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

Abstract

The disclosure provides a slurry diffusion interpretation method and system based on the combination of resistivity and radar signals, relating to the technical field of geotechnical engineering, comprising: comprehensively analyzing and judging the water-rich area of the face to be excavated to obtain non-water-rich strata and water-rich strata; for non-water-rich strata, using an electrode network arranged on the surface of the face, based on resistivity changes, the diffusion path and diffusion range of the slurry are inverted; for water-rich strata, while arranging the electrode network, radar sensors are arranged in the peripheral area of the face, the diffusion path and diffusion range of the slurry are preliminarily positioned by resistivity, and then the diffusion path and diffusion boundary are constrained and corrected in combination with the radar signals of the radar sensors; the present application adopts corresponding tracking interpretation methods for different stratum environments, especially for non-water-rich strata, through the combination of resistivity and radar signals, the joint interpretation of slurry diffusion is realized, and the accuracy and robustness of slurry diffusion tracking are improved.

Description

TECHNICAL FIELD

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

[0002] When tunnel engineering passes through complex geological conditions, it often encounters problems such as poor stability of surrounding rock, serious water seepage, and development of cavities and fractures. In order to ensure the safety of tunnel construction and the stability of long-term operation, grouting reinforcement technology has become a common engineering means. By injecting cement slurry, chemical slurry and other materials into the surrounding rock or stratum of the tunnel, the fractures and cavities can be effectively filled, the bearing capacity of the surrounding rock can be enhanced, the occurrence of seepage water can be prevented, and the settlement of the stratum can be controlled. However, the key to the grouting process lies in mastering the diffusion path and coverage range of the slurry to ensure that the slurry can effectively reach the target area and form a stable reinforcement effect.

[0003] Currently, the tracking of slurry diffusion still faces many challenges; the migration process of slurry in the underground is affected by multiple factors such as stratum structure, geological characteristics, porosity and fracture network, and it is difficult to accurately predict its diffusion path and coverage range, especially in water-rich strata or weak surrounding rock, the presence of stratum water and the change of conductivity exacerbate the difficulty of slurry diffusion tracking. Traditional grouting monitoring methods have obvious limitations and cannot achieve comprehensive, accurate and real-time monitoring, which cannot meet the high-precision requirements of tunnel grouting engineering; for example, pressure monitoring and flow control can only provide information on the changes of fluid parameters, and it is difficult to directly reflect the diffusion path of underground slurry; excavation exposure can directly observe the distribution of slurry, but it is limited to local areas, and has the problems of destructiveness, high cost and inability to 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 range.

[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 in tunnel grouting monitoring; by arranging an electrode array in the grouting area, combined with the change of resistivity, the range of slurry diffusion can be indirectly inferred; however, in water-rich strata, due to the similar conductivity of slurry and water, the resistivity signal is difficult to accurately capture the boundary of slurry diffusion, resulting in deficiencies in accuracy and robustness of existing slurry diffusion tracking schemes. SUMMARY

[0005] In order to solve the above problems, the present disclosure proposes a slurry diffusion interpretation method and system based on joint resistivity and radar signals, which adopts corresponding tracking interpretation methods for different stratum environments, and especially for non-water-rich strata, realizes joint interpretation of slurry diffusion by combining resistivity and radar signals, and improves the accuracy and robustness of slurry diffusion tracking.

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

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

[0008] The water-rich comprehensive research and judgment is performed on the region to be excavated of the working face in a manner of combination of geological exploration, geophysical exploration and drilling exploration, to obtain the non-water-rich stratum and the water-rich stratum;

[0009] For the non-water-rich stratum, the electrode net arranged on the surface of the working face is used to monitor the resistivity change in the grouting process, and the diffusion path and diffusion range of the slurry are inversely calculated based on the resistivity change;

[0010] For the water-rich stratum, the radar sensor is arranged in the peripheral region of the working face while the electrode net is arranged, the diffusion path and diffusion range of the slurry are preliminarily positioned by the resistivity, and then the radar signals of the radar sensor are combined to constrain and correct the diffusion path and diffusion boundary, so as to finally realize the visualization of the slurry diffusion process.

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

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

[0013] The water-rich research and judgment module is configured to perform the water-rich comprehensive research and judgment on the region to be excavated of the working face in a manner of combination of geological exploration, geophysical exploration and drilling exploration, to obtain the non-water-rich stratum and the water-rich stratum;

[0014] The non-water-rich module is configured to, for the non-water-rich stratum, use the electrode net arranged on the surface of the working face to monitor the resistivity change in the grouting process, and inversely calculate the diffusion path and diffusion range of the slurry based on the resistivity change;

[0015] The water-rich module is configured to, for the water-rich stratum, arrange the radar sensor in the peripheral region of the working face while the electrode net is arranged, preliminarily position the diffusion path and diffusion range of the slurry by the resistivity, then combine the radar signals of the radar sensor to probe the structure of the stratum before and after the injected slurry, constrain and correct the diffusion path and diffusion boundary, and finally realize the visualization of the slurry diffusion process.

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

[0017] A computer program product comprises a computer program, which, when executed by a processor, implements the slurry diffusion interpretation method based on the combination of resistivity and radar signals.

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

[0019] A non-transitory computer readable storage medium for storing computer instructions, which, when executed by a processor, implement the slurry diffusion interpretation method based on joint resistivity and radar signals.

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

[0021] An electronic device, comprising a processor, a memory and a computer program, wherein the processor is connected with the memory, and 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 to enable the electronic device to implement the slurry diffusion interpretation method based on joint resistivity and radar signals.

[0022] Compared with the prior art, the present disclosure has the beneficial effects that:

[0023] The present disclosure proposes a slurry diffusion interpretation method based on joint resistivity and radar signals, which realizes tracking of slurry diffusion by interpreting resistivity signals for non-water-rich strata, and realizes joint interpretation of slurry diffusion by obtaining resistivity and three-dimensional radar detection signals in the slurry diffusion process for water-rich strata, effectively solving the problems of increased difficulty in resistivity signal analysis and difficulty in capturing the slurry and water interface due to low contrast between water environment and slurry conductivity in water-rich strata.

[0024] The present disclosure adopts corresponding tracking interpretation schemes for different stratum environments, in non-water-rich strata, an electrode array is arranged outside the injected medium, and a resistivity signal analysis system is used to track the diffusion path of the slurry, while in water-rich strata, due to the similar conductivity of the slurry and water, the resistivity signal is used to preliminarily locate the slurry diffusion, and then the three-dimensional radar signal is used to accurately explore the structure before and after the injected slurry, distinguish the slurry diffusion boundary and diffusion path, and finally realize accurate visualization of the slurry diffusion process. BRIEF DESCRIPTION OF DRAWINGS

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

[0026] Figure 1 The present disclosure is a method flowchart. DETAILED DESCRIPTION

[0027] The present disclosure will be further described below in combination with the accompanying drawings and embodiments.

[0028] It should be noted that the following detailed description is illustrative only, and is intended to provide further description in connection with the present disclosure. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure belongs.

[0029] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0030] Embodiment 1

[0031] A slurry diffusion interpretation method based on joint resistivity and radar signal is provided in an embodiment of the present disclosure, as shown in Figure 1 includes:

[0032] In combination with geological exploration, geophysical exploration and drilling, the water-rich comprehensive research and judgment of the area to be excavated at the working face is carried out, and the non-water-rich stratum and the water-rich stratum are obtained.

[0033] For the non-water-rich stratum, the electrode net arranged on the surface of the working face is used to monitor the resistivity change in the grouting process, and the diffusion path and diffusion range of the slurry are inversely calculated based on the resistivity change.

[0034] For the water-rich stratum, the radar sensor is arranged in the peripheral area of the working face while the electrode net is arranged, the diffusion path and diffusion range of the slurry are preliminarily positioned by resistivity, and then the diffusion path and diffusion boundary are constrained and corrected by combining the radar signal of the radar sensor, so as to finally realize the visualization of the slurry diffusion process.

[0035] As an embodiment, the slurry diffusion interpretation method based on joint resistivity and radar signal of the present disclosure adopts corresponding tracking interpretation methods for different stratum environments, especially for the non-water-rich stratum, realizes the joint interpretation of slurry diffusion by combining resistivity and radar signal, improves the accuracy and robustness of slurry diffusion tracking, and the specific implementation process is as follows:

[0036] Step 1: According to the results of geological exploration, combined with conventional working face geophysical and drilling means, the water-rich comprehensive research and judgment of the area to be excavated at the working face is carried out, and the non-water-rich stratum and the water-rich stratum are obtained.

[0037] Step 2: For the non-water-rich stratum with poor stability such as broken surrounding rock, weak stratum and karst cave, the electrode net is arranged on the surface of the working face, and the layout range and density of the electrodes should be able to cover the expected slurry diffusion range.

[0038] Specifically, by the electrode optimization arrangement system, an optimal electrode network arrangement scheme is formed according to the stratum characteristics of the region to be excavated and preset monitoring requirements, and the actual electrode network arrangement is performed according to the optimal scheme.

[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 establishes a mathematical model based on electrode positions and slurry diffusion ranges to describe precision optimization, data coverage optimization and the like, by using a particle swarm optimization algorithm, according to stratum characteristics such as electrical conductivity, porosity and fracture distribution of the stratum, and preset monitoring requirements such as electrode quantity, precision requirement, monitoring coverage and the like, and the predicted diffusion radius of the slurry.

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

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

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

[0044] Each electrode arrangement scheme is expressed as a particle vector, and each element of the vector corresponds to the position, quantity and spacing of the electrodes.

[0045] An initial particle swarm is generated, and the position and speed of each particle are randomly allocated within a set range; each particle has two attributes: position (containing information of the position, quantity and spacing of the electrodes) and speed (indicating the moving speed of the particle in the search space, used to update the position of the particle in the next iteration).

[0046] The position (electrode arrangement scheme) of each particle is evaluated for fitness according to the objective function, and the motion of the particle is updated according to its historical optimal solution (personal optimal solution) and the optimal solution of the entire particle swarm (global optimal solution).

[0047] In each iteration, the speed and position update formula of the particle is:

[0048]

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

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

[0051] The electrode positioning and installation unit determines the electrode position by laser ranging technology and installs the electrode to the predetermined position according to the optimal electrode arrangement scheme; during the installation process, the contact quality detector and the pressure sensor are used to monitor the contact between the electrode and the stratum in real time, ensuring stable contact.

[0052] Step 3: Collect resistivity data in the initial stage before grouting to obtain the initial resistivity distribution; during the grouting process, collect resistivity data in real time, record the resistivity changes during the slurry diffusion, process the resistivity change image of the slurry diffusion through the tomographic imaging algorithm, and obtain the diffusion path, diffusion range, and diffusion speed of the slurry.

[0053] Specifically, step 3.1: Monitor the resistivity changes during the grouting process, specifically:

[0054] (1) Collect resistivity data in the initial stage before grouting to obtain the initial resistivity distribution.

[0055] First, use the current source in the electrode network to generate a known current (I), and calculate the resistivity of the stratum through the voltage difference (ΔV).

[0056] During the grouting process, as the slurry diffuses, the resistivity of the stratum changes. The injected slurry has a certain conductivity, and when the slurry enters the stratum, it changes the resistivity distribution of the stratum; generally, the resistivity of the slurry is lower, so after the slurry is injected, the resistivity of the region will be reduced, making it easier for the current to pass through that region, and the voltage difference will change accordingly.

[0057] Therefore, during the grouting process, the voltage difference (ΔV) will dynamically change with the diffusion of the slurry, reflecting the changes in the resistivity of the stratum.

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

[0059] (2) During the grouting process, collect resistivity data in real time and record the resistivity changes during the slurry diffusion. Step 3.2: Invert the diffusion path and diffusion range of the slurry based on the resistivity changes, specifically:

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

[0061] (2) Based on the resistivity change image, determine the regions where the resistivity has decreased significantly, which correspond to the injection regions of the slurry and the range to which the slurry has diffused.

[0062] For example: in a certain area, the resistivity is significantly reduced, 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 time; as the slurry continues to be injected, the resistivity of the diffusion area will change dynamically, and by continuously updating the resistivity data, the tracking of the slurry diffusion path is realized, the spatial distribution and change trend of the resistivity change are analyzed, and the time evolution process of the slurry diffusion is obtained.

[0064] Step 4: For water-rich strata, electrode nets are arranged on the surface of the working face, and radar sensors are arranged in the peripheral area of the working face. The arrangement of electrodes and the coverage range of radar signals should be able to cover the expected slurry diffusion range. In particular, for the arrangement of radar sensors, suitable antennas are selected according to the characteristics of the strata to improve the signal penetration.

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

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

[0067] The sensor selection module selects appropriate types of radar sensors (such as ground penetrating radar, three-dimensional radar, or imaging radar, etc.) according to the characteristics of water-rich strata and the requirements of monitoring depth, resolution, etc.

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

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

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

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

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

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

[0074] An initial particle swarm is generated, and the position and velocity of each particle are randomly assigned within a set range. Each particle has two attributes: position (containing the position and angle of the radar sensor) and velocity (representing the particle's moving speed in the search space, used to update the particle's position in the next iteration).

[0075] The position of each particle (electrode arrangement scheme) is evaluated according to the objective function, and the particle's movement is updated according to its historical optimal solution (personal optimal solution) and the optimal solution of the entire particle swarm (global optimal solution).

[0076] In each iteration, the particle's velocity and position update formula is:

[0077]

[0078] where, is the particle's velocity in the tth round; is the particle's position in the tth round; pbest i is the particle's optimal position; w is the inertia weight; c1 and c2 are learning factors that control the adjustment degree of the particle to the personal optimal solution and the global optimal solution; r1 and r2 are random numbers that ensure the randomness of the search.

[0079] After each iteration, the particle swarm gradually converges to an optimal solution, and the optimal radar arrangement scheme is found.

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

[0081] Through the radar sensor to emit electromagnetic wave signal, receive the signal reflected by the stratum, and convert it into digital signal, get the radar signal; through signal processing to filter out noise and enhance signal quality, support the stratum structure analysis of slurry diffusion path and real-time tracking of slurry.

[0082] The frequency of radar signal acquisition is adaptively set according to the water enrichment condition of the stratum. If the water enrichment degree of the stratum is high, high frequency is used to scan the stratum with radar, and if the water enrichment degree of the stratum is low, low frequency is used to scan the stratum with radar. Combined with real-time resistivity data, the joint tracking of slurry diffusion is realized.

[0083] Step 5: Collect initial resistivity data and radar data before grouting to obtain initial resistivity distribution and initial radar image.

[0084] In particular, for radar signal acquisition, the radar sensor is used to scan the tunnel face at multiple angles and positions, and finally obtain the initial resistivity distribution of the tunnel face and the location and structural characteristics of the water-conducting channel and cavity in front of the tunnel face based on the radar image.

[0085] Step 6: Data synchronization and calibration to ensure that the resistivity data and radar signals are consistent in time and space, and if not, necessary calibration is performed.

[0086] Specifically, by preprocessing and standardizing the resistivity data and radar image, the consistency of the data format and resolution is ensured; by using spatial registration technology, the data points of resistivity data and radar data are mapped to the same spatial coordinate system for alignment, ensuring that they correspond at the same depth and other spatial positions, while correcting whether the time of the two types of data is the same time.

[0087] The original format of the resistivity data is matrix form (containing spatial coordinate points and resistivity data of each coordinate point), and the original format of the radar signal is image format, which needs to be converted to matrix format; if the resolution of the radar image is lower than that of the resistivity data, the resolution of the image is increased using the interpolation method; if the resolution of the radar image is higher than that of the resistivity data, the resolution is lowered using the pixel averaging method; further format conversion is realized, and 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 one embodiment)

[0088] Step 7: Through joint tracking of resistivity and radar signals, accurate monitoring of the slurry diffusion path in complex strata is realized.

[0089] Specifically, step 7.1: Based on the initial resistivity distribution and radar data, the water-rich area and water channel boundary are identified.

[0090] Step 7.2: Determine the area where the resistivity decreases significantly during grouting, which corresponds to the injection area of the slurry and the range of slurry diffusion. For example: in a certain 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. During the grouting 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 water-rich strata may be affected by water conductivity, the tracking accuracy is low, therefore, combined with high-resolution radar signals, the slurry diffusion path and water-conducting channel boundary are constrained and corrected, and the following are the constraint and correction steps:

[0092] The water channel, fracture and stratum spatial structure information captured by the radar image are obtained as the water channel boundary constraint condition, the resistivity inversion images at the same time and the same spatial position are superimposed, the slurry-water interface in the slurry diffusion direction is determined by the resistivity inversion result, the water channel boundary obtained by the radar signal is used as the slurry diffusion boundary of the water channel that has been plugged, and the system dynamically adjusts the boundary condition by using the radar and resistivity data, and optimizes the inversion result of the diffusion range.

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

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

[0095] Embodiment 2

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

[0097] The water enrichment judgment module is configured to comprehensively judge the water enrichment of the region to be excavated at the working face by combining geological exploration, geophysical exploration and drilling, and obtain non-water-enriched strata and water-enriched strata.

[0098] The non-water-enriched module is configured to monitor the resistivity change in the grouting process by using the electrode net arranged on the surface of the working face for the non-water-enriched strata, and inversely calculate the diffusion path and diffusion range of the slurry based on the resistivity change.

[0099] The water-enriched module is configured to arrange radar sensors in the peripheral region of the working face while arranging the electrode net for the water-enriched strata, preliminarily locate the diffusion path and diffusion range of the slurry by using the resistivity, combine the radar signals of the radar sensors to explore the stratum structure before and after the injected slurry, constrain and correct the diffusion path and diffusion boundary, and finally realize the visualization of the slurry diffusion process.

[0100] Embodiment 3

[0101] In an embodiment of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the slurry diffusion interpretation method based on joint resistivity and radar signals.

[0102] Embodiment 4

[0103] In an embodiment of the present disclosure, a non-transitory computer readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement the method for interpreting slurry diffusion based on combination of resistivity and radar signals.

[0104] Embodiment 5

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

[0106] The present disclosure is described with reference to the flowcharts and / or block diagrams of the method, device (system), and computer program product according to embodiments of the present disclosure. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams 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 apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that carries out the functions specified in one or more flows and / or blocks.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed by the computer or other programmable data processing apparatus provide a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 one or more flows and / or blocks

[0108] The above description of the specific embodiments of the present disclosure is described with reference to the accompanying drawings, but is not a limitation on the scope of protection of the present disclosure. Those skilled in the art should understand that various modifications or changes made on the basis of the technical solutions of the present disclosure without inventive labor are still within the scope of protection of the present disclosure.

Claims

1. A slurry diffusion interpretation method based on resistivity and radar signal combination, characterized in that, The method comprises the following steps: Rich water comprehensive research and judgment is carried out on the area to be excavated at the working face by means of geological exploration, geophysical exploration and drilling exploration, and non-rich water stratum and rich water stratum are obtained; For the non-rich water stratum, the electrode net arranged on the surface of the working face is used to monitor the resistivity change in the grouting process, and the diffusion path and diffusion range of the slurry are inversely calculated based on the resistivity change; For the rich water stratum, the electrode net is arranged at the same time, and the radar sensor is arranged in the peripheral area of the working face, the diffusion path and diffusion range of the slurry are preliminarily positioned by resistivity, and then the diffusion path and diffusion boundary are constrained and corrected by combining the radar signal of the radar sensor, so that the visualization of the slurry diffusion process is realized; The electrode net and the radar sensor are arranged according to the stratum characteristics of the area to be excavated and the preset monitoring requirements by the electrode arrangement optimization unit and the radar sensor optimization arrangement system, and the optimal electrode net arrangement scheme and the radar sensor arrangement scheme are formed, and the actual arrangement is carried out according to the optimal scheme; The electrode arrangement optimization unit establishes a mathematical model based on the electrode position and the slurry diffusion range to describe the precision optimization and data coverage optimization according to the conductivity, porosity and fracture distribution of the stratum characteristics, the preset electrode quantity, precision requirement, monitoring requirement and the predicted diffusion radius of the slurry by the particle swarm optimization algorithm; The radar sensor optimization arrangement system integrates the particle swarm optimization algorithm to determine the optimal arrangement position and quantity of the radar sensor, and establishes a mathematical model based on the radar sensor position and the radar signal radiation area to describe the precision optimization and data coverage optimization.

2. The method of claim 1, wherein the method is based on a combination of resistivity and radar signals. The monitoring of the resistivity change in the grouting process comprises the following steps: Initial stage resistivity data acquisition is carried out before grouting to obtain the initial resistivity distribution; During the grouting process, the resistivity data are collected in real time, and the resistivity change during the slurry diffusion is recorded.

3. The method of claim 1, wherein the method is based on a combination of resistivity and radar signals. The diffusion path and diffusion range of the slurry are inversely calculated based on the resistivity change, which comprises the following steps: The resistivity change image of the slurry diffusion is generated by the tomographic imaging algorithm; Based on the resistivity change image, the diffusion process model of the slurry is constructed, 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.

4. The method of claim 1, wherein the method is a combined resistivity and radar signal based slurry diffusion interpretation method. The method further comprises the following steps: When the resolution of the diffusion path obtained by resistivity inversion is insufficient or inconsistent with the boundary reflected by the radar signal, the boundary reflected by the radar signal is used as the constraint of the diffusion boundary, and the distance of the slurry diffusion is adjusted according to the resistivity inversion result.

5. The method of claim 1, wherein the method is a combined resistivity and radar signal based slurry dispersion interpretation method. The resistivity data and the radar signal are aligned in time and space. The method comprises the following steps:

6. A slurry diffusion interpretation system based on resistivity combined with radar signals, characterized in that, The rich water research and judgment module is configured to carry out rich water comprehensive research and judgment on the area to be excavated at the working face by means of geological exploration, geophysical exploration and drilling exploration, and non-rich water stratum and rich water stratum are obtained; The non-rich water module is configured to monitor the resistivity change in the grouting process by using the electrode net arranged on the surface of the working face for the non-rich water stratum, and the diffusion path and diffusion range of the slurry are inversely calculated based on the resistivity change; ​ The water-rich module is configured to: for a water-rich stratum, arrange a radar sensor in a peripheral area of a working face while arranging an electrode net, preliminarily locate a diffusion path and a diffusion range of slurry through resistivity, and then combine a radar signal of the radar sensor to probe a structure of a stratum before and after the injected slurry, constrain and correct the diffusion path and the diffusion boundary, and finally realize visualization of a slurry diffusion process.

7. A non-transitory computer-readable storage medium, comprising: The non-transitory computer readable storage medium is used to store computer instructions, and the computer instructions are executed by a processor to implement the slurry diffusion interpretation method based on the combination of resistivity and radar signals according to any one of claims 1-5.

8. An electronic device, comprising: The non-transitory computer readable storage medium is used to store computer instructions, and the computer instructions are executed by a processor to implement the slurry diffusion interpretation method based on the combination of resistivity and radar signals according to any one of claims 1-5. The non-transitory computer readable storage medium is used to store computer instructions, and the computer instructions are executed by a processor to implement the slurry diffusion interpretation method based on the combination of resistivity and radar signals according to any one of claims 1-5. The non-transitory computer readable storage medium is used to store computer instructions, and the computer instructions are executed by a processor to implement the slurry diffusion interpretation method based on the combination of resistivity and radar signals according to any one of claims 1-5.

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