IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-29681-y.html
   My bibliography  Save this article

Seismic precursors to the Whakaari 2019 phreatic eruption are transferable to other eruptions and volcanoes

Author

Listed:
  • Alberto Ardid

    (University of Canterbury)

  • David Dempsey

    (University of Canterbury)

  • Corentin Caudron

    (Université Libre de Bruxelles)

  • Shane Cronin

    (University of Auckland)

Abstract

Volcanic eruptions that occur without warning can be deadly in touristic and populated areas. Even with real-time geophysical monitoring, forecasting sudden eruptions is difficult, because their precursors are hard to recognize and can vary between volcanoes. Here, we describe a general seismic precursor signal for gas-driven eruptions, identified through correlation analysis of 18 well-recorded eruptions in New Zealand, Alaska, and Kamchatka. The precursor manifests in the displacement seismic amplitude ratio between medium (4.5–8 Hz) and high (8–16 Hz) frequency tremor bands, exhibiting a characteristic rise in the days prior to eruptions. We interpret this as formation of a hydrothermal seal that enables rapid pressurization of shallow groundwater. Applying this model to the 2019 eruption at Whakaari (New Zealand), we describe pressurization of the system in the week before the eruption, and cascading seal failure in the 16 h prior to the explosion. Real-time monitoring for this precursor may improve short-term eruption warning systems at certain volcanoes.

Suggested Citation

  • Alberto Ardid & David Dempsey & Corentin Caudron & Shane Cronin, 2022. "Seismic precursors to the Whakaari 2019 phreatic eruption are transferable to other eruptions and volcanoes," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29681-y
    DOI: 10.1038/s41467-022-29681-y
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-29681-y
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-29681-y?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. 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. A. Mark Jellinek & David Bercovici, 2011. "Seismic tremors and magma wagging during explosive volcanism," Nature, Nature, vol. 470(7335), pages 522-525, February.
    3. Markus Reichstein & Gustau Camps-Valls & Bjorn Stevens & Martin Jung & Joachim Denzler & Nuno Carvalhais & Prabhat, 2019. "Deep learning and process understanding for data-driven Earth system science," Nature, Nature, vol. 566(7743), pages 195-204, February.
    4. S. Mostafa Mousavi & William L. Ellsworth & Weiqiang Zhu & Lindsay Y. Chuang & Gregory C. Beroza, 2020. "Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    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. Corentin Caudron & Yosuke Aoki & Thomas Lecocq & Raphael Plaen & Jean Soubestre & Aurelien Mordret & Leonard Seydoux & Toshiko Terakawa, 2022. "Hidden pressurized fluids prior to the 2014 phreatic eruption at Mt Ontake," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    2. Wang, Jun & Cao, Junxing, 2024. "Reservoir properties inversion using attention-based parallel hybrid network integrating feature selection and transfer learning," Energy, Elsevier, vol. 304(C).
    3. Licheng Liu & Wang Zhou & Kaiyu Guan & Bin Peng & Shaoming Xu & Jinyun Tang & Qing Zhu & Jessica Till & Xiaowei Jia & Chongya Jiang & Sheng Wang & Ziqi Qin & Hui Kong & Robert Grant & Symon Mezbahuddi, 2024. "Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    4. Rozenstein, Offer & Fine, Lior & Malachy, Nitzan & Richard, Antoine & Pradalier, Cedric & Tanny, Josef, 2023. "Data-driven estimation of actual evapotranspiration to support irrigation management: Testing two novel methods based on an unoccupied aerial vehicle and an artificial neural network," Agricultural Water Management, Elsevier, vol. 283(C).
    5. Jiang, Hou & Lu, Ning & Huang, Guanghui & Yao, Ling & Qin, Jun & Liu, Hengzi, 2020. "Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data," Applied Energy, Elsevier, vol. 270(C).
    6. Wen Zhang & Jing Li & Yunhao Chen & Yang Li, 2019. "A Surrogate-Based Optimization Design and Uncertainty Analysis for Urban Flood Mitigation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4201-4214, September.
    7. Feng, Jiaojiao & Wang, Weizhen & Xu, Feinan & Wang, Shengtang, 2024. "Evaluating the ability of deep learning on actual daily evapotranspiration estimation over the heterogeneous surfaces," Agricultural Water Management, Elsevier, vol. 291(C).
    8. Mohanad A. Deif & Ahmed A. A. Solyman & Mohammed H. Alsharif & Seungwon Jung & Eenjun Hwang, 2021. "A Hybrid Multi-Objective Optimizer-Based SVM Model for Enhancing Numerical Weather Prediction: A Study for the Seoul Metropolitan Area," Sustainability, MDPI, vol. 14(1), pages 1-17, December.
    9. Zhang, Shuangyi & Li, Xichen, 2021. "Future projections of offshore wind energy resources in China using CMIP6 simulations and a deep learning-based downscaling method," Energy, Elsevier, vol. 217(C).
    10. Florian Reiner & Martin Brandt & Xiaoye Tong & David Skole & Ankit Kariryaa & Philippe Ciais & Andrew Davies & Pierre Hiernaux & Jérôme Chave & Maurice Mugabowindekwe & Christian Igel & Stefan Oehmcke, 2023. "More than one quarter of Africa’s tree cover is found outside areas previously classified as forest," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    11. Wang, Yukuan & Liu, Jingxian & Liu, Ryan Wen & Wu, Weihuang & Liu, Yang, 2023. "Interval prediction of vessel trajectory based on lower and upper bound estimation and attention-modified LSTM with bayesian optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    12. Gianluca Biggi & Martina Iori & Julia Mazzei & Andrea Mina, 2024. "Green Intelligence: The AI content of green technologies," LEM Papers Series 2024/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    13. He, Xinlei & Liu, Shaomin & Xu, Tongren & Yu, Kailiang & Gentine, Pierre & Zhang, Zhe & Xu, Ziwei & Jiao, Dandan & Wu, Dongxing, 2022. "Improving predictions of evapotranspiration by integrating multi-source observations and land surface model," Agricultural Water Management, Elsevier, vol. 272(C).
    14. Wang, Yangjun & Liu, Kefeng & Zhang, Ren & Qian, Longxia & Shan, Yulong, 2021. "Feasibility of the Northeast Passage: The role of vessel speed, route planning, and icebreaking assistance determined by sea-ice conditions for the container shipping market during 2020–2030," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    15. Richards, Daniel Rex & Lavorel, Sandra, 2022. "Integrating social media data and machine learning to analyse scenarios of landscape appreciation," Ecosystem Services, Elsevier, vol. 55(C).
    16. Abrasaldo, Paul Michael B. & Zarrouk, Sadiq J. & Kempa-Liehr, Andreas W., 2024. "A systematic review of data analytics applications in above-ground geothermal energy operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    17. Galaz, Victor & Centeno, Miguel A. & Callahan, Peter W. & Causevic, Amar & Patterson, Thayer & Brass, Irina & Baum, Seth & Farber, Darryl & Fischer, Joern & Garcia, David & McPhearson, Timon & Jimenez, 2021. "Artificial intelligence, systemic risks, and sustainability," Technology in Society, Elsevier, vol. 67(C).
    18. Wan, Zijing & Wei, Fulong & Peng, Jiale & Deng, Chao & Ding, Siqi & Xu, Dongwei & Luo, Xiaobing, 2023. "Application of physical model-based machine learning to the temperature prediction of electronic device in oil-gas exploration logging," Energy, Elsevier, vol. 282(C).
    19. Hood, Raleigh R. & Shenk, Gary W. & Dixon, Rachel L. & Smith, Sean M.C. & Ball, William P. & Bash, Jesse O. & Batiuk, Rich & Boomer, Kathy & Brady, Damian C. & Cerco, Carl & Claggett, Peter & de Mutse, 2021. "The Chesapeake Bay program modeling system: Overview and recommendations for future development," Ecological Modelling, Elsevier, vol. 456(C).
    20. Jennie Molinder & Sebastian Scher & Erik Nilsson & Heiner Körnich & Hans Bergström & Anna Sjöblom, 2020. "Probabilistic Forecasting of Wind Turbine Icing Related Production Losses Using Quantile Regression Forests," Energies, MDPI, vol. 14(1), pages 1-19, December.

    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:13:y:2022:i:1:d:10.1038_s41467-022-29681-y. 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.