Abstract

The earthquake cycle of stress accumulation and release is associated with the elastic rebound hypothesis proposed by H.F. Reid following the M7.9 San Francisco earthquake of 1906. However, observing details of the actual values of time- and space-dependent tectonic stress is not possible at the present time. In two previous papers, we have proposed methods to image the earthquake cycle in California by means of proxy variables. These variables are based on correlations in patterns of small earthquakes that occur nearly continuously in time. The purpose of the present paper is to compare these two methods by evaluating their information content using decision thresholds and Receiver Operating Characteristic methods together with Shannon information entropy. Using seismic data from 1940 to present in California, we find that both methods provide nearly equivalent information on the rise and fall of earthquake correlations associated with major earthquakes in the region. We conclude that the resulting timeseries can be viewed as proxies for the cycle of stress accumulation and release associated with major tectonic activity.

Article Highlights

• The current state of the earthquake cycle of tectonic stress accumulation and release is unobservable

• We review two methods for visualizing the current state of the earthquake cycle from correlation in small earthquake patterns

• Machine learning techniques indicate that signals in a correlation time series corresponding to future large earthquakes can be detected

中文翻译：

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通过机器学习可视化地震周期来临近预报地震：两种方法的比较

摘要

应力积累和释放的地震周期与 HF Reid 在 1906 年旧金山 M7.9 地震后提出的弹性回弹假说有关。 然而，观察与时空相关的构造应力实际值的细节是不可能的目前。在之前的两篇论文中，我们提出了通过代理变量对加利福尼亚地震周期进行成像的方法。这些变量基于在时间上几乎连续发生的小地震模式的相关性。本文的目的是通过使用决策阈值和接收器操作特征方法以及香农信息熵评估它们的信息内容来比较这两种方法。使用 1940 年至今在加利福尼亚州的地震数据，我们发现这两种方法提供了与该地区大地震相关的地震相关性的上升和下降的几乎相同的信息。我们得出的结论是，由此产生的时间序列可以被视为与主要构造活动相关的应力积累和释放周期的代理。

文章亮点

• 构造应力积累和释放的地震循环现状是不可观测的

• 我们回顾了两种从小地震模式的相关性中可视化地震周期当前状态的方法

• 机器学习技术表明可以检测到与未来大地震对应的相关时间序列中的信号