IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-025-55994-9.html
   My bibliography  Save this article

Automatic speech recognition predicts contemporaneous earthquake fault displacement

Author

Listed:
  • Christopher W. Johnson

    (EES-17 National Security Earth Science)

  • Kun Wang

    (EES-17 National Security Earth Science
    Energy Sciences Research Division)

  • Paul A. Johnson

    (EES-17 National Security Earth Science)

Abstract

Significant progress has been made in probing the state of an earthquake fault by applying machine learning to continuous seismic waveforms. The breakthroughs were originally obtained from laboratory shear experiments and numerical simulations of fault shear, then successfully extended to slow-slipping faults. Here we apply the Wav2Vec-2.0 self-supervised framework for automatic speech recognition to continuous seismic signals emanating from a sequence of moderate magnitude earthquakes during the 2018 caldera collapse at the Kīlauea volcano on the island of Hawai’i. We pre-train the Wav2Vec-2.0 model using caldera seismic waveforms and augment the model architecture to predict contemporaneous surface displacement during the caldera collapse sequence, a proxy for fault displacement. We find the model displacement predictions to be excellent. The model is adapted for near-future prediction information and found hints of prediction capability, but the results are not robust. The results demonstrate that earthquake faults emit seismic signatures in a similar manner to laboratory and numerical simulation faults, and artificial intelligence models developed for encoding audio of speech may have important applications in studying active fault zones.

Suggested Citation

  • Christopher W. Johnson & Kun Wang & Paul A. Johnson, 2025. "Automatic speech recognition predicts contemporaneous earthquake fault displacement," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-55994-9
    DOI: 10.1038/s41467-025-55994-9
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-025-55994-9
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-025-55994-9?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. Kun Wang & Christopher W. Johnson & Kane C. Bennett & Paul A. Johnson, 2021. "Predicting fault slip via transfer learning," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    2. Claudia Hulbert & Bertrand Rouet-Leduc & Romain Jolivet & Paul A. Johnson, 2020. "An exponential build-up in seismic energy suggests a months-long nucleation of slow slip in Cascadia," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    3. 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.
    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. Gou, Liangjie & Yang, Zhaozhong & Min, Chao & Yi, Duo & Li, Xiaogang & Kong, Bing, 2024. "A novel domain adaptation method with physical constraints for shale gas production forecasting," Applied Energy, Elsevier, vol. 371(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.
    3. Daniel Rathmaier & Fawz Naim & Andikan Charles William & Dwaipayan Chakraborty & Christopher Conwell & Matthias Imhof & Gordon M. Holmes & Luis E. Zerpa, 2024. "A Reservoir Modeling Study for the Evaluation of CO 2 Storage Upscaling at the Decatur Site in the Eastern Illinois Basin," Energies, MDPI, vol. 17(5), pages 1-18, March.
    4. 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.
    5. Asher, Eitan E. & Havlin, Shlomo & Moshel, Shay & Ashkenazy, Yosef, 2023. "Increased earthquake rate prior to mainshocks," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    6. 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).
    7. Li, Dan & Li, Yijun & Wang, Chaoqun & Chen, Min & Wu, Qi, 2023. "Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks," Applied Energy, Elsevier, vol. 331(C).
    8. 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.
    9. Weiqiang Zhu & Ettore Biondi & Jiaxuan Li & Jiuxun Yin & Zachary E. Ross & Zhongwen Zhan, 2023. "Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    10. Prabhav Borate & Jacques Rivière & Chris Marone & Ankur Mali & Daniel Kifer & Parisa Shokouhi, 2023. "Using a physics-informed neural network and fault zone acoustic monitoring to predict lab earthquakes," Nature Communications, Nature, vol. 14(1), pages 1-12, 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:16:y:2025:i:1:d:10.1038_s41467-025-55994-9. 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.