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Real-time discrimination of earthquake foreshocks and aftershocks

Author

Listed:
  • Laura Gulia

    (ETH Zurich)

  • Stefan Wiemer

    (ETH Zurich)

Abstract

Immediately after a large earthquake, the main question asked by the public and decision-makers is whether it was the mainshock or a foreshock to an even stronger event yet to come. So far, scientists can only offer empirical evidence from statistical compilations of past sequences, arguing that normally the aftershock sequence will decay gradually whereas the occurrence of a forthcoming larger event has a probability of a few per cent. Here we analyse the average size distribution of aftershocks of the recent Amatrice–Norcia and Kumamoto earthquake sequences, and we suggest that in many cases it may be possible to discriminate whether an ongoing sequence represents a decaying aftershock sequence or foreshocks to an upcoming large event. We propose a simple traffic light classification to assess in real time the level of concern about a subsequent larger event and test it against 58 sequences, achieving a classification accuracy of 95 per cent.

Suggested Citation

  • Laura Gulia & Stefan Wiemer, 2019. "Real-time discrimination of earthquake foreshocks and aftershocks," Nature, Nature, vol. 574(7777), pages 193-199, October.
    • Handle: RePEc:nat:nature:v:574:y:2019:i:7777:d:10.1038_s41586-019-1606-4
    DOI: 10.1038/s41586-019-1606-4
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    Citations

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    Cited by:

    1. Matteo Picozzi & Antonio Giovanni Iaccarino, 2021. "Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network," Forecasting, MDPI, vol. 3(1), pages 1-20, January.
    2. Elio Roca-Flores & Gerardo G. Naumis, 2021. "Assessing statistical hurricane risks: nonlinear regression and time-window analysis of North Atlantic annual accumulated cyclonic energy rank profile," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(3), pages 2455-2465, September.
    3. Matteo Taroni & Giorgio Vocalelli & Andrea De Polis, 2021. "Gutenberg–Richter B-Value Time Series Forecasting: A Weighted Likelihood Approach," Forecasting, MDPI, vol. 3(3), pages 1-9, August.
    4. 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.
    5. 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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