Stochastic declustering of space-time earthquake occurrences
Stochastic declustering of space-time earthquake occurrences
复制标题
DOI：
10.1198/016214502760046925
复制
发表时间：
2002-06-01
影响因子：
3.7
通讯作者：
Vere-Jones, D
中科院分区：
文献类型：
--
作者：
Zhuang, J;Ogata, Y;Vere-Jones, D
本文与地震目录中背景地震发生的空间强度功能的客观估计有关，该目录包括时空中的许多聚类事件，还具有用于从原始目录中产生被解释的目录的算法。时空分支过程模型（ETAS模型）用于描述每个事件如何生成后代事件。结果表明，如果可以估计总空间地震强度和分支结构，则可以评估背景强度函数。实际上，整个时空过程分为两个子过程，背景事件和聚类事件。提出的算法结合了使用时空ETAS模型的聚类结构的参数最大似然估计，以及对背景地震性的非参数估计，我们称之为可变的加权核心估计值。为了证明目前的方法，我们估计了新西兰中部和日本中部和西部地区的背景地震活动，然后使用这些估计值生成背景事件的目录。
This article is concerned with objective estimation of the spatial intensity function of the background earthquake occurrences from an earthquake catalog that includes numerous clustered events in space and time, and also with an algorithm for producing declustered catalogs from the original catalog. A space-time branching process model (the ETAS model) is used for describing how each event generates offspring events. It is shown that the background intensity function can be evaluated if the total spatial seismicity intensity and the branching structure can be estimated. In fact, the whole space-time process is split into two subprocesses, the background events and the clustered events. The proposed algorithm combines a parametric maximum likelihood estimate for the clustering structures using the space-time ETAS model and a nonparametric estimate of the background seismicity that we call a variable weighted kernel estimate. To demonstrate the present methods, we estimate the background seismic activities in the central region of New Zealand and in the central and western regions of Japan, then use these estimates to produce catalogs of background events.