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Short-term foreshocks and their predictive value

Dive into the realm of short-term foreshocks, their defining characteristics, and predictive capabilities. Understand their spatial-temporal patterns and explore the predictive value regarding impending mainshocks. Learn about seismic moment release and foreshock occurrence, illustrated with notable examples like L’Aquila and Chile. Uncover the significance of seismicity changes, space-time analysis, and the evolving field of predictive seismic modeling.

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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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