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Ilya Zaliapin

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

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Ilya Zaliapin

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

  2. Outline Earthquake clusters: existence, detection, stability 1 1 Clusters in southern California 2 2 • Main types of clusters • Topological cluster characterization Cluster type vs. physical properties of the lithosphere 3 3 Evolution of clustering with relation to large events 4 4

  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. Earthquake cluster analysis 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 time Spatial distance Gutenberg-Richter law Distance from an earthquake j to an earlier earthquake i : Definition: Property: [M. Baiesi and M. Paczuski, PRE, 69, 066106 (2004)] [Zaliapin et al., PRL, 101, 018501(2008)]

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

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

  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 et al., PRL (2008) Zaliapin and Ben-Zion, JGR (2013)

  9. Seismicity as a flow of clusters

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

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

  12. Quality and stability of cluster identification

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

  14. Main types of EQ clusters • 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. Swarm vs. burst like clusters: Topologic representation M5.51 M5.75 Burst-like Swarm-like M5.51 M5.75 Time Time L= 417, tree depth = 9, ave. depth = 3.8 L= 572, tree depth = 44, ave. depth = 30.3

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

  17. Cluster type vs. physical properties of the crust

  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

  22. Evolution of seismic clustering

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

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

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

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

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

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

  29. L L EMC

  30. “San Jacinto Fault” HM L N SH EMC

  31. “San Jacinto Fault” EMC SH Families with size L > 10 L N HM Families with 3 < m < 4

  32. 100 km from Superstition Hills, M6.6 of 1987 SH L N HM EMC Topological depth d > 6, mainshock m< 5

  33. Salton Trough

  34. Salton Trough SH L N HM EMC Average leaf depth > 1, Family size > 5

  35. Baja California

  36. Baja California SH L N HM EMC Average leaf depth > 1, Family size > 5

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

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

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

  40. Thank you!

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