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Continuous chatter of the Cascadia subduction zone revealed by machine learning

Nature Geoscience volume 12, pages 75–79 (2019)Cite this article

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Abstract

Tectonic faults slip in various manners, which range from ordinary earthquakes to slow slip events to aseismic fault creep. Slow slip and associated tremor are common to many subduction zones, and occur down-dip from the neighbouring locked zone where megaquakes take place. In the clearest cases, such as Cascadia, identified tremor occurs in discrete bursts, primarily during the slow slip event. Here we show that the Cascadia subduction zone is apparently continuously broadcasting a low-amplitude, tremor-like signal that precisely informs of the fault displacement rate throughout the slow slip cycle. Using a method based on machine learning previously developed in the laboratory, we analysed large amounts of raw seismic data from Vancouver Island to separate this signal from the background seismic noise. We posit that this provides indirect real-time access to fault physics on the down-dip portion of the megathrust, and thus may prove useful in determining if and how a slow slip may couple to or evolve into a major earthquake.

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Fig. 1: Map and schematic of the region analysed: Vancouver Island and the subduction zone.
Fig. 2: Estimating the GPS displacement rate from the continuous seismic data.
Fig. 3: Strong correlations between the statistics of the seismic data and the GPS displacement rate.

Data availability

All the data used are publicly available and can be found online. The seismic data are from the CNSN27 (www.earthquakescanada.nrcan.gc.ca), and the GPS data are from the Western Canada Deformation Array operated by the Geological Survey of Canada, preprocessed by the United States Geological Survey28 (https://earthquake.usgs.gov/monitoring/gps/Pacific_Northwest, NA-fixed trended data). The work flow described in Methods uses open source software (python and python packages including scikit-learn37 and obspy36). We are currently unable to make the python script associated with this paper available, but we aim to make it available in the near future. Please contact bertrandrl@lanl.gov for details.

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Acknowledgements

This work was funded by Institutional Support (LDRD) at Los Alamos and the DOE Office of Basic Research, Geoscience Program. We are grateful to H. Dragert, H. Kao, T. Melbourne, J. Gomberg, D. Trugman, R. Guyer, A. Delorey, J. Murray, I. McBrearty and C. Lee for fruitful comments and discussions. We thank W. Frank for his extensive review of our work and his suggestions. We thank T. Cote, X. Jin and M. Kolaj from the CNSN for their data and help.

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Author notes

  1. These authors contributed equally: Bertrand Rouet-Leduc, Claudia Hulbert.

Authors and Affiliations

  1. Los Alamos National Laboratory, Geophysics Group, Los Alamos, New Mexico, USA

    Bertrand Rouet-Leduc, Claudia Hulbert & Paul A. Johnson

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  1. Bertrand Rouet-Leduc
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  2. Claudia Hulbert
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Contributions

All the authors devised the original study. B.R.L. and C.H. conducted the machine-learning work and all the authors contributed to writing the manuscript.

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Correspondence to Bertrand Rouet-Leduc.

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Supplementary Information

Further information on machine learning and GPS displacement and Supplementary Figures 1–6.

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Rouet-Leduc, B., Hulbert, C. & Johnson, P.A. Continuous chatter of the Cascadia subduction zone revealed by machine learning. Nature Geosci 12, 75–79 (2019). https://doi.org/10.1038/s41561-018-0274-6

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