## 1 Introduction  
With the development process of urbanization, people require more scientific and reasonable planning and design for urban infrastructure. This requires us to carry out more accurate geological surveys and comprehensive measurements. At the same time, it is also necessary for us to arrange the corresponding monitoring and recording work due to the increase in the service life of buildings. The external surfaces of existing building structures can be directly observed and analyzed with the naked eye, but it is difficult to assess material loss within. Similarly, for underground structures, while previous geological survey reports and engineering records can provide insight, data loss and changes in underground media due to external influences such as the environment are also common. This undoubtedly requires us to choose an appropriate testing method. Ground-penetrating radar (GPR) is used as a geophysical method to identify near-surface subsurface target bodies by exciting high-frequency pulsed electromagnetic waves [1, 2]. In recent years, its applications have become more and more extensive. For example, it has been applied in the analysis of historical buildings [3, 4] and typical case detection [5, 6], for crack detection of urban highway roads [7, 8, 9, 10], and in the evaluation of the maintenance effect of concrete-based construction materials [11, 12]. Compared with traditional detection methods in the civil engineering field, it has unique advantages such as non-destructiveness and high resolution in recognizing the target body. Therefore, the use of the GPR method will be a wiser choice. GPR imaging involves analysis of electromagnetic waves reflected from the subsurface medium. Normally, we scan and collect data along survey lines and proceed to two-dimensional (2D) imaging analysis. The information in the profile is observed to infer the subsurface medium characteristics. Based on the magnitude of the difference between the dielectric constant of the target and the surrounding medium, we can find the corresponding \"hyperbolic\" reflections, and thus make preliminary inferences about the location and shape of the target [13; 14]. However, it is difficult to obtain the exact geometry of the target from the profile data obtained from a single measurement line. In this case, we can recognize the target more intuitively by adopting a three-dimensional (3D) imaging method [15; 16; 17], which arranges the 2D images in the form of slices. At present, 3D imaging application technology based on GPR is becoming increasingly mature, with detection in archaeological applications [18; 19], including the detection of underground graves [20; 21], etc. The resolution of the radar data collected using GPR equipment at the actual site is directly related to the center frequency of the selected GPR equipment. At the same time, due to the complexity of the underground medium of the site, the accuracy of the information brought by C-scan is still debatable. At this point, we can use the corresponding attribute extraction methods for further analysis. Through the Hilbert transform, the real signal can be transformed into a complex signal, and instantaneous amplitude, instantaneous frequency, and instantaneous phase can be extracted as instantaneous properties to predict the properties of underground media [22; 23; 24; 25]. The time-varying centroid frequency can be used to track the change in signal dominant frequency with time and identify the target [26; 27; 28]. At the same time, the use of machine learning [29; 30] can optimize the effectiveness of the selected method. In this study, we established multiple survey lines in a grid-like intersection at the experimental site and utilized 3D imaging to visualize the target body geometry and other relevant information. The experiment aims to analyze the current underground structure of the site.