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Distinguishing Artifacts of Earthquake Catalogs From Genuine Seismicity Patterns Ilya Zaliapin Department of Mathematics and Statistics University of Nevada,

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Presentation on theme: "Distinguishing Artifacts of Earthquake Catalogs From Genuine Seismicity Patterns Ilya Zaliapin Department of Mathematics and Statistics University of Nevada,"— Presentation transcript:

1 Distinguishing Artifacts of Earthquake Catalogs From Genuine Seismicity Patterns 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 Goal: To review location and registration errors in earthquake catalogs of southern California and examine effects of these errors on statistical analyses of seismicity. Data: Catalogs of Hauksson et al. [2012, 2013], Richards-Dinger and Shearer [2000], and ANSS. Method: The offspring/parent identification refer to our recent methodology for detection and classification of seismic clusters [Zaliapin and Ben-Zion, 2013a]. The novelty of this approach 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. In this work we show that The location errors are significantly reduced in the central Southern California due to better quality of seismic network (see also Hauksson et al., 2012) – Panel 2 The location errors significantly affect the estimated distance-to-parent and may distort the analyzes of offspring/aftershock/foreshock spatial distribution and decay – Panel 3 The location errors may lead to incorrect parent-offspring assignment and result in underestimation of earthquake triggering productivity, as well as overestimation of background rates – Panel 4 The combined registration/location errors affect the frequency-magnitude offspring distribution for parents of all magnitudes (not only large ones). This may affect the analyses of spatio-temporal b-value variability – Panel 5. The reported artifacts are robustly observed in alternate catalogs of southern California, and hence are not due to a particular relocation method – Panel 6 The study is a first step toward a comprehensive analysis of catalog errors and related artifacts of statistical analyses of seismic clustering. 1. Data and offspring identification approach The main data set is the relocated catalog of Hauksson et al. [2013]. We analyze 117,076 events with magnitude m ≥ 2; Cluster identification is done according to Zaliapin et al. [2008], Zaliapin and Ben-Zion [2013a]. Specifically, we identify the single parent of each event according to the nearest-neighbor earthquake distance (in time-space- magnitude domain) introduced by 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 5. Artifact 3: Short-term incompleteness The research is supported by the SCEC, project 15120; the United States Geological Survey Grant G09AP00019; and the National Science Foundation grant DMS-0934871. 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) 7. References and acknowledgement The studies that employ this cluster analysis include: Gu et al. [2013]; Mignan [2012]; Reverso et al. [2015]; 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 (2013) Triggering cascades and statistical properties of aftershocks, J. Geophys. Res., 118(8), 4278-4295. 3.Hauksson, E., W. Yang, and P. M. Shearer (2013), Waveform Relocated Earthquake Catalog for Southern California, 1981-2013; available from http://www.data.scec.org/research-tools/downloads.html 4.Hauksson, E. and W. Yang, and P.M. Shearer, (2012) Waveform Relocated Earthquake Catalog for Southern California (1981 to 2011). Bull. Seismol. Soc. Am., 102(5), 2239-2244. 5.Mignan, A. (2012) Functional Shape of the Earthquake Frequency-Magnitude Distribution and Completeness Magnitude, J. Geophys. Res., 117, B009347, doi: 10.1029/2012JB009347 6.Richards-Dinger, K. B. and Shearer, P. M. (2000). Earthquake locations in southern California obtained using source-specific station terms. J. Geophys. Res., 105(B5), 10939-10960. 7.Reverso, T., Marsan, D., & Helmstetter, A. (2015). Detection and characterization of transient forcing episodes affecting earthquake activity in the Aleutian Arc system. Earth and Planetary Science Letters, 412, 25-34. 8.Zaliapin, I., A. Gabrielov, H. Wong, and V. Keilis-Borok (2008). Clustering analysis of seismicity and aftershock identification, Phys. Rev. Lett., 101. 9.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. 10.Zaliapin, I. and Y. Ben-Zion (2013a) Earthquake clusters in southern California, I: Identification and stability. J. Geophys. Res., 118, 2847-2864. 11.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. 4. Artifact 2: Underestimated clustering and overestimated background rates Proportion of events with close parent (Panel E) significantly decreases with absolute error. Average number of offspring per event decreases with relative location error (Panel F). Each event is the catalog is assigned absolute and relative (w.r.t. the other events in the similar event cluster) error [Hauksson et al., 2012]. Here we only consider horizontal errors; the analyses and conclusions for vertical errors are similar (not shown). The joint distribution of absolute and relative errors is shown below – the error are only weakly dependent. It is well-known that a number of small-magnitude events is not registered after a large one. This leads to apparent changes in the b-value illustrated in Panel A. The time-dependence of this short-term incompleteness is shown in Panel B. Interestingly, the effect of incompleteness is also seen for small magnitude parents (not shown). The incompleteness may explain apparent b-value changes for background vs. clustered events reported by Gu et al., [2013]. B Offspring of parents with magnitude 4 < m < 6 Poster 101, Exhibit Hall A 3. Artifact 1: Increased distance-to-parent The absolute and relative errors significantly affect the estimated distance- to-parent. In particular, the rescaled distance to parent R increases more than two orders of magnitude when the absolute error increases from 100m to over 1 km, or when the relative error increases from 1m to 100m. This effect should be taken into account when studying spatial distribution of offspring/aftershock/foreshocks. For example, the rates of spatial decay are expected to decrease with increasing location error. (We consider here only horizontal absolute and relative errors. The results for vertical errors are similar and are not reported in this poster.) Both absolute and relative errors depend on (i) no. of P and S picks used to locate event, (ii) no. of “similar events”, and (iii) no. of differential times. The values of each of the above three characteristics significantly increase in the central Southern California, which results in much lower location errors. No. P & S picksAbsolute ErrorRelative Error 2. Location errors: Spatial controls Proportion of singles – events with no close parent/offspring (solid blue) and internal cluster events – events with close parent and offspring (dashed green) as a function of absolute (Panel A) and relative (Panel B) horizontal error in entire southern California, as well as the same proportion vs. relative errors in individual regions: Salton Sea (Panel C) and San Jacinto (Panel D). The proportion of singles increases with location error, independently of average regional level of singles. The proportion of internal cluster events decreases with location error, independently of average regional level of clustering. Our results suggests that location errors contaminate cluster identification, leading to significant underestimation of the number of clustered events (and offspring productivity) and overestimation of background activity. A 6. Alternative catalogs of southern California The results reported in Panels 2 – 4 are robustly reproduced in four alternative catalogs: Hauksson et al. [2013], Richards-Dinger and Shearer [2000], ANSS-1 (1961-1981), and ANSS-2 (1981-2013). The four examined catalogs can be ordered (Panel A) according to the overall location quality, reflected in the distance-to-parent distribution. Some general cluster statistics (e.g., proportion of singles) remains the same in alternate catalogs (Panel B), despite differences in parent- offspring assignments. The ANSS-2 present an exception due to its very low location quality.


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