Title: Nowcasting Earthquakes: Imaging the Earthquake Cycle in California With Machine Learning

Journal Article · · Earth and Space Science
ORCiD logo [1]; ORCiD logo [2];  [3];  [4]; ORCiD logo [5]
  1. University of California, Davis, CA (United States); California Institute of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Laboratory (JPL); Santa Fe Institute (SFI), Santa Fe, NM (United States); University of California Davis
  2. California Institute of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Laboratory (JPL)
  3. Indiana University, Bloomington, IN (United States)
  4. University of California, Davis, CA (United States)
  5. City University of New York (CUNY), NY (United States)
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We propose a new machine learning-based method for nowcasting earthquakes to image the time-dependent earthquake cycle. The result is a timeseries that may correspond to the process of stress accumulation and release. The timeseries are constructed by using principal component analysis of regional seismicity. The patterns are found as eigenvectors of the cross-correlation matrix of a collection of seismicity timeseries in a coarse grained regional spatial grid (pattern recognition via unsupervised machine learning). The eigenvalues of this matrix represent the relative importance of the various eigenpatterns. Using the eigenvectors and eigenvalues, we compute the weighted correlation timeseries of the regional seismicity. This timeseries has the property that the weighted correlation generally decreases prior to major earthquakes in the region, and increases suddenly just after a major earthquake occurs. As in a previous paper, we find that this method produces a nowcasting timeseries that resembles the hypothesized regional stress accumulation and release process characterizing the earthquake cycle. We then address the problem of whether the timeseries contain information regarding future large earthquakes. For this, we compute a receiver operating characteristic and determine the decision thresholds for several future time periods of interest (optimization via supervised machine learning). We find that signals can be detected that can be used to characterize the information content of the timeseries. These signals may be useful in assessing present and near-future seismic hazards.

Research Organization:
University of California, Davis, CA (United States)
Sponsoring Organization:
National Aeronautics and Space Administration (NASA); National Science Foundation (NSF); Southern California Earthquake Center; USDOE Office of Science (SC); United States Geological Survey (USGS)
Grant/Contract Number:
SC0017324
OSTI ID:
1978503
Journal Information:
Earth and Space Science, Journal Name: Earth and Space Science Journal Issue: 12 Vol. 8; ISSN 2333-5084
Publisher:
American Geophysical Union (AGU)Copyright Statement
Country of Publication:
United States
Language:
English

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