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  • Seismological Society of America (SSA)  (3)
  • 2010-2014  (3)
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  • 2010-2014  (3)
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  • 1
    Publication Date: 2011-10-01
    Description: Branching point process models such as the epidemic-type aftershock sequence (ETAS) models introduced by Ogata (1988, 1998) are often used in the description, characterization, simulation, and declustering of modern earthquake catalogs. The present work investigates how the parameters in these models vary across different tectonic zones. After considering divisions of the surface of the Earth into several zones based on the plate-boundary model of Bird (2003), ETAS models are fit to the occurrence times and locations of shallow earthquakes within each zone. Computationally, the expectation-maximization (EM) type algorithm of Veen and Schoenberg (2008) is employed for the purpose of model fitting. The fits and variations in parameter estimates for distinct zones are compared, and seismological implications are discussed. In particular, we find that estimated background seismicity rates range by a factor of nearly 500 for interplate and trench events, respectively; the estimated productivity parameter, governing the relationship between the magnitude of an earthquake and its expected number of direct aftershocks, ranges by a factor of more than five from events in the slow-moving zone to events in active continental areas, suggesting a much higher rate of swarming in the ridges than in the trenches and active continental zones. Despite the pronounced differences between the seismicity patterns and parameter estimates in the different zones, the ETAS model with few parameters and with the same functional form seems to fit reasonably well to the seismicity in each zone.
    Print ISSN: 0037-1106
    Electronic ISSN: 1943-3573
    Topics: Geosciences , Physics
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  • 2
    Publication Date: 2011-06-01
    Description: We constructed 5- and 10-yr smoothed-seismicity forecasts of moderate-to-large California earthquakes, and we examined the importance of several assumptions and choices. To do this, we divided the available catalog into learning and testing periods and optimized parameters to best predict earthquakes in the testing period. Fourteen different 5-yr testing periods were considered, in which the number of earthquakes varies from 18 to 63. We then compared the likelihood gain per target earthquake for the various choices. In this study, we assumed that the spatial, temporal, and magnitude distributions were independent of one another, so that the joint probability distribution could be factored into those three components. We compared several disjoint test periods of the same length to determine the variability of the likelihood gain. The variability is large enough to mask the effects of some modeling choices. Stochastic declustering of the learning catalog produced a significantly better forecast, and representing larger earthquakes by their rupture surfaces provided a slightly better result, all other choices being equal. Inclusion of historical earthquakes and the use of an anisotropic smoothing kernel based on focal mechanisms failed to improve the forecast consistently. We chose a lower threshold magnitude of 4.7 for our learning catalog so that our results could be compared in the future to other forecasts relying on shorter catalogs with a smaller magnitude threshold.
    Print ISSN: 0037-1106
    Electronic ISSN: 1943-3573
    Topics: Geosciences , Physics
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  • 3
    Publication Date: 2011-08-01
    Description: We present two models for estimating the probabilities of future earthquakes in California, to be tested in the Collaboratory for the Study of Earthquake Predictability (CSEP). The first is a time-independent model of adaptively smoothed seismicity that we modified from Helmstetter et al. (2007). The model provides five-year forecasts for earthquakes with magnitudes M[≥]4.95. We show that large earthquakes tend to occur near the locations of small M[≥]2 events, so that a high-resolution estimate of the spatial distribution of future large quakes is obtained from the locations of the numerous small events. We further assume a universal Gutenberg-Richter magnitude distribution. In retrospective tests, we show that a Poisson distribution does not fit the observed rate variability, in contrast to assumptions in current earthquake predictability experiments. We therefore issued forecasts using a better-fitting negative binomial distribution for the number of events. The second model is a time-dependent epidemic-type aftershock sequence (ETAS) model that we modified from Helmstetter et al. (2006) and that provides next-day forecasts for M[≥]3.95. In this model, the forecasted rate is the sum of a background rate (proportional to the time-independent model rate) and of the expected rate of triggered events due to all prior earthquakes. Each earthquake triggers events with a rate that increases exponentially with its magnitude and decays in time according to the Omori-Utsu law. An isotropic kernel models the spatial density of aftershocks for small (M[≤]5.5) events, while for larger quakes, we smooth early aftershocks to forecast later events. We estimate parameter values by optimizing retrospective forecasts and find that the short-term model realizes a probability gain of about 6.0 per earthquake over the time-independent model.
    Print ISSN: 0037-1106
    Electronic ISSN: 1943-3573
    Topics: Geosciences , Physics
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