Tunnel induced polarization inversion imaging method and apparatus

Abstract

The application provides a tunnel induced polarization inversion imaging method and device, and the tunnel induced polarization inversion imaging method comprises the following steps: establishing a resistivity initial model m0; performing inversion iteration according to the resistivity initial model m0 and outputting a resistivity model m1; establishing a polarization rate initial model η0; constructing a polarization rate reference model η based on the resistivity model m1 r2 ; performing inversion iteration according to the polarization rate reference model η r2 , and outputting a polarization rate inversion result η1. The tunnel induced polarization inversion imaging method is based on the idea of sequential inversion, a polarization rate inversion reference model is constructed under the premise of obtaining a resistivity inversion result, a polarization rate inversion reference model η r2 is constructed and a polarization rate inversion reference model constraint inversion equation is derived, the tunnel induced polarization inversion imaging method ensures that inversion converges in the correct direction, and solves the problem that traditional polarization rate inversion is prone to local optimization and difficult to converge.

Description

Technical Field

[0001] This invention relates to the field of geophysical exploration technology, specifically to a method and equipment for tunnel-induced polarization inversion imaging. Background Technology

[0002] Induced polarization (IP) is an electrical exploration method that uses the differences in induced polarization effects among different rocks and ores as its material basis to explore underground geological conditions by observing and studying induced polarization anomalies. After years of development, tunnel IPV methods, especially multi-pore combined IPV tomography, have become an effective method for identifying hazardous water bodies. Its resistivity, polarizability, and other parameters are sensitive to the water content of the formation, and multi-pore combined IPV tomography can be used to invert and image the water body in front of the tunnel.

[0003] Existing tunnel-induced polarization inversion imaging methods and equipment have the following problems:

[0004] In terms of data processing and imaging, the general approach is to first invert the resistivity, obtain the resistivity inversion model, and then use it as the basis for polarizability forward modeling before inverting the polarizability. However, imaging introduces many multiple solutions to resistivity inversion, which affects the accuracy of polarizability forward modeling and makes it difficult for polarizability inversion to converge.

[0005] II. While the aforementioned multi-porous combined excited polarization tomography scanning detection has the advantage of high-precision detection, it also suffers from problems such as uneven sensitivity distribution and positive / negative differences (e.g., Figure 1 As shown in the figure, this may ultimately lead to the inversion results showing distortion of the target's shape, which may not accurately reflect the key information such as the shape, position, and boundary of the detected target.

[0006] Third, in order to avoid the large electromagnetic interference generated by the high-power transmitter affecting the receiver's acquisition of high-precision data, the existing technology usually separates the power supply device (transmitter) and the measuring device (receiver) into two separate machines, which results in the large overall size of conventional excitation polarization instruments and makes them difficult to carry. Summary of the Invention

[0007] To address one or more of the problems existing in the prior art, the present invention provides a tunneling-induced polarization inversion imaging method, the method comprising the following steps:

[0008] Establish an initial resistivity model m0;

[0009] The resistivity model m0 is inverted and iterated, and the resistivity model m1 is output.

[0010] Establish an initial polarizability model η0;

[0011] A polarizability reference model η is constructed based on the resistivity model m1. r2 ;

[0012] According to the polarizability reference model η r2 The inversion process is iterated, and the polarizability inversion result η1 is output.

[0013] According to the tunnel-induced polarization inversion imaging method of the present invention, an initial resistivity model m0 is first established, forward modeling is performed based on the initial resistivity model m0, and then inversion iteration is performed to output a resistivity model m1. Then, a polarizability reference model η is constructed based on the resistivity model m1. r2 Based on the initial polarizability model η0 and the reference polarizability model η r2 The polarizability inversion calculation is performed, and the polarizability model η1 obtained from the inversion calculation iteration is output. The tunnel-induced polarizability inversion imaging method of this invention, based on the idea of ​​sequential inversion, constructs a polarizability inversion reference model after obtaining the resistivity inversion results. Based on the polarizability inversion reference model η... r2 A reference model constrained inversion equation for polarizability inversion was constructed and derived. This tunnel-induced polarizability inversion imaging method ensures that the inversion converges in the correct direction, solving the problem that traditional polarizability inversion is prone to getting trapped in local optima and is difficult to converge.

[0014] In some embodiments of the present invention, the polarizability reference model η r2 The calculation formula is as follows:

[0015]

[0016] In the formula η r2 The polarizability inversion reference model is constructed based on the target of water-bearing structure detection. σ is the conductivity inversion model, which is the reciprocal of the resistivity inversion model. min(σ) is the minimum conductivity inversion model, max(σ) is the maximum conductivity inversion model, and u and μ are the upper and lower limits of the polarizability reference model, respectively.

[0017] In some embodiments of the present invention, the polarizability reference model η is used. r2 In the inversion iteration and output polarizability inversion result η1 step, the polarizability reference model η calculated by equation (1) is used. r2 Substituting this into the derived polarizability inversion equation...

[0018]

[0019] In the formula Here, W represents the polarizability reference model constraint term, ε is the damping factor, and W... de J is the data weighting matrix, J is the sensitivity matrix (also known as the Jacobian matrix), and W is the data weighting matrix. ms Let d be the model constraint matrix. obs For actual observation data, d preThe forward simulation prediction data is from the current polarizability inversion model; η r1 For a uniform model where the value is the background polarizability, η r2 For the polarizability reference model calculated based on Equation 1, λX -2 +λY -2 and λ(X) -1 -Y -1 )e is the polarizability inversion boundary constraint term, β is the damping factor of the model term, and Δη is the polarizability model increment of the inversion.

[0020] In some embodiments of the present invention, the polarizability reference model η is used. r2 The inversion iteration and output of the polarizability inversion result η1 includes the following steps:

[0021] Based on the initial polarizability model η0 and the polarizability reference model η r2 The polarizability simulation observation data and sensitivity matrix are used for inversion iteration, and the polarizability inversion result η1 is output.

[0022] In some embodiments of the present invention, the polarizability reference model η is used. r2 Before the incremental steps of calculating the polarizability reference model using polarizability simulation observation data and sensitivity matrix calculation, the following steps are included:

[0023] The polarizability simulation observation data are calculated based on the initial polarizability model η0 and the resistivity model m1.

[0024] In some embodiments of the present invention, the sensitivity matrix is ​​calculated using the following formula:

[0025] J1 = W J *J0 (3)

[0026] Where J1 is the new sensitivity matrix, J0 is the initial sensitivity matrix, and W J W is a sensitivity weighting matrix. J The calculation formula is as follows:

[0027]

[0028] The sensitivity weighting matrix is ​​an N×M matrix, where N and M represent the amount of data and the number of model parameters, respectively. ij The calculation formula is as follows:

[0029]

[0030] Among them, J ij This represents the sensitivity value generated by the i-th data in the j-th model grid.

[0031] In some embodiments of the present invention, the step of inverting and iterating based on the initial resistivity model m0 and outputting the resistivity model m1 includes the following steps:

[0032] Forward modeling is performed based on the initial resistivity model m0, and resistivity simulation observation data are obtained.

[0033] Calculate the sensitivity matrix based on the reciprocity criterion;

[0034] Based on resistivity simulation observation data and sensitivity matrix, inversion calculation is performed to obtain resistivity increment Δm, and then the resistivity model m1 is updated.

[0035] Determine whether the resistivity model m1 meets the inversion requirements;

[0036] If the resistivity model m1 meets the inversion requirements, then output the resistivity model m1;

[0037] If the resistivity model m1 does not meet the inversion requirements, the observed data will be calculated again and the next inversion iteration will begin.

[0038] In some embodiments of the present invention, the polarizability reference model η is used. r2 The inversion iteration and output of the polarizability inversion result η1 includes the following steps:

[0039] Determine whether the polarizability model η1 satisfies the inversion requirements;

[0040] If the polarizability model η1 satisfies the inversion requirement, then output the polarizability model η1;

[0041] If the polarizability model η1 does not meet the inversion requirements, the simulated observation data will be calculated again and the next inversion iteration will begin.

[0042] A second aspect of the present invention provides a tunnel-induced polarization inversion imaging control device, the tunnel-induced polarization inversion imaging control device being used to implement the tunnel-induced polarization inversion imaging method according to any of the above embodiments, the tunnel-induced polarization inversion imaging control device comprising:

[0043] The construction module is used to establish an initial resistivity model m0, an initial polarizability model η0, and a polarizability reference model η based on the resistivity model m1. r2 ;

[0044] The calculation output module is used to perform inversion iteration based on the initial resistivity model m0 and output the resistivity model m1, based on the polarizability reference model η. r2 The inversion process is iterated, and the polarizability inversion result η1 is output.

[0045] A third aspect of the present invention provides a tunneling excitation polarization apparatus for implementing the tunneling excitation polarization inversion imaging method according to any of the above embodiments, the tunneling excitation polarization apparatus comprising:

[0046] Circuit board;

[0047] A transmitting module, which is disposed on the circuit board, is used to transmit signals;

[0048] A receiving module is disposed on the circuit board, and a transmitting module is disposed at an interval from the receiving module. The receiving module is used to receive signals.

[0049] An electromagnetic shielding module is disposed between the transmitting module and the receiving module. Attached Figure Description

[0050] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0051] Figure 1 This refers to an electrode arrangement and its sensitivity distribution in the existing tunneling-induced polarization inversion imaging method.

[0052] Figure 2 This is a flowchart of the tunnel-induced polarization inversion imaging method described in this invention.

[0053] Figure 3 This is a three-dimensional schematic diagram of the tunnel-induced polarization inversion imaging device described in this invention;

[0054] Figure 4 This is a planar schematic diagram of the tunnel-induced polarization inversion imaging device described in this invention.

[0055] The components include: 9. Instrument panel; 10. 220V power supply interface; 11. Power switch; 12. Power indicator light; 13. Cable 1-32 interface; 14. Cable 33-64 interface; 15. Cable 65-96 interface; 16. Cable 97-128 interface; 17. Grounding electrode interface; 18. USB programmable interface; 19. Circuit board; 20. LHE60-20B12; 21. DC-DC converter; 22. Cable 1-32 interface; 23. Cable 33-64 interface; 24. Cable 65-96 interface; 25. 26. Cable 97-128 interface; 27. Grounding electrode; 28. Transmitting module; 29. ​​Electromagnetic shielding module; 30. 64-channel voltage divider follower circuit; 31. Data acquisition card power supply port; 32. Data acquisition card digital output port; 33. Data acquisition card analog input port; 34. Constant current control unit; 35. 500V switching power supply; 36. Data acquisition card analog output port; 37. Data acquisition card power supply port; 38. Data acquisition card digital output port; 39. Data acquisition card analog input port; 40. USB programmable interface; 41. Connection point; 42. Cabinet; 43. Receiving module. Detailed Implementation

[0056] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0057] The following disclosure provides many different implementations or examples for carrying out different structures of the present invention. Of course, these are merely examples and are not intended to limit the invention. Preferred embodiments of the invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the invention.

[0058] Figure 2 This is a flowchart of the tunneling-induced polarization inversion imaging method described in this invention, as follows: Figure 2 As shown, the tunneling-induced polarization inversion imaging method includes the following steps:

[0059] Establish an initial resistivity model m0;

[0060] The resistivity model m0 is inverted and iterated, and the resistivity model m1 is output.

[0061] Establish an initial polarizability model η0;

[0062] A polarizability reference model η is constructed based on the resistivity model m1. r2 ;

[0063] Based on the initial polarizability model η0 and the polarizability reference model ηr2 The inversion iteration is performed, and the polarizability inversion result η1 is output. According to the tunnel-induced polarization inversion imaging method of the present invention, firstly, an initial resistivity model m0 is established. Forward calculations are performed based on the initial resistivity model m0, and then inversion iterations are performed to output the resistivity model m1. Then, a polarizability reference model η is constructed based on the resistivity model m1 and the polarizability threshold. r2 Based on the initial polarizability model η0 and the reference polarizability model η r2 The polarizability inversion calculation is performed, and the polarizability model η1 obtained from the inversion calculation iteration is output. The tunnel-induced polarizability inversion imaging method of this invention, based on the idea of ​​sequential inversion, constructs a polarizability inversion reference model after obtaining the resistivity inversion results. Based on the polarizability inversion reference model η... r2 A reference model constrained inversion equation for polarizability inversion was constructed and derived. This tunnel-induced polarizability inversion imaging method ensures that the inversion converges in the correct direction, solving the problem that traditional polarizability inversion is prone to getting trapped in local optima and is difficult to converge.

[0064] Specifically, the polarizability reference model η r2 The model is constructed based on resistivity and polarizability thresholds. The polarizability threshold is an empirical value obtained from experiments. For example, if the maximum value of polarizability is 0.5, then a polarizability reference model of (0,0.5) is constructed based on the polarizability threshold (0,0.5).

[0065] In some embodiments of the present invention, constraints for the polarizability inversion calculation, namely a polarizability reference model, are constructed based on the resistivity model m1 and the initial polarizability model η0. Specifically, water-bearing structures ahead of tunnels often exhibit typical characteristics of low resistivity and high polarizability in their induced polarization parameters. Constraints for the polarizability reference model are established based on these typical characteristics. The calculation formula for the polarizability reference model is as follows:

[0066]

[0067] In the formula η r2 The polarizability inversion reference model is constructed based on the target of water-bearing structure detection. σ is the conductivity inversion model, which is the reciprocal of the resistivity inversion model. min(σ) is the minimum conductivity inversion model, max(σ) is the maximum conductivity inversion model, and u and μ are the upper and lower limits of the polarizability reference model, respectively.

[0068] Based on equation (1), the polarizability inversion reference model constraint inversion equation was constructed and derived. This tunnel-excited polarization inversion imaging method ensures that the inversion converges in the correct direction.

[0069] The calculated polarizability reference model η r2 Substituting this into the derived polarizability inversion equation:

[0070]

[0071] In the formula ε is the polarizability reference model constraint term, and ε is the damping factor, whose main function is to adjust the weight of this reference model term in the inversion. The meanings of other parameters are as follows: W de J is the data weighting matrix, whose main function is to limit the influence of noise in the data on the inversion; J is the sensitivity matrix, also known as the Jacobian matrix, which mainly reflects the impact of model changes on the observed data; W ms This is the model constraint matrix, primarily used for stability inversion; d obs and d pre These are measured observation data and forward simulation prediction data from the current inversion model, respectively; η r1 and η r2 There are two sets of reference models, η r1 The value is a uniform model based on the background polarizability, mainly used for stable inversion to eliminate false anomalies, η r2 The reference model for polarizability is calculated based on Equation 1; λX -2 +λY -2 and λ(X) -1 -Y -1 e is the polarizability inversion boundary constraint term, whose main function is to constrain the polarizability value to always meet its natural boundary condition of 0-1; β is the damping factor of the model term, which is determined empirically; Δη is the polarizability model increment of the inversion. The inversion is carried out in an iterative manner, and the solved model increment will be accumulated on the previous inversion polarizability model to realize the update of the inversion polarizability model. As a result, the inversion polarizability model will gradually approach the real model.

[0072] In some embodiments of the present invention, the polarizability reference model η is used. r2 The inversion iteration and output of the polarizability inversion result η1 includes the following steps:

[0073] Based on the initial polarizability model η0 and the polarizability reference model η r2 The polarizability simulation observation data and sensitivity matrix are used for inversion iteration, and the polarizability inversion result η1 is output.

[0074] Specifically, the polarizability reference model η r2This is only one part of the polarizability model inversion equation. To ensure the normal inversion calculation of the polarizability model, it is also necessary to combine parameters such as polarizability simulation observation data, sensitivity matrix, and polarizability forward modeling data to calculate the polarizability model inversion equation. Then, the polarizability model obtained from the inversion result is compared with the real model. If the polarizability model obtained from the inversion is close enough to the real model, the polarizability model is output. If the difference between the polarizability model obtained from the inversion and the real model is large, the forward calculation of the polarizability simulation observation data continues, and the next polarizability inversion iteration begins until the polarizability model obtained from the inversion is close enough to the real polarizability model.

[0075] In some embodiments of the present invention, the polarizability reference model η is used. r2 Before performing inversion iterations on the polarizability simulation observation data and sensitivity matrix, and outputting the polarizability inversion results, the following steps are included:

[0076] Polarizability was calculated based on simulated observation data using the finite element method.

[0077] Specifically, firstly, an initial polarizability model η0 is established, and then a polarizability reference model η is constructed based on the resistivity model m1 and the initial polarizability model η0. r2 Then, based on the resistivity model m1 obtained from the resistivity model inversion, the initial polarizability model η0 is calculated using forward modeling to obtain the polarizability model observation data. The polarizability observation data is calculated using the finite element method. After obtaining the simulated polarizability observation data, a sensitivity matrix is ​​calculated based on the reciprocity criterion to weight the sensitivity.

[0078] In some embodiments of the present invention, the polarizability reference model η is used. r2 Before performing inversion iterations on the polarizability simulation observation data and sensitivity matrix, and outputting the polarizability inversion results, the following steps are included:

[0079] The sensitivity matrix is ​​calculated based on the reciprocity criterion.

[0080] Before the polarizability model inversion iteration, it is necessary to perform a polarizability reference model η. r2 The polarizability reference model η is calculated or constructed using parameters such as simulated observation data and sensitivity matrix. r2 The model is constructed based on resistivity model m1 and initial polarizability model η0. Polarizability observation data are calculated using the finite element method, and the sensitivity matrix is ​​calculated based on the reciprocity criterion.

[0081] Specifically, the calculation formula in the step of calculating the sensitivity matrix based on the reciprocity criterion is as follows:

[0082] J1 = W J *J0 (3)

[0083] Where J1 is the new sensitivity matrix, J0 is the initial sensitivity matrix, and W J The sensitivity weighting matrix. Matrix W J The sensitivity matrix (i.e. Jacobi matrix) J0 is weighted in the inversion equation by element-wise multiplication, where J0 can be calculated using the reciprocity criterion.

[0084] W J The calculation formula is as follows:

[0085]

[0086] Based on tunnel environment modeling, the sensitivity of a single observation method under uniform medium conditions ahead of the tunnel is analyzed, and its sensitivity distribution characteristics are obtained. The sensitivity weighting matrix is ​​an N×M matrix, where N and M represent the amount of data and the number of model parameters, respectively, corresponding to the dimension J[N×M] of the sensitivity matrix, and the matrix W... J Elements are calculated in row order.

[0087] w ij The calculation formula is as follows:

[0088]

[0089] Among them, J ij This represents the sensitivity value generated by the i-th data in the j-th model grid.

[0090] After obtaining the simulated observation data of polarizability and the sensitivity matrix, the above parameters are substituted into the polarizability inversion equation (Equation 2) to obtain a new polarizability inversion equation.

[0091] In some embodiments of the present invention, the step of inverting and iterating based on the initial resistivity model m0 and outputting the resistivity model m1 includes the following steps:

[0092] Forward modeling is performed based on the initial resistivity model m0, and resistivity simulation observation data are obtained.

[0093] Calculate the sensitivity matrix based on the reciprocity criterion;

[0094] Inversion calculations are performed based on observation data and sensitivity matrix, and the resistivity model m1 is updated.

[0095] Determine whether the resistivity model m1 meets the inversion requirements;

[0096] If the resistivity model m1 meets the inversion requirements, then output the resistivity model m1;

[0097] If the resistivity model m1 does not meet the inversion requirements, the observed data will be calculated again and the next inversion iteration will begin.

[0098] Specifically, resistivity is forward-modeled based on the initial resistivity model m0 to obtain simulated resistivity observation data. Then, a sensitivity matrix is ​​calculated according to the reciprocity criterion to perform sensitivity weighting on the resistivity model inversion calculation. After obtaining the resistivity observation data and the sensitivity matrix, the resistivity model is inverted to obtain the resistivity model increment Δm. The resistivity model increment Δm is then superimposed on the initial resistivity model m0 to obtain the resistivity model m1. If the resistivity model m1 is sufficiently close to the true resistivity model, then the resistivity model m1 is directly output. If the resistivity model m1 differs significantly from the true resistivity model, then the resistivity model m1 does not meet the inversion requirements. In this case, it is necessary to return to the step of calculating the simulated resistivity observation data and perform the next polarizability inversion iteration until the polarizability model m1 meets the inversion requirements and is output.

[0099] In some embodiments of the present invention, the polarizability reference model η is used. r2 The inversion iteration and output of the polarizability inversion result η1 includes the following steps:

[0100] Determine whether the polarizability model η1 satisfies the inversion requirements;

[0101] If the polarizability model η1 satisfies the inversion requirement, then output the polarizability model η1;

[0102] If the polarizability model η1 does not meet the inversion requirements, the simulated observation data will be calculated again and the next inversion iteration will begin.

[0103] Specifically, the polarizability reference model increment Δη is obtained based on the inversion equation of the polarizability model. This increment Δη is then superimposed onto the initial polarizability model η0 to obtain the updated polarizability model η1. The polarizability model η1 is then compared with the true polarizability model. If the polarizability model η1 is sufficiently close to the true polarizability model, then it meets the inversion requirements, and can be output. If the polarizability model η1 differs significantly from the true polarizability model, it does not meet the inversion requirements. In this case, the process returns to the step of calculating the simulated polarizability observation data and performs the next polarizability inversion iteration until the polarizability model η1 meets the inversion requirements and is output.

[0104] A second aspect of the present invention provides a tunnel-induced polarization inversion imaging control device, the tunnel-induced polarization inversion imaging control device being used to implement the tunnel-induced polarization inversion imaging method according to any of the above embodiments, the tunnel-induced polarization inversion imaging control device comprising:

[0105] The construction module is used to establish an initial resistivity model m1, an initial polarizability model η0, and to construct a polarizability reference model η based on the resistivity model m1 and the initial polarizability model η0. r2 ;

[0106] The calculation output module is used for inversion iteration based on the initial resistivity model m0 and outputting the resistivity model m1, based on the initial polarizability model η0 and the polarizability reference model η. r2 The inversion process is iterated, and the polarizability inversion result η1 is output.

[0107] According to the tunnel-induced polarization inversion imaging control device of the present invention, firstly, an initial resistivity model m0 is established through a construction module. Then, a calculation and output module performs forward modeling based on the initial resistivity model m0, followed by inversion iteration and outputting a resistivity model m1. Then, the construction module constructs a polarizability reference model η based on the resistivity model m1 and the initial polarizability model η0. r2 The calculation output module is based on the initial polarizability model η0 and the polarizability reference model η. r2 The polarizability inversion calculation is performed, and the polarizability model η1 obtained from the inversion calculation iteration is output. The tunnel-excited polarizability inversion imaging control device of this invention, based on the idea of ​​sequential inversion, constructs a polarizability inversion reference model using a construction module after obtaining the resistivity inversion results. The calculation output module calculates the polarizability inversion reference model η1. r2 A polarizability inversion reference model constrained inversion equation was constructed and derived. The imaging method implemented by the tunnel-excited polarizability inversion imaging control device ensures that the inversion converges in the correct direction, solving the problem that traditional polarizability inversion is prone to getting trapped in local optima and is difficult to converge.

[0108] like Figure 3 , Figure 4 As shown, a third aspect of the present invention provides a tunneling excitation polarization apparatus for implementing the tunneling excitation polarization inversion imaging method according to any of the above embodiments, the tunneling excitation polarization apparatus comprising:

[0109] Circuit board 19;

[0110] Transmitting module 27, which is disposed on the circuit board 19, is used to transmit signals;

[0111] The receiving module 43 is disposed on the circuit board 19, and the transmitting module 27 is disposed at an interval from the receiving module 43. The receiving module 43 is used to receive signals.

[0112] An electromagnetic shielding module 28 is disposed between the transmitting module 27 and the receiving module 43.

[0113] The tunneling polarization device of the present invention mainly includes a circuit board 19, a transmitting module 27, a receiving module 43, and an electromagnetic shielding module 28. The transmitting module 27 and the receiving module 43 are spaced apart on the circuit board 19, and the electromagnetic shielding module 28 is disposed between the transmitting module 27 and the receiving module 43. That is, the electromagnetic shielding module 28 separates the transmitting module 27 and the receiving module 43 to prevent electromagnetic interference between them and ensure the normal operation of each module. The tunneling polarization device of the present invention integrates the transmitting module 27 and the receiving module 43 into one unit, reducing the overall size of the device. At the same time, the electromagnetic shielding module 28 between the transmitting module 27 and the receiving module 43 can avoid electromagnetic interference between them and improve their reliability.

[0114] In some embodiments of the present invention, the tunneling excitation polarization device further includes a housing 42, an instrument panel 9, and a base plate 33. The instrument panel 9, the circuit board 19, and the base plate 33 are arranged sequentially from top to bottom along the height of the housing 42. The instrument panel 9 is connected to the top of the housing 42 via a connection point 41.

[0115] In some embodiments of the present invention, the instrument panel 9 is provided with a 220V power supply interface 10, a power switch 11, a power indicator light 12, a cable 1-32 interface 13, a cable 33-64 interface 14, a cable 65-96 interface 15, a cable 97-128 interface 16, a grounding electrode interface 17, and a USB programmable interface 18. The instrument panel 9 mainly provides interfaces for connecting to external devices such as power supplies, cables, and main control computers. The 220V power supply interface 10 connects to an external 220V AC power supply. The power switch 11 controls whether the instrument is powered on. The power indicator light 12 indicates whether the instrument is powered on; a lit indicator light means the instrument is powered on, and an unlit indicator light means the instrument is powered off. The cable 1-32 interface 13, cable 33-64 interface 14, cable 65-96 interface 15, and cable 97-128 interface 16 are external cable interfaces. During detection, interfaces 13-15 are generally connected to the cables inside the borehole, and interface 97-128 interface 16 is connected to the cables at the working face.

[0116] In some embodiments of the present invention, the circuit board 19 is further provided with an LHE60-20B12 (20), a DC-DC converter (21), a cable 1-32 interface 22, a cable 33-64 interface 23, a cable 65-96 interface 24, a cable 97-128 interface 25, a ground electrode 26, a 64-channel voltage divider follower circuit 29, a data acquisition card power supply port 30, a data acquisition card digital output port 31, and a data acquisition card analog input port 32. Specifically, the circuit board 19 is connected to an external power supply through the LHE60-20B12 (20), and the cables connected by the cable 1-32 interface 13, the cable 33-64 interface 14, the cable 65-96 interface 15, and the cable 97-128 interface 16 are connected to the circuit board 19 through the cable 1-32 interface 22, the cable 33-64 interface 23, the cable 65-96 interface 24, and the cable 97-128 interface 25. The ground electrode interface 17 is connected to the circuit board 19 through the ground electrode 26.

[0117] The transmitting module 27 and the receiving module 43 are spaced apart, and the electromagnetic shielding module 28 is placed between the transmitting module 27 and the receiving module 43 to prevent magnetic interference between the transmitting module 27 and the receiving module 43, ensuring the normal operation of the transmitting module 27 and the receiving module 43, improving the reliability of the tunnel excitation polarization device, and ensuring the accuracy of the measurement data. The power supply port 30, the digital output port 31, and the analog input port 32 of the acquisition card are the acquisition card interfaces, which are connected to the power supply port 37, the digital output port 38, and the analog input port 39 of the acquisition card, respectively, to communicate data between the circuit board and the base plate 33.

[0118] In some embodiments of the present invention, the base plate 33 is provided with a constant current control unit 34, a 500V switching power supply 35, an analog output port 36 of the acquisition card, a power supply port 37 of the acquisition card, a digital output port 38 of the acquisition card, an analog input port 39 of the acquisition card, and a USB programmable interface 40.

[0119] The 220V power supply interface 10 is connected to an external power source, and then a constant current control unit 34 and a 500V switching power supply 35 are connected inside the instrument to provide power to the instrument as a whole. The constant current control unit 34 converts the AC power into a DC power source for detection.

[0120] During detection, the USB programmable interface 18 first receives the detection command from the host computer. The signal is then transferred to the USB programmable interface 40 and transmitted to various modules via the acquisition card's analog output port 36, power supply port 37, digital output port 38, and analog input port 39 to begin acquisition. After the signal is transmitted to the transmitting module 27 and the receiving module 43, a relay controls the designated electrode to supply power. The signal is then connected to cable interfaces 1-32 (22), 33-64 (23), 65-96 (24), and 97-128 (25), and then transferred to cable interfaces 1-32 (13), 33-64 (14), 65-96 (15), and 97-128 (16) to complete the power supply operation to the designated electrode. Then, the transmitting module 27 and the receiving module 43 receive the detection command and open the circuit of the corresponding electrode. The measurement signal is transferred from the electrode to the circuit board 19 via the cable 1-32 interface 13, cable 33-64 interface 14, cable 65-96 interface 15, cable 97-128 interface 16 and the 1-32 interface 22, cable 33-64 interface 23, cable 65-96 interface 24, and cable 97-128 interface 25. It is then transmitted to the main control computer through the acquisition card power supply port 30, acquisition card digital output port 31, acquisition card analog input port 32, acquisition card power supply port 37, acquisition card digital output port 38, acquisition card analog input port 39, as well as the USB programmable interface 40 and USB programmable interface 18. The detection data is then displayed on the computer, completing one data detection and acquisition process.

[0121] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A tunnel-excited polarization inversion imaging method, characterized in that, The method includes the following steps: Establish an initial resistivity model m0; The resistivity model m0 is inverted and iterated, and the resistivity model m1 is output. Establish an initial polarizability model η0; A polarizability reference model η is constructed based on the resistivity model m1. r2 ; Based on the initial polarizability model η0 and the polarizability reference model η r2 The inversion process is iterated, and the polarizability inversion result η1 is output.

2. The tunnel-induced polarization inversion imaging method according to claim 1, characterized in that, The polarizability reference model η r2 The calculation formula is as follows: （1） In the formula Here, σ represents the polarizability inversion reference model constructed based on the target of water-bearing structure detection, which is the reciprocal of the resistivity inversion model. min(σ) represents the minimum conductivity inversion model, and max(σ) represents the maximum conductivity inversion model. u and μ are the polarizability reference model η, respectively. r2 The upper and lower limits.

3. The tunnel-induced polarization inversion imaging method according to claim 2, characterized in that, According to the polarizability reference model η r2 In the inversion iteration and output polarizability inversion result η1 step, the polarizability reference model η calculated by equation (1) is used. r2 Substituting this into the derived polarizability inversion equation: （2） In the formula Here, ε is the polarizability reference model constraint term, and ε is the damping factor. Weighted matrix of data, The sensitivity matrix is ​​also known as the Jacobian matrix. The model constraint matrix, These are actual observation data. This is the forward simulation prediction data for the current polarizability inversion model; For a uniform model where the value is the background polarization rate, The polarizability reference model is calculated based on Equation 1. and For the polarizability inversion boundary constraint term, The damping factor of the model term. This is the increment of the inverted polarizability model.

4. The tunnel-induced polarization inversion imaging method according to claim 3, characterized in that, According to the polarizability reference model η r2 The inversion iteration and output of the polarizability inversion result η1 includes the following steps: Based on the initial polarizability model η0 and the polarizability reference model η r2 The polarizability simulation observation data and sensitivity matrix are used for inversion iteration, and the polarizability inversion result η1 is output.

5. The tunnel-induced polarization inversion imaging method according to claim 4, characterized in that, According to the polarizability reference model η r2 Before the incremental steps of calculating the polarizability reference model using polarizability simulation observation data and sensitivity matrix calculation, the following steps are included: The polarizability simulation observation data are calculated based on the initial polarizability model η0 and the resistivity model m1.

6. The tunnel-induced polarization inversion imaging method according to claim 4, characterized in that, The formula for calculating the sensitivity matrix is ​​as follows: （3） in, For the new sensitivity matrix, The initial sensitivity matrix, For sensitivity weighting matrix, The calculation formula is as follows: （4） The sensitivity weighting matrix is ​​an N×M matrix, where N and M represent the amount of data and the number of model parameters, respectively. The calculation formula is as follows: （5） in, This represents the sensitivity value generated by the i-th data in the j-th model grid.

7. The tunnel-induced polarization inversion imaging method according to claim 1, characterized in that, The process of inverting and iterating based on the initial resistivity model m0 and outputting the resistivity model m1 includes the following steps: Forward modeling is performed based on the initial resistivity model m0, and resistivity simulation observation data are obtained. Calculate the sensitivity matrix based on the reciprocity criterion; Based on resistivity simulation observation data and sensitivity matrix, inversion calculation is performed to obtain resistivity increment Δm, and then the resistivity model m1 is updated. Determine whether the resistivity model m1 meets the inversion requirements; If the resistivity model m1 meets the inversion requirements, then output the resistivity model m1; If the resistivity model m1 does not meet the inversion requirements, the observed data will be calculated again and the next inversion iteration will begin.

8. The tunnel-induced polarization inversion imaging method according to claim 4, characterized in that, According to the polarizability reference model η r2 The inversion iteration and output of the polarizability reference model η1 includes the following steps: Determine whether the polarizability model η1 satisfies the inversion requirements; If the polarizability model η1 satisfies the inversion requirement, then output the polarizability model η1; If the polarizability model η1 does not meet the inversion requirements, the simulated observation data will be calculated again and the next inversion iteration will begin.

9. A tunnel-excited polarization inversion imaging control device, characterized in that, The tunnel-induced polarization inversion imaging control device is used to implement the tunnel-induced polarization inversion imaging method according to any one of claims 1 to 8, and the tunnel-induced polarization inversion imaging control device comprises: The construction module is used to establish an initial resistivity model m1, an initial polarizability model η0, and to construct a polarizability reference model η based on the resistivity model m1 and the initial polarizability model η0. r2 ; The calculation output module is used to perform inversion iteration based on the initial resistivity model m0 and output the resistivity model m1, based on the polarizability reference model η. r2 The inversion process is iterated, and the polarizability inversion result η1 is output.

10. A tunneling-induced polarization device, characterized in that, The tunneling excitation polarization device is used to implement the tunneling excitation polarization inversion imaging method according to any one of claims 1 to 8, and the tunneling excitation polarization device comprises: Circuit board; A transmitting module, which is disposed on the circuit board, is used to transmit signals; A receiving module is disposed on the circuit board, and a transmitting module is disposed at an interval from the receiving module. The receiving module is used to receive signals. An electromagnetic shielding module is disposed between the transmitting module and the receiving module.

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