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arXiv physics/0408018physics.geo-phcond-mat.stat-mech
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地震和余震的复杂网络

Complex networks of earthquakes and aftershocks

Marco Baiesi, Maya Paczuski

摘要:

我们调用一个度量来量化任何两次地震之间的相关性。这提供了一个简单和直接的替代使用时空窗口来检测余震序列,并消除了需要区分主震从余震。有向地震网络是通过在强烈相关的事件对之间放置一个从过去指向未来的链接来构建的。每个链接都有一个权重,给出相关性的相对强度,使得任何节点的传入链接之和对于余震等于1,或者如果事件没有相关的前驱,则为零。设置相关阈值以在不丢失重要信息的情况下大幅减小数据集的大小。事件可以是许多先前事件的余震,也可以产生许多余震。传入和传出链路的数量的概率分布都是无标度的,并且网络是高度聚集的。大森定律适用于余震率,直到去相关时间与初始地震的震级$m$成比例,即$t_{\rm cutoff} \sim 10^{βm}$与$β\simeq 3/4$。另一个标度定律将地震和余震之间的距离与初始地震的震级联系起来。我们的结果与有限余震区的假设是不一致的。我们还发现了地震活动主要由小地震触发的证据。我们的方法,使用复杂网络的现代理论的概念,加上一个度量来估计相关性,开辟了新的研究途径,以及新的工具来了解地震活动性。

英文摘要:

We invoke a metric to quantify the correlation between any two earthquakes. This provides a simple and straightforward alternative to using space-time windows to detect aftershock sequences and obviates the need to distinguish main shocks from aftershocks. Directed networks of earthquakes are constructed by placing a link, directed from the past to the future, between pairs of events that are strongly correlated. Each link has a weight giving the relative strength of correlation such that the sum over the incoming links to any node equals unity for aftershocks, or zero if the event had no correlated predecessors. A correlation threshold is set to drastically reduce the size of the data set without losing significant information. Events can be aftershocks of many previous events, and also generate many aftershocks. The probability distribution for the number of incoming and outgoing links are both scale free, and the networks are highly clustered. The Omori law holds for aftershock rates up to a decorrelation time that scales with the magnitude, $m$, of the initiating shock as $t_{\rm cutoff} \sim 10^{βm}$ with $β\simeq 3/4$. Another scaling law relates distances between earthquakes and their aftershocks to the magnitude of the initiating shock. Our results are inconsistent with the hypothesis of finite aftershock zones. We also find evidence that seismicity is dominantly triggered by small earthquakes. Our approach, using concepts from the modern theory of complex networks, together with a metric to estimate correlations, opens up new avenues of research, as well as new tools to understand seismicity.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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