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Slip rate deficit and earthquake potential on shallow megathrusts

Abstract

Most destructive tsunamis are caused by seismic slip on the shallow part of offshore megathrusts. The likelihood of this behaviour is partly determined by the interseismic slip rate deficit, which is often assumed to be low based on frictional studies of shallow fault material. Here, we present a new method for inferring the slip rate deficit from geodetic data that accounts for the stress shadow cast by frictionally locked patches, and show that this approach greatly improves our offshore resolution. We apply this technique to the Cascadia and Japan Trench megathrusts and find that, wherever locked patches are present, the shallow fault generally has a slip rate deficit between 80 and 100% of the plate convergence rate, irrespective of its frictional properties. This finding rules out areas of low kinematic coupling at the trench considered by previous studies. If these areas of the shallow fault can slip seismically, the global tsunami hazard could be higher than currently recognized. Our method identifies critical locations where seafloor observations could yield information about frictional properties of these faults so as to better understand their slip behaviour.

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Fig. 1: Illustration of the stress shadow effect on a megathrust fault.
Fig. 2: Synthetic test in two dimensions.
Fig. 3: Three methods for inferring slip deficit.
Fig. 4: Adding stress constraints to the inversion greatly reduces the range of acceptable models.

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Data availability

All data analysed in this paper are available as supplementary information in previously published studies: for Cascadia, McCaffrey et al.8 (https://doi.org/10.1029/2012JB009473); for Japan, Kreemer et al.30 (https://doi.org/10.1002/2014GC005407). The shaded topography and bathymetry in Fig. 4 and Extended Data Figs. 14 are from SRTM15+44, available at https://topex.ucsd.edu/WWW_html/srtm15_plus.html.

Code availability

MATLAB code for running the stress-constrained inversion method, including all examples described above, is available at https://github.com/ericlindsey/stress-shadows. All figures were prepared using version 6 of Generic Mapping Tools53, available at https://www.generic-mapping-tools.org/.

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Acknowledgements

This research is supported by the National Research Foundation of Singapore and the Singapore Ministry of Education under the Research Centres of Excellence initiative, and by a Singapore NRF Investigatorship award no. NRF-NRFI05-2019-0009 to E.M.H. R.B. acknowledges support by NSF award no. EAR-1801720. J.D.P.M. acknowledges support by NERC award no. NE/R00515X/1. This work comprises Earth Observatory of Singapore contribution no. 337. We acknowledge helpful discussions with M. Métois, M. Chlieh, J.-P. Avouac, J.-M. Nocquet, M. Herman and many others.

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Authors and Affiliations

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E.O.L., R.V.A., J.A.H., K.E.B., R.M., R.B. and E.M.H. designed the study. E.O.L., R.M. and J.D.P.M. developed the inverse method. E.O.L. conducted the data analysis and created the figures. E.O.L. wrote the initial manuscript and all authors commented and contributed to the final version.

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Correspondence to Eric O. Lindsey.

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The authors declare no competing interests.

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Peer review information Nature Geoscience thanks Laura Wallace and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Stefan Lachowycz.

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Extended data

Extended Data Fig. 1 Minimum and Maximum kinematic coupling models for Cascadia.

Minimum and maximum allowable coupling models under the assumption of no stress constraints (panels a-b) or with stress constraints (panels d-e), and the resulting computed model variability (panels c and f). The largest difference between unconstrained and constrained models appears in the minimum allowable coupling model; the maximum models typically do not encounter the stress constraint during the inversion, except where it controls the down-dip gradient in coupling (for example near 44° N, 125º W). Panels c and f are shown in Fig. 4 as panels c and b, respectively. Background bathymetry and topography are from the SRTM15+ dataset44.

Extended Data Fig. 2 Minimum and Maximum kinematic coupling models for Japan.

Same as Extended Data Fig. 1, for the Japan Trench case.

Extended Data Fig. 3 Comparison of the stress-constrained model with GPS-A data offshore Japan.

Validation of the Japan Trench model with offshore GPS-A data from Yokota et al.37. Panel a shows the stress-constrained model from Fig. 4, based only on the land-based GPS observations, along with the GPS observations (gray arrows) and model predictions (red arrows). Panel b shows the resulting model when the offshore GPS-A data are included, and panel c shows the difference of the two. There is a slight increase in coupling offshore when the GPS-A data are included. Background bathymetry and topography are from the SRTM15+ dataset44.

Extended Data Fig. 4 Comparison of the unconstrained model with GPS-A data offshore Japan.

Same as Extended Data Fig. 3, for an unconstrained model with slip-deficit penalty. Note a much larger increase in offshore kinematic coupling when the GPS-A data are included (panel b), indicating that the land-data-only model (panel a) had underestimated the true kinematic coupling offshore.

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Supplementary Figs. 1–7.

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Lindsey, E.O., Mallick, R., Hubbard, J.A. et al. Slip rate deficit and earthquake potential on shallow megathrusts. Nat. Geosci. 14, 321–326 (2021). https://doi.org/10.1038/s41561-021-00736-x

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