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Modeling of magnitude distributions by the generalized truncated exponential distribution

机译:通过广义截断指数分布对量级分布进行建模

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摘要

The probability distribution of the magnitude can be modeled by an exponential distribution according to the Gutenberg-Richter relation. Two alternatives are the truncated exponential distribution (TED) and the cutoff exponential distribution (CED). The TED is frequently used in seismic hazard analysis although it has a weak point: when two TEDs with equal parameters except the upper bound magnitude are mixed, then the resulting distribution is not a TED. Inversely, it is also not possible to split a TED of a seismic region into TEDs of subregions with equal parameters except the upper bound magnitude. This weakness is a principal problem as seismic regions are constructed scientific objects and not natural units. We overcome it by the generalization of the abovementioned exponential distributions: the generalized truncated exponential distribution (GTED). Therein, identical exponential distributions are mixed by the probability distribution of the correct cutoff points. This distribution model is flexible in the vicinity of the upper bound magnitude and is equal to the exponential distribution for smaller magnitudes. Additionally, the exponential distributions TED and CED are special cases of the GTED. We discuss the possible ways of estimating its parameters and introduce the normalized spacing for this purpose. Furthermore, we present methods for geographic aggregation and differentiation of the GTED and demonstrate the potential and universality of our simple approach by applying it to empirical data. The considerable improvement by the GTED in contrast to the TED is indicated by a large difference between the corresponding values of the Akaike information criterion.
机译:大小的概率分布可以根据古腾堡-里希特关系,通过指数分布来建模。两种选择是截断指数分布(TED)和截止指数分布(CED)。尽管TED有一个弱点,但却经常用于地震危险性分析:当将两个具有相同参数(除了上限幅度)的TED混合在一起时,结果分布就不是TED。相反,除了上限幅度之外,也不可能将地震区域的TED分成具有相等参数的子区域的TED。这种弱点是一个主要问题,因为地震区域是被构造为科学对象而不是自然单位。我们通过上述指数分布的广义化来克服它:广义截断指数分布(GTED)。其中,相同的指数分布通过正确截止点的概率分布进行混合。此分布模型在上限大小附近是灵活的,并且对于较小的大小等于指数分布。此外,指数分布TED和CED是GTED的特殊情况。我们讨论了估计其参数的可能方法,并为此目的引入了归一化间距。此外,我们介绍了GTED的地理汇总和区分方法,并通过将其应用于经验数据来证明我们简单方法的潜力和普遍性。与TED相比,GTED的显着改进是由Akaike信息标准的相应值之间的巨大差异表示的。

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