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Bayesian Approach for Estimating the Distribution of Magnitudes, Interevent Times and Distances of Earthquake Sequences
Cybernetics and Systems ( IF 1.7 ) Pub Date : 2020-09-17 , DOI: 10.1080/01969722.2020.1814582
Jorge Morales 1 , Wen Yu 1 , Luciano Telesca 2
Affiliation  

Abstract A Bayesian approach has been applied to estimate the distribution of magnitudes, interevent distances and times of earthquakes occurred in 2017 in central Italy by using a small amount of random samples drawn from the distribution of the same seismic parameters for the earthquakes occurred in 2014-2016. We applied the method to the whole and aftershock-depleted seismicity by using the exponential and the normal model to fit the distributions of the seismic parameters. Our findings indicate that the exponential model fits the distributions of the seismic parameters much better than the normal model. Furthermore, in the whole seismicity case, the method requires at least 2100 to 2300 random samples to estimate the distributions of the seismic parameters of earthquakes occurred in 2017 with an estimation error less than 0.01; while in the aftershock-depleted case, a minimum number of random samples varying between 360 and 1470 occurred in 2014-2017 is required to estimate the distributions of the seismic parameters of earthquakes occurred in 2017 with an estimation error less than 0.01.

中文翻译:

估计地震序列震级、发生时间和距离分布的贝叶斯方法

摘要 利用贝叶斯方法,利用从2014-2017年地震相同地震参数分布中抽取的少量随机样本,估计了2017年意大利中部地震发生的震级、震级、间隔距离和发生次数的分布。 2016 年。利用指数模型和正态模型拟合地震参数的分布,将该方法应用于整体和余震耗尽的地震活动。我们的研究结果表明,指数模型比正常模型更适合地震参数的分布。此外,在整个地震活动情况下,该方法至少需要2100到2300个随机样本来估计2017年发生的地震的地震参数分布,估计误差小于0.01;
更新日期:2020-09-17
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