IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v3y2021i1p2-36d474465.html
   My bibliography  Save this article

Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network

Author

Listed:
  • Matteo Picozzi

    (Department of Physics, University of Naples Federico II, Complesso Universitatio Monte S. Angelo, via Cinthia, 80126 Naples, Italy)

  • Antonio Giovanni Iaccarino

    (Department of Physics, University of Naples Federico II, Complesso Universitatio Monte S. Angelo, via Cinthia, 80126 Naples, Italy)

Abstract

Earthquakes prediction is considered the holy grail of seismology. After almost a century of efforts without convincing results, the recent raise of machine learning (ML) methods in conjunction with the deployment of dense seismic networks has boosted new hope in this field. Even if large earthquakes still occur unanticipated, recent laboratory, field, and theoretical studies support the existence of a preparatory phase preceding earthquakes, where small and stable ruptures progressively develop into an unstable and confined zone around the future hypocenter. The problem of recognizing the preparatory phase of earthquakes is of critical importance for mitigating seismic risk for both natural and induced events. Here, we focus on the induced seismicity at The Geysers geothermal field in California. We address the preparatory phase of M~4 earthquakes identification problem by developing a ML approach based on features computed from catalogues, which are used to train a recurrent neural network (RNN). We show that RNN successfully reveal the preparation of M~4 earthquakes. These results confirm the potential of monitoring induced microseismicity and should encourage new research also in predictability of natural earthquakes.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:1:p:2-36:d:474465
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/3/1/2/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-9394/3/1/2/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Laura Gulia & Stefan Wiemer, 2019. "Real-time discrimination of earthquake foreshocks and aftershocks," Nature, Nature, vol. 574(7777), pages 193-199, October.
    2. Phoebe M. R. DeVries & Fernanda Viégas & Martin Wattenberg & Brendan J. Meade, 2018. "Deep learning of aftershock patterns following large earthquakes," Nature, Nature, vol. 560(7720), pages 632-634, August.
    3. Shanker, M. & Hu, M. Y. & Hung, M. S., 1996. "Effect of data standardization on neural network training," Omega, Elsevier, vol. 24(4), pages 385-397, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhu, Jingbao & Sun, Wentao & Li, Shanyou & Yao, Kunpeng & Song, Jindong, 2024. "Threshold-based earthquake early warning for high-speed railways using deep learning," Reliability Engineering and System Safety, Elsevier, vol. 250(C).

    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. Luis Alberto Geraldo-Campos & Juan J. Soria & Tamara Pando-Ezcurra, 2022. "Machine Learning for Credit Risk in the Reactive Peru Program: A Comparison of the Lasso and Ridge Regression Models," Economies, MDPI, vol. 10(8), pages 1-21, July.
    2. Zhou, Yuhao & Wang, Yanwei, 2022. "An integrated framework based on deep learning algorithm for optimizing thermochemical production in heavy oil reservoirs," Energy, Elsevier, vol. 253(C).
    3. Samuka Mohanty & Rajashree Dash, 2023. "A New Dual Normalization for Enhancing the Bitcoin Pricing Capability of an Optimized Low Complexity Neural Net with TOPSIS Evaluation," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    4. Joana Dias & Humberto Rocha & Brígida Ferreira & Maria Lopes, 2014. "A genetic algorithm with neural network fitness function evaluation for IMRT beam angle optimization," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(3), pages 431-455, September.
    5. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    6. Miguel Núñez-Peiró & Anna Mavrogianni & Phil Symonds & Carmen Sánchez-Guevara Sánchez & F. Javier Neila González, 2021. "Modelling Long-Term Urban Temperatures with Less Training Data: A Comparative Study Using Neural Networks in the City of Madrid," Sustainability, MDPI, vol. 13(15), pages 1-23, July.
    7. Wenjuan Sun & Paolo Bocchini & Brian D. Davison, 2020. "Applications of artificial intelligence for disaster management," 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. 103(3), pages 2631-2689, September.
    8. Semenoglou, Artemios-Anargyros & Spiliotis, Evangelos & Makridakis, Spyros & Assimakopoulos, Vassilios, 2021. "Investigating the accuracy of cross-learning time series forecasting methods," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1072-1084.
    9. Shigeyuki Hamori & Takahiro Kume, 2018. "Artificial Intelligence And Economic Growth," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 256-278, December.
    10. Satoshi Matsumoto & Yoshihisa Iio & Shinichi Sakai & Aitaro Kato, 2024. "Strength dependency of frequency–magnitude distribution in earthquakes and implications for stress state criticality," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    11. 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.
    12. 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.
    13. James R. Coakley & Carol E. Brown, 2000. "Artificial neural networks in accounting and finance: modeling issues," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(2), pages 119-144, June.
    14. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    15. 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.
    16. Társilo Girona & Kyriaki Drymoni, 2024. "Abnormal low-magnitude seismicity preceding large-magnitude earthquakes," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    17. Zhang, Gioqinang & Hu, Michael Y., 1998. "Neural network forecasting of the British Pound/US Dollar exchange rate," Omega, Elsevier, vol. 26(4), pages 495-506, August.
    18. Qian, Cheng & Xu, Binghui & Chang, Liang & Sun, Bo & Feng, Qiang & Yang, Dezhen & Ren, Yi & Wang, Zili, 2021. "Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries," Energy, Elsevier, vol. 227(C).
    19. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins," Forecasting, MDPI, vol. 5(2), pages 1-15, June.
    20. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins," Papers 2307.08853, arXiv.org.

    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:gam:jforec:v:3:y:2021:i:1:p:2-36:d:474465. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.