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Similarity of fast and slow earthquakes illuminated by machine learning

Abstract

Tectonic faults fail in a spectrum of modes, ranging from earthquakes to slow slip events. The physics of fast earthquakes are well described by stick–slip friction and elastodynamic rupture; however, slow earthquakes are poorly understood. Key questions remain about how ruptures propagate quasi-dynamically, whether they obey different scaling laws from ordinary earthquakes and whether a single fault can host multiple slip modes. We report on laboratory earthquakes and show that both slow and fast slip modes are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure. Using machine learning, we find that acoustic emissions generated during shear of quartz fault gouge under normal stress of 1–10 MPa predict the timing and duration of laboratory earthquakes. Laboratory slow earthquakes reach peak slip velocities of the order of 1 × 10−4 m s−1 and do not radiate high-frequency elastic energy, consistent with tectonic slow slip. Acoustic signals generated in the early stages of impending fast laboratory earthquakes are systematically larger than those for slow slip events. Here, we show that a broad range of stick–slip and creep–slip modes of failure can be predicted and share common mechanisms, which suggests that catastrophic earthquake failure may be preceded by an organized, potentially forecastable, set of processes.

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Fig. 1: Laboratory experiments.
Fig. 2: Detail of aperiodic stick–slip events showing alternating fast and slow slip.
Fig. 3: Acoustic signature foretells failure mode for laboratory events.
Fig. 4: Laboratory earthquake prediction on testing set.

Data availability

The data are available from the Penn State Rock Mechanics laboratory (www3.geosc.psu.edu/~cjm38/).

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Acknowledgements

We thank Institutional Support (LDRD) and DOE Fossil Energy for funding the work at Los Alamos, and the National Science Foundation and the LANL-CSES program for funding the work at Penn State. We thank J. Gomberg, A. Delorey, I. McBrearty, R. Guyer, C. Lee and J. Leeman for discussions and comments.

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C.H., B.R.-L. and C.X.R. conducted the machine learning analysis. J.R., D.C.B., P.A.J. and C.M. conducted the experiments. P.A.J. and C.M. supervised the project. C.H., C.M. and P.A.J. wrote the manuscript along with all authors.

Corresponding author

Correspondence to Claudia Hulbert.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Experimental Information and Supplementary Figures 1–5.

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Hulbert, C., Rouet-Leduc, B., Johnson, P.A. et al. Similarity of fast and slow earthquakes illuminated by machine learning. Nature Geosci 12, 69–74 (2019). https://doi.org/10.1038/s41561-018-0272-8

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