Slurry diffusion tracking method and system integrating optical fiber sensing and electrical resistance tomography technology

Abstract

The present disclosure provides a slurry diffusion tracking method and system that integrates fiber optic sensing and electrical resistance tomography technology, and relates to the field of geotechnical engineering technology, including: identifying areas with high geological complexity based on geological exploration results of the area to be grouting; acquiring resistivity data in real time through an electrode network uniformly arranged on the surface of the tunnel face, and using electrical resistance tomography to obtain global resistivity change data during the grouting process; for areas with high geological complexity, acquiring fiber optic sensing data in real time using fiber optic sensors arranged in the borehole to be grouting, and then obtaining local strain, temperature and pressure data; performing data fusion analysis based on the global resistivity change data and the local strain, temperature and pressure data, and ultimately obtaining the slurry diffusion range; based on the acquisition of global resistivity, the present invention uses fiber optic sensing to monitor local strain, temperature and pressure data during the slurry diffusion process, thereby achieving global and local refined tracking of the slurry.

Description

Technical Field

[0001] The present disclosure relates to the field of geotechnical engineering technology, and in particular to a slurry diffusion tracking method and system integrating optical fiber sensing and electrical resistance tomography technology. Background Art

[0002] In recent years, significant progress has been made in the field of tunnel transportation, particularly in transportation hubs, mountain railways, and urban underground space development. Tunnel construction has become a crucial means of enhancing transportation capacity and promoting regional economic development. Common geological hazards associated with tunnel construction include water inrush, mud inrush, collapse, and rockburst, particularly in areas with weak surrounding rock, high in-situ stress, and water-rich strata. Water and mud inrush are caused by excessive groundwater pressure or insufficient surrounding rock strength, while collapses are often caused by poor surrounding rock stability or improper support. Rockbursts are caused by the release of stress from high-in-situ stress rocks. Effective geological surveys, grouting reinforcement, and construction adjustments are key to preventing these hazards.

[0003] The primary function of grouting technology is to inject slurry into the ground to fill cracks and voids, thereby strengthening the formation, reducing permeability, and controlling deformation. Grouting not only improves the bearing capacity of the surrounding rock but also effectively controls groundwater seepage, making it a crucial tool in tunnel construction and ground reinforcement projects. Efficient grouting requires real-time monitoring of the slurry's diffusion state to ensure that it evenly and fully fills the target area, avoiding quality issues caused by insufficient or excessive grouting.

[0004] However, existing grouting monitoring technologies have numerous shortcomings. For example, traditional monitoring methods primarily rely on sensors deployed on the surface or in boreholes, which struggle to fully capture the diffusion behavior of slurry under complex geological conditions. Furthermore, traditional methods suffer from low spatial resolution and sensitivity, making it difficult to accurately capture real-time dynamic information on slurry diffusion. This is particularly true in deep, complex formations, where monitoring accuracy and reliability are significantly limited. Therefore, effectively tracking the diffusion path and concentration distribution of slurry has become a pressing challenge for the engineering community.

[0005] Fiber-optic sensing technology has attracted widespread attention for its application in slurry diffusion monitoring due to its high sensitivity, resistance to electromagnetic interference, and ability to conduct long-distance distributed monitoring. Fiber-optic sensing tracks the diffusion path of slurries by monitoring changes in temperature or strain. However, relying solely on fiber-optic sensing technology in complex geological environments often makes it difficult to accurately obtain specific slurry concentration distribution information and imaging of spatial diffusion patterns. Furthermore, measurement accuracy may be affected in complex formations with strong interference sources.

[0006] Therefore, the existing slurry diffusion tracking technology lacks stability and accuracy. Summary of the Invention

[0007] To address the above-mentioned issues, the present disclosure proposes a slurry diffusion tracking method and system that integrates fiber optic sensing and electrical resistance tomography technology. Based on the acquisition of global resistivity, fiber optic sensing is used to monitor local strain, temperature, and pressure data during the slurry diffusion process, thereby achieving global and local refined tracking of the slurry.

[0008] According to some embodiments, the present disclosure adopts the following technical solutions:

[0009] The slurry diffusion tracking method that integrates fiber optic sensing and electrical resistance tomography technology includes:

[0010] Based on the geological exploration results of the area to be grouting, identify areas with high geological complexity in the area to be grouting;

[0011] The resistivity data is acquired in real time by using an electrode network evenly distributed on the tunnel face surface, and the global resistivity change data during the grouting process is obtained using electrical resistance tomography.

[0012] For areas with highly complex geological conditions, fiber optic sensors placed in the borehole to be grouted are used to obtain fiber optic sensor data in real time, thereby obtaining local strain, temperature and pressure data;

[0013] Based on the global resistivity change data and local strain, temperature and pressure data, data fusion analysis is performed to finally obtain the slurry diffusion range.

[0014] According to some embodiments, the present disclosure adopts the following technical solutions:

[0015] The slurry diffusion tracking system integrates fiber optic sensing and electrical resistance tomography technology, including area module, resistance module, fiber optic module and range module:

[0016] The regional module is configured to: identify regions with high geological complexity in the region to be grouting based on geological exploration results of the region to be grouting;

[0017] The resistance module is configured to acquire resistivity data in real time through an electrode network uniformly arranged on the tunnel face surface, and to obtain global resistivity change data during the grouting process using electrical resistance tomography technology;

[0018] The fiber optic module is configured to: For areas with high geological complexity, it uses fiber optic sensors placed in the borehole to be grouted to acquire fiber optic sensor data in real time, thereby obtaining local strain, temperature and pressure data;

[0019] The slurry diffusion range analysis module is configured to perform data fusion analysis based on global resistivity change data and local strain, temperature and pressure data, and ultimately obtain the slurry diffusion range.

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

[0021] A computer program product includes a computer program, which, when executed by a processor, implements the slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography.

[0022] According to some embodiments, the present disclosure adopts the following technical solutions:

[0023] 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 tracking method integrating optical fiber sensing and electrical resistance tomography is implemented.

[0024] According to some embodiments, the present disclosure adopts the following technical solutions:

[0025] An electronic device includes: a processor, a memory, and a computer program; wherein the processor is connected to 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 to enable the electronic device to implement the slurry diffusion tracking method that integrates optical fiber sensing and electrical resistance tomography technology.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The disclosed slurry diffusion tracking method that integrates optical fiber sensing and electrical resistance tomography technology arranges resistive ERT electrodes on the surface of the tunnel face to globally capture resistivity change data, arranges optical fibers in boreholes in areas with complex local formation conditions to monitor local strain, temperature, and pressure data during the slurry diffusion process, and ultimately achieves global and local refined tracking of the slurry through data fusion analysis of global resistivity change data and local strain, temperature, and pressure data.

[0028] The present invention provides two methods for data fusion analysis. One is the diffusion range prediction based on feature extraction and fusion, which uses intelligent methods such as machine learning models and deep learning models to perform feature extraction and fusion, and introduces constraints based on physical laws to constrain the training of the model, thereby improving accuracy and real-time performance; the other is the diffusion range construction based on data analysis, which uses data analysis to first determine the rough diffusion range based on the preprocessed global resistivity change data, and then determine the precise slurry front position and slurry diffusion boundary based on the preprocessed local strain, temperature and pressure data. The further obtained diffusion path is accumulated in time to refine the rough diffusion range. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings, which constitute a part of the present disclosure, are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0030] Figure 1 This is a flow chart of a method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0032] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure belongs.

[0033] 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 intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0034] Explanation of terms:

[0035] The diffusion path is the dynamic grouting route, and the diffusion range is the slurry diffusion area within a certain period of time, which is the final grouting result.

[0036] Example 1

[0037] In one embodiment of the present disclosure, a slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography is provided, comprising:

[0038] Based on the geological exploration results of the area to be grouting, identify areas with high geological complexity in the area to be grouting;

[0039] The resistivity data is acquired in real time by using an electrode network evenly distributed on the tunnel face surface, and the global resistivity change data during the grouting process is obtained using electrical resistance tomography.

[0040] For areas with highly complex geological conditions, fiber optic sensors placed in the borehole to be grouted are used to obtain fiber optic sensor data in real time, thereby obtaining local strain, temperature and pressure data;

[0041] Based on the global resistivity change data and local strain, temperature and pressure data, data fusion analysis is performed to finally obtain the slurry diffusion range.

[0042] As an embodiment, the disclosed slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology, based on obtaining global resistivity, uses optical fiber sensing to monitor local strain, temperature and pressure data during the slurry diffusion process, thereby achieving global and local refined tracking of the slurry, such as Figure 1 The specific implementation process is as follows:

[0043] Step S1: Based on the geological exploration results of the area to be grouting, identify areas with high geological complexity in the area to be grouting.

[0044] Furthermore, the areas with high geological complexity are marked for stratum complexity based on underground structure and stratum characteristic data obtained from geological exploration results, and areas with different geological conditions are divided into high complexity and low complexity areas.

[0045] Specifically, carry out preliminary geological exploration and identification of complex areas, obtain underground structure and formation characteristic data based on the results of preliminary geological exploration, mark the complex formation areas, and divide areas with different geological conditions into high-complexity and low-complexity areas. High-complexity areas include but are not limited to: fracture zones, broken zones, water-rich formations, low-permeability rock formations, weak formations, heterogeneous rock formations, etc., to provide a basis for the subsequent electrode arrangement of optical fiber and electrical resistance tomography.

[0046] Step S2: Real-time resistivity data is acquired through an electrode network uniformly arranged on the tunnel face surface, and global resistivity change data during the grouting process is obtained using electrical resistance tomography.

[0047] Furthermore, the electrical resistance tomography technology uses the Gauss-Newton inversion algorithm to infer the resistivity distribution of the underground strata based on the resistivity data. Through a continuous inversion process, the resistivity distribution map at each moment is obtained, and then the global resistivity change data during the grouting process is obtained.

[0048] Specifically, the electrode grid is uniformly arranged on the tunnel face surface, and the layout range and density of the electrodes can cover the expected slurry diffusion range.

[0049] Before grouting, resistivity data is collected in the initial stage and the initial resistivity distribution map is calculated. During the grouting process, resistivity data is collected in real time and the resistivity distribution map at each moment is calculated to obtain the global resistivity change data during the grouting process.

[0050] Among them, the Gauss-Newton inversion algorithm is used to calculate the resistivity distribution of the underground formation. Through the continuous inversion process, the resistivity distribution map at each moment can be obtained; and the resistivity change data is used to determine the areas where the resistivity drops significantly. These areas correspond to the slurry injection area and the range to which the slurry diffuses.

[0051] Step S3: For areas with highly complex geological conditions, optical fiber sensors arranged in the borehole to be grouted are used to obtain optical fiber sensing data in real time, thereby obtaining local strain, temperature and pressure data.

[0052] Specifically, on the premise of global fiber optic sensing arrangement on the face, for areas with highly complex geological conditions, after drilling the grouting borehole, optical fiber is arranged on the borehole wall. For boreholes using small tube grouting, optical fiber is also arranged on the small tube wall. For boreholes with severe hole collapse and large amount of water and sand gushing, the optical fiber in the borehole is arranged more densely.

[0053] Before grouting, the optical fiber signal is calibrated based on the initial resistivity distribution map.

[0054] The calibration here includes two aspects:

[0055] (1) Calibrate the measurement parameters of the optical fiber signal, including measurement accuracy, signal output accuracy and stability.

[0056] (2) Perform a signal benchmark test on the optical fiber before grouting, and use the optical fiber signal generated by the natural environment of the formation and environmental changes as a benchmark to separate it from the optical fiber signal generated during the grouting process.

[0057] Step S4: Based on the global resistivity change data and the local strain, temperature and pressure data, data fusion analysis is performed to finally obtain the slurry diffusion range.

[0058] Specifically, during the grouting process, resistivity data and fiber optic sensor data are collected in real time. The global resistivity change data of the ERT system is integrated and analyzed with the local strain, temperature, and pressure data of the fiber optic sensor to obtain the diffusion range of the slurry. The specific steps are as follows:

[0059] 1. Preprocessing

[0060] Ensure the time synchronization between the electrical resistance tomography data acquisition and the fiber optic sensor, and ensure that the global resistivity and local strain, temperature, and pressure data are aligned on the time axis.

[0061] The fiber optic sensing data is filtered and noise eliminated, and the spatial resolution and temporal resolution of different data are resampled to ensure consistency in space and time.

[0062] 2. Data fusion after preprocessing

[0063] This embodiment provides two methods: a method based on feature extraction and fusion and a method based on data analysis, which are described below respectively.

[0064] Method 1: Diffusion range prediction based on feature extraction and fusion

[0065] In three-dimensional space, the spatial mapping relationship between data points is established by using the spatial distribution of the ERT system's electrode positions and optical fiber layout. The global resistivity change data and local strain, temperature, and pressure data are spliced ​​into a multidimensional feature vector. The vector is then input into a machine learning model (such as a decision tree or support vector machine) or a deep learning model (such as a multimodal neural network) for fusion processing to obtain the diffusion range. The specific steps are as follows:

[0066] (1) First, the resistivity data is preprocessed and converted into two-dimensional or three-dimensional grid data, and the global spatial diffusion feature vector F is extracted through the convolutional neural network (CNN) Resistivity ; The local strain, temperature, pressure and other data monitored by the optical fiber are processed into time series data, and the long short-term memory network is used to extract the local time series feature vector F Fiber .

[0067] (2) The attention mechanism is used to fuse the global and local feature vectors, giving different weights to the global feature vector and the local feature vector (the resistivity data weight w Resistivity , fiber data weight w Fiber ), and output the fused feature vector F Fusion .

[0068] w Resistivity +w Fiber =1

[0069] F Fusion =w Resistivity ·F Resistivity +w Fiber ·F Fiber

[0070] (3) The fused feature vector is input into the fully connected layer to achieve continuous output of the slurry diffusion range.

[0071] During the training of the above learning model, constraints based on physical laws are added. The resistivity diffusion characteristics must comply with the diffusion equation constraints, and the optical fiber strain, temperature, and pressure signals must satisfy Hooke's law. Constraints are implemented by adding regularization terms to the loss function, resulting in a loss function related to physical laws, which can be expressed as follows:

[0072] L physics =λ1·L diffusion +λ2·L strain

[0073] Among them, λ1 and λ2 are physical constraint weight coefficients, L diffusion With L strain Represents the loss term for the diffusion equation and strain constraint.

[0074] Ultimately, by minimizing the total loss function, we ensure that the model not only accurately fits the data but also follows the actual physical laws, thereby improving the accuracy and physical consistency of the prediction. The total loss function is:

[0075] L total =L data (F Fusion )+L physics

[0076] Among them, L total Represents the total loss, L data is the loss of data generated during the fitting process, L physics Losses related to physical laws.

[0077] Method 2: Diffusion range construction based on data analysis

[0078] A combination of rough construction and fine construction is adopted, specifically:

[0079] (1) Based on the pre-processed global resistivity change data, the area where the resistivity drops significantly is determined, which corresponds to the slurry injection area and the range where the slurry diffuses, and a rough diffusion range is obtained.

[0080] (2) Based on the pre-processed local strain, temperature and pressure data, the abnormal change points of strain, temperature and pressure are extracted, and the precise slurry front position and slurry diffusion boundary are determined in combination with the grouting theory to refine the rough diffusion range.

[0081] Based on the slurry diffusion range obtained from resistivity data, in order to finely characterize the slurry diffusion front, the data of abnormal temperature and pressure change points captured by the fiber optic sensing data are cleaned and preprocessed. A low-pass filtering algorithm is used to remove high-frequency noise. The data over a long period of time is divided into multiple time windows or spatial windows for local extraction and analysis. The spatial coordinate positions of the data points are spatially calibrated with the resistivity data to ensure that the data change rate is obtained by calculation under the same spatial coordinates. When the change rate exceeds a certain critical value, the point is considered to be an abnormal change point. The abnormal change point is extracted and the abnormal change point of the fiber optic sensing path is projected onto the resistivity image to ultimately determine the precise position of the slurry front.

[0082] In order to finely characterize the slurry diffusion boundary, the strain, temperature, and pressure monitoring change data extracted along the grouting path and the extracted abnormal change points are analyzed. The strain gradient of the strain data is used to identify areas with large strain changes. The data is normalized with the parameters such as the surrounding rock permeability and elastic modulus along the optical fiber layout obtained in the early geological exploration, and each data is assigned a weight. The weight is obtained through repeated experiments. For example, high permeability areas may have a greater impact on slurry diffusion, so the permeability of this area may need to be given a higher weight; in low permeability areas, strain changes may be more sensitive, so a higher weight should be given; for hard rock formations, the elastic modulus has a greater impact, so the weight of the elastic modulus can be appropriately increased. For example, using optical fiber monitoring strain data, surrounding rock permeability, and elastic modulus to achieve boundary refinement, the weighted calculation formula can be:

[0083] W total =w ∈ ·E ∈ +w K ·E K +w E ·E E

[0084] Among them, W total Represents the final weighted value, E ∈ represents the normalized strain, E K represents the normalized permeability, E E represents the normalized elastic modulus, w ∈ 、w K 、w E denote the weights of strain, permeability and elastic modulus, respectively.

[0085] Building a physical deformation model:

[0086] ΔX=f(∈,σ,K,E)

[0087] Where ΔX represents the deformation of the diffusion boundary, and f(∈,σ,K,E) represents the deformation function with respect to strain, stress, permeability, and elastic modulus.

[0088] The boundary position after deformation is:

[0089] X new =X old +ΔX

[0090] Among them, X old Represented as the position of the original diffusion boundary, X new Indicates the position of the boundary after deformation.

[0091] The deformed boundary position is projected onto the resistivity image to determine the precise slurry diffusion boundary.

[0092] Based on the determined slurry front position and slurry diffusion boundary, the diffusion path at a certain moment is determined, and the diffusion path is accumulated over time to obtain the final diffusion range, thereby achieving accurate capture of the slurry diffusion process.

[0093] Example 2

[0094] In one embodiment of the present disclosure, a slurry diffusion tracking system integrating fiber optic sensing and electrical resistance tomography technology is provided, including a region module, a resistance module, a fiber optic module, and a range module:

[0095] The regional module is configured to: identify regions with high geological complexity in the region to be grouting based on geological exploration results of the region to be grouting;

[0096] The resistance module is configured to acquire resistivity data in real time through an electrode network uniformly arranged on the tunnel face surface, and to obtain global resistivity change data during the grouting process using electrical resistance tomography technology;

[0097] The fiber optic module is configured to: For areas with high geological complexity, it uses fiber optic sensors placed in the borehole to be grouted to acquire fiber optic sensor data in real time, thereby obtaining local strain, temperature and pressure data;

[0098] The range module is configured to perform data fusion analysis based on the global resistivity change data and the local strain, temperature and pressure data, and finally obtain the slurry diffusion range.

[0099] Example 3

[0100] 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 tracking method integrating optical fiber sensing and electrical resistance tomography is implemented.

[0101] Example 4

[0102] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided. 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 tracking method that integrates fiber optic sensing and electrical resistance tomography technology is implemented.

[0103] Example 5

[0104] In one embodiment of the present disclosure, an electronic device is provided, comprising: a processor, a memory, and a computer program; wherein the processor is connected to 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 to enable the electronic device to implement the slurry diffusion tracking method that integrates optical fiber sensing and electrical resistance tomography technology.

[0105] 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 produce 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.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0107] 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. Those skilled in the art 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 tracking method integrating optical fiber sensing and electrical resistance tomography technology, characterized in that: include: Based on the geological exploration results of the area to be grouting, identify areas with high geological complexity in the area to be grouting; The resistivity data is acquired in real time by using an electrode network evenly distributed on the tunnel face surface, and the global resistivity change data during the grouting process is obtained using electrical resistance tomography. For areas with highly complex geological conditions, fiber optic sensors placed in the borehole to be grouted are used to obtain fiber optic sensor data in real time, thereby obtaining local strain, temperature and pressure data; Based on the global resistivity change data and local strain, temperature and pressure data, data fusion analysis is performed to finally obtain the slurry diffusion range; The data fusion analysis uses a data analysis-based approach to construct the slurry diffusion range, specifically: Based on the pre-processed global resistivity change data, the area where the resistivity drops significantly is determined, which corresponds to the slurry injection area and the range where the slurry diffuses, and the rough diffusion range is obtained; Based on the pre-processed local strain, temperature and pressure data, the abnormal change points of strain, temperature and pressure are extracted. The precise slurry front position and slurry diffusion boundary are determined by combining the grouting theory, and the rough diffusion range is refined. The extracted strain, temperature, and pressure monitoring change data along the grouting path and the extracted abnormal change points are analyzed. The strain gradient of the strain data is used to identify areas with large strain changes. The data is normalized with the surrounding rock permeability and elastic modulus parameters along the optical fiber layout obtained in the early geological exploration, and a weight is assigned to each data. The weighted calculation formula is: in, represents the final weighted value, represents the normalized strain, represents the normalized permeability, represents the normalized elastic modulus, 、 、 denote the weights of strain, permeability, and elastic modulus, respectively; Building a physical deformation model: in, represents the deformation of the diffusion boundary, represents the deformation function with respect to strain, stress, permeability, and elastic modulus; The boundary position after deformation is: in, is represented as the location of the original diffusion boundary, Indicates the boundary position after deformation; The deformed boundary position is projected onto the resistivity image to determine the precise slurry diffusion boundary; Based on the determined slurry front position and slurry diffusion boundary, the diffusion path at a certain moment is determined, and the diffusion path is accumulated over time to obtain the final diffusion range, thereby achieving accurate capture of the slurry diffusion process.

2. The slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology as claimed in claim 1, characterized in that: The areas with high geological complexity are marked with stratum complexity based on underground structure and stratum characteristic data obtained from geological exploration results, and areas with different geological conditions are divided into high complexity and low complexity areas.

3. The slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology as claimed in claim 1, characterized in that: The electrical resistance tomography technology uses the Gauss-Newton inversion algorithm to infer the resistivity distribution of underground strata based on resistivity data. Through a continuous inversion process, the resistivity distribution map at each moment is obtained, and then the global resistivity change data during the grouting process is obtained.

4. The slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology as claimed in claim 1, characterized in that: The optical fiber sensor arranged in the borehole to be grouting includes: After drilling the hole to be grouting, arrange the optical fiber on the hole wall; For boreholes that use small conduit grouting, optical fibers are arranged on the wall of the small conduit; For boreholes with severe collapse and large amounts of water and sand gushing, the optical fibers in the boreholes are arranged more densely.

5. The slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology as claimed in claim 1, characterized in that: The data fusion analysis includes: Preprocessing of global resistivity variation data to align them in time and space with local strain, temperature, and pressure data; Feature extraction is performed on the pre-processed global resistivity change data and local strain, temperature and pressure data respectively; Fuse the extracted features; Based on the fused features, the serous diffusion range is predicted.

6. A slurry diffusion tracking system integrating optical fiber sensing and electrical resistance tomography technology, characterized by: A slurry diffusion tracking method using the fusion of optical fiber sensing and electrical resistance tomography technology as described in any one of claims 1 to 5, comprising: The regional module is configured to: identify regions with high geological complexity in the region to be grouting based on geological exploration results of the region to be grouting; The resistance module is configured to acquire resistivity data in real time through an electrode network uniformly arranged on the tunnel face surface, and to obtain global resistivity change data during the grouting process using electrical resistance tomography technology; The fiber optic module is configured to: For areas with high geological complexity, it uses fiber optic sensors placed in the borehole to be grouted to acquire fiber optic sensor data in real time, thereby obtaining local strain, temperature and pressure data; The range module is configured to perform data fusion analysis based on the global resistivity change data and the local strain, temperature and pressure data, and finally obtain the slurry diffusion range.

7. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology according to any one of claims 1 to 5 is implemented.

8. 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 tracking method integrating optical fiber sensing and electrical resistance tomography technology as described in any one of claims 1 to 5 is implemented.

9. An electronic device, characterized in that: include: A processor, a memory, and a computer program; wherein the processor is connected to 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 to enable the electronic device to implement the slurry diffusion tracking method that integrates optical fiber sensing and electrical resistance tomography technology as described in any one of claims 1 to 5.

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