# Recognition of Tunnel Fracture Zones in Seismic Waves and Ground-Penetrating Radar Data
## 1 Introduction  
Tunnel construction areas are often geologically complex, and the rock body in the construction area contains numerous rock fragmentation zones, which often leads to the collapse of these zones. Therefore, accurately forecasting the fracture zones within the rock mass becomes an indispensable step. The accuracy of tunnel prediction can be improved by using a combination of geophysical methods. The seismic wave method is often used in long-distance detection to identify hazardous areas in front of tunnels, as it is more sensitive to fractured rock masses. Currently, the seismic reflection method represented by tunnel seismic prediction (TSP) [1], known as true reflection tomography (TRT) [2], is the dominant method in the advanced prediction of hazardous areas in tunnels. With the development of seismic exploration technology for tunnels, migration imaging in the critical stage of tunnel seismic processing is becoming increasingly precise, and the diffraction stacking migration method [3] has been widely applied. In addition, seismic advanced prediction methods also include tunnel seismic tomography (TST) systems [4], TGP tunnel construction advanced geological prediction (TGP), and other advanced detection methods for use in tunnels. In 2015, [PERSON] et al. [5] predicted changes in the geological conditions in front of the palm face in advance based on the engineering geological background of the tunnel, combined with the seismic wave reflection characteristics of typical geological hazards in karst tunnels. They thus proved the feasibility of using earthquake methods for advanced prediction. In 2017, [PERSON] et al. [6] used active-source surface-tunnel-surface seismic data to analyze the structure of the rock between a tunnel and the surface; these authors used physical parameters,such as the P-wave and S-wave velocities, to characterize fracture zones. These fracture zones were characterized on a scale corresponding to seismic exploration waves. In 2020, [PERSON] et al. [7] applied a seismic wave advanced prediction method to tunnel detection, identified unstable areas, and successfully detected and verified the positions of river faults and fracture zones. In 2023, [PERSON] et al. [8] proposed a 3c-3d tunnel seismic imaging method using acoustic wave equations for forward and backward wave field extrapolation. This method effectively eliminated the imaging artifacts of converted waves and accurately determined the spatial positions of faults and cracks in front of the tunnel. In 2014, [PERSON] et al. [9] proposed an optimized classification method for advanced prediction in tunnels based on the fuzzy analytic hierarchy process (FAHP). By using the comprehensive weight method to determine the reasonable weights of each evaluation index, an optimized FAHP evaluation model for the rock surrounding a given tunnel was established, and a hierarchical analysis of surrounding rock optimization classification was achieved. Detection with ground-penetrating radar (GPR) [10] is the main method of short-range prediction. This method involves analyzing waveform characteristics through changes in the reflection coefficient and attenuation coefficient of the medium [11,12]; predicting fractured rock masses, water bodies, and karst caves before the excavation of underground tunnels; and inspecting tunnel lining defects. In 2011, [PERSON] et al. [13] evaluated the applicability of the GPR method for locating large fractures. Their findings indicated that ground-penetrating radar is effective and useful in measuring the surfaces of tunnels. In 2011, [PERSON] et al. [14] achieved the automatic identification and positioning of tunnel lining layers through the characteristics of variations in the Fresnel reflection coefficient of electromagnetic waves in the tunnel lining and the peak-point characteristics of a single waveform. In 2019, [PERSON] et al.