Detecting Aseismic Fault Slip and Magmatic Intrusion From Seismicity

Data

A. L. Llenos1, J. J. McGuire2

1 MIT/WHOI Joint Program in Oceanography2 Woods Hole Oceanographic Institution

AGU Fall Meeting10 December 2007

Aseismic processes trigger seismicity

Recent studies show that aseismic deformation can trigger large amounts of seismicity. Examples include:

Bourouis & Bernard (2007)

Afterslip of subduction zone earthquakes

Magmatic intrusion

Toda et al. (2002)

Aseismic creep

Lohman & McGuire (2007)

Hsu et al. (2006)

Fluid flow

• Seismicity rate R(t) related to stressing rate S(t)

• S can be integrated from observed seismicity rate R

• But: R = RC +RA +RT

– RC = coseismic (earthquake-earthquake)

– RT = tectonic (background rate)– RA = aseismic (dike intrusions,

creep)• Catalogs with many aftershocks

– In high branching ratio n catalogs (Helmstetter & Sornette, 2002), RC obscures RA

– S then reflects primarily coseismic stress rates, not aseismic

Seismicity to stress rates: Rate-state model(Dieterich, 1994)

)(1

dSdtA

d

S

rR

r

Catalog 1 (n=0.1)

Catalog 2 (n=0.95)

ETAS model can isolate aseismic part (Ogata, 1988; Helmstetter and Sornette, 2002)

• Epidemic-Type Aftershock Sequence model– Stochastic point process model– Omori’s Law – seismicity rate decays exponentially

following a main shock– Each aftershock can produce its own aftershocks

• For an earthquake catalog, observed times ti and magnitudes mi are used to make maximum likelihood estimates of the ETAS parameters (K, c, p, , )

• Aseismic transient (RA) reflected in

ttp

i

mm

i

i

ctt

KttR

)(

10),(),(

)( 0

xx

RCRA+RT

• State-space model– Measurement equation

• Relates observed data to underlying process

– State transition equation• Describes evolution of state variables

• State variables: background stress rate, stress, aseismic stress rate, and seismicity state variable (rate-state)

• Estimate time history of x using the extended Kalman filter – Important assumption: Gaussian observation and process noise

• Maximum likelihood estimation (Ogata et al., 1993)– Model parameters: K, c, p, , A– Point process likelihood function– Gridsearch over parameters to find MLE using 64-processor Linux

cluster

Approach: Combine ETAS and rate-state models

krkttt

mmp

ik

kk S

r

ctt

KRd

ki

ci

:

10

11 kkk xtx SSS p x

T

i

N

i

dtttAcKpL0

1

log,,,,,log

Synthetic Test

n = 0.2

n = 0.9 = 0.8

p = 1.2

c = 0.0001 day

A = 0.1 MPa

S = 0.2 MPa/yr

S max = 20 MPa/yr

Synthetic Test Results

n = 0.2

n = 0.9

Can resolve stress rate changes of 1-2 orders of magnitude even in high branching ratio catalogs

Data Application: Salton Trough

• Transition from strike-slip to divergent plate boundaries– High heat flow (Kisslinger &

Jones, 1991)• Earthquake swarms driven by

aseismic creep (Lohman & McGuire, 2007)

• Test detection of stress rate transients in 20-yr catalog

Lohman & McGuire (2007)

Stress Rate Inversion Results

Lohman & McGuire (2007)

Binned seismicity

Stress rate estimate

=> constant stressing rate

=> Jump of ~3 orders of

magnitude in 2005

Obsidian Buttes swarm, 2005(Lohman & McGuire, 2007)

• >1000 events over the course of 2 weeks in 2005

• Combination of geodetic and seismicity data suggest swarm driven by aseismic creep

• Magnitude and duration of stress transient

Lohman & McGuire (2007)

Future work

• Synthetic tests show that combining the ETAS and rate-state models into a single algorithm removes the effects of aftershock (coseismic) rates and can resolve changes in aseismic stressing rate of 1-2 orders of magnitude even in high branching ratio catalogs

• Salton Trough results demonstrate its potential use as an aseismic stress transient detector

Ozawa et al. (2003)

Summary

• Find stress rate transients (and compare to geodetic studies)

• Extend to spatial dimensions• Estimates of rate-state

parameters (A)• Evaluate rate-state model (by

comparing to geodetic data)• Evaluate seismicity triggering

due to aseismic processes