Xianlei Xu

Ultra-deep ground-penetrating radar (GPR) serves as a crucial technical tool for detailed exploration of deep geological structures in mines. Conventional equivalent sampling methods suffer from significant errors in capturing deep reflection signals due to propagation attenuation in the medium. While real-time sampling technology substantially enhances data acquisition efficiency, its resolution capability for deep weak signals remains limited by the voltage amplitude of direct waves. This paper presents a segmented-gain high-speed real-time acquisition system designed for capturing deep, weak signals in GPR. By employing a segmented-gain acquisition control method, it effectively collects deep radar echo signals to enhance the resolution capability for deep signal acquisition. Additionally, a segmented data fusion method is proposed. To validate the segmented-gain technique's effectiveness, the gain characteristics of deep weak signals were systematically analyzed using a 100MHz ultra-deep GPR antenna as an example, confirming the radar's detection capabilities. Experimental results demonstrate that deep weak signals acquired with segmented-gain technology exhibit an average gain of 35dB compared to those collected in the no-gain mode. In confined-space air experiments, the proposed method achieved a detection depth of 26.02m with a detection error of 1.08% in gain mode. This compares to a detection depth of 12.24m and an error of 5.56% in no-gain mode, representing a 13.78m increase in detection depth and a 4.48% reduction in error. In engineering applications, the system enables geological stratum detection at depths up to 18m. This technology provides effective technical support for ultra-deep geological precision surveying in mines.

Tunnel grouting is a commonly used measure for mitigating groundwater leakage in tunnels. However, the actual grout diffusion range within the fissure between the secondary lining and the waterproofing membrane is hidden from direct observation. Conventional geophysical methods struggle to provide a quantitative evaluation of grout diffusion, while the most common drilling-based assessment is not only destructive to the tunnel but also unable to reconstruct the overall grout distribution from a few localized borehole observations. To address this problem, this paper proposes a quantitative grout-diffusion monitoring method based on time-lapse ground-penetrating radar. Multiple time-lapse datasets were acquired during grouting, and a wavelet time–energy density based Structure Similarity Index Measure was introduced to characterize subtle electromagnetic response changes induced by grout injection. Attribute slices were then reconstructed via interpolation, and, combined with the recorded cumulative grouting volume, a quantitative method was developed to estimate the extent of grout-enriched anomalies. Field test results show that the proposed method can reveal the spatiotemporal evolution of grout diffusion inside the lining. The quantitatively delineated grout boundary shows good spatiotemporal agreement with the grout leakage observed at the observation holes. The method provides a non-invasive, high-resolution, and engineering-feasible tool for evaluating grouting effectiveness and supporting targeted grouting construction.

Accurate identification of the coal-rock interface in mines is one of the critical challenges for intelligent and safe coal mining. Air-coupled Ground Penetrating Radar (GPR) has shown unique advantages in coal-rock interface identification. However, constrained by the complex and strong interference geological environment in mines (such as roof supports, gangue in coal seams, faults, and layer pinch-outs), GPR data often suffers from low signal-to-noise ratios (SNR), which severely affects the performance of horizon tracking and directly leads to incorrect horizon identification. This paper proposes a robust coal-rock interface identification model for GPR data with complex interference and low SNR. Compared to conventional coal-rock horizon identification algorithms, this model can: 1) construct an adaptive stacked waveform as a dynamic reference, guiding the tracking by calculating the similarity between the current waveform and this reference, thereby adapting to lateral signal variations and improving the reliability of judgments; 2) implement a jump strategy based on local trend prediction. When the similarity falls below a threshold, indicating tracking difficulties, the algorithm intelligently predicts the current position using the local geological trend estimated from previously tracked horizon points (least squares fitting). This allows it to effectively cross low SNR, structural interference, or data-missing regions while maintaining the continuity of geological structures. Extensive testing and comparative analysis on simulated data with different noise levels, gangue interference, and bad traces, as well as on actual coal-rock interface model data, mine data, and road horizon data, demonstrate that: the proposed method achieves continuous and accurate horizon tracking, especially under low SNR (SNR15%) conditions, where its robustness is significantly superior to traditional tracking methods. The proposed method shows clear advantages in avoiding non-geological 'steps' and preserving geological trends, with fewer jumps and comparable computational efficiency to traditional horizon tracking methods (completing 4200 traces of profile tracking in only 0.215s). Furthermore, it can accurately track low SNR actual mine coal-rock interface horizons and road horizons with pipeline interference, further demonstrating its adaptability in geological radar horizon tracking applications. This research provides an efficient tool for automatic and precise interpretation of GPR coal-rock interfaces under complex conditions, holding significant value for improving geological exploration accuracy and supporting intelligent mining.

During the identification of shallow coal-rock layers using ground penetrating radar (GPR), inversion of the air and coal thicknesses by GPR response is the key to achieving intelligent control over the orientation of the Shearer drum and GPR. The relationship between the offset GPR response and the real thicknesses of complicated shallow layers is uncertain and there is random noise present in the environment. A novel method based on artificial neural network (ANN) is proposed to invert the real thicknesses by the apparent thicknesses picked up from the GPR response. First, a relationship model between the real thicknesses and the air-coupled GPR apparent thicknesses of the complex double layers is established through qualitative analysis. Then two ANN architecture schemes are proposed for inverting the real thicknesses of air and coal, respectively. Afterwards, the ANN models are trained by simulation data and are then improved with regard to network input, network structure, and dataset size. The robustness of this approach is then examined under various trace interval and signal-to-noise ratio (SNR) scenarios. Finally, the proposed method is applied to physical model experiments and contrasted with traditional methods. The results show that the proposed method significantly improves the accuracy of the air and coal thicknesses measured by air-coupled GPR. The maximum and average errors of the air thicknesses are 4.97 mm and 1.15 mm, respectively, with those of the coal thicknesses of 6.04 mm and 1.60 mm. This method can achieve high-precision measurement of shallow layer thicknesses, supporting coal mining automation.

This study is based on in situ structural test sections and systematically explains the construction processes and key control points of different waterproofing methods by optimizing the self-waterproofing of structural concrete, controlling the installation process of external waterproofing membranes, and managing quality throughout the construction process. For various materials such as polymer-coated waterstops, steel-edged rubber waterstops, and composite grouting pipes with water-swelling strips, the waterproofing performance under the corresponding processes was analyzed through a combination of experiments and numerical simulations. The research focuses on investigating the influence of material selection and construction techniques on waterproofing effectiveness, clarifying the applicable conditions and performance differences among various materials and techniques. The results indicate that polymer-coated waterstops perform significantly better than other materials; self-compacting concrete causes minimal disturbance to waterstops, which is beneficial for waterproofing, but it exhibits deficiencies in early-age crack resistance; refined control of construction techniques plays a decisive role in the overall performance of the waterproofing system. Consequently, detailed construction quality control specifications for the main structure and its components were developed.

This study investigates the performance of various waterproofing techniques based on an in-situ experimental tunnel section. Through optimized design of structural concrete permeability resistance, controlled installation of external waterproof membranes, and rigorous quality management throughout construction, the research systematically elaborates the construction procedures and key control points for different waterproofing methods. A comparative analysis of the waterproofing performance of multiple materials—including polymer-coated waterstops, steel-edged rubber waterstops, and water-swellable sealants combined with grouting pipes—was conducted using both experimental testing and numerical simulation under their respective construction processes. The study focuses on the influence of material selection and construction techniques on waterproofing effectiveness, clarifying the applicable conditions and performance variations among different materials and methods. Results demonstrate that polymer-coated waterstops exhibit significantly superior performance compared to other materials. While self-compacting concrete causes minimal disturbance to waterstops and is thus beneficial for waterproofing, it shows deficiencies in early-age crack resistance. The refined control of construction processes proves decisive for the overall performance of the waterproofing system. Ultimately, this research establishes detailed quality control specifications for both main structural elements and detailed structural components.