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Machine learning and earthquake forecasting—next steps

Author

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  • Gregory C. Beroza

    (Stanford University)

  • Margarita Segou

    (Lyell Centre)

  • S. Mostafa Mousavi

    (Stanford University)

Abstract

A new generation of earthquake catalogs developed through supervised machine-learning illuminates earthquake activity with unprecedented detail. Application of unsupervised machine learning to analyze the more complete expression of seismicity in these catalogs may be the fastest route to improving earthquake forecasting.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24952-6
    DOI: 10.1038/s41467-021-24952-6
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    Cited by:

    1. Asher, Eitan E. & Havlin, Shlomo & Moshel, Shay & Ashkenazy, Yosef, 2023. "Increased earthquake rate prior to mainshocks," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    2. 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.

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