1. Analysis of complex seismicity pattern generated by fluid diffusion and aftershock triggering Sebastian Hainzl Toni Kraft System Statsei4

2. Introduction A Closed System = “plate boundary scenario” Assumption: tectonic loading + earthquake induced effects Statistical Earthquake Models: - long-term mainshock occurrence: Stress-Release model (Vere-Jones, 1978) - short-term clustering: ETAS model (Ogata, 1988) Epidemic Type Aftershock Sequences  talk: Bebbington  poster: Kuehn & Hainzl

3. Introduction B Open System = “intraplate scenario” Assumption: tectonic loading + earthquake induced effects + external forcing Examples: - volcano related seismicity - postglacial rebound - fluid intrusion

4. Introduction In the latter case, statistical modeling has to take care of the spatiotemporally varying external forcing. Two examples are shown: 1) Unknown external force: (Hainzl & Ogata, JGR 2005) “Vogtland Swarm Activity” 2) Known hypothetical source: “Seismicity at Mt. Hochstaufen”

5. 1) Vogtland swarm activity 1896/97, 1903, 1908/09, 1985/86, 2000 episodic occurrence of earthquake swarms: Possible mechanism: “...fluid overpressure in the brittle crust” (Braeuer et al., JGR 2003) swarm 2000 magnitude time / date (Hainzl & Ogata 2005)

6. Statistical modeling by means of the ETAS model Each earthquake has a magnitude- dependent ability to trigger aftershocks: f(M) = K exp( a M ) The aftershock rate decays according to the modified Omori law: h(t) = (c+t) -p-p 1) Vogtland swarm activity external triggering tectonic loading + pore pressure increase aftershock triggering induced stress + pressure changes (Hainzl & Ogata 2005)

7. Method to extract the forcing signal: fit of the ETAS model by maximum likelihood method estimation of the ETAS parameter in a moving time window Results: external triggering accounts only for a few percent of all events 1. method is successfully tested for model simulations: Fluid signal can be reconstructed! 3. temporal variation of the forcing signal is correlated with phases of (i) diffusion-like spatiotemporal migration (Parotidis et al. 2003) (ii) enhanced tensile components (Roessler et al. 2005) 2. time [days] forcing rate [#/day] 1) Vogtland swarm activity (Hainzl & Ogata 2005)

8. 1) Vogtland swarm activity Unknown driving force: reconstruction of the spatiotemporal pattern of the external force is possible revealed pattern can be compared with competing source models Indirect test of seismicity models

9. 2) Seismicity at Mt. Hochstaufen - spatially isolated activity - earthquakes are felt since more than 700 years - seasonally variations  hypothesis: rainfall induced (Kraft et al., 2006)

10. 2) Seismicity at Mt. Hochstaufen Analysis of the high-quality data from year 2002 INPUT: daily measured rainfall OUTPUT: earthquake catalog > 1100 events > 500 locations

11. 2) Seismicity at Mt. Hochstaufen

12.  lambda=0.3, c=4600 day/bar, D= 0.32 m2/s  80% rain-triggered & 20% background events

13. 2) Seismicity at Mt. Hochstaufen: RESULTS rain pressure comparison: pressure increase & earthquake rate

14. 2) Seismicity at Mt. Hochstaufen: RESULTS Coefficient of Correlation as a function of the delay time between daily seismic rate & daily rain

15. 2) Seismicity at Mt. Hochstaufen: RESULTS  high correlation with the pore pressure diffusion model Coefficient of Correlation as a function of the delay time between daily seismic rate & daily rain daily seismic rate & pore pressure increase

16. Summary: - direct test of the hypothesis of rain-triggered activity - model yields high correlation with observation - this suggests that very tiny stress changes are able to trigger earthquakes 2) Seismicity at Mt. Hochstaufen: