Electromagnetic Inverse Scattering of Buried Conductors in Half-Space Using TE Waves and Deep Convolutional Neural Networks

Electromagnetic Inverse Scattering (EMIS) is a key technology for reconstructing the geometry of buried conductors based on measured scattered fields. This research employs a Deep Convolutional Neural Network (DCNN) to address the intrinsic nonlinear characteristics in reconstructing perfect conductors in a half-space using transverse electric (TE) waves. First, TE electromagnetic waves are transmitted to illuminate the conductor, and the corresponding scattered fields are collected. Subsequently, the scattered field is fed into the DCNN to reconstruct the precise shape of the conductor. Considering that real-world measurements may contain noise, we add 5%, 10%, and 20% noise levels in the simulation. Numerical results for various conductor geometries, including shield, oval, arrow, heart, and four-petal shapes, demonstrate the reconstruction capability of the proposed method under the investigated Gaussian noise levels. These results demonstrate the feasibility of applying a DCNN to the reconstruction of buried conductors under TE-wave illumination.