Observing spatio-temporal clustering and separation using interevent distributions of regional earthquakes

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Abstract

Past studies that attempted to quantify the spatio-temporal organization of seismicity have defined the conditions by which an event and those that follow it can be related in space and/or time. In this work, we use the simplest measures of spatio-temporal separation: the interevent distances R and interevent times T between pairs of successive events. We observe that after a characteristic value R*, the distributions of R begin to follow that of a randomly shuffled sequence, suggesting that events separated by R > R* are more likely to be uncorrelated events generated independent of one another. Interestingly, the conditional T distributions for short-distance (long-distance) events, R ≤ R* (R > R*), peak at correspondingly short (long) T values, signifying the spatio-temporal clustering (separation) of correlated (independent) events. By considering different threshold magnitudes within a range that ensures substantial catalogue completeness, invariant quantities related to the spatial and temporal spacing of correlated events and the rate of generation of independent events emerge naturally. © Author(s) 2014.

Figures

  • Figure 1. Cumulative magnitude distributions and representative R–T plots for the (a)–(b) Philippines, PH; (c)–(d) Japan, JP; and (e)– (f) southern California, SC. The threshold magnitudes considered for analyses are shaded in (a), (c), and (e). These are within the power-law regimes of the magnitude distributions and have a sufficient number of events to ensure substantial completeness. TheR–T scatter plots in (b), (d), and (f), obtained for the highest threshold magnitudes considered for each of the catalogues, reveal a visually discernible separation into two clusters situated at different regimes: for short-R–short-T and long-R–long-T .
  • Figure 2. Interevent distance R histograms (left panels) and probability density (right panels) plots for (a)–(b) PH, (c)–(d) JP, and (e)–(f) SC, for the corresponding magnitude ranges shown in Fig. 1. Symbols are distributions obtained from the original sequences, while connected symbols are from the shuffled sequences. The original and shuffled distributions begin to follow the same trend after characteristic R∗ values, as indicated by the arrows in (a), (c), and (e). In (g), the value of R∗ is shown not to vary significantly within the threshold magnitude ranges considered. The average values of R∗ are shown as broken lines in the right panels: (b) R∗PH = 125± 1 km, (d) R ∗ JP = 164± 7 km, and (f) and R∗SC = 79± 6 km. These R ∗ values are used to separate the “short” and “long” R regimes.
  • Figure 3. Representative interevent time T histograms (left panels) and probability density (right panels) plots superimposed with the conditional distributions of Tin and Tout, for the case of the smallest M considered: (a)–(b) PH, M = 4.5; (c)–(d) JP, M = 2.5; and (e)–(f) SC, M = 2.5. Hollow symbols are total distributions, thin broken lines are the distributions obtained from the shuffling procedure, and connected filled symbols are the conditional distributions. Using R∗ to separate the interevent times resulted in the the decomposition of the histograms into two component histograms with different characteristic times, as observed by Touati et al. (2009) in the ETAS model. The normalized probability densities further show that events separated by short (long) distances are also more likely to be separated by short (long) time intervals. The shuffled sequences show random (Poisson) statistics, with reduced occurrences of very short and very long interevent times. (g) The fraction of Tout, γ , is roughly constant within the range of M considered: γPH = 0.7; γJP = 0.8; and γSC = 0.4.
  • Figure 4. Conditional histograms of Tin (left panels, hollow symbols) and Tout (right panels, filled symbols), with the corresponding probability density plots (inset): (a)–(b) PH, (c)–(d) JP, and (e)–(f) SC. For higher M , events with smaller magnitudes are neglected; this results in the broadening of the tail of Tin histograms (left panels) and the complete shifting of the Tout distributions to longer values (right panels). In (g)–(h), the peak interevent time for all histograms are tracked, showing the almost negligible shift of the peaks of Tin and the continuous increase in the peaks of Tout.

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CITATION STYLE

APA

Batac, R. C., & Kantz, H. (2014). Observing spatio-temporal clustering and separation using interevent distributions of regional earthquakes. Nonlinear Processes in Geophysics, 21(4), 735–744. https://doi.org/10.5194/npg-21-735-2014

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