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The break of earthquake asperities imaged by distributed acoustic sensing

Author

Listed:
  • Jiaxuan Li

    (California Institute of Technology)

  • Taeho Kim

    (California Institute of Technology)

  • Nadia Lapusta

    (California Institute of Technology
    California Institute of Technology)

  • Ettore Biondi

    (California Institute of Technology)

  • Zhongwen Zhan

    (California Institute of Technology)

Abstract

Rupture imaging of megathrust earthquakes with global seismic arrays revealed frequency-dependent rupture signatures1–4, but the role of high-frequency radiators remains unclear3–5. Similar observations of the more abundant crustal earthquakes could provide critical constraints but are rare without ultradense local arrays6,7. Here we use distributed acoustic sensing technology8,9 to image the high-frequency earthquake rupture radiators. By converting a 100-kilometre dark-fibre cable into a 10,000-channel seismic array, we image four high-frequency subevents for the 2021 Antelope Valley, California, moment-magnitude 6.0 earthquake. After comparing our results with long-period moment-release10,11 and dynamic rupture simulations, we suggest that the imaged subevents are due to the breaking of fault asperities—stronger spots or pins on the fault—that substantially modulate the overall rupture behaviour. An otherwise fading rupture propagation could be promoted by the breaking of fault asperities in a cascading sequence. This study highlights how we can use the extensive pre-existing fibre networks12 as high-frequency seismic antennas to systematically investigate the rupture process of regional moderate-sized earthquakes. Coupled with dynamic rupture modelling, it could improve our understanding of earthquake rupture dynamics.

Suggested Citation

  • Jiaxuan Li & Taeho Kim & Nadia Lapusta & Ettore Biondi & Zhongwen Zhan, 2023. "The break of earthquake asperities imaged by distributed acoustic sensing," Nature, Nature, vol. 620(7975), pages 800-806, August.
  • Handle: RePEc:nat:nature:v:620:y:2023:i:7975:d:10.1038_s41586-023-06227-w
    DOI: 10.1038/s41586-023-06227-w
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    Cited by:

    1. Weiqiang Zhu & Ettore Biondi & Jiaxuan Li & Jiuxun Yin & Zachary E. Ross & Zhongwen Zhan, 2023. "Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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