# Electromagnetic Imaging for Buried Conductors Using Deep Convolutional Neural Networks
In 2019, [PERSON] proposed to use a self-adaptive dynamic differential evolution method to reconstruct the periodic homogeneous dielectric object buried in rough surfaces [9]. However, these methods will spend a lot of time in iterative calculation of the complex Green's function. In 2020, [PERSON] proposed an accurate and robust method to reconstruct buried objects for half-space scenarios. First, the linear sampling method (LSM) was employed to estimate the targets. A relaxed error threshold was defined for LSM to ensure all real targets had been included. Second, differential evolution optimization was used to refine the LSM results [10]. In 2022, [PERSON] proposed a method for reconstructing media objects buried in three layers of media by using 2D projection as well as the shadow projections of 3D images. The proposed method could reconstruct the size and dimension of uniform objects significantly but could not identify the material of the object [11]. For neural network mechanisms, in 2021, [PERSON] provided a robust and efficient data-based strategy for underground metal target detection on portable devices with limited computing capability and energy supply applications. This cross-combination strategy of dimensionality reduction methods and machine learning models provided a means to find the optimal machine learning model for underground target detection [12]. Nevertheless, it could not reconstruct the shape of the object. One of the studies in half-space is using ground penetrating radar (GPR) to detect buried objects and earth layers by transmitting the electromagnetic wave time domain pulses of different frequencies. This also demonstrates that electromagnetic backscattering can be used in a wide range of applications such as military surveys, medical imaging, and industrial applications such as underground gas or electrical pipelines. In GPR, [PERSON] proposed a pipeline for buried landmine detection based on the convolutional neural network (CNN) and GPR images. The validation of the presented system was carried out on real GPR acquisitions, and it was possible to reach 95% of detection accuracy in 2017 [13]. In 2018, [PERSON] and [PERSON] developed a method for identifying subsurface objects from GPR images using deep neural networks in the time domain. In this study, features of subsurface objects were extracted and learned from generated GPR images by a 9-layer convolutional neural network. The results showed that the CNN could identify different materials with approximately 80% accuracy in heterogeneous subsurface media [14]. [PERSON] proposed a novel multilevel deep learning-based algorithm for GPR B-scan by buried object detection. An efficient layer-by-layer training approach was formulated to learn the deep dictionaries and different classifiers of types of shapes of buried objects [15]. However, it could not reconstruct the arbitrary shape of metal. In 2021, [PERSON] investigated the recovery performance of a specific and unconventional contactless multistatic GPR system for the subsurface imaging of antitank and antipersonnel plastic mines [16]. In GPR application, [PERSON] proposed a CNN model for predicting the size of buried objects from the GPR B-Scan. This method demonstrated good performance in predicting buried object sizes in 2022 [17]. In the above articles, only the position or size of the objects were being reconstructed, not the shape of the objects. In brief, GPR transmits time domain pulses of electromagnetic waves at various frequencies for buried objects and soil layers. Compared with GPR, we apply the time-harmonic field, which has only a single frequency, to transmit the electromagnetic waves. This implies that it would be more difficult to reconstruct the object in the frequency domain. In this paper, we propose a seep convolutional neural network (DCNN) to solve the buried conductor in the frequency domain. We use the measured scattered fields as the input data to the DCNN for training.