Research on Underground Pipeline Detection Method Based on Background Matrix Subtraction and Three-Dimensional Velocity Spectrum

Abstract

Ground-penetrating radar (GPR) can detect urban underground pipelines and image their spatial distribution. However, due to the interference of direct wave and ground reflected wave in radar profile, the detection accuracy of underground pipeline depth is low. In order to improve the reliability and accuracy of the interpretation of ground penetrating radar data, it is necessary to suppress the noise, and then to detect underground pipelines. Firstly, on the basis of data collection, a background matrix subtraction (BMS) method is proposed to suppress noise signals, and the Noise reduction effect is compared and analyzed by two groups of simulation data and two groups of measured data examples with the method of reducing average channel and singular decomposition. Then, a three-dimensional velocity spectrum (3DVS) method is proposed to estimate the buried depth of underground pipeline in radar profile, and the propagation velocity of electromagnetic wave in underground media is calculated by automatically scanning the hyperbolic reflection signal. The estimated velocity is used to carry out back-propagation migration (BPM) processing on the ground penetrating radar profile, and the underground pipeline is accurately detected. Finally, the underground pipeline detection method based on BMS and 3DVS is applied to a residential area in Nanjing, Jiangsu, China. The detection results show that the effect of BMS is obviously better than that of mean-reducing method and singular decomposition method, which can suppress the noise well on the basis of ensuring that the effective signal is not lost. It is helpful to identify the characteristics of target signal in ground penetrating radar profile, and improve the accuracy and reliability of data interpretation. The error between the velocity value estimated by the 3DVS method and the real value is less than 3.8%, and the buried depth error of pipeline target in the obtained data is 1.4%, which indicates that the algorithm has practical application value and can realize the accurate positioning of underground pipelines.