Small-loop transient electromagnetic (TEM) method can be used for urban underground space detection. Given the mutual inductance between transmitting and receiving coils, the early secondary field signals are contaminated by primary field signals. To compensate for the primary field, bucking coil technology has been utilized in the airborne TEM system due to attractive features of the technology, which include a compensation effect and a strong coupling strength between the receiving coil and the underground body. However, many problems arise when the bucking coil technology is used directly in a ground small-loop TEM system. One is that the size of the receiving coil is limited, resulting in insufficient bandwidth; the other is that the bucking coil brings the near-field effect, which causes the loss of shallow information. To solve these problems, in this article, we propose a new TEM structure with noncoplanar bucking compensation. Compared with the traditional design, the new structure overcomes not only the near-field effect but also reduces the constraint of the receiving coil size. Furthermore, the approach can suppress the primary field coupling more effectively, and has a better fault tolerance for the installation accuracy. The simulation and field experiment results verify the effectiveness and practicability of the new structure.

Road collapse and underground water inrush accidents pose a serious threat to urban safety and development. Transient electromagnetic (TEM) method is an effective geophysical method for detecting urban underground space. However, due to the complexity of urban geological environment, the conventional TEM detection methods are difficult to meet the needs of efficient and high-resolution detection of urban underground space. To solve this problem, an adaptive high-resolution (AHR) TEM detection method based on pseudoseismic wavelet transform technology is proposed in this study. The simulation results show that compared with the conventional technologies, the AHR detection method can more accurately reflect the resistivity distribution information in complex geological environment. The proposed method is implemented to detect an urban underground cavity, and the imaging results are consistent with the actual results. The research results will provide technical support for the early warning of road collapse and water inrush in urban industrial development.

Ground penetrating radar (GPR) and transient electromagnetic (TEM) technologies can detect urban underground space efficiently and with good sensitivity. These two technologies have complementary advantages, but they are often used independently in urban underground space detection and are rarely used for the integrated interpretation of urban geology. To realize the high-resolution measurement of urban underground space, we take the shallow structure information of GPR as the input information of TEM inversion and use the transdimensional Bayesian (Trans-Bayes) inversion method to realize an integrated interpretation of TEM and GPR data. The simulation results show that, compared with inversion using only TEM data, an integrated inversion improves the accuracy of the inversion results, retains the lateral smoothness of the 2-D model, and reduces the multiple solutions of the TEM inversion. In addition, the richer the layer interface information provided by GPR, the more significant the improvement in inversion quality. We successfully applied GPR and TEM technologies to the accurate detection of an urban underground air-raid shelter area and verified the superiority of GPR in improving the resolution of TEM shallow geological imaging. The combination of GPR and TEM provides a new idea for the high-resolution measurement of urban underground space geology and has practical application value in the accurate early warning of underground water gushing and road collapse accidents.

Machine learning inversion (ML-inversion) has been widely used in the interpretation of geophysical data. However, because the transient electromagnetic (TEM) is not sensitive to the response of the small-size or large-depth abnormal body, there are some problems, such as poor imaging accuracy and low resolution when directly processing raw TEM data with machine learning (ML) method. To solve this problem, we propose to use the pseudo seismic wavelet (PSW) data obtained from the TEM wave field transformation to perform TEM ML-inversion. Simulation and example application verify that compared with the TEM data, the PSW data has higher recognition sensitivity to the electrical changes of underground anomalies, and the ML-inversion based on the PSW data can significantly improve the TEM imaging accuracy. Our study demonstrates that the PSW data can be used to achieve TEM ML-inversion, which may provide a new way for the further combination of electromagnetic data processing and ML technology.

Transient electromagnetic (TEM) method is a geophysical technique suitable for efficient detection of urban underground space, which can be used for advance detection of road collapse and underground water inrush accidents. However, due to the complexity of urban geology, it is difficult for conventional three dimensions (3D) imaging techniques to take into account both imaging efficiency and imaging accuracy. To address this issue, this study proposes a fast, high-resolution TEM quasi-3D imaging strategy suitable for urban geology. We convert the TEM data into pseudo-seismic wavelet (PSW) data to identify the geological correlation of the 3D spatial survey area, and then impose adaptive spatial constraints on the 3D imaging. Simulation and application case results show that, compared with conventional imaging techniques, our proposed new strategy can effectively improve imaging resolution while ensuring computational efficiency. The research results provide a feasible technical solution for rapid high-resolution 3D detection of urban geology.

The transient electromagnetic method (TEM) is one of the geophysical methods that can quickly detect underground space information. It obtains the resistivity structure of underground medium by inversion technology. The traditional inversion methods can only give a unique solution conforming to TEM data, but cannot solve the multiplicity problem of solutions. TEM Bayesian inversion technology can provide uncertainty information to solve this problem. However, its long calculation time makes it difficult to apply to engineering detection with high real-time performance. To solve these problems, a TEM one-dimensional inversion mixture density network (TEMIMDNet) is proposed in this paper. This method combines Bayesian theory with deep learning methods. By inputting TEM data into the trained network, the statistical parameters of the posterior probability density of the corresponding geological model can be quickly obtained. Thus, the uncertainty information of the geological model can be obtained. This method overcomes the problem of low efficiency of traditional Bayesian inversion. The simulated experiment shows that the TEMIMDNet method can not only obtain the probability density function graph of the geological model but also the average relative error between the maximum a posteriori model and the corresponding TEM response, which is less than 0.02. The field experiment shows that the TEMIMDNet method can output the statistical parameters of 52 survey points in only 4 ms, and the imaging results are consistent with the spatially constrained inversion method and OCCAM.