Fast Electromagnetic Inversion of Inhomogeneous Scatterers Embedded in Layered Media by Born Approximation and 3-D U-Net
| Autor: | Jiawen Li, Feng Han, Yanjin Chen, Junping Xiao, Qing Huo Liu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Physics
Scattering Iterative method Monte Carlo method Mathematical analysis 0211 other engineering and technologies Inverse transform sampling Inversion (meteorology) 02 engineering and technology Iterative reconstruction Geotechnical Engineering and Engineering Geology Convolutional neural network Electrical and Electronic Engineering Born approximation 021101 geological & geomatics engineering |
| Zdroj: | IEEE Geoscience and Remote Sensing Letters. 17:1677-1681 |
| ISSN: | 1558-0571 1545-598X |
| DOI: | 10.1109/lgrs.2019.2953708 |
| Popis: | This letter presents a 3-D electromagnetic inversion method based on the Born approximation (BA) and a convolutional neural network (CNN), the 3-D U-Net. In the training stage, the BA is first used to obtain the preliminary 3-D images of a series of homogeneous scatterers with regular shapes that are further improved by the Monte Carlo method. Then, these images are used to train the 3-D U-Net. In the testing stage, inhomogeneous scatterers with complex shapes are reconstructed by both the trained 3-D U-Net and the traditional iterative method, variational Born iteration method (VBIM). Their performance is evaluated and compared. |
| Databáze: | OpenAIRE |
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