Theoretical studies indicate (e.g., Weertman, 1980; Ben-Zion and Andrews, 1997; Ben-Zion 2001; Ampuero and Ben-Zion 2008; Brietzke et al. 2009) that ruptures on bimaterial faults that separate different lithologies have larger slip-velocity and slip in the direction of particle motion in the compliant solid (referred to as the preferred direction). This can have significant effects on the seismic shaking hazard at major metropolitan areas (e.g., Los Angeles, San Francisco, Istanbul) near large bimaterial faults (e.g., the San Andreas and North Anatolian faults). The asymmetric along-strike rupture behavior on bimaterial faults is expected to produce enhanced dynamic triggering in the preferred propagation direction (Figure 1). Some evidence for asymmetric dynamic triggering on the Northern San Andreas fault has been documented by Rubin and Gillard (2000) and Schorlemmer and Ben-Zion (2008). Here we examined the along-strike symmetry properties of seismicity on the Parkfield section of the San Andreas fault with a clear velocity contrast across the fault, and the Eastern California Shear Zone without an overall lithology contrast. Ilya Zaliapin Department of Mathematics and Statistics University of Nevada Reno Introduction and motivation Clarifying what genuine properties of seismicity patterns are remains an extremely challenging problem because of the inherent complexity of the earthquake process combined with the limited and noisy available data. Purely statistical studies tend to analyze seismicity associated with large spatial domains to increase the amount of data. However, this approach can mix different populations of earthquakes (e.g., those on large plate- bounding faults vs. small intra-plate faults or seismicity on cold vs. hot regions). Here we attempt to increase the information content of the available data by establishing correlations between spatio-temporal patterns of seismicity and geophysical properties of the crust. In particular, we examine the relations between asymmetric dynamic triggering patterns along faults and velocity structure images. Figure 1: Strain Model Contour plot of second invariant of strain rate model reflecting secular deformation constrained by GPS velocities and Quaternary fault slip rates (used faults shown by thin lines). Annual Meeting September 12-16, 2009 Palm Springs, CA Figure 2: Fault zones. Indexes in squares indicate aftershock- dominated zones; they are not analyzed. After Powers (2009). Figure 1. XXXX [Andrews and Ben-Zion, 1997; Ben- Zion, 2001] Figure 4: Observed values of the ratio  and confidence regions according to a truncated Pareto model. Yehuda Ben-Zion Department of Earth Sciences University of Southern California Summary We attempt to establish correlations between spatio-temporal patterns of seismicity and geophysical properties of the crust. In this study, we examine the relations between asymmetric dynamic triggering patterns along faults and velocity structure images. We use the regional southern California (CA) catalog of Lin et al. (2007) and the catalogs of Power and Jordan (2009) for various specific fault-zones in CA. The analyses are based on the earthquake clustering technique of Zaliapin et al. (2008) that employs the Baiesi-Paczuski (2004) distance between earthquakes and allows one to distinguish between the clustered and homogeneous parts of an earthquake catalog. The initial results indicate the presence of asymmetric triggering in early-time close aftershocks along the the San Andreas fault and absence of asymmetry for the Eastern California Shear Zone. Methodology (1) Catalog: We analyze seismicity located along well-defines faults, which makes the interpretation of results particularly straightforward. Specifically, we work with 52 seismic zones defined by Powers (2009), and only analyze the earthquake location along the fault (Figure 2). (2) Clustering: Triggering analysis of seismicity is commonly complicated by the problem of associating events with possible predecessors (mainshocks or parents). We estimate seismic clusters using the method of Zaliapin et al. (2008). The distance  ij between event i and a later event j is measured by the Baiesi-Paczuski metric where t ij and r ij are time and spatial distance between events and m i is the magnitude of the first one. We further define the normalized distance and time between events as The joint 2D distribution of the normalized times and distances is used to detect earthquake clusters (Figure 3). For each cluster that consists of a mainshock (parent) and n aftershocks (descendants) we define the asymmetry index Large positive (negative) values of I A indicate that aftershocks tend to happen with positive (negative) displacement relative to the respective mainshock. Results Figure 4: Asymmetry results for different fault zones (zone index is from Figure 2). Zone 1: No asymmetry detected Zone 51: NW (positive) asymmetryZone 5: SE (negative) asymmetry Panel a: Each point represents a single descendant event; parents are stacked at the origin. Red points – clustered seismicity, which is used for triggering analysis. Panel b: Each point represents a single cluster, formed by some of the red points in panel (a) Panel c: Time-space map of a selected cluster from panel (b) (see black arrows). Each point represents an earthquake; red dot – parent event, blue dots – descendants used to compute the asymmetry index. The Y-axis shows the distance in km along the fault zone shown in Figure 2. Panel a: see details in Zone 1 part Panel b: see details in Zone 1 part Panel a: see details in Zone 1 part Panel b: see details in Zone 1 part 3-sigma error bars Mean asymmetry index averaged over 496 different choices of clusters (red points in panels (a) above) Empirical 10% and 90% quantiles Zone 1: Zone 5: Zone 11: Zone 51: Zone 15: Figure 3: Three examples of asymmetry analysis: positive (zone 51) and negative (zone 5) asymmetry and no asymmetry (zone 1). Zone 5: Negative triggering Zone 51: Positive triggering REFERENCES [1] Ampuero, J.-P. and Y. Ben-Zion, 2008, Cracks, pulses and macroscopic asymmetry of dynamic rupture on a bimaterial interface with velocity-weakening friction, Geophys. J. Int., 173, 674–692, doi: /j X x. [2] Baiesi, M and M. Paczuski, 2004, Scale-free networks of earthquakes and aftershocks. Phys. Rev. E, 69, [3] Ben-Zion, Y., 2001, Dynamic Rupture in Recent Models of Earthquake Faults, J. Mech. Phys. Solids, 49, [4] Ben-Zion, Y. and D. J. Andrews, 1998, Properties and Implications of Dynamic Rupture Along a Material Interface, Bull. Seism. Soc. Am., 88, [5] Brietzke, G.B., Cochard, A. and Igel, H., 2009, Importance of bimaterial interfaces for earthquake dynamics and strong ground motion, Geophys. J. Int., 178, , doi: /j X x. [6] Rubin, A. and D. Gillard, 2000, Aftershock asymmetry/rupture directivity along central San Andreas fault microearthquakes, J. Geophys. Res., 105, 19,095-19,109. [7] Schorlemmer, D. and Y. and Ben-Zion, 2008, Directivity effects of fault velocity contrast on triggered seismicity, Seism. Res. Lett., 79 (2), 295. [8] Weertman, J., 1980, Unstable slippage across a fault that separates elastic media of different elastic constants, J. Geophys. Res., 85, [9] Lin, G., P. Shearer, and E. Hauksson (2007). Applying a three-dimensional velocity model, waveform cross-correlation, and cluster analysis to locate southern California seismicity from 1981 to 2005, J. Geophys. Res. 112, B12309, doi /2007JB [10] Powers, P. M. and T. H. Jordan, Distribution of Seismicity Across Strike-Slip Faults in California, J. Geophys. Res, in review, [11] Power, P. M. SEISMICITY DISTRIBUTION NEAR STRIKE-SLIP FAULTS IN CALIFORNIA, PhD thesis, University of Southern California, 2009 [12] Zaliapin, I., A. Gabrielov, H. Wong, and V. Keilis-Borok, 2008, Clustering analysis of seismicity and aftershock identification, Phys. Rev. Lett., 101, doi: /PhysRevLett This research is supported by SCEC project “Correlation between seismic clustering properties and regional physical conditions”