Road safety is important for the rapid development of the economy and society. Thus, it is of great significance to monitor the dynamic changing processes of road diseases, such as cavities, to provide a basis for the daily maintenance of roads and prevent any possible car accidents. The ground penetrating radar (GPR) technology is widely used in road disease detection due to its advantages of nondestructiveness, rapidness, and high resolution. Traditionally, one-time 2D GPR detection cannot obtain the 3D spatial changes of subgrades. Thus, we developed a road subgrade monitoring method based on the time-lapse full-coverage (TLFC) 3D GPR technique by focusing on solving the key problems of time and spatial position mismatches in experimental data. Moreover, we used the time zero consistency correction, 3D data combination, and spatial position matching methods, as they greatly improve the 3D imaging quality of underground spaces. Finally, the time-lapse attribute analysis method was used in the TLFC 3D GPR data to obtain detailed characteristics and an overall rule of the dynamic subgrade change. Overall, this research proves that TLFC 3D GPR is an optimal choice for road subgrade monitoring.

The development of urban underground space (UUS) has become an effective way to solve the problem of urban space. Seismic methods have achieved good results in underground engineering construction, active fault detection, urban geologic structure detection, and other fields. Active source seismic methods in UUS include reflection methods, refraction methods, surface wave methods, andvertical seismic profile methods. Interest in the ambient noise method is also rising gradually, with it becoming an important method of urban underground space detection in the future because it overcomes the problem that the active source method is interfered with by urban noise. We evaluate these methods and develop application examples of UUS.

Ground-penetrating radar (GPR) profiling is the primary tool to provide detail information of the internal structure and characteristics inside a sand dune in the desert area. However, with the severe elevation change in a short horizontal distance and on the rugged and complex surface topography of the sand dunes, getting clear imaging of the internal structure of sand dunes from the GPR profiling is still a big challenge. The classic imaging technique such as the Kirchhoff migration has been applied to process GPR data on sand dunes, with limited success. The reverse-time migration (RTM) technique is the most advanced imaging technique that can handles GPR data acquired on rugged surface with severe topographic relief to generate subsurface structural images with high fidelity and has been tried to process GPR data on sand dunes. The results are encouraging, and the imaging quality is significantly improved. The finite-difference time-domain (FDTD) method is the major numerical tool for forward and backward continuations of the wave field during the RTM process. There are two aspects for using FDTD in RTM of GPR data on sand dunes still need improvement: the numerical scattering caused by the staircase approximation of the ground surface by using gridding in the Cartesian coordinate, and the negligence of the possible anisotropy of the electromagnetic material properties due to the calcareous cementation bedding inside a sand dune. In this paper, we develop the RTM algorithm based on the staggered grid FDTD that handles the rugged topographic surface by using the curvilinear coordinate, and the possible anisotropic radar wave velocity of the sand dune media. We first demonstrate the equivalency of the nonuniform, isotropic medium and the uniform, anisotropic medium for justifying using the uniform, anisotropic velocity in the RTM by synthetic modeling. Next, we validate our approach of using the synthetic data with the comparison of using the Cartesian coordinate and the curvilinear coordinate in an isotropic medium. The results indicate that the RTM algorithm using the curvilinear coordinate can efficiently eliminate the adverse effect of the staircase approximated boundary of the topography surface. Finally, we processed the real GPR data collected on a sand dune in the Badain Jaran desert by using the curvilinear coordinate and the uniform, anisotropic velocity in FDTD forward and backward wave field continuation. Comparison of the results indicates that the RTM imaging using the boundary-conforming curvilinear coordinate and anisotropic velocity gains more coherent and higher resolution images for the calcareous cementation layers and the water table.

As the amount of ground-penetrating radar (GPR) data increases significantly with the high demands of nondestructive detection methods under urban roads, a method suitable for time-lapse data dynamic monitoring should be developed to quickly identify targets on GPR profiles and compare time-lapse datasets. This study conducted a field experiment aiming to monitor one backfill pit using three-dimensional GPR (3D GPR), and the time-lapse data collected over four months were used to train U-Net, a fast neural network based on convolutional neural networks (CNNs). Consequently, a trained network model that could effectively segment the backfill pit from inline profiles was obtained, whose Intersection over Union (IoU) was 0.83 on the test dataset. Moreover, segmentation masks were compared, demonstrating that a change in the southwest side of the backfill pit may exist. The results demonstrate the potential of machine learning algorithms in time-lapse 3D GPR data segmentation and dynamic monitoring.

An unmanned aerial vehicle (UAV) seismic source, suitable for complex terrains due to its eco-friendly, cost-effective, and efficient nature, operates by hovering and releasing impact objects to generate seismic waves. The aerodynamic behavior of these impact objects is vital for shotpoint position accuracy and drag magnitude. Excessive drag will result in the loss of seismic source energy, whereas the deviations of shotpoint position will disrupt the geometric structure of the observation system, significantly impacting the quality of seismic data acquisition. Through theoretical calculations, the influencing factors of air resistance and cross-wind deflection are analyzed. Air resistance increases with the increase in mass, cross-sectional area, speed, and drag coefficient. Increasing the mass and throwing height can increase the upper limit of the output, but after the air resistance and gravity are balanced, the output kinetic energy of the drone’s seismic source will no longer increase when the throwing height is increased. Using computational fluid dynamics simulations and field tests, this study examines the aerodynamics of four prevalent impact object designs. The results show that shuttle and spherical structures have the least drag during descent and are less influenced by crosswinds, thus being optimal for this application. Field measurement data are also presented for reference.