Nowcasting Earthquakes by Visualizing the Earthquake Cycle with Machine
Learning:A Comparison of Two Methods
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 previous research, we have proposed two
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. One of these
is based on the construction of a time series by the unsupervised
detection of small earthquake clusters. The other is based on expanding
earthquake seismicity in PCA-derived patterns, to construct a weighted
correlation time series. The purpose of the present research 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 time series can be viewed as proxies for the cycle of stress
accumulation and release associated with major tectonic activity. The
figure shows the PCA patterns of small earthquakes associated with 5
major M>7 earthquakes in California since 1950.