IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-41403-6.html
   My bibliography  Save this article

Broad fault zones enable deep fluid transport and limit earthquake magnitudes

Author

Listed:
  • Konstantinos Leptokaropoulos

    (University of Southampton
    The MathWorks)

  • Catherine A. Rychert

    (University of Southampton
    Woods Hole Oceanographic Institution)

  • Nicholas Harmon

    (University of Southampton
    Woods Hole Oceanographic Institution)

  • David Schlaphorst

    (Universidade de Lisboa)

  • Ingo Grevemeyer

    (RD4—Marine Geodynamics)

  • John-Michael Kendall

    (University of Oxford)

  • Satish C. Singh

    (Institut de Physique du Globe de Paris, CNRS)

Abstract

Constraining the controlling factors of fault rupture is fundamentally important. Fluids influence earthquake locations and magnitudes, although the exact pathways through the lithosphere are not well-known. Ocean transform faults are ideal for studying faults and fluid pathways given their relative simplicity. We analyse seismicity recorded by the Passive Imaging of the Lithosphere-Asthenosphere Boundary (PI-LAB) experiment, centred around the Chain Fracture Zone. We find earthquakes beneath morphological transpressional features occur deeper than the brittle-ductile transition predicted by simple thermal models, but elsewhere occur shallower. These features are characterised by multiple parallel fault segments and step overs, higher proportions of smaller events, gaps in large historical earthquakes, and seismic velocity structures consistent with hydrothermal alteration. Therefore, broader fault damage zones preferentially facilitate fluid transport. This cools the mantle and reduces the potential for large earthquakes at localized barriers that divide the transform into shorter asperity regions, limiting earthquake magnitudes on the transform.

Suggested Citation

  • Konstantinos Leptokaropoulos & Catherine A. Rychert & Nicholas Harmon & David Schlaphorst & Ingo Grevemeyer & John-Michael Kendall & Satish C. Singh, 2023. "Broad fault zones enable deep fluid transport and limit earthquake magnitudes," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41403-6
    DOI: 10.1038/s41467-023-41403-6
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-41403-6
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-41403-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ingo Grevemeyer & Lars H. Rüpke & Jason P. Morgan & Karthik Iyer & Colin W. Devey, 2021. "Extensional tectonics and two-stage crustal accretion at oceanic transform faults," Nature, Nature, vol. 591(7850), pages 402-407, March.
    2. Rachel E. Abercrombie & Göran Ekström, 2001. "Earthquake slip on oceanic transform faults," Nature, Nature, vol. 410(6824), pages 74-77, March.
    3. Marsaglia, George & Marsaglia, John, 2004. "Evaluating the Anderson-Darling Distribution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 9(i02).
    4. Matthew R. Agius & Catherine A. Rychert & Nicholas Harmon & Saikiran Tharimena & J.-Michael Kendall, 2021. "A thin mantle transition zone beneath the equatorial Mid-Atlantic Ridge," Nature, Nature, vol. 589(7843), pages 562-566, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. BenSaïda, Ahmed & Slim, Skander, 2016. "Highly flexible distributions to fit multiple frequency financial returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 203-213.
    2. Fernández de Marcos Giménez de los Galanes, Alberto, 2022. "Data-driven stabilizations of goodness-of-fit tests," DES - Working Papers. Statistics and Econometrics. WS 35324, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Shibin Zhang & Xin M. Tu, 2022. "Tests for comparing time‐invariant and time‐varying spectra based on the Anderson–Darling statistic," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(3), pages 254-282, August.
    4. Zinoviy Landsman & Udi Makov & Tomer Shushi, 2017. "Extended Generalized Skew-Elliptical Distributions and their Moments," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 79(1), pages 76-100, February.
    5. Hocine Khelifa & Eric Vagnon & Abderrahmane Beroual, 2023. "Effect of Fullerene and Graphene Nanoparticles on the AC Dielectric Strength of Natural Ester," Energies, MDPI, vol. 16(4), pages 1-11, February.
    6. Fernández-de-Marcos, Alberto & García-Portugués, Eduardo, 2023. "Data-driven stabilizations of goodness-of-fit tests," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    7. Dobric, Jadran & Schmid, Friedrich, 2007. "A goodness of fit test for copulas based on Rosenblatt's transformation," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4633-4642, May.
    8. Sung Ik Kim, 2022. "ARMA–GARCH model with fractional generalized hyperbolic innovations," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-25, December.
    9. Xiaochuan Tian & Mark D. Behn & Garrett Ito & Jana C. Schierjott & Boris J. P. Kaus & Anton A. Popov, 2024. "Magmatism controls global oceanic transform fault topography," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    10. Daniele Coin, 2017. "A goodness-of-fit test for Generalized Error Distribution," Temi di discussione (Economic working papers) 1096, Bank of Italy, Economic Research and International Relations Area.
    11. Kristian Hindberg & Jan Hannig & Fred Godtliebsen, 2019. "A novel scale-space approach for multinormality testing and the k-sample problem in the high dimension low sample size scenario," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-20, January.
    12. Gomes-Gonçalves, Erika & Gzyl, Henryk & Mayoral, Silvia, 2015. "Two maxentropic approaches to determine the probability density of compound risk losses," Insurance: Mathematics and Economics, Elsevier, vol. 62(C), pages 42-53.
    13. Asmerilda Hitaj & Lorenzo Mercuri & Edit Rroji, 2019. "Sensitivity analysis of Mixed Tempered Stable parameters with implications in portfolio optimization," Computational Management Science, Springer, vol. 16(1), pages 71-95, February.
    14. Zhikai Wang & Satish C. Singh, 2022. "Seismic evidence for uniform crustal accretion along slow-spreading ridges in the equatorial Atlantic Ocean," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    15. A. H Nzokem, 2024. "Fitting the seven-parameter Generalized Tempered Stable distribution to the financial data," Papers 2410.19751, arXiv.org.
    16. Manon Bickert & Mary-Alix Kaczmarek & Daniele Brunelli & Marcia Maia & Thomas F. C. Campos & Susanna E. Sichel, 2023. "Fluid-assisted grain size reduction leads to strain localization in oceanic transform faults," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    17. Kim, Young Shin & Lee, Jaesung & Mittnik, Stefan & Park, Jiho, 2015. "Quanto option pricing in the presence of fat tails and asymmetric dependence," Journal of Econometrics, Elsevier, vol. 187(2), pages 512-520.
    18. Grundke, Peter, 2010. "Top-down approaches for integrated risk management: How accurate are they?," European Journal of Operational Research, Elsevier, vol. 203(3), pages 662-672, June.
    19. Hasan A. Fallahgoul & Young S. Kim & Frank J. Fabozzi & Jiho Park, 2019. "Quanto Option Pricing with Lévy Models," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1279-1308, March.
    20. Grace, Adam W. & Wood, Ian A., 2012. "Approximating the tail of the Anderson–Darling distribution," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4301-4311.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41403-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.