IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i23p3873-d1539992.html
   My bibliography  Save this article

Geophysical Frequency Domain Electromagnetic Field Simulation Using Physics-Informed Neural Network

Author

Listed:
  • Bochen Wang

    (School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
    Hunan Key Laboratory of Nonferrous Resources and Geological Hazard Exploration, Changsha 410083, China)

  • Zhenwei Guo

    (School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
    Hunan Key Laboratory of Nonferrous Resources and Geological Hazard Exploration, Changsha 410083, China)

  • Jianxin Liu

    (School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
    Hunan Key Laboratory of Nonferrous Resources and Geological Hazard Exploration, Changsha 410083, China)

  • Yanyi Wang

    (School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
    Hunan Key Laboratory of Nonferrous Resources and Geological Hazard Exploration, Changsha 410083, China)

  • Fansheng Xiong

    (Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, Beijing 101408, China)

Abstract

Simulating electromagnetic (EM) fields can obtain the EM responses of geoelectric models at different times and spaces, which helps to explain the dynamic process of EM wave propagation underground. EM forward modeling is regarded as the engine of inversion. Traditional numerical methods have certain limitations in simulating the EM responses from large-scale geoelectric models. In recent years, the emerging physics-informed neural networks (PINNs) have given new solutions for geophysical EM field simulations. This paper conducts a preliminary exploration using PINN to simulate geophysical frequency domain EM fields. The proposed PINN performs self-supervised training under physical constraints without any data. Once the training is completed, the responses of EM fields at any position in the geoelectric model can be inferred instantly. Compared with the finite-difference solution, the proposed PINN performs the task of geophysical frequency domain EM field simulations well. The proposed PINN is applicable for simulating the EM response of any one-dimensional geoelectric model under any polarization mode at any frequency and any spatial position. This work provides a new scenario for the application of artificial intelligence in geophysical EM exploration.

Suggested Citation

  • Bochen Wang & Zhenwei Guo & Jianxin Liu & Yanyi Wang & Fansheng Xiong, 2024. "Geophysical Frequency Domain Electromagnetic Field Simulation Using Physics-Informed Neural Network," Mathematics, MDPI, vol. 12(23), pages 1-18, December.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:23:p:3873-:d:1539992
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/23/3873/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/23/3873/
    Download Restriction: no
    ---><---

    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:jmathe:v:12:y:2024:i:23:p:3873-:d:1539992. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.