1. Ilya Zaliapin Department of Mathematics and Statistics University of Nevada, Reno SAMSI workshop Dynamics of Seismicity Thursday, October 10, 2013 Yehuda Ben-Zion Department of Earth Sciences University of Southern California Spatio-temporal evolution of seismic clusters in southern and central California

2. Earthquake clusters: existence, detection, stability Clusters in southern California 1 2 3 1 2 3 Outline o Main types of clusters o Topological cluster characterization Evolution of clustering with relation to large events 44 Cluster type vs. physical properties of the lithosphere

3. Data Southern California catalog: Hauksson, Yang, Shearer (2012) available from SCEC data center; 111,981 earthquakes with m 2 Heat flow data from www.smu.edu/geothermal

4. Baiesi and Paczuski, PRE, 69, 066106 (2004) Zaliapin et al., PRL, 101, 018501 (2008) Zaliapin and Ben-Zion, GJI, 185, 1288–1304 (2011) Zaliapin and Ben-Zion, JGR, 118, 2847-2864 (2013) Zaliapin and Ben-Zion, JGR, 118, 2865-2877 (2013)

5. (Fractal) dimension of epicenters Intercurrence timeSpatial distanceGutenberg-Richter law [M. Baiesi and M. Paczuski, PRE, 69, 066106 (2004)] [Zaliapin et al., PRL, 101, 018501 (2008)] Distance from an earthquake j to an earlier earthquake i : Definition: Property:

6. Separation of clustered and background parts in southern California Earthquake j Parent (nearest neighbor) i Zaliapin and Ben-Zion, JGR (2012) Zaliapin et al., PRL (2008)

7. Background and clustered parts in models Zaliapin and Ben-Zion, JGR (2013) Zaliapin et al., PRL (2008) Homogeneous Poisson processETAS model

8. Separation of clustered and background parts in southern California Background = weak links (as in stationary, inhomogeneous Poisson process) Clustered part = strong links (events are much closer to each other than in the background part) Zaliapin and Ben-Zion, JGR (2013) Zaliapin et al., PRL (2008)

10. weak link strong link Cluster #3Cluster #2 Cluster #1 Identification of clusters: data driven Time

11. Foreshocks Aftershocks Mainshock Identification of event types: problem driven Time Single

13. ETAS declustering: Example 29,671 events 9,536 mainshocks

14. Burst-like clusters Represent brittle fracture. Large b-value (b=1), small number of events, small proportion of foreshocks, short duration, small area, isotropic spatial distribution. Tend to occur in regions with low heat flow, non-enhanced fluid content, relatively large depth => increased effective viscosity. Swarm-like clusters Represent brittle-ductile fracture. Small b-value (b=0.6), large number of events, large proportion of foreshocks, long duration, large area, anisotropic channel-like spatial pattern. Tend to occur in regions with high heat flow, increased fluid content, relatively shallow depth => decreased effective viscosity. Singles Highly numerous in all regions; some but not all are related to catalog resolution. Clusters of the largest events Most prominent clusters; object of the standard cluster studies. Not representative of the majority of clusters (mixture of types 1-2).

15. M5.75 M5.51 M5.75 L= 417, tree depth = 9, ave. depth = 3.8L= 572, tree depth = 44, ave. depth = 30.3 Swarm vs. burst like clusters: Topologic representation Burst-likeSwarm-like Time

16. Average leaf depth (number of generations from a leaf to the root): Bimodal structure HYS (2012), m M 2 Large topological depth: Swarm-like clusters Small topological depth: Burst-like clusters ETAS model

18. Heat flow in southern California http://www.smu.edu/geothermal

19. Preferred spatial location of burst/swarm like clusters 195 clusters with m 4, N 10; spatial average within 50 km

20. Moment of foreshocks relative to that of mainshock 195 clusters with m 4, N 10; spatial average within 50 km

21. Family size 112 Δ- clusters with m 4, N 10; spatial average within 50 km

23. X-zone D-zone Time Space N-zone Statistical analysis of premonitory patterns: zero-level approach

24. D = 2 years, X = 1 year, R = 200 km, M=6.5 mainshocks with m>3 are examined All mainshocks Topological depth (average leaf depth)

25. Δ = X = 3 years, R = 100km m > 3, N > 20 ANOVA p =7x10 -7 : Significant difference Large families, N > 20 Topological depth (average leaf depth)

26. Δ = X = 2 years, R = 100km m > 3 All mainshocks Proportion of families

27. Δ = X = 2 years, R = 100km m > 3, N >1 Families (N > 1) Proportion of large families (N>=5)

28. Large earthquakes in California, M6.5 2) Landers, M7.3, 1992 4) Hector Mine, M7.1, 1999 1) Superstition Hills, M6.6, 1987 5) El Mayor Cucapah, M7.2, 2010 3) Northridge, M6.7, 1994

29. L EMC L

30. San Jacinto Fault SH EMC L N HM

31. Families with 3 < m < 4 Families with size L > 10 San Jacinto Fault SH EMC LNHM

32. Topological depth d > 6, mainshock m< 5 100 km from Superstition Hills, M6.6 of 1987 SHEMCLNHM

33. Salton Trough

34. Average leaf depth > 1, Family size > 5 SHEMCLNHM Salton Trough

35. Baja California

36. Average leaf depth > 1, Family size > 5 Baja California SHEMCLNHM

37. Average leaf depth > 1, Family size > 5 R < 5 km R < 20 km R < 100 km R < 300 km El Mayor Cucapah, M7.2

38. Topological depth d > 5 20 km from Landers, M7.3 of 1992 In this region: 613 mainshocks; 139 families; 11 mainshocks/10 families with m>3.5 Remote aftershock of Superstition Hill, M6.6 of 1987 Landers, M7.3 of 1992 Remote foreshock to Hector Mine, M7.1 of 1999 SHEMCLNHM

39. Seismic clusters in southern California 1 2 3 1 2 3 Summary o Four types of clusters: Burst-like clusters Swarm-like clusters Singles Largest regional clusters o Topological cluster characterization o Swarm-like clusters decreased effective viscosity o Burst-like clusters increased effective viscosity Spatial variability: Relation to physical properties of the crust Temporal variability: Relation to large events