aquila, 2009

  19. Predictive value: magnitude • Mo ≠ Mf ; Mo ≠ duration (f) • However, Mo may depend on foreshock area! Mo ranges from 4.5 to 9.0

  20. Foreshock rate? • Current statistics indicates Fr around 40-50% • Earlier statistics indicated Fr around 10-20% Catalog Problems Foreshock recognition strongly depends on recording capabilities • In well monitored areas no foreshocks were recognized, e.g. in Parkfield, 2004, M6.0 No catalog problems Source properties determines the no foreshock incidence

  21. Conclusions • Foreshocks have characteristic 3D patterns • In time: power-law mode • In size: b-value drops • In space: move towards mainshock epicenter • There is evidence that the foreshock area depends on Mo • The predictive value of foreshocks now becomes evident, which is promising for the mainshock prediction

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