Slurry diffusion interpretation method and system based on resistivity and radar signal combination

Abstract

The invention provides a slurry diffusion interpretation method and system based on resistivity and radar signal combination, and relates to the technical field of geotechnical engineering.The method comprises the steps that water-rich comprehensive study and judgment are conducted on a to-be-excavated area of a tunnel face, and a non-water-rich stratum and a water-rich stratum are obtained; aiming at a non-water-rich stratum, an electrode net arranged on the surface of a tunnel face is utilized to invert a diffusion path and a diffusion range of slurry based on resistivity change; for a water-rich stratum, while an electrode net is arranged, a radar sensor is arranged in a peripheral area of a tunnel face, a diffusion path and a diffusion range of slurry are preliminarily positioned through resistivity, and then the diffusion path and a diffusion boundary are restrained and corrected in combination with radar signals of the radar sensor; according to the method, corresponding tracking interpretation methods, especially non-water-rich stratums are adopted for different stratum environments, combined interpretation of slurry diffusion is achieved in the mode that the resistivity is combined with radar signals, and the accuracy and robustness of slurry diffusion tracking are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of geotechnical engineering technology, and in particular to a slurry diffusion interpretation method and system based on the combination of resistivity and radar signals. Background Art

[0002] When tunnel projects pass through complex geological conditions, they often encounter problems such as poor surrounding rock stability, severe water seepage, and the development of voids and fissures. In order to ensure the construction safety and long-term operational stability of tunnels, grouting reinforcement technology has become a common engineering method. By injecting cement slurry, chemical slurry and other materials into the surrounding rock or strata around the tunnel, cracks and holes can be effectively filled, the bearing capacity of the surrounding rock can be enhanced, water seepage can be prevented, and the settlement of the strata can be controlled. However, the key to the grouting process is to master the diffusion path and coverage of the slurry to ensure that the slurry can effectively reach the target area and form a stable reinforcement effect.

[0003] At present, the tracking of slurry diffusion still faces many challenges; the migration process of slurry underground is affected by multiple factors such as stratum structure, geological characteristics, porosity and fracture network, and its diffusion path and coverage are difficult to accurately predict, especially in water-rich strata or weak surrounding rocks. The presence of stratum water and changes in conductivity increase the difficulty of slurry diffusion tracking. Traditional grouting monitoring methods have obvious limitations, making it difficult to achieve comprehensive, accurate and real-time monitoring, and unable to meet the high-precision requirements of tunnel grouting projects; for example, pressure monitoring and flow control can only provide information on changes in fluid parameters, and it is difficult to directly reflect the diffusion path of underground slurry; although excavation can directly observe the distribution of slurry, it is limited to local areas, and is destructive, costly and cannot provide real-time feedback; drilling sampling also only provides local information, and the samples are discontinuous, making it difficult to fully reflect the diffusion situation in a large area.

[0004] In order to improve the accuracy and efficiency of tunnel grouting, it is particularly important to develop effective slurry diffusion tracking technology. Physical methods such as resistivity monitoring and radar detection have been gradually applied to tunnel grouting monitoring. By deploying electrode arrays in the grouting area and combining the changes in resistivity, the range of slurry diffusion can be indirectly inferred. However, in water-rich formations, due to the similar conductivity of slurry and water, the resistivity signal is difficult to accurately capture the boundary of slurry diffusion, resulting in the existing slurry diffusion tracking schemes being insufficient in accuracy and robustness. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a slurry diffusion interpretation method and system based on the combination of resistivity and radar signals. According to different formation environments, corresponding tracking and interpretation methods are adopted, especially for non-water-rich formations. By combining resistivity and radar signals, the joint interpretation of slurry diffusion is realized, thereby improving the accuracy and robustness of slurry diffusion tracking.

[0006] According to some embodiments, the present disclosure adopts the following technical solutions:

[0007] The slurry diffusion interpretation method based on the combination of resistivity and radar signals includes:

[0008] By combining geological exploration, geophysical exploration and drilling, a comprehensive assessment of the water-rich area of ​​the face to be excavated is conducted to obtain non-water-rich strata and water-rich strata;

[0009] For non-water-rich formations, the electrode network arranged on the surface of the tunnel face is used to monitor the resistivity changes during the grouting process, and the diffusion path and range of the slurry are inverted based on the resistivity changes;

[0010] For water-rich formations, radar sensors are arranged in the peripheral area of ​​the tunnel face while arranging the electrode grid. The diffusion path and diffusion range of the slurry are preliminarily located by resistivity, and the diffusion path and diffusion boundary are constrained and corrected in combination with the radar signal of the radar sensor, ultimately realizing the visualization of the slurry diffusion process.

[0011] According to some embodiments, the present disclosure adopts the following technical solutions:

[0012] The slurry diffusion interpretation system based on the combination of resistivity and radar signals includes:

[0013] The water-rich assessment module is configured to: conduct a comprehensive assessment of the water-rich area of ​​the face to be excavated by combining geological exploration, geophysical exploration, and drilling to obtain non-water-rich strata and water-rich strata;

[0014] The non-water-rich module is configured to: for non-water-rich formations, use an electrode network arranged on the surface of the tunnel face to monitor the resistivity change during the grouting process, and invert the diffusion path and diffusion range of the slurry based on the resistivity change;

[0015] The water-rich module is configured as follows: for water-rich formations, while arranging the electrode grid, radar sensors are arranged in the peripheral area of ​​the tunnel face, and the diffusion path and diffusion range of the slurry are preliminarily located by resistivity. The radar signal of the radar sensor is then used to explore the stratum structure before and after the slurry is injected, and the diffusion path and diffusion boundary are constrained and corrected, ultimately realizing the visualization of the slurry diffusion process.

[0016] According to some embodiments, the present disclosure adopts the following technical solutions:

[0017] A computer program product comprises a computer program. When the computer program is executed by a processor, the method for interpreting slurry diffusion based on the combination of resistivity and radar signal is implemented.

[0018] According to some embodiments, the present disclosure adopts the following technical solutions:

[0019] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the slurry diffusion interpretation method based on the combination of resistivity and radar signals is implemented.

[0020] According to some embodiments, the present disclosure adopts the following technical solutions:

[0021] An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device implements the slurry diffusion interpretation method based on the combination of resistivity and radar signals.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] The present invention proposes a slurry diffusion interpretation method based on the combination of resistivity and radar signals. For non-water-rich formations, slurry diffusion is tracked by interpreting resistivity signals; for water-rich formations, slurry diffusion is jointly interpreted by acquiring the resistivity and three-dimensional radar detection signals during the slurry diffusion process, effectively solving the problem of increased difficulty in resistivity signal analysis and difficulty in capturing the slurry-water interface in water-rich formations due to the low contrast between the water environment and the slurry conductivity.

[0024] The present invention adopts corresponding tracking and interpretation schemes for different formation environments. In non-water-rich formations, an electrode array is arranged outside the injection medium, and a resistance signal analysis system is used to track the diffusion path of the slurry. In water-rich formations, since the conductivity of the slurry is similar to that of the water body, the slurry diffusion is preliminarily located by the resistivity signal, and then the front and rear structures of the injected slurry are accurately explored in combination with the three-dimensional radar signal to distinguish the slurry diffusion boundary and the diffusion path, and finally the accurate visualization of the slurry diffusion process is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0026] Figure 1 The present invention is a method flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0028] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0029] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0030] Example 1

[0031] In one embodiment of the present disclosure, a slurry diffusion interpretation method based on the combination of resistivity and radar signals is provided, such as Figure 1 As shown, including:

[0032] By combining geological exploration, geophysical exploration and drilling, a comprehensive assessment of the water-rich area of ​​the face to be excavated is conducted to obtain non-water-rich strata and water-rich strata;

[0033] For non-water-rich formations, the electrode network arranged on the surface of the tunnel face is used to monitor the resistivity changes during the grouting process, and the diffusion path and range of the slurry are inverted based on the resistivity changes;

[0034] For water-rich formations, radar sensors are arranged in the peripheral area of ​​the tunnel face while arranging the electrode grid. The diffusion path and diffusion range of the slurry are preliminarily located by resistivity, and the diffusion path and diffusion boundary are constrained and corrected in combination with the radar signal of the radar sensor, ultimately realizing the visualization of the slurry diffusion process.

[0035] As an embodiment, the slurry diffusion interpretation method based on the combination of resistivity and radar signal disclosed in the present invention adopts corresponding tracking interpretation methods for different formation environments, especially non-water-rich formations, and realizes the joint interpretation of slurry diffusion by combining resistivity and radar signals, thereby improving the accuracy and robustness of slurry diffusion tracking. The specific implementation process is as follows:

[0036] Step 1: Based on the geological exploration results and combined with conventional geophysical drilling methods, a comprehensive assessment of the water-rich area of ​​the face to be excavated is conducted to obtain non-water-rich strata and water-rich strata.

[0037] Step 2: For non-water-rich formations with poor stability such as broken surrounding rocks, weak formations, and karst cavities, an electrode grid is arranged on the surface of the tunnel face. The layout range and density of the electrodes should be able to cover the expected slurry diffusion range.

[0038] Specifically, through the electrode optimization layout system, the optimal electrode network layout plan is formed according to the stratum characteristics of the area to be excavated and the preset monitoring requirements, and the actual electrode network layout is carried out according to the optimal plan.

[0039] The electrode optimization arrangement system is composed of an electrode arrangement optimization unit and an electrode positioning and installation unit.

[0040] The electrode arrangement optimization unit uses the particle swarm optimization algorithm to establish a mathematical model based on electrode position and slurry diffusion range to describe accuracy optimization, data coverage optimization, etc. according to the formation characteristics such as conductivity, porosity, fracture distribution, and the preset monitoring requirements such as the number of electrodes, accuracy requirements, and monitoring coverage, as well as the expected diffusion radius of the slurry.

[0041] As an optional embodiment, the objective function of the mathematical model can be expressed as:

[0042] f(x) = w1·precision error + w2·coverage + w3·uniformity error

[0043] Among them, w1, w2, and w3 are weight coefficients.

[0044] Each electrode arrangement is represented as a particle vector, where each element of the vector corresponds to the position, number and spacing of the electrode.

[0045] Generate an initial particle swarm, and the position and speed of each particle are randomly assigned within a set range; each particle has two attributes: position (including the position, number, and spacing information of the electrodes) and speed (indicating the speed at which the particle moves in the search space, which is used to update the position of the particle in the next round of iteration).

[0046] The position of each particle (electrode arrangement plan) will be evaluated for fitness according to the objective function, and the movement of the particle will be updated according to its historical optimal solution (personal optimal solution) and the optimal solution among all particles (global optimal solution).

[0047] In each iteration, the particle's velocity and position are updated as follows:

[0048]

[0049] in, is the velocity of the particle in round t; is the position of the particle in round t; pbest i is the optimal position of the particle; w is the inertia weight; c1 and c2 are learning factors that control the degree of adjustment of the particle to the personal optimal solution and the global optimal solution; r1 and r2 are random numbers to ensure the randomness of the search.

[0050] After each round of iteration, the particle swarm gradually converges to an optimal solution and finds the optimal electrode arrangement scheme.

[0051] The electrode positioning and installation unit determines the electrode position based on the optimal electrode layout plan using laser ranging technology and installs the electrode to the predetermined position. During the installation process, the contact condition between the electrode and the formation is monitored in real time using a contact quality detector and a pressure sensor to ensure stable contact.

[0052] Step 3: Before grouting, resistivity data is collected in the initial stage to obtain the initial resistivity distribution. At the beginning of grouting, resistivity data is collected in real time to record the resistivity change during slurry diffusion. Through tomography algorithm processing, a resistivity change image of slurry diffusion is generated to obtain the diffusion path, diffusion range and diffusion speed of the slurry.

[0053] Specifically, step 3.1: monitoring the resistivity change during the grouting process, specifically:

[0054] (1) Before grouting, the resistivity data is collected in the initial stage to obtain the initial resistivity distribution.

[0055] First, a known current (I) is generated by a current source in the electrode network, and the resistivity of the formation is calculated by the voltage difference (ΔV).

[0056] During the grouting process, as the slurry diffuses, the resistivity of the formation will change. The injected slurry has a certain conductivity. When the slurry enters the formation, it will change the resistivity distribution of the formation. Usually, the resistivity of the slurry is low, so after the slurry is injected, the resistivity of the area will be reduced, making it easier for current to pass through the area, and the voltage difference will change accordingly.

[0057] Therefore, during the grouting process, the voltage difference (ΔV) will change dynamically as the slurry diffuses, reflecting the change in formation resistivity.

[0058] Then, the Gauss-Newton inversion algorithm is used to infer the initial resistivity distribution of the underground formation.

[0059] (2) During the grouting process, real-time resistivity data is collected to record the resistivity changes during slurry diffusion. Step 3.2: Based on the resistivity changes, the diffusion path and diffusion range of the slurry are inverted, specifically:

[0060] (1) By continuously calling the Gauss-Newton inversion algorithm, the resistivity distribution map at each moment is generated, and then the resistivity change image of the slurry diffusion is obtained.

[0061] (2) Based on the resistivity variation image, the areas where the resistivity drops significantly are determined. These areas correspond to the injection areas of the slurry and the range to which the slurry diffuses.

[0062] For example, in a specific area, the resistivity decreases significantly, and the resistivity value of the area is lower than a certain threshold (the threshold is based on previous experiments or previous data acquisition), and it is inferred that the area is the range of slurry diffusion.

[0063] During the slurry injection process, the real-time tracking of the slurry diffusion path can be achieved by collecting time-series data of resistivity images multiple times; each resistivity image inversion result provides a snapshot of the underground resistivity distribution at a specific moment; with the continuous injection of slurry, the resistivity of the diffusion area will change dynamically. By continuously updating the resistivity data, the slurry diffusion path can be tracked, the spatial distribution and change trend of the resistivity change can be analyzed, and the time evolution process of slurry diffusion can be obtained.

[0064] Step 4: For water-rich formations, arrange the electrode network on the surface of the tunnel face and arrange the radar sensor in the peripheral area of ​​the tunnel face. The arrangement of the electrodes and the coverage of the radar signal should be able to cover the expected slurry diffusion range; in particular, for the arrangement of the radar sensor, select a suitable antenna according to the formation characteristics to improve the signal penetration.

[0065] Specifically, through the radar sensor optimization layout system, the optimal radar sensor layout plan is formed according to the geological characteristics of the area to be excavated and the preset monitoring requirements, and the actual radar sensor layout is carried out according to the optimal plan.

[0066] The radar sensor optimization layout system consists of a sensor selection module and a layout decision and optimization module.

[0067] The sensor selection module selects the appropriate type of radar sensor (such as ground penetrating radar, 3D radar or imaging radar, etc.) according to the characteristics of the water-rich strata and the depth and resolution requirements to be monitored.

[0068] The layout decision and optimization module integrates the particle swarm optimization algorithm to determine the optimal layout location and number of radar sensors to ensure that all areas of interest are covered. The specific steps are as follows:

[0069] A mathematical model based on the radar sensor position and the radar signal radiation area is established to describe accuracy optimization, data coverage optimization, etc.

[0070] As an optional embodiment, the objective function of the mathematical model can be expressed as:

[0071] f(x) = w1·precision error + w2·coverage + w3·uniformity error

[0072] Among them, w1, w2, and w3 are weight coefficients.

[0073] Each radar deployment scheme is represented as a particle vector, and each element of the vector corresponds to the position and angle of the radar.

[0074] Generate an initial particle swarm, and the position and velocity of each particle are randomly assigned within the set range. Each particle has two attributes: position (including the position and angle of the radar sensor) and velocity (indicates the speed at which the particle moves in the search space, which is used to update the particle's position in the next iteration).

[0075] The position of each particle (electrode arrangement plan) will be evaluated for fitness according to the objective function, and the movement of the particle will be updated according to its historical optimal solution (personal optimal solution) and the optimal solution among all particles (global optimal solution).

[0076] In each iteration, the particle's velocity and position are updated as follows:

[0077]

[0078] in, is the velocity of the particle in round t; is the position of the particle in round t; pbest i is the optimal position of the particle; w is the inertia weight; c1c2 is the learning factor, which controls the degree of adjustment of the particle to the personal optimal solution and the global optimal solution; r1r2 is a random number to ensure the randomness of the search.

[0079] After each round of iteration, the particle swarm gradually converges to an optimal solution and finds the optimal radar deployment plan.

[0080] According to the working range and detection angle of the sensor, the sensor spacing and direction are reasonably set; the total station is used for precise positioning.

[0081] The radar sensor emits electromagnetic wave signals, receives signals reflected from the formation, and converts them into digital signals to obtain radar signals. Signal processing is used to filter out noise and enhance signal quality, supporting formation structure analysis of the slurry diffusion path and real-time tracking of slurry.

[0082] The radar signal acquisition frequency is adaptively set according to the water-rich situation of the formation. If the formation is highly water-rich, a high frequency radar scan is used for the formation. If the formation is less water-rich, a low frequency radar scan is used. Combined with real-time resistivity data, joint tracking of slurry diffusion is achieved.

[0083] Step 5: Before grouting, collect resistivity data and radar data in the initial stage to obtain the initial resistivity distribution and initial radar image.

[0084] In particular, for radar signal collection, radar sensors are used to scan the tunnel face at multiple angles and orientations, and ultimately the initial resistivity of the tunnel face and the location and structural characteristics of the water-conducting channels and cavities in the surrounding rock in front of the tunnel face based on radar images are obtained.

[0085] Step 6: Synchronize and calibrate the data to ensure that the resistivity data and radar signals have consistent benchmarks in time and space. If they are inconsistent, perform necessary calibration and alignment.

[0086] Specifically, the resistivity data and radar images are preprocessed and standardized to ensure that they are consistent in data format and resolution. The spatial registration technology is used to map the data points of the resistivity data and radar data to the same spatial coordinate system for alignment to ensure that they correspond to the same spatial position such as the same depth. At the same time, the time of the two data is corrected to see if they are at the same moment.

[0087] The original format of the resistivity data is in matrix form (containing spatial coordinate points and resistivity data of each coordinate point), and the original format of the radar signal is in image format, which needs to be converted into a matrix format; if the resolution of the radar image is lower than that of the resistivity data, the interpolation method is used to increase the resolution of the image; if the resolution of the radar image is higher than that of the resistivity data, the pixel averaging method is used to reduce the resolution; further realizing the format conversion, the gray value of each pixel represents the reflection intensity of a certain position underground, so the gray value matrix of the image can be directly used as the data matrix of the radar image (only an embodiment)

[0088] Step 7: Accurately monitor the slurry diffusion path in complex formations by jointly tracking resistivity and radar signals.

[0089] Specifically, step 7.1: identify water-rich areas and water channel boundaries based on initial resistivity distribution and radar data.

[0090] Step 7.2: During the grouting process, determine the areas where the resistivity drops significantly. These areas correspond to the injection area of ​​the slurry and the range to which the slurry diffuses. For example, in a specific area, the resistivity drops significantly, and the resistivity value of the area is lower than a certain threshold (the threshold is based on previous experiments or previous data acquisition), and then it is inferred that the area is the range of slurry diffusion. During the slurry injection process, the initial diffusion path of the slurry is captured by collecting time series data of resistivity images multiple times.

[0091] Since the resistivity signal in the water-rich formation may be affected by the water conductivity and the tracking accuracy is low, the slurry diffusion path and the boundary of the water channel are constrained and corrected in combination with the high-resolution radar signal. The following are the constraint and correction steps:

[0092] The water channels, cracks, and stratum spatial structure information captured by radar images are used as boundary constraints of the water channel. The resistivity inversion images at the same time and spatial position are superimposed. The slurry-water interface in the slurry diffusion direction is based on the resistivity inversion result. The slurry diffusion boundary of the blocked water channel is based on the water channel boundary obtained by the radar signal. In addition, the system uses radar and resistivity data to dynamically adjust the boundary conditions due to the deformation of the stratum structure caused by the increase in grouting pressure, and optimizes the inversion results of the diffusion range.

[0093] Step 7.3: Finally, the continuous inversion results of the two data are superimposed and the complete diffusion path is constructed to track the slurry diffusion.

[0094] The inversion results at each moment are integrated using data fusion methods such as color mapping and transparent stacking to generate visual two-dimensional or three-dimensional images, providing a comprehensive display of the formation structure and slurry diffusion path.

[0095] Example 2

[0096] In one embodiment of the present disclosure, a slurry diffusion interpretation system based on the combination of resistivity and radar signals is provided, comprising:

[0097] The water-rich assessment module is configured to: conduct a comprehensive assessment of the water-rich area of ​​the face to be excavated by combining geological exploration, geophysical exploration, and drilling to obtain non-water-rich strata and water-rich strata;

[0098] The non-water-rich module is configured to: for non-water-rich formations, use an electrode network arranged on the surface of the tunnel face to monitor the resistivity change during the grouting process, and invert the diffusion path and diffusion range of the slurry based on the resistivity change;

[0099] The water-rich module is configured as follows: for water-rich formations, while arranging the electrode grid, radar sensors are arranged in the peripheral area of ​​the tunnel face, and the diffusion path and diffusion range of the slurry are preliminarily located by resistivity. The radar signal of the radar sensor is then used to explore the stratum structure before and after the slurry is injected, and the diffusion path and diffusion boundary are constrained and corrected, ultimately realizing the visualization of the slurry diffusion process.

[0100] Example 3

[0101] In one embodiment of the present disclosure, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the slurry diffusion interpretation method based on the combination of resistivity and radar signals is implemented.

[0102] Example 4

[0103] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the slurry diffusion interpretation method based on the combination of resistivity and radar signals is implemented.

[0104] Example 5

[0105] In one embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the slurry diffusion interpretation method based on the combination of resistivity and radar signals.

[0106] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0108] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A slurry diffusion interpretation method based on resistivity and radar signals, characterized in that: include: By combining geological exploration, geophysical exploration and drilling, a comprehensive assessment of the water-rich area of ​​the face to be excavated is conducted to obtain non-water-rich strata and water-rich strata; For non-water-rich formations, the electrode network arranged on the surface of the tunnel face is used to monitor the resistivity changes during the grouting process, and the diffusion path and range of the slurry are inverted based on the resistivity changes; For water-rich formations, radar sensors are arranged in the peripheral area of ​​the tunnel face while arranging the electrode grid. The diffusion path and diffusion range of the slurry are preliminarily located by resistivity, and the diffusion path and diffusion boundary are constrained and corrected in combination with the radar signal of the radar sensor, ultimately realizing the visualization of the slurry diffusion process.

2. The slurry diffusion interpretation method based on resistivity and radar signal combination as claimed in claim 1, characterized in that: The electrode network and radar sensor form an optimal electrode network layout plan and radar sensor layout plan according to the stratum characteristics of the area to be excavated and the preset monitoring requirements, and the actual layout is carried out according to the optimal plan.

3. The slurry diffusion interpretation method based on resistivity and radar signal combination as claimed in claim 1, characterized in that: The resistivity change during the monitoring grouting process is specifically: Conduct resistivity data collection in the initial stage before grouting to obtain the initial resistivity distribution; During the grouting process, resistivity data is collected in real time to record the resistivity changes during slurry diffusion.

4. The slurry diffusion interpretation method based on resistivity and radar signal combination according to claim 1, characterized in that: The diffusion path and diffusion range of the slurry based on the resistivity change inversion are specifically: Through the tomography algorithm, an image of resistivity changes of slurry diffusion is generated; Based on the resistivity change image, a diffusion process model of the slurry is constructed, and the diffusion path of the slurry is updated according to the real-time resistivity data, and finally the diffusion path and diffusion range of the slurry are obtained.

5. The slurry diffusion interpretation method based on resistivity and radar signal combination according to claim 1, characterized in that: Also includes: When the diffusion path obtained by resistivity inversion has insufficient resolution or is inconsistent with the boundary reflected by the radar signal, the boundary reflected by the radar signal is preferentially used as the constraint of the diffusion boundary, and the slurry diffusion distance is adjusted according to the resistivity inversion results.

6. The slurry diffusion interpretation method based on resistivity and radar signal combination according to claim 1, characterized in that: Also includes: Resistivity data and radar signals are aligned in time and space.

7. A slurry diffusion interpretation system based on resistivity and radar signals, characterized in that: include: The water-rich assessment module is configured to: conduct a comprehensive assessment of the water-rich area of ​​the face to be excavated by combining geological exploration, geophysical exploration, and drilling to obtain non-water-rich strata and water-rich strata; The non-water-rich module is configured to: for non-water-rich formations, use an electrode network arranged on the surface of the tunnel face to monitor the resistivity change during the grouting process, and invert the diffusion path and diffusion range of the slurry based on the resistivity change; The water-rich module is configured as follows: for water-rich formations, while arranging the electrode grid, radar sensors are arranged in the peripheral area of ​​the tunnel face, and the diffusion path and diffusion range of the slurry are preliminarily located by resistivity. The radar signal of the radar sensor is then used to explore the stratum structure before and after the slurry is injected, and the diffusion path and diffusion boundary are constrained and corrected, ultimately realizing the visualization of the slurry diffusion process.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the slurry diffusion interpretation method based on the combination of resistivity and radar signals as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the slurry diffusion interpretation method based on the combination of resistivity and radar signals as described in any one of claims 1 to 6 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the slurry diffusion interpretation method based on the combination of resistivity and radar signals as described in any one of claims 1 to 6.

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