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Geologic interpretation results from conventional ground-penetrating radar (GPR) detection methods tend to have a certain degree of uncertainty. In order to improve the reliability of ground-penetrating radars in the detection of rock mass fissures in grottoes, this study proposes a ground-penetrating radar detection method with the logging data constraint, which is applied to detect the fractured rock mass in the Baodingshan Scenic Area of Dazu Rock Carvings, Chongqing, China. First, conventional logging and borehole televiewer data were compared and verified, yielding detailed lithological and wellbore fissure information. Next, electromagnetic wave velocity was calibrated using GPR profile and the depth of the stratigraphic interface determined by borehole data. Utilizing this calibrated velocity, we are able to accurately calculate the depth values of anomalies in GPR interpretation profiles. Subsequently, we compared the preliminary GPR interpretation profile with the borehole televiewer images. After eliminating false anomalies caused by interference, we obtained more reliable location information for detection targets such as fissures, fracture zones, and weak interlayers. The results of fissure detection in the Dazu Rock Carvings indicate that the detection results of ground-penetrating radar are verified and supplemented under the constraints of stratigraphic and well-wall fissure information obtained by logging. This effectively mitigates the influence of multiplicity and false anomalies of GPR detection on interpretation results. GPR with the logging data constraint enhances the accuracy of the fissure detection results, providing novel technical means for the protection and restoration of grotto relics.

Ground-penetrating radar (GPR) is an important tool for measuring soil water content (SWC) at the field scale. The amplitude analysis of the early time signal (ETS) of GPR may provide a rapid way to estimate SWC. By assuming a homogeneous medium, various studies have been conducted on the relationship between the amplitude of ETS and the topsoil layer’s electromagnetic parameters (dielectric permittivity and conductivity) through numerical simulations, laboratory experiments, and field experiments. Soil is a typical inhomogeneous medium, and soil cultivation is a factor affecting its heterogeneity. In this context, we discuss the ability of the amplitude of ETS to estimate the water content of heterogeneous soil. First, we establish a multi-scale stochastic medium model with the inhomogeneous distribution of dielectric permittivity and conductivity and simulate the GPR response by the finite-difference time-domain (FDTD) method to observe the influence of medium heterogeneity on the GPR response. The heterogeneity of the soil models is evaluated by a geostatistical analysis described by two parameters, correlation length and variability. Then, we analyze the relationship between variability and the average envelope amplitude (AEA) of ETS. A strong soil heterogeneity increases the error of the AEA method in estimating SWC. Finally, the AEA method is used to estimate the SWC of two adjacent fields with different heterogeneities, which were caused by different cultivation methods. The results of the numerical simulation and field experiment indicate that the soil heterogeneity can have an impact on the estimation of SWC using EST, with an error lower than 3% within a depth range of 1/2 λ to λ (wavelength). This suggests that the EST of GPR can be applied to soil layers with relatively large lateral changes in water content.

Ground-penetrating radar (GPR) is an efficient and nondestructive geophysical method with great potential for detecting soil water content at the farmland scale. However, a key challenge in soil detection is obtaining soil water content rapidly and in real-time. In recent years, deep learning methods have become more widespread in the earth sciences, making it possible to use them for soil water content inversion from GPR data. In this paper, we propose a neural network framework GPRSW based on deep learning of GPR data. GPRSW is an end-to-end network that directly inverts volumetric soil water content (VSWC) through single-channel GPR data. Synthetic experiments show that GPRSW accurately identifies different VSWC boundaries in the model in time depth. The predicted VSWC and model fit well within 40 ns, with a maximum error after 40 ns of less than 0.10 cm3 × cm−3. To validate our method, we conducted GPR measurements at the experimental field of the Academy of Agricultural Sciences in Gongzhuling City, Jilin Province and applied GPRSW to VSWC measurements. The results show that predicted values of GPRSW match with field soil samples and are consistent with the overall trend of the TDR soil probe samples, with a maximum difference not exceeding 0.03 cm3 × cm−3. Therefore, our study shows that GPRSW has the potential to be applied to obtain soil water content from GPR data on farmland.