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Earthquakes: Recurrence and Interoccurrence Times

机译:地震:复发和同时发生的时间

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The purpose of this paper is to discuss the statistical distributions of recurrence times of earthquakes. Recurrence times are the time intervals between successive earthquakes at a specified location on a specified fault. Although a number of statistical distributions have been proposed for recurrence times, we argue in favor of the Weibull distribution. The Weibull distribution is the only distribution that has a scale-invariant hazard function. We consider three sets of characteristic earthquakes on the San Andreas fault: (1) The Parkfield earthquakes, (2) the sequence of earthquakes identified by paleoseismic studies at the Wrightwood site, and (3) an example of a sequence of micro-repeating earthquakes at a site near San Juan Bautista. In each case we make a comparison with the applicable Weibull distribution. The number of earthquakes in each of these sequences is too small to make definitive conclusions. To overcome this difficulty we consider a sequence of earthquakes obtained from a one million year “Virtual California” simulation of San Andreas earthquakes. Very good agreement with a Weibull distribution is found. We also obtain recurrence statistics for two other model studies. The first is a modified forest-fire model and the second is a slider-block model. In both cases good agreements with Weibull distributions are obtained. Our conclusion is that the Weibull distribution is the preferred distribution for estimating the risk of future earthquakes on the San Andreas fault and elsewhere.
机译:本文的目的是讨论地震复发时间的统计分布。重复时间是指定断层上指定位置的连续地震之间的时间间隔。尽管已为递归时间建议了许多统计分布,但我们主张采用Weibull分布。威布尔分布是唯一具有尺度不变危害函数的分布。我们考虑了圣安德烈亚斯断层上的三组特征性地震:(1)帕克菲尔德地震,(2)在赖特伍德(Wrightwood)地点通过古地震研究确定的地震序列,以及(3)一个微重复地震序列的例子。在圣胡安包蒂斯塔(San Juan Bautista)附近的地点。在每种情况下,我们都会与适用的Weibull分布进行比较。这些序列中每个序列的地震次数太少,无法得出明确的结论。为了克服这一困难,我们考虑了从一百万年的圣安德烈亚斯地震的“虚拟加利福尼亚”模拟中获得的一系列地震。发现与威布尔分布非常吻合。我们还获得了另外两个模型研究的复发统计数据。第一个是修改后的森林火灾模型,第二个是滑块模型。在这两种情况下,都获得了与威布尔分布的良好协议。我们的结论是,威布尔分布是估计圣安德烈亚斯断层和其他地区未来地震风险的首选分布。

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