analysis of complex seismicity pattern generated by fluid diffusion and aftershock triggering...

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: