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Ergodic seismic precursors and transfer learning for short term eruption forecasting at data scarce volcanoes

Author

Listed:
  • Alberto Ardid

    (University of Canterbury)

  • David Dempsey

    (University of Canterbury)

  • Corentin Caudron

    (Université libre de Bruxelles
    WEL Research Institute)

  • Shane Cronin

    (University of Auckland)

  • Ben Kennedy

    (University of Canterbury)

  • Társilo Girona

    (University of Alaska Fairbanks)

  • Diana Roman

    (Carnegie Institution)

  • Craig Miller

    (Te Pū Ao | GNS Science)

  • Sally Potter

    (Te Pū Ao | GNS Science)

  • Oliver D. Lamb

    (Te Pū Ao | GNS Science)

  • Anto Martanto

    (Center for Volcanology and Geological Hazard Mitigation)

  • Yesim Cubuk-Sabuncu

    (Icelandic Met Office)

  • Leoncio Cabrera

    (Pontificia Universidad Católica de Chile)

  • Sergio Ruiz

    (Universidad de Chile)

  • Rodrigo Contreras

    (Universidad Católica de Temuco
    Universidad Católica de Temuco)

  • Javier Pacheco

    (National University of Costa Rica)

  • Mauricio M. Mora

    (University of Costa Rica)

  • Silvio Angelis

    (University of Liverpool
    Istituto Nazionale di Geofisica e Vulcanologia)

Abstract

Seismic data recorded before volcanic eruptions provides important clues for forecasting. However, limited monitoring histories and infrequent eruptions restrict the data available for training forecasting models. We propose a transfer machine learning approach that identifies eruption precursors—signals that consistently change before eruptions—across multiple volcanoes. Using seismic data from 41 eruptions at 24 volcanoes over 73 years, our approach forecasts eruptions at unobserved (out-of-sample) volcanoes. Tested without data from the target volcano, the model demonstrated accuracy comparable to direct training on the target and exceeded benchmarks based on seismic amplitude. These results indicate that eruption precursors exhibit ergodicity, sharing common patterns that allow observations from one group of volcanoes to approximate the behavior of others. This approach addresses data limitations at individual sites and provides a useful tool to support monitoring efforts at volcano observatories, improving the ability to forecast eruptions and mitigate volcanic risks.

Suggested Citation

  • Alberto Ardid & David Dempsey & Corentin Caudron & Shane Cronin & Ben Kennedy & Társilo Girona & Diana Roman & Craig Miller & Sally Potter & Oliver D. Lamb & Anto Martanto & Yesim Cubuk-Sabuncu & Leon, 2025. "Ergodic seismic precursors and transfer learning for short term eruption forecasting at data scarce volcanoes," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56689-x
    DOI: 10.1038/s41467-025-56689-x
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    References listed on IDEAS

    as
    1. D. E. Dempsey & S. J. Cronin & S. Mei & A. W. Kempa-Liehr, 2020. "Automatic precursor recognition and real-time forecasting of sudden explosive volcanic eruptions at Whakaari, New Zealand," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    2. Willy Aspinall, 2010. "A route to more tractable expert advice," Nature, Nature, vol. 463(7279), pages 294-295, January.
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