HR: 1455h
AN: S13E-06    [Abstracts]
TI: Use of dependence probabilities to detect near-fault bias in earthquake triggering
AU: * Powers, P M
EM: pmpowers@usc.edu
AF: University of Southern California, Dept. of Earth Sciences 3651 Trousdale Pkwy. (ZHS-117), Los Angeles, CA 90089,
AU: Jordan, T H
EM: tjordan@usc.edu
AF: University of Southern California, Dept. of Earth Sciences 3651 Trousdale Pkwy. (ZHS-117), Los Angeles, CA 90089,
AB: Models of triggered seismicty, such as ETAS, do a good job of predicting observed earthquake patterns in time and space, excepting the largest events. However, these models are typically spatially isotropic and so far do not incorporate the fault structure that controls earthquake distribution. We have demonstrated that the average rate of small earthquakes decays with distance from strike-slip faults in California according to a power law of the form R~(x²+d²)^{- γ/2}, where x is distance from a fault, γ is the decay rate of seismicity, and d is a near-fault inner scale. To determine if aftershock statistics reflect the observed scaling, we decluster our data set using traditional methods (e.g. Reasenberg[1984]) and find that γ is higher for triggered events, indicating a near-fault bias. We also select aftershocks of small to moderate earthquakes, using short time and distance windows, and observe a bias of events towards and along strike-slip faults. These results suggest a more appropriate ETAS spatial kernel would have the form φ~x₀^<em> -γr^<em> -β where x₀ is distance from a fault, γ is the fault-seismicity parameter, r is distance from a mainshock, and β controls the radially symmetric decay of aftershocks away from a mainshock. To distinguish such a model from the null hypothesis that there is no fault bias in earthquake triggering, we require a better, probability based (non-binary) means of separating triggered from independent events. Because our data is derived from small regions around strike-slip fault segments, we simplify the process by ignoring events whose triggering intensity function gradient is sufficiently low across the region of interest (i.e. all events of interest are affected equally). Furthermore, because only the spatial component of the intensity function controls the gradient, the intensity function can be reduced to I=Δr β/r where Δr is the distance across the region of interest, and r and β are defined as above. Results will be presented for different sets of California faults, illustrating interesting regional variations.
DE: 7223 Earthquake interaction, forecasting, and prediction (1217, 1242)
SC: Seismology [S]
MN: 2007 Fall Meeting