Detecting Aseismic Fault Slip and Magmatic Intrusion From Seismicity Data A. L. Llenos 1, J. J. McGuire 2 1 MIT/WHOI Joint Program in Oceanography 2 Woods Hole Oceanographic Institution AGU Fall Meeting 10 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 = R C +R A +R T –R C = coseismic (earthquake- earthquake) –R T = tectonic (background rate) –R A = aseismic (dike intrusions, creep) Catalogs with many aftershocks –In high branching ratio n catalogs (Helmstetter & Sornette, 2002), R C obscures R A –S then reflects primarily coseismic stress rates, not aseismic Seismicity to stress rates: Rate-state model (Dieterich, 1994) 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 t i and magnitudes m i are used to make maximum likelihood estimates of the ETAS parameters (K, c, p, , ) Aseismic transient (R A ) reflected in  RCRC R A +R T

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

Synthetic Test n = 0.2 n = 0.9  = 0.8 p = 1.2 c = 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