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ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
Claim

A deep learning-based parameter inversion for improved Tunnel Electromagnetic Imaging

Research datakeyboard_double_arrow_right Dataset 01 Jun 2024Publisher:Zenodo
Authors: Tang, Rongjiang;

doi: 10.5281/zenodo.11407019

A deep learning-based parameter inversion for improved Tunnel Electromagnetic Imaging

Abstract

Running FTEM_bacth_train.ipynb you can train the UNet frameworks, which transform three-component time-domain induced electromotive force data and three-component frequency-domain magnetic field data, into two mapping results. For details, please refer to the manuscript "A deep learning-based parameter inversion for improved Tunnel Electromagnetic Imaging"

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average