Nowcasting Earthquakes: Imaging the Earthquake Cycle in California with Machine Learning

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 1950 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 stressaccumulation and release associated with major tectonic activity.

应力积累与释放的地震周期，与H.F. Reid在1906年旧金山7.9级地震后提出的弹性回跳假说（elastic rebound hypothesis）相关。然而，目前尚无法观测时空依赖性构造应力实际值的细节。在前两篇论文中，我们提出了通过代理变量（proxy variables）对加州地震周期进行成像的方法，这些变量基于几乎持续发生的小地震模式的相关性。本文的目的是通过决策阈值、接收器操作特征（Receiver Operating Characteristic，ROC）方法及香农信息熵（Shannon information entropy）评估两种方法的信息含量，进而对其展开比较。利用加州1950年至今的地震数据，我们发现两种方法提供的信息几乎等价，均涉及该区域大地震相关的地震相关性升降。我们得出结论：所得时间序列可视为与主要构造活动相关的应力积累与释放周期的代理。