The determination of quantitative relationship between soil dielectric constant and water content is an important basis for measuring soil water content based on ground penetrating radar (GPR) technology. The calculation of soil volumetric water content using GPR technology is usually based on the classic Topp formula. However, there are large errors between measured values and calculated values when using the formula, and it cannot be flexibly applied to different media. To solve these problems, first, a combination of GPR and shallow drilling is used to calibrate the wave velocity to obtain an accurate dielectric constant. Then, combined with experimental moisture content, the intelligent group algorithm is applied to accurately build mathematical models of the relative dielectric constant and volumetric water content, and the Topp formula is revised for sand and clay media. Compared with the classic Topp formula, the average error rate of sand is decreased by nearly 15.8%, the average error rate of clay is decreased by 31.75%. The calculation accuracy of the formula has been greatly improved. It proves that the revised model is accurate, and at the same time, it proves the rationality of the method of using GPR wave velocity calibration method to accurately calculate the volumetric water content.

Accurately predicting geological structures (GS) ahead of tunnel excavation is of paramount importance for ensuring safety in coal mines. Standard time or frequency analysis methods face significant challenges due to limitations in downhole environments and ground penetrating radar (GPR) instrument performance, hindering the acquisition of precise geological information. Time-frequency (TF) analysis, capable of simultaneously examining non-stationary signals in both time and frequency domains, has become an important tool for interpreting GPR data. High-order synchrosqueezing transform (HSST) has a solid mathematical foundation and higher TF resolution than linear TF transforms and synchrosqueezing transforms. In this study, we propose a method utilizing HSST to extract TF features of geological structure responses and reconstruct them into novel Bscan for advanced detection of geological structures. Experimental examples using GPR forward simulation data containing GSs demonstrate the method’s precise characterization and intuitive visualization capabilities for GS interfaces, even in noise-polluted environments. Additionally, we present a real GPR data example that demonstrates the method’s ability to intuitively and accurately characterize the front GS interface. Field revelations provide robust verification of the method’s accuracy and practical applicability.