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Revealing the spatiotemporal complexity of the magnitude distribution and b-value during an earthquake sequence

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  • Marcus Herrmann

    (dell’Ambiente e delle Risorse; Università degli Studi di Napoli ‘Federico II’)

  • Ester Piegari

    (dell’Ambiente e delle Risorse; Università degli Studi di Napoli ‘Federico II’)

  • Warner Marzocchi

    (dell’Ambiente e delle Risorse; Università degli Studi di Napoli ‘Federico II’)

Abstract

The Magnitude–Frequency-Distribution (MFD) of earthquakes is typically modeled with the (tapered) Gutenberg–Richter relation. The main parameter of this relation, the b-value, controls the relative rate of small and large earthquakes. Resolving spatiotemporal variations of the b-value is critical to understanding the earthquake occurrence process and improving earthquake forecasting. However, this variation is not well understood. Here we present remarkable MFD variability during the complex 2016/17 central Italy sequence using a high-resolution earthquake catalog. Isolating seismically active volumes (‘clusters’) reveals that the MFD differed in nearby clusters, varied or remained constant in time depending on the cluster, and increased in b-value in the cluster where the largest earthquake eventually occurred. These findings suggest that the fault system’s heterogeneity and complexity influence the MFD. Our findings raise the question “b-value of what?”: interpreting and using MFD variability needs a spatiotemporal scale that is physically meaningful, like the one proposed here.

Suggested Citation

  • Marcus Herrmann & Ester Piegari & Warner Marzocchi, 2022. "Revealing the spatiotemporal complexity of the magnitude distribution and b-value during an earthquake sequence," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32755-6
    DOI: 10.1038/s41467-022-32755-6
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    References listed on IDEAS

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    1. Danijel Schorlemmer & Stefan Wiemer & Max Wyss, 2005. "Variations in earthquake-size distribution across different stress regimes," Nature, Nature, vol. 437(7058), pages 539-542, September.
    2. Gregory C. Beroza & Margarita Segou & S. Mostafa Mousavi, 2021. "Machine learning and earthquake forecasting—next steps," Nature Communications, Nature, vol. 12(1), pages 1-3, December.
    3. Laura Gulia & Stefan Wiemer, 2019. "Real-time discrimination of earthquake foreshocks and aftershocks," Nature, Nature, vol. 574(7777), pages 193-199, October.
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    1. C. Collettini & M. R. Barchi & N. Paola & F. Trippetta & E. Tinti, 2022. "Rock and fault rheology explain differences between on fault and distributed seismicity," Nature Communications, Nature, vol. 13(1), pages 1-11, December.

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