HR: 17:30h
AN: NG54A-07    [Abstracts]
TI: Earthquake Prediction in Large-scale Faulting Experiments
AU: * Junger, J
EM: jennifer\_junger@brown.edu
AF: Brown University, Department of Geological Sciences, Providence, RI 02912-1846 United States
AU: Kilgore, B
EM: bkilgore@usgs.gov
AF: U.S. Geological Survey, 345 Middlefield Road, Menlo Park, CA 94025 United States
AU: Beeler, N
EM: nbeeler@usgs.gov
AF: U.S. Geological Survey, 345 Middlefield Road, Menlo Park, CA 94025 United States
AU: Dieterich, J
EM: jdieterich@usgs.gov
AF: U.S. Geological Survey, 345 Middlefield Road, Menlo Park, CA 94025 United States
AB: We study repeated earthquake slip of a 2 m long laboratory granite fault surface with approximately homogenous frictional properties. In this apparatus earthquakes follow a period of controlled, constant rate shear stress increase, analogous to tectonic loading. Slip initiates and accumulates within a limited area of the fault surface while the surrounding fault remains locked. Dynamic rupture propagation and slip of the entire fault surface is induced when slip in the nucleating zone becomes sufficiently large. We report on the event to event reproducibility of loading time (recurrence interval), failure stress, stress drop, and precursory activity. We tentatively interpret these variations as indications of the intrinsic variability of small earthquake occurrence and source physics in this controlled setting. We use the results to produce measures of earthquake predictability based on the probability density of repeating occurrence and the reproducibility of near-field precursory strain. At 4 MPa normal stress and a loading rate of 0.0001 MPa/s, the loading time is $\sim$25 min, with a coefficient of variation of around 10%. Static stress drop has a similar variability which results almost entirely from variability of the final (rather than initial) stress. Thus, the initial stress has low variability and event times are slip-predictable. The variability of loading time to failure is comparable to the lowest variability of recurrence time of small repeating earthquakes at Parkfield (Nadeau et al., 1998) and our result may be a good estimate of the intrinsic variability of recurrence. Distributions of loading time can be adequately represented by a log-normal or Weibel distribution but long term prediction of the next event time based on probabilistic representation of previous occurrence is not dramatically better than for field-observed small- or large-magnitude earthquake datasets. The gradually accelerating precursory aseismic slip observed in the region of nucleation in these experiments is consistent with observations and theory of Dieterich and Kilgore (1996). Precursory strains can be detected typically after 50% of the total loading time. The Dieterich and Kilgore approach implies an alternative method of earthquake prediction based on comparing real-time strain monitoring with previous precursory strain records or with physically-based models of accelerating slip. Near failure, time to failure t is approximately inversely proportional to precursory slip rate V. Based on a least squares fit to accelerating slip velocity from ten or more events, the standard deviation of the residual between predicted and observed log t is typically 0.14. Scaling these results to natural recurrence suggests that a year prior to an earthquake, failure time can be predicted from measured fault slip rate with a typical error of 140 days, and a day prior to the earthquake with a typical error of 9 hours. However, such predictions require detecting aseismic nucleating strains, which have not yet been found in the field, and on distinguishing earthquake precursors from other strain transients. There is some field evidence of precursory seismic strain for large earthquakes (Bufe and Varnes, 1993) which may be related to our observations. In instances where precursory activity is spatially variable during the interseismic period, as in our experiments, distinguishing precursory activity might be best accomplished with deep arrays of near fault instruments and pattern recognition algorithms such as principle component analysis (Rundle et al., 2000).
DE: 8199 General or miscellaneous
DE: 7223 Seismic hazard assessment and prediction
DE: 7299 General or miscellaneous
DE: 7209 Earthquake dynamics and mechanics
DE: 7215 Earthquake parameters
SC: Nonlinear Geophysics [NG]
MN: 2004 AGU Fall Meeting