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Fault-network geometry influences earthquake frictional behaviour

Nature volume 631, pages 106–110 (2024)Cite this article

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Abstract

Understanding the factors governing the stability of fault slip is a crucial problem in fault mechanics1,2,3. The importance of fault geometry and roughness on fault-slip behaviour has been highlighted in recent lab experiments4,5,6,7 and numerical models8,9,10,11, and emerging evidence suggests that large-scale complexities in fault networks have a vital role in the fault-rupture process12,13,14,15,16,17,18. Here we present a new perspective on fault creep by investigating the link between fault-network geometry and surface creep rates in California, USA. Our analysis reveals that fault groups exhibiting creeping behaviour show smaller misalignment in their fault-network geometry. The observation indicates that the surface fault traces of creeping regions tend to be simple, whereas locked regions tend to be more complex. We propose that the presence of complex fault-network geometries results in geometric locking that promotes stick-slip behaviour characterized by earthquakes, whereas simpler geometries facilitate smooth fault creep. Our findings challenge traditional hypotheses on the physical origins of fault creep explained primarily in terms of fault friction19,20,21 and demonstrate the potential for a new framework in which large-scale earthquake frictional behaviour is determined by a combination of geometric factors and rheological yielding properties.

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Fig. 1: Fault misalignment and surface creep rates along main faults in California.
Fig. 2: Fault misalignment versus creep rates.
Fig. 3: Schematic illustrations of explanations of seismogenic behaviour.

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Data availability

The surface creep data used in this study are available from ref. 33 (https://www.usgs.gov/data/creep-rate-models-california-faults-2023-us-national-seismic-hazard-model). The surface fault traces are from the USGS Quaternary Fault and Fold Database of the United States (https://www.usgs.gov/programs/earthquake-hazards/faults). The earthquake catalogue data can be downloaded from the Northern California Earthquake Data Center (NCEDC, https://doi.org/10.7932/NCEDC) and the Southern California Earthquake Center (SCEDC, https://doi.org/10.7909/C3WD3xH1). Source data are provided with this paper.

Code availability

Codes used in this research are available on Zenodo at https://doi.org/10.5281/zenodo.10982013 (ref. 59).

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Acknowledgements

The work presented in this paper was supported by National Science Foundation grants EAR-2146640 and EAR-2231705.

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Authors and Affiliations

  1. Department of Earth, Environmental, and Planetary Sciences, Brown University, Providence, RI, USA

    Jaeseok Lee, Victor C. Tsai & Greg Hirth

  2. Nevada Seismological Laboratory, University of Nevada, Reno, NY, USA

    Avigyan Chatterjee & Daniel T. Trugman

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  1. Jaeseok Lee
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Contributions

V.C.T. conceived and designed the study. J.L. led the investigation, including data analysis, visualization and interpretation. D.T.T. and A.C. contributed to the statistical analysis and interpretation of fault complexity and creep rate data. G.H. helped with the interpretation of results in the framework of rock mechanics and frictional theory. J.L. took the lead in drafting the manuscript. All authors provided input on the analysis, reviewed the results, contributed to editing the manuscript and approved the final version of the manuscript. V.C.T., D.T.T. and G.H. secured funding to support the project.

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Correspondence to Victor C. Tsai.

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Nature thanks Romain Jolivet and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Scatter plots for fault misalignment and fault density.

a, Scatter plot of surface creep rate versus fault misalignment. b, Scatter plot of surface creep rate normalized by accumulated seismic moment versus fault misalignment. The negative correlation between fault misalignment and normalized creep remains consistent. c, Scatter plot of surface creep rate and fault misalignment versus fault density. Fault density correlates with fault misalignment but does not show any correlation with creep rates. a–c, Spearman’s rank correlation (RC) coefficients between the variables are in the subplot titles and the red error bar plots indicate the means and standard deviations for the binned intervals.

Extended Data Fig. 2 Fault misalignment and creep outside California.

a, Fault misalignment and fault creep rates along the North Anatolian Fault. Inner circles indicate surface creep rates51,52,53 and outer circles indicate measured fault misalignments. Surface fault traces are coloured in white54. b, Scatter plot of fault misalignment and surface creep rates along the North Anatolian Fault. Spearman’s rank correlation (RC) coefficient between the two is indicated in the subplot title. c, Fault misalignment and fault creep rates along the Chaman Fault. Inner circles indicate surface creep rates55 and outer circles indicate measured fault misalignments. Surface fault traces are coloured in white58. d, Scatter plot of fault misalignment and surface creep rates along the Chaman Fault. Spearman’s RC coefficient between the two is indicated in the subplot title.

Extended Data Fig. 3 Average surface creep rates and fault misalignment for different fault segments in California.

Spearman’s rank correlation (RC) coefficient between the two is indicated in the subplot title and the dashed black line indicates a monotonic cubic polynomial of best fit60. The shaded green area is a 95% confidence interval around the best fit. The inset map in the upper-right corner depicts the fault segments using the same colours as in the main plot.

Extended Data Fig. 4 Creep rate sampling.

Comparison of surface creep rates sampled at 10-km intervals along faults in California (red) with the compiled measurements from ref. 33 (black). The number of estimates for each fault is indicated in the subplot titles. Estimates and errors at the sampled locations are calculated as the weighted average of measurements within 10 km.

Extended Data Fig. 5 Tests of robustness.

a, Variation in the mean and standard deviation of fault misalignment for locked and creeping faults for different creep cutoff thresholds. b, Changes in the mean and standard deviation of fault misalignment for locked and creeping faults (threshold: 3 mm per year), considering various radius circles for measuring fault-network misalignment. The distinct distribution of fault misalignment between locked and creeping faults remains consistent, regardless of the chosen cutoff threshold or radius circle used to measure fault complexity. As the radius increases, the fault misalignment in creeping faults with simple geometries remains relatively constant. By contrast, for locked faults with complex geometries, fault misalignment increases as a result of the violation of the fractality assumption at smaller scales, attributed to limited resolution.

Extended Data Fig. 6 Fault metric regions.

Fault metrics are computed within the red circles.

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Lee, J., Tsai, V.C., Hirth, G. et al. Fault-network geometry influences earthquake frictional behaviour. Nature 631, 106–110 (2024). https://doi.org/10.1038/s41586-024-07518-6

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