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Nowcasting Earthquakes by Visualizing the Earthquake Cycle with Machine Learning:A Comparison of Two Methods
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  • John Rundle,
  • Andrea Donnellan,
  • Geoffrey Fox,
  • James Crutchfield
John Rundle
University of California Davis

Corresponding Author:[email protected]

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Andrea Donnellan
Jet Propulsion Laboratory, California Institute of Technology
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Geoffrey Fox
Indiana University Bloomington
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James Crutchfield
University of California Davis
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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.