Short-term foreshocks and their predictive value

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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

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.

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

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?

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)

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

Chile, 1 Apr. 2014, M8.1

Tohoku, 11 March 2011, M9.0

S. California, 4 Apr 2010, M7.20

S. California 26 Apr 1981, M5.75

South Greece, 14.8.2011, M4.5

South Greece, 14.8.2011, M4.5

Basilicata (Italy), M5.0, 25.10.2012

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)

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

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)

Application in L’ Aquila, 2009

Evolution of Betweeness Centrality L’ Aquila, 2009

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

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

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