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

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Presentation transcript:

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

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

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

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”

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)

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)

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)

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

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)

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

2) Seismicity at Mt. Hochstaufen

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

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

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

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

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: