Three-dimensional transient electromagnetic detection method for tunnel advanced geological forecast

Abstract

The invention discloses a three-dimensional transient electromagnetic detection method for tunnel advanced geological forecast. The transient electromagnetic exploration method has the advantages of low construction cost, high safety coefficient, reliable detection result and the like. The method comprises the following steps: installing a plurality of loop sources in an area nearby a tunnel face in a set mode, and obtaining stratum information data of the area nearby the tunnel face; and analyzing the data by adopting a three-dimensional transient electromagnetic forward and reverse modeling method to obtain related information of the unfavorable geologic body in front of the tunnel face. According to the method, the efficiency and the accuracy of transient electromagnetic tunnel advanced prediction are improved.

Description

Technical Field

[0002] This invention belongs to the field of engineering survey technology, specifically relating to a three-dimensional transient electromagnetic detection method for advanced geological prediction of tunnels. Background Technology

[0003] Due to its advantages such as low construction cost, high safety factor, and reliable detection results, transient electromagnetic exploration has become one of the main technical means for tunnel advance detection. However, limited by technical means and computational conditions, tunnel advance detection mostly employs one-dimensional inversion and imaging methods to interpret the data. Among these, Tan Daiming (2009) systematically studied the transient electromagnetic theory of tunnel advance water exploration in the entire space; Ouyang Jing (2014) developed a one-dimensional transient electromagnetic inversion algorithm for tunnels in the entire space environment; Zhong Wenjun et al. (2022) applied the one-dimensional transient electromagnetic inversion technology to tunnel advance water exploration projects; and Yang Fujie et al. (2022) studied the adaptive regularized inversion theory of transient electromagnetic transient sources for conical field sources, improving the data interpretation efficiency of tunnel electromagnetic advance detection. However, all of the above studies assume that the model around the tunnel is layered, an assumption that does not match the actual underground geological structure. Considering the complex working environment inside the tunnel and the high resolution requirements for data interpretation in advance detection, traditional one-dimensional inversion methods are insufficient to meet the needs of tunnel advance detection in the new era. Developing new technologies for interpreting tunnel advance detection data is urgently needed. Electromagnetic data 3D inversion technology, with its advantages of high resolution, detailed model description, and high interpretation reliability, is an effective tool for the detailed interpretation of tunnel advance detection data.

[0004] In recent years, with the rapid development of computer hardware technology and the rapid improvement of computing power, geophysicists have gradually shifted their research focus on data interpretation technology from traditional one-dimensional inversion algorithms to three-dimensional fine inversion interpretation algorithms. Among them, Yin et al. (2016) realized the three-dimensional inversion of airborne electromagnetic data and performed fine interpretation of multi-wave emission electromagnetic data; Liu et al. (2018) introduced wavelet transform into the three-dimensional inversion of electromagnetic data and realized the multi-scale fine interpretation of electromagnetic data; Yue Mingxin et al. (2019) conducted a systematic study on the three-dimensional forward and inversion technology of ground-controlled source electromagnetic method in complex media; Xing Xiuju et al. (2020) applied three-dimensional data acquisition and data imaging technology to tunnel advanced detection, but it is still a pseudo-three-dimensional imaging technology and has not achieved true three-dimensional fine inversion; Zhang et al. (2021) solved the problem of large-scale airborne electromagnetic data three-dimensional inversion based on unstructured grid algorithm; Hui Zhejian et al. (2022) successfully developed a marine electromagnetic three-dimensional inversion algorithm and applied it to the interpretation of marine electromagnetic exploration data in the South my country Sea. Thanks to the tireless efforts of the scholars mentioned above, three-dimensional electromagnetic inversion technology has become relatively mature and has been applied to detailed exploration in various scenarios, including air, ocean, and land. However, due to the unique characteristics of tunnel electromagnetic exploration in terms of working environment and data acquisition methods, conventional electromagnetic three-dimensional inversion technology and equipment cannot directly solve the problem of detailed interpretation of tunnel advance exploration data. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention provides a three-dimensional transient electromagnetic detection method for tunnel advanced geological prediction. Multiple loop sources are installed in the vicinity of the tunnel face in a predetermined manner to acquire stratigraphic information data of the vicinity. A three-dimensional transient electromagnetic forward and inverse modeling method is used to analyze the data, thereby improving the efficiency and accuracy of transient electromagnetic tunnel advanced prediction.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A three-dimensional transient electromagnetic detection method for advanced geological prediction of tunnels includes the following steps: Step S1: Based on the principle of eccentric self-compensation, a primary field self-compensation system is designed using the principle of non-coplanar eccentricity of the transmitting and receiving coils. The non-coplanar structure realizes weak coupling between the transmitting and receiving coils, and the optimal relative eccentricity position between the two is obtained based on the mutual inductance theory. Step S2: A loop source is set up at the working face. Each loop source has multiple receiving coils. Electromagnetic waves are emitted through the transmitting coil, and the receiving coils obtain the information of the induced electromagnetic wave field in front of the working face. Step S3: For the obtained electromagnetic wave information, perform three-dimensional transient electromagnetic forward modeling using the finite element algorithm, and realize three-dimensional electromagnetic data inversion technology based on numerical optimization and L2 norm model constraint algorithm to achieve fine inversion and interpretation of electromagnetic data of complex tunnel model. Step S4: Based on the three-dimensional transient electromagnetic inversion information obtained in step S3, obtain relevant information about the adverse geological bodies in front of the tunnel face.

[0007] Furthermore, in step S1, receiving coils of different sizes have different zero-point compensation positions. As the size of the receiving coil increases, the area limited by the contour lines will gradually decrease. There are two zero-compensation positions at the same height, corresponding to different receiving coil sizes.

[0008] Furthermore, in step S2, a return source is set up at the working face, and multiple receiving coils are set up inside the return source. The non-coplanar receiving coils are set up as in S1, and electromagnetic waves are emitted through the transmitting coil to obtain the induced electromagnetic wave field information of multiple measuring points at one time.

[0009] Furthermore, step S3 specifically includes: Step 3.1: Simulate the electromagnetic response under complex terrain conditions using the vector finite element method based on unstructured tetrahedral elements; Step 3.2: Perform three-dimensional inversion on the response data of the array electromagnetic system based on numerical optimization algorithm.

[0010] Furthermore, in step 3.1, the complex tunnel model is first discretized using an unstructured mesh, and then the double curl equation satisfied by the electromagnetic field is addressed: (1) The governing equations for forward modeling are derived using the Galerkin algorithm, namely: (2) By calculating the integral in equation (2) in each subdivided element, we can obtain a small set of linear equations corresponding to all elements: (3) By assembling the linear equations corresponding to each unit, the large-scale linear equations corresponding to the forward modeling problem can be derived. (4) By solving the large linear equation system (4) using the direct solver MUMPS, the electric field values ​​corresponding to all edges of the grid can be obtained; then, using Faraday's law of electromagnetic induction, the magnetic field response at the receiver location can be calculated; Equation (4) contains the derivative of the electric field with respect to time, and to calculate the electromagnetic field response at different times, the second-order post-Euler formula is used: (5) Discretizing the time derivative term of the electric field yields the following recurrence relation for the electric field: (6) By recursively solving a large set of linear equations (6), the response simulation of an array-type electromagnetic system can be realized.

[0011] Furthermore, in step 3.2, based on regularization theory, the following electromagnetic data inversion objective function can be constructed: (7) After performing a Taylor expansion of equation (7) and taking its minimum, the Gauss-Newton three-dimensional inversion equation can be derived as follows: (8) The key to solving equation (8) lies in calculating the sensitivity matrix J. Considering the enormous computational cost required to directly calculate the sensitivity matrix, this project proposes to use the adjoint forward modeling algorithm to calculate the inverse sensitivity matrix. From the definition of the sensitivity matrix, we can obtain... (9) definition Then V can be obtained by solving a large system of linear equations: (10).

[0012] Furthermore, in step S3, K is the left-hand coefficient matrix of the large linear equation system given in formula (6); since the forward modeling equation (6) is solved in the form of time recursion, the accompanying forward modeling equation system (10) can also be solved in the form of recursion; after obtaining the sensitivity matrix, the model correction amount Δm can be obtained by solving the equation system (8), and then the model can be continuously updated using the model correction amount, and finally the optimal model that fully fits the data can be obtained, realizing the inversion of the measured data of the array electromagnetic system.

[0013] Furthermore, in step S4, the adverse geological information ahead of the tunnel face is determined based on the resistivity magnitude and the trend of the contour lines in the contour map derived from the measured values.

[0014] The beneficial effects of this invention are: This invention installs multiple loop sources in the vicinity of the tunnel face in a predetermined manner to acquire stratigraphic information data of the vicinity of the tunnel face; and uses a three-dimensional transient electromagnetic forward and inverse modeling method to analyze the data, thereby improving the efficiency and accuracy of transient electromagnetic tunnel advance prediction. Attached Figure Description

[0015] Figure 1 This is a structural diagram of the three-dimensional transient electromagnetic coil for tunnel advanced geological prediction according to the present invention; Figure 2 This is a design drawing of the three-dimensional transient electromagnetic transceiver coil scheme for tunnel advanced geological prediction according to the present invention; Figure 3 This is a flowchart of the three-dimensional transient electromagnetic forward and inverse modeling process for tunnel advanced geological prediction according to the present invention. Detailed Implementation

[0016] The present invention will now be described in detail with reference to specific embodiments.

[0017] This invention provides a three-dimensional transient electromagnetic detection method for tunnel advanced geological prediction. Multiple loop sources are installed in the area near the tunnel face in a predetermined manner to acquire stratigraphic information data of the area near the tunnel face. The three-dimensional transient electromagnetic forward and inverse modeling method is used to analyze the data, thereby improving the efficiency and accuracy of transient electromagnetic tunnel advanced prediction.

[0018] like Figure 3 As shown, the three-dimensional transient electromagnetic detection method for tunnel advanced geological prediction of the present invention includes the following steps: Step S1: To address the issues of early signal distortion and shallow blind zones caused by the coupling of the TEM transceiver antenna in the tunnel small loop, a primary field self-compensation system is designed based on the principle of eccentric self-compensation and the principle of non-coplanar eccentricity of the transceiver coils. The non-coplanar structure achieves weak coupling between the transmitting and receiving coils, and the optimal relative eccentricity position of the two is obtained based on the mutual inductance theory to eliminate primary field interference and reduce the blind zone of tunnel advanced geological prediction detection. Transient electromagnetic methods, due to their sensitivity to low-resistivity bodies and their non-destructive, low-cost, and high-efficiency characteristics, are increasingly widely used in detecting adverse geological formations such as water-bearing or mud-filled faults, fissures, and fracture zones ahead of tunnel faces. However, the working environment inside tunnels is complex, and traditional one-dimensional detection and inversion techniques have poor anti-interference capabilities, a 10m detection blind zone, and coarse inversion model results, making it difficult to meet the requirements of advanced geological prediction in areas with complex geological conditions. For traditional coplanar buckling coil structures, due to the limitations of buckling coils, the size of the receiving coil cannot be too large, thus requiring an increase in the number of winding turns to improve the effective receiving area, resulting in a loss of receiving bandwidth. This paper analyzes the influence of the size and height of the receiving coil on the total magnetic flux. Figure 1 As shown, in step S1, receiving coils of different sizes have different zero-compensation positions. When the size of the receiving coil increases, the area limited by the contour lines will gradually decrease. At an appropriate height, there are two zero-compensation positions at the same height, corresponding to different receiving coil sizes.

[0019] Step S2: A loop source is set up at the working face. Each loop source has multiple receiving coils. Electromagnetic waves are emitted through the transmitting coil, and the receiving coils obtain the information of the induced electromagnetic wave field in front of the working face. like Figure 2 As shown, in step S2, a loop source is set up at the working face, and multiple receiving coils are set up inside the loop source. The non-coplanar receiving coils are set up as in S1. Electromagnetic waves are emitted through the transmitting coil to obtain the induced electromagnetic wave field information of multiple measuring points at one time, realizing true three-dimensional transient electromagnetic advance detection.

[0020] Step S3: For the acquired electromagnetic wave information, perform three-dimensional transient electromagnetic forward modeling using the finite element method. Based on numerical optimization and L2 norm model constraint algorithms, implement three-dimensional electromagnetic data inversion technology to achieve refined inversion and interpretation of electromagnetic data from complex tunnel models. Specifically, Step S3 involves: Step 3.1: Simulate the electromagnetic response under complex terrain conditions using the vector finite element method based on unstructured tetrahedral elements; in Step 3.1, firstly, the complex tunnel model is discretized using an unstructured mesh, and then the double curl equation satisfied by the electromagnetic field is analyzed: (1) The governing equations for forward modeling are derived using the Galerkin algorithm, namely: (2) By calculating the integral in equation (2) in each subdivided element, we can obtain a small set of linear equations corresponding to all elements: (3) By assembling the linear equations corresponding to each unit, the large-scale linear equations corresponding to the forward modeling problem can be derived. (4) By solving the large linear equation system (4) using the direct solver MUMPS, the electric field values ​​corresponding to all edges of the grid can be obtained; then, using Faraday's law of electromagnetic induction, the magnetic field response at the receiver location can be calculated; Equation (4) contains the derivative of the electric field with respect to time, and to calculate the electromagnetic field response at different times, the second-order post-Euler formula is used: (5) Discretizing the time derivative term of the electric field yields the following recurrence relation for the electric field: (6) By recursively solving a large set of linear equations (6), the response simulation of an array-type electromagnetic system can be realized.

[0021] Step 3.2: Perform three-dimensional inversion of the response data of the array-type electromagnetic system based on a numerical optimization algorithm. In Step 3.2, based on regularization theory, the following objective function for electromagnetic data inversion can be constructed: (7) After performing a Taylor expansion of equation (7) and taking its minimum, the Gauss-Newton three-dimensional inversion equation can be derived as follows: (8) The key to solving equation (8) lies in calculating the sensitivity matrix J. Considering the enormous computational cost required to directly calculate the sensitivity matrix, this project proposes to use the adjoint forward modeling algorithm to calculate the inverse sensitivity matrix. From the definition of the sensitivity matrix, we can obtain... (9) definition Then V can be obtained by solving a large system of linear equations: (10) K is the coefficient matrix on the left side of the large linear equation system given in formula (6). Since the forward modeling equation (6) is solved in the form of time recursion, the accompanying forward modeling equation system (10) can also be solved in the form of recursion. After obtaining the sensitivity matrix, the model correction amount Δm can be obtained by solving the equation system (8). Then, the model is continuously updated using the model correction amount. Finally, the optimal model that fully fits the data can be obtained, and the inversion of the measured data of the array electromagnetic system can be realized.

[0022] Step S4: Based on the three-dimensional transient electromagnetic inversion information obtained in step S3, acquire relevant information about adverse geological bodies ahead of the tunnel face. In step S4, based on the resistivity magnitude and contour line trend in the contour map derived from the measured values, determine the adverse geological information ahead of the tunnel face.

[0023] The content of this invention is not limited to the embodiments listed. Any equivalent modifications made by those skilled in the art to the technical solutions of this invention by reading this specification are covered by the claims of this invention.

Claims

1. A three-dimensional transient electromagnetic detection method for advanced geological prediction of tunnels, characterized in that: Includes the following steps: Step S1: Based on the principle of eccentric self-compensation, a primary field self-compensation system is designed using the principle of non-coplanar eccentricity of the transmitting and receiving coils. The non-coplanar structure realizes weak coupling between the transmitting and receiving coils, and the optimal relative eccentricity position between the two is obtained based on the mutual inductance theory. Step S2: A loop source is set up at the working face. Each loop source has multiple receiving coils. Electromagnetic waves are emitted through the transmitting coil, and the receiving coils obtain the information of the induced electromagnetic wave field in front of the working face. Step S3: For the obtained electromagnetic wave information, perform three-dimensional transient electromagnetic forward modeling using the finite element algorithm, and realize three-dimensional electromagnetic data inversion technology based on numerical optimization and L2 norm model constraint algorithm to achieve fine inversion and interpretation of electromagnetic data of complex tunnel model. Step S4: Based on the three-dimensional transient electromagnetic inversion information obtained in step S3, obtain relevant information about the adverse geological bodies in front of the tunnel face.

2. The three-dimensional transient electromagnetic detection method for tunnel advanced geological prediction according to claim 1, characterized in that: In step S1, receiving coils of different sizes have different zero-point compensation positions. As the size of the receiving coil increases, the area limited by the contour lines will gradually decrease. There are two zero-compensation positions at the same height, corresponding to different receiving coil sizes.

3. The three-dimensional transient electromagnetic detection method for tunnel advanced geological prediction according to claim 2, characterized in that: In step S2, a return source is set up at the working face, and multiple receiving coils are set up inside the return source. The non-coplanar receiving coils are set up as in S1. Electromagnetic waves are emitted through the transmitting coil to obtain the induced electromagnetic wave field information of multiple measuring points at one time.

4. The three-dimensional transient electromagnetic detection method for tunnel advanced geological prediction according to claim 3, characterized in that: Step S3 specifically involves: Step 3.1: Simulate the electromagnetic response under complex terrain conditions using the vector finite element method based on unstructured tetrahedral elements; Step 3.2: Perform three-dimensional inversion on the response data of the array electromagnetic system based on numerical optimization algorithm.

5. A three-dimensional transient electromagnetic detection method for tunnel advanced geological prediction according to claim 4, characterized in that: In step 3.1, the complex tunnel model is first discretized using an unstructured mesh, and then the double curl equation satisfied by the electromagnetic field is addressed: （1） The governing equations for forward modeling are derived using the Galerkin algorithm, namely: （2） By calculating the integral in equation (2) in each subdivided element, we can obtain a small set of linear equations corresponding to all elements: （3） By assembling the linear equations corresponding to each unit, the large-scale linear equations corresponding to the forward modeling problem can be derived. （4） By solving the large linear equation system (4) using the direct solver MUMPS, the electric field values ​​corresponding to all edges of the grid can be obtained; then, using Faraday's law of electromagnetic induction, the magnetic field response at the receiver location can be calculated; Equation (4) contains the derivative of the electric field with respect to time, and to calculate the electromagnetic field response at different times, the second-order post-Euler formula is used: （5） Discretizing the time derivative term of the electric field yields the following recurrence relation for the electric field: （6） By recursively solving a large set of linear equations (6), the response simulation of an array-type electromagnetic system can be realized.

6. A three-dimensional transient electromagnetic detection method for tunnel advanced geological prediction according to claim 5, characterized in that: In step 3.2, based on regularization theory, the following electromagnetic data inversion objective function can be constructed: （7） After performing a Taylor expansion of equation (7) and taking its minimum, the Gauss-Newton three-dimensional inversion equation can be derived as follows: （8） The key to solving equation (8) lies in calculating the sensitivity matrix J; considering the huge amount of computation required to directly calculate the sensitivity matrix, this project plans to use the adjoint forward modeling algorithm to calculate the inverse sensitivity matrix; from the definition of the sensitivity matrix, we can obtain （9） definition Then V can be obtained by solving a large system of linear equations: （10）。 7. The three-dimensional transient electromagnetic detection method for tunnel advanced geological prediction according to claim 6, characterized in that: In step S3, K is the left-hand coefficient matrix of the large linear equation system given in formula (6); since the forward modeling equation (6) is solved in the form of time recursion, the accompanying forward modeling equation system (10) can also be solved in the form of recursion. After obtaining the sensitivity matrix, the model correction amount Δm can be obtained by solving the system of equations (8). The model correction amount is then used to continuously update the model, and finally the optimal model that fully fits the data can be obtained, thus realizing the inversion of measured data of the array electromagnetic system.

8. The three-dimensional transient electromagnetic detection method for tunnel advanced geological prediction according to claim 7, characterized in that: In step S4, the adverse geological information ahead of the tunnel face is determined based on the resistivity and the trend of the contour lines in the contour map derived from the measured values.

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