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
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-house FORMA 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-value changes (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, , M4.5

12. South Greece, , M4.5

13. Basilicata (Italy), M5.0,

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? Fr around 10-20% Current statistics indicates
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