About

John B. Rundle is a Distinguished Professor at UC Davis in the Departments of Physics and Earth and Planetary Sciences. He earned his Ph.D. from the University of California at Los Angeles in 1976. His research focuses on the dynamics of complex systems within the geosciences, particularly using statistical physics to understand earthquakes and other driven threshold systems. For nearly 40 years, his work has involved studying phase transitions, including first and second order types, through the application of field theories developed in physics such as particle physics and cosmology. Rundle has a special interest in developing methods for earthquake forecasting by analyzing chaos and complexity in driven nonlinear systems, utilizing large-scale numerical simulations. More recently, he has extended his research to view crashes in economic and financial systems as analogous to earthquakes and phase transitions, coining the term 'econoquake.' His contributions have been recognized through honors such as being named a Fellow of the American Geophysical Union in 2008 and a Fellow of the American Association for the Advancement of Science in 2017. He was also a co-recipient of NASA's Software of the Year in 2012 as part of the Quake Sim team.

Research topics

• Computer Science
• Geology
• Artificial Intelligence
• Seismology
• Machine Learning
• Data Mining
• Geodesy
• Meteorology
• Algorithm
• Geography

Selected publications

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

  Earth and Space Science · 2021 · 47 citations

  1st authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Machine Learning

  Abstract 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 (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020ea001097 ), 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.

  DOI

• Nowcasting Earthquakes by Visualizing the Earthquake Cycle with Machine Learning: A Comparison of Two Methods

  Surveys in Geophysics · 2021 · 37 citations

  1st authorCorresponding
  • Computer Science
  • Machine Learning
  • Geology
  DOI

• Clustering Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California's Major Faults

  Earth and Space Science · 2021 · 23 citations

  • Computer Science
  • Computer Science
  • Geology

  We present a data-driven approach to clustering or grouping Global Navigation Satellite System (GNSS) stations according to observed velocities, displacements or other selected characteristics. Clustering GNSS stations provides useful scientific information, and is a necessary initial step in other analysis, such as detecting aseismic transient signals (Granat et al., 2013, https://doi.org/10.1785/0220130039). Desired features of the data can be selected for clustering, including some subset of displacement or velocity components, uncertainty estimates, station location, and other relevant information. Based on those selections, the clustering procedure autonomously groups the GNSS stations according to a selected clustering method. We have implemented this approach as a Python application, allowing us to draw upon the full range of open source clustering methods available in Python's scikit-learn package (Pedregosa et al., 2011, https://doi.org/10.5555/1953048.2078195). The application returns the stations labeled by group as a table and color coded KML file and is designed to work with the GNSS information available from GeoGateway (Donnellan et al., 2021, https://doi.org/10.1007/s12145-020-00561-7; Heflin et al., 2020, https://doi.org/10.1029/2019ea000644) but is easily extensible. We demonstrate the methodology on California and western Nevada. The results show partitions that follow faults or geologic boundaries, including for recent large earthquakes and post-seismic motion. The San Andreas fault system is most prominent, reflecting Pacific-North American plate boundary motion. Deformation reflected as class boundaries is distributed north and south of the central California creeping section. For most models a cluster boundary connects the southernmost San Andreas fault with the Eastern California Shear Zone (ECSZ) rather than continuing through the San Gorgonio Pass.

  DOI

• The complex dynamics of earthquake fault systems: new approaches to forecasting and nowcasting of earthquakes

  Reports on Progress in Physics · 2021 · 89 citations

  1st authorCorresponding
  • Seismology
  • Geology
  • Meteorology

  Charles Richter's observation that 'only fools and charlatans predict earthquakes,' reflects the fact that despite more than 100 years of effort, seismologists remain unable to do so with reliable and accurate results. Meaningful prediction involves specifying the location, time, and size of an earthquake before it occurs to greater precision than expected purely by chance from the known statistics of earthquakes in an area. In this context, 'forecasting' implies a prediction with a specification of a probability of the time, location, and magnitude. Two general approaches have been used. In one, the rate of motion accumulating across faults and the amount of slip in past earthquakes is used to infer where and when future earthquakes will occur and the shaking that would be expected. Because the intervals between earthquakes are highly variable, these long-term forecasts are accurate to no better than a hundred years. They are thus valuable for earthquake hazard mitigation, given the long lives of structures, but have clear limitations. The second approach is to identify potentially observable changes in the Earth that precede earthquakes. Various precursors have been suggested, and may have been real in certain cases, but none have yet proved to be a general feature preceding all earthquakes or to stand out convincingly from the normal variability of the Earth's behavior. However, new types of data, models, and computational power may provide avenues for progress using machine learning that were not previously available. At present, it is unclear whether deterministic earthquake prediction is possible. The frustrations of this search have led to the observation that (echoing Yogi Berra) 'it is difficult to predict earthquakes, especially before they happen.' However, because success would be of enormous societal benefit, the search for methods of earthquake prediction and forecasting will likely continue. In this review, we note that the focus is on anticipating the earthquake rupture before it occurs, rather than characterizing it rapidly just after it occurs. The latter is the domain of earthquake early warning, which we do not treat in detail here, although we include a short discussion in the machine learning section at the end.

  DOI

• Automated Estimation and Tools to Extract Positions, Velocities, Breaks, and Seasonal Terms From Daily GNSS Measurements: Illuminating Nonlinear Salton Trough Deformation

  Earth and Space Science · 2020 · 54 citations

  • Computer Science
  • Geology
  • Geodesy

  Abstract This paper describes the methods used to estimate positions, velocities, breaks, and seasonal terms from daily Global Navigation Satellite System (GNSS) measurements. Break detection and outlier removal have been automated so that decades of daily measurements from thousands of stations can be processed in a few hours. New measurements are added, and parameters are updated every week. Model parameters allow separation of interseismic, annual, coseismic, and postseismic signals. Tools available through GeoGateway ( http://geo-gateway.org ) allow rapid visualization and analysis of these terms for results that can be subsetted in time or space. Results show highly variable and nonlinear motion for GPS stations in southern California. The variable motion is related to seasonal motions, distributed tectonic motion, earthquakes, and postseismic motions that can continue for years. In some areas results suggest that additional processes are responsible for the observed motions. In general, following earthquakes, stations return to their long‐term motions after 2–3 years, though some exceptions occur. The use of the tools shows nonlinear motion in the Salton Trough of southern California related to the 2010 M7.2 El Mayor‐Cucapah earthquake, 2012 Brawley earthquake swarm, and a creep event on the Superstition Hills fault in 2017.

  DOI

Frequent coauthors

• Donald L. Turcotte

  University of California, Davis

  228 shared

• Andrea Donnellan

  Jet Propulsion Laboratory

  217 shared

• K. F. Tiampo

  University of Colorado Boulder

  199 shared

• W. Klein

  Naval Research Laboratory Information Technology Division

  185 shared

• Geoffrey Fox

  96 shared

• J. R. Holliday

  76 shared

• Jay Parker

  66 shared

• José Fernández

  Instituto de Geociencias

  56 shared

Awards & honors

• Fellow, American Association for the Advancement of Science,…
• Fellow of the American Geophysical Union, 2008
• Quake Sim team: co-recipient of NASA's Software of the Year,…

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