analysis of complex seismicity pattern generated by fluid diffusion and aftershock triggering...
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
mag
nitu
de
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
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 forcingsignal 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]
forc
ing
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
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: