Abstract. We test the concept that seismicity prior to a large earthquake can be understood in terms of the statistical physics of a critical phase transition. In this model, the cumulative seismic strain release increases as a power law time to failure before the final event. Furthermore, the region of correlated seismicity predicted by this model is much greater than would be predicted from simple elastodynamic interactions. We present a systematic procedure to test for the accelerating seismicity predicted by the critical point model and to identify the region approaching criticality, based on a comparison between the observed cumulative energy (Benioff strain) release and the power law behavior predicted by theory. This method is used to find the critical region before all earthquakes along the San Andreas system since 1950 with M > 6.5. The statistical significance of our results is assessed by performing the same procedure on a large number of randomly generated synthetic catalogs. The null hypothesis, that the observed acceleration in all these earthquakes could result from spurious patterns generated by our procedure in purely random catalogs, is rejected with 99.5 % confidence. An empirical relation between the logarithm of the critical region radius (R) and the magnitude of the final event (M) is found, such that log R •: 0.5M, suggesting that the largest probable event in a given region scales with the size of the regional fault network.

Paleoseismological data suggest the occurrence of four bursts of seismic moment release in the Los Angeles region during the past 12,000 yr. The historic period appears to be part of an ongoing lull that has persisted for about the past 1000 yr. These periods of rapid seismic displacement in the Los Angeles region have occurred during the lulls between similar bursts of activity observed on the eastern California shear zone in the Mojave Desert, which is now seismically active. A kinematic model in which the faults of the greater San Andreas system suppress activity on faults in the eastern California shear zone, and vice versa, can explain the apparent switching of activity between the two fault networks. Combined with the observation that short-term geodetic and longer-term geologic rates co-vary on major southern California fault systems, this suggests that either (1) a temporal cluster of seismic displacements on upper-crustal faults increases ductile deformation on their downward extensions, or (2) rapid ductile slip in the lower crust beneath faults loads the upper crust, driving a seismic cluster. We suggest that alternating periods of rapid seismic displacement may be the expected mode of seismicity when two fault systems accommodate the same plate-boundary motion, and slip on one system suppresses slip on the other.

The stress field that existed before a large earthquake can be calculated based on the known source parameters of the event. This stress field can be used to define a region that shows greater seismic moment rate changes prior to the event than arbitrarily shaped regions, allowing us to link two previously unrelated subjects: Coulomb stress interactions and accelerating seismicity before large earthquakes. As an example, we have examined all M•_6.5 earthquakes in California since 1950. While we illustrate the model using seismicity in California, the technique is general and can be applied to any tectonically active region. We show that where sufficient knowledge of the regional tectonics exists, this method can be used to augment current techniques for seismic hazard estimation.

Oblique motion along tectonic boundaries is commonly partitioned into slip on faults with different senses of motion. The origin of slip partitioning is important to structural geology, tectonophysics, and earthquake mechanics. Partitioning can be explained by the upward elastoplastic propagation of oblique slip from a fault or shear zone at depth. The strain field ahead of the propagating fault separates into zones of predominantly normal, reverse, and strike-slip faulting. The model successfully predicts the distribution of fault types along parts of the San Andreas and Haiyuan faults.