1. Spatio-temporal evolution of seismic clusters in southern and central California Ilya Zaliapin Department of Mathematics and Statistics University of Nevada, Reno zal@unr.edu http://www.unr.edu/~zal Yehuda Ben-Zion Department of Earth Sciences University of South California benzion@usc.edu http://earth.usc.edu/~ybz/ Summary We examine evolutionary patterns of seismic clusters. The analyses are based on our recent methodology for detection and classification of seismic clusters and new high-quality relocated catalogs of California. The novelty of this study is in systematic uniform analysis of thousands of robustly detected seismic clusters of small-to-medium magnitude events, as opposed to the handful of largest clusters analyzed in most studies. Our previous research [Zaliapin and Ben-Zion, 2013a,b] established the existence of three basic types of earthquake clusters (burst-like, swarm-like, and singles) of small-to-medium magnitude, and demonstrated that the cluster type is closely related to the heat flow and other properties governing the effective viscosity of a region. We also have shown that the observed swarm type clusters are not reproduced well by the ETAS model. This project focuses on dynamic patterns. The main challenge is to untangle and classify the multitude of phenomena that cause the observed changes of clustering. This includes (i) true non-stationarities of cluster properties reflecting, e.g., increasing stress on a fault, (ii) effects due to seasonal and other period loadings, (iii) artificial patterns caused by catalog incompleteness (which can, e.g., be modulated by seasonal variations of noise), (iv) various triggering effects by transient loadings, (v) artifacts of cluster identification methods, etc. We report here on the following: o Significant non-stationarities in the number of mainshocks that can hardly be due entirely to instrumental problems, effects of large events, or cluster method deficiencies (Panel 2) o Significant increase of clustering (measured by the proportion of non-single mainshocks) on selected faults in the temporal vicinity of the largest events: Landers M7.3 and El Mayor-Cucapah M7.2 (Panel 3) o Seasonal fluctuations of small-magnitude mainshock flow most probably coupled to seismic noise (Panel 4) o Prominent patterns of small-magnitude mainshocks most probably caused by instrumental issues (Panel 4) o Increase of clustering (measured by the number of families and number of aftershocks) of small-magnitude events in Parkfield zone prior to the M6 event of 2004. The results point to some genuine evolutionary patterns of seismicity with possible relation to the large events. The reported findings will be further explored toward developing formal forecast techniques 1. Data and cluster identification We use the waveform relocated catalog of Hauksson et al. [2012] and analyze 111,981 events with magnitude m ≥ 2; see the epicenter map, time-magnitude plot below. (See panel 5 for other catalogs and small-magnitude analysis). Cluster identification is done according to Zaliapin et al. [2008], Zaliapin and Ben-Zion [2013a]. The method is based on the earthquake nearest-neighbor distance η in time-space-magnitude domain [Baiesi and Paczuski, 2004]. The 2D distribution of the time (T) and space (R) components of the nearest-neighbor distance in the observed catalogs is prominently bi-modal (see figure below), with upper mode corresponding to background seismicity and lower mode to the clustered seismicity [Zaliapin et al., 2008, Zaliapin and Ben- Zion, 2011, 2013a]. This bimodality is used to separate the analyzed catalog into sequence of individual clusters (see definitions below). The bimodality of the earthquake nearest-neighbor distances allows one to decompose a seismic catalog into individual clusters (families) as shown below. Large distance Short distance Cluster #3 Cluster #2 Cluster #1 Foreshocks Aftershocks Mainshock Time We identify, within each family, its mainshock (first event with the largest magnitude), aftershocks and foreshocks, as illustrated above. This is a problem- specific definition, and it may vary for other applications. 4. Time variability related to instrumental artifacts (magnitudes less than 1.5) SCEC Annual Meeting September 8-11, 2013 Palm Springs, CA Poster 075 The research is supported by the SCEC, project 13036; the United States Geological Survey Grant G09AP00019; and the National Science Foundation grant DMS-0934871. 3. Evolution of clustering with possible relation to the largest earthquakes 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) 6. References and acknowledgement The studies that employ this cluster analysis include: Gu et al. [2012]; Mignan [2012]; Zaliapin and Ben-Zion [2011, 2013a,b]. 1.Baiesi, M and M. Paczuski (2004) Scale-free networks of earthquakes and aftershocks. Phys. Rev. E, 69, 066106. 2.Gu, C., M. Baiesi and J. Davidsen (2012) Triggering cascades and statistical properties of aftershocks, In review. 3.Hauksson, E. and W. Yang, and P.M. Shearer, (2012) Waveform Relocated Earthquake Catalog for Southern California (1981 to 2011). Bull. Seismol. Soc. Am., in press. 4.Mignan, A. (2012) Functional Shape of the Earthquake Frequency-Magnitude Distribution and Completeness Magnitude, J. Geophys. Res., 117, B009347, doi: 10.1029/2012JB009347 5.Thurber, C.H., Zhang, H., Waldhauser, F., Hardebeck, J., Michael, A. & Eberhart-Phillips,D. (2006) Three-dimensional compressionalwavespeed model, earthquake relocations, and focal mechanisms for the Parkfield, California, region, Bull. seism. Soc. Am., 96(4B), S38–S49. 6.Waldhauser, F. and D.P. Schaff (2008), Large-scale relocation of two decades of Northern California seismicity using cross-correlation and double-difference methods, J. Geophys. Res., 113, B08311, doi:10.1029/2007JB005479. 7.Zaliapin, I., A. Gabrielov, H. Wong, and V. Keilis-Borok (2008). Clustering analysis of seismicity and aftershock identification, Phys. Rev. Lett., 101. 8.Zaliapin, I. and Y. Ben-Zion (2011). Asymmetric distribution of early aftershocks on large faults in California, Geophys. J. Intl., 185, 1288- 1304, doi: 10.1111/j.1365-246X.2011.04995.x. 9.Zaliapin, I. and Y. Ben-Zion (2013a) Earthquake clusters in southern California, I: Identification and stability. J. Geophys. Res., 118, 2847- 2864. 10.Zaliapin, I. and Y. Ben-Zion (2013b) Earthquake clusters in southern California, II: Classification and relation to physical properties of the crust. J. Geophys. Res., 118, 2865-2877. Proportion of non-single clusters (families) in a moving window of 1 year with a step of 1 month in different regions High proportions indicate increased level of clustering – tendency of events to happen in families rather than individually 5. Small magnitude patterns (Parkfield) 99% 95% 50% Bootstrap quantiles 5% 1% 2. Non-stationarity of earthquake flow The 3 left panels show the number of mainshocks in a moving window of 1 year with a step of 1 month. The confidence intervals correspond to a stationary flow obtained by drawing random stationary times of events and keeping their original magnitudes and locations. The empirical quantiles are computed with 10,000 realizations of a random flow. The temporal fluctuations of the mainshock flow have high statistical significance (cannot be explained by a random rate fluctuations) The pattern of M>4 mainshocks suggests seasonal modulations. This possibility is to be further explored. Mainshocks m ≥ 2 Mainshocks m ≥ 3 Mainshocks m ≥ 4 Superstition Hills, M6.6 Landers, M7.3 Northridge, M6.7 Hector Mine, M7.1 El Mayor-Cucapah, M7.2 99% 95% 50% Bootstrap quantiles using 10,000 simulations 5% 1% The fluctuations can be hardly due entirely to catalog incompleteness (see the analysis on San Jacinto below) The fluctuations do not seem to be caused by the largest earthquakes in the region (red vertical lines) Time-latitude sequence and frequency-magnitude plot on San Jacinto fault One clearly sees a burst of activity during 1984-1990 that does not seem to be related to incompleteness Mainshocks m≥2 The confidence intervals correspond to a synthetic flow obtained by reshuffling the family sizes and keeping their original times. The empirical quantiles are computed with 1000 realizations of a random flow In all examined regions, the clustering is significantly high several years prior to Landers (M7.3, 1992) In Mojave (top panel) the clustering dropped significantly after the Landers earthquake of 1992, M7.3 -- a regime change? In Baja California (bottom panel), another significant increase is observed prior to the El Mayor-Cucapah event (M7.2, 2010) Mainshocks with m ≤ 1.5 on San Jacinto fault Red dots correspond to M5 events along San Jacinto (including the Superstition Hills, 1987, M6.6) The abrupt gaps (checkerboard pattern) seems to be caused by instrumental/registration errors and incompleteness Deviations of the number of mainshocks with m ≤ 1.5 in a 3-month moving window from that in a 1-year window, normalized by the 1-year window value (so 0.5 means 50% change from the annual number, etc) The periodic oscillations with period of 1 year are most probably related to event detection problems caused by annually fluctuating seismic noise Number of families in a moving window of 1 year with a step of 1 month A significant increase is observed prior to Parkfield M6 of 2004 Similar results are seen for the number of mainshocks and number of singles (not shown) Confidence limits are explained in panel 3 Number of aftershocks from mainshocks with magnitude less than 3 in a moving window of 1 year with a step of 1 month The only significant increase is seen prior to Parkfield M6 of 2004 A similar result is seen for all aftershocks (not shown) Confidence limits are explained in panel 4 The result also suggests the existence of regular seasonal modulations. This possibility is to be further explored. Patterns similar to those reported in panels 2 and 3 are seen in small magnitudes as well. This panel shows results for Parkfield zone using the catalog of Thurber et al. (2006) with m ≥ 0 (GR is linear down to m=1). The same results are obtained using the catalog of Waldhauser and Shaff (2008) with m ≥ 0 (GR is linear down to m=1). These results also can be reproduced using the HYS (2012) catalog; although this catalog has a big gap in M2 events during 1997-2004 not corroborated by Thurber and Waldhauser/Shaff catalogs. Power spectrum