Trans-Scale Magnetotelluric Inversion via Deep Learning Guided by the Principle of Physical Similarity and Application

Hui Chen; Chongwei Yuan; Juzhi Deng; Hui Yu; Tuanfu Gui; Min Yin

doi:https://doi.org/10.5194/egusphere-egu26-9602

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EGU26-9602, updated on 14 Mar 2026

https://doi.org/10.5194/egusphere-egu26-9602

EGU General Assembly 2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Trans-Scale Magnetotelluric Inversion via Deep Learning Guided by the Principle of Physical Similarity and Application

Hui Chen^1,2, Chongwei Yuan², Juzhi Deng^1,2, Hui Yu^1,2, Tuanfu Gui², and Min Yin²

Hui Chen et al.

¹National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing, East China University of Technology, Nanchang, China(huich@ecut.edu.cn)
²School of Geophysics and Space Exploration, East China University of Technology, Nanchang, China.

How to cite: Chen, H., Yuan, C., Deng, J., Yu, H., Gui, T., and Yin, M.: Trans-Scale Magnetotelluric Inversion via Deep Learning Guided by the Principle of Physical Similarity and Application, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9602, https://doi.org/10.5194/egusphere-egu26-9602, 2026.

This abstract has been withdrawn after no-show on 11 Aug 2026.