Experimental device and experimental method for fracture evolution and seepage coupling model on confined water

Abstract

The invention provides a confined water fracture evolution and seepage coupling model experiment device and experiment method, and relates to the technical field of geotechnical engineering.The experiment device comprises an optical fiber monitoring module, an electrical method monitoring module and a data collecting and processing module, and the optical fiber monitoring module comprises an optical fiber sensor and an optical fiber demodulator; the electrical method monitoring module comprises an electrode array and a resistance measuring instrument and is used for determining the association between the path of seepage water and the crack extension, and the data acquisition and processing module fuses optical fiber monitoring data and electrical method monitoring data and sends the fused data to the data acquisition and processing module. And generating a multi-dimensional data model of mine floor fracture extension and seepage flow, and formulating corresponding coal mine water disaster prevention and control strategies and monitoring schemes according to different seepage migration modes obtained by clustering analysis. For an area with a high-risk seepage mode, the monitoring frequency is enhanced, and targeted prevention and control measures are taken, so that the safety production of a coal mine can be guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of geotechnical engineering, and in particular, provides an experimental device and method for a coupled model of fissure evolution and seepage under confined water. Background Art

[0002] The seepage of confined water in the mine floor and the expansion of fissures are important inducements for mine water inrush accidents. Existing monitoring methods mostly rely on single technologies, such as temperature monitoring, resistivity monitoring or deformation monitoring, making it difficult to accurately locate and dynamically track fissure expansion and seepage paths, and the monitoring range and accuracy are limited. Therefore, a new method is needed to monitor mine water disasters, achieve multi-dimensional monitoring of seepage in the mine floor and fissure expansion, and provide reliable technical support for mine safety production. Summary of the Invention

[0003] In order to achieve efficient and real-time monitoring of the seepage process of confined water in the mine floor and fissure evolution, the present invention provides an experimental device and method for a coupled model of fissure evolution and seepage under confined water, and the specific technical solutions are as follows.

[0004] An experimental device for a coupled model of fissure evolution and seepage under confined water includes an optical fiber monitoring module, an electrical method monitoring module, and a data acquisition and processing module. The optical fiber monitoring module includes an optical fiber sensor and an optical fiber demodulator. The optical fiber sensor is arranged along the fissures and seepage areas of the mine floor, and the optical fiber sensor and the optical fiber demodulator are connected through an optical cable. The optical fiber monitoring module conducts high-precision dynamic monitoring of the fissure expansion and seepage path of the mine floor; the electrical method monitoring module includes an electrode array and a resistance measuring instrument. The electrode array is composed of multiple electrodes arranged, and the electrodes are installed in the mine floor. The electrical method monitoring module real-time collects data on the resistivity of the rock mass, generates a resistivity distribution map through data inversion calculation, and determines the path of the seepage water and its association with fissure expansion; the data acquisition and processing module fuses the optical fiber monitoring data and the electrical method monitoring data to generate a multi-dimensional data model of fissure expansion and seepage water flow on the mine floor.

[0005] Preferably, the optical fiber sensor is linearly arranged or arranged in a king-shaped coverage in the area where fissures are concentrated; in the area where fissures are sparse, the optical fiber sensor is arranged at the position of the path where seepage is concentrated; the optical fiber sensor covers faults, goafs, and areas with sudden changes in lithology.

[0006] Preferably, the electrodes are arranged in a straight line or in a grid pattern in the mine floor through drilling, and the resolution of monitoring is improved by increasing the setting density of the electrodes; the resistivity parameters obtained by the electrical method monitoring module are used to judge the distribution of moisture in the rock mass, and the lower the resistivity, the higher the water content of the rock mass.

[0007] An experimental method for a coupled model of fissure evolution and seepage under confined water uses the above-mentioned experimental device for a coupled model of fissure evolution and seepage under confined water, and includes:

[0008] In the multi-dimensional data model of mine floor fissure expansion and seepage water flow, data is read and stored through the data collection function collect_data; the integrity of the data is checked using the check_data_integrity function; all numerical data is standardized using the standardize_data function; the correlation coefficient between each monitoring data and seepage migration is calculated using the correlation_analysis function, and characteristic parameters directly reflecting the dynamics and underlying characteristics of seepage are determined in combination with the correlation coefficient, and characteristic parameters are selected for clustering analysis.

[0009] Preferably, the parameters obtained by the data acquisition and processing module include strain, temperature, vibration, humidity, water quality, seepage velocity, seepage direction, resistivity, and voltage.

[0010] Preferably, during the process of checking the integrity of the data, after identifying missing values, for the missing values, the fill_missing_values function is called and filled using the mean, median, or interpolation method; or through the delete_invalid_records function, they are deleted according to the actual situation, and at the same time, the identify_outliers function is used to set thresholds or use box plot statistical methods to identify and remove obvious outliers.

[0011] Preferably, in the clustering analysis, the K-means clustering algorithm is run at different K values through the calculate_sse function, and the SSE corresponding to each K value is calculated. SSE represents the sum of the squared errors from the data points to their respective cluster centers. Then, the plot_sse function is used to plot the curve of SSE changing with the K value, and the K value corresponding to the inflection point of the curve is the number of clusters.

[0012] Further preferably, after the number of clusters is determined to be K, the kmeans_clustering function is used, the selected characteristic data and the determined number of clusters K are input, and the K-means clustering algorithm is run. This function divides the data points into K cluster clusters, minimizing the sum of the distances from each data point to its respective cluster center. The clustering process updates the cluster centers iteratively until the convergence condition is reached.

[0013] Further preferably, the cluster_statistics function is written to perform statistical analysis on the data in each cluster cluster, calculate statistics such as the mean, median, and standard deviation of each characteristic, and by comparing the differences in statistics between different cluster clusters, observe the different performances of each characteristic in different clusters to determine the seepage migration pattern;

[0014] Use the cross_analysis function to perform correlation analysis on the clustering results and other monitoring data that were not used for clustering. Through cross-analysis, determine the characteristics under different seepage migration patterns.

[0015] Further preferably, use the silhouette_score function to calculate the silhouette coefficient to evaluate the compactness and separation degree of clustering, or use the leave_one_out_cross_validation function for leave-one-out cross-validation to test the stability of the clustering results; according to the different seepage migration patterns obtained from the clustering analysis, write the generate_prevention_strategy function to determine the corresponding coal mine water disaster prevention strategies and monitoring plans.

[0016] The beneficial effects of an experimental device and experimental method for a coupled model of fissure evolution and seepage under confined water provided by the present invention are that the experimental device can flexibly adjust the configuration of each module according to the actual geological conditions and monitoring requirements of the mine floor. The fiber optic monitoring module and the electrical method monitoring module can both operate independently or work together; the device also combines the fiber optic monitoring module, the electrical method monitoring module, and the data acquisition and processing module to capture the seepage path, fissure expansion, and rock mass deformation of the mine floor in real time, analyze various data, and generate warning signals; the number, arrangement method, and monitoring range of fiber optic sensors can be adjusted according to the fissure and seepage distribution to achieve optimized coverage and monitoring cost control. The arrangement density and scale of the pole array can be flexibly changed according to the monitoring target to ensure the accuracy of monitoring. Description of the Drawings

[0017] Figure 1 It is a schematic diagram of the detection devices of the fiber optic monitoring module and the electrical method monitoring module;

[0018] Figure 2 It is a schematic diagram of the principle of the electrical method monitoring module;

[0019] In the figure: 1 - fiber optic demodulator; 2 - fiber optic sensor; 3 - data acquisition unit; 4 - electrode; 5 - resistance measuring instrument; 6 - data processing terminal; 7 - data line. Detailed Embodiments

[0020] Combined with Figure 1 and Figure 2 as shown, the detailed embodiments of an experimental device and experimental method for a coupled model of fissure evolution and seepage under confined water provided by the present invention are described.

[0021] An experimental device for a coupled model of fissure evolution and seepage under confined water pressure includes an optical fiber monitoring module, an electrical method monitoring module, and a data acquisition and processing module. The optical fiber monitoring module includes an optical fiber sensor and an optical fiber demodulator. The optical fiber sensor is arranged along the fissures and seepage areas of the mine floor, and the optical fiber sensor and the optical fiber demodulator are connected through an optical cable. The optical fiber monitoring module conducts high-precision dynamic monitoring of the fissure expansion and seepage path of the mine floor; the electrical method monitoring module includes an electrode array and a resistance measuring instrument. The electrode array is composed of multiple electrodes arranged, and the electrodes are installed in the mine floor. The electrical method monitoring module collects the data of the resistivity of the rock mass in real time, generates a resistivity distribution map through data inversion calculation, and determines the path of the seepage water and its relationship with the fissure expansion; the data acquisition and processing module integrates the optical fiber monitoring data and the electrical method monitoring data to generate a multi-dimensional data model of the fissure expansion and seepage water flow of the mine floor.

[0022] The optical fiber sensors are linearly arranged or arranged in a king-shaped coverage in the area where fissures are concentrated; in the area where fissures are sparse, the optical fiber sensors are arranged at the path positions where seepage is concentrated; the optical fiber sensors cover fault zones, goafs, and areas with sudden lithological changes. Specifically, the optical fiber sensors can be arranged along the fissures and seepage areas of the mine floor and connected to the optical fiber demodulator through an optical cable, and are used to monitor the temperature change, strain state, and fissure expansion dynamics of the mine floor in real time. The optical fiber sensors can capture various data in the fissure and seepage areas through distributed optical fiber sensing technology. The optical fiber sensors can sense the subtle strain caused by the expansion or compression of the fissures on the mine floor in real time, and judge the direction, rate, and stability of the rock mass by measuring the strain change. The optical fiber sensors locate the seepage path and water source by detecting the temperature change caused by the seepage water flow. The confined water flowing in the fissures usually causes abnormal local temperature, and the sensors can accurately locate these areas. The optical fiber sensors can sense the micro-vibration signals caused by the water flow and provide dynamic information such as the intensity, flow rate, and range of the water flow activity.

[0023] The optical fiber demodulator is responsible for receiving the optical signals from the optical fiber sensors and demodulating them into electrical signals. The demodulator supports multi-channel input and can process the data of multiple optical fiber sensors simultaneously. The demodulated signals are transmitted to the data acquisition unit through data lines, providing reliable data support for subsequent data fusion and analysis.

[0024] The electrodes are arranged on the mine floor through drilling in a linear or grid pattern, increasing the density of electrode settings to improve the resolution of monitoring. The resistivity parameters obtained by the electrical monitoring module are used to judge the distribution of moisture in the rock mass, where the lower the resistivity, the higher the water content in the rock mass. The resistance measuring instrument is connected to the electrode array, collecting the resistivity data of the rock mass in real time, and performing inversion calculations on the data through algorithms to generate a resistivity distribution map, showing the path of seepage water and its correlation with crack propagation, providing basic data for subsequent data processing and used to identify potential water hazard risk areas. The electrodes are arranged at different depths on the mine floor through drilling, forming a linear or grid pattern, increasing the density of electrodes in key areas to improve the resolution and accuracy of data. When conducting large-scale monitoring, the electrodes can be arranged sparsely appropriately to expand the monitoring coverage. The electrical monitoring module provides an important means for the deep monitoring of crack propagation and seepage water, effectively identifying the dynamic changes of the mine floor through high-resolution resistivity data.

[0025] The data acquisition and processing module is responsible for the integration, analysis, and early warning of multi-source data. The data acquisition unit is connected to the fiber optic demodulator through an optical cable and to the resistance measuring instrument through a wire, ensuring the consistency of the data from the fiber optic monitoring module and the electrical monitoring module in terms of time and space, providing guarantee for data fusion. The multi-source monitoring data is uniformly converted into a standardized format for subsequent analysis and storage. The data processing terminal is connected to the data acquisition unit through a data line, deeply analyzing the data using advanced fusion algorithms and machine learning methods, combining the fiber optic monitoring and electrical monitoring data to generate a multi-dimensional data model of crack propagation and seepage water flow on the mine floor.

[0026] An experimental method for coupled crack evolution and seepage flow under confined water pressure, using the above-mentioned experimental device for coupled crack evolution and seepage flow model under confined water pressure, includes:

[0027] Step 1, data collection.

[0028] In the multi-dimensional data model of crack propagation and seepage water flow on the mine floor, data is read and stored through the data acquisition function collect_data; the parameters obtained by the data acquisition and processing module include strain, temperature, vibration, humidity, water quality, seepage velocity, seepage direction, resistivity, and voltage. Data is regularly collected from the electrical monitoring equipment and fiber optic monitoring system at the coal mine site at a set reasonable time interval, and the function used needs to ensure accurate connection to various monitoring equipment.

[0029] Step 2, data preprocessing.

[0030] First, use the check_data_integrity function to check the integrity of the data; during the process of checking the data integrity, after identifying missing values, for the missing values, call the fill_missing_values function and use the mean, median or interpolation method for filling; or through the delete_invalid_records function, delete according to the actual situation, and at the same time, set the threshold through the identify_outliers function or use the box plot statistical method to identify and remove obvious outliers.

[0031] Then, use the standardize_data function to standardize all numerical data; this function converts the data into a standard normal distribution with a mean of 0 and a standard deviation of 1, so that each feature has the same weight in the clustering analysis and avoids the deviation of the clustering result caused by the difference in dimension.

[0032] Step three, feature selection.

[0033] Use the correlation_analysis function to calculate the correlation coefficient between each monitoring data and the seepage migration, combine the correlation coefficient to determine the characteristic parameters that directly reflect the dynamics and underlying characteristics of the seepage, and select the characteristic parameters for clustering analysis. For example, combined with the experience of the seepage migration law of the coal mine floor water, select the characteristics that are closely related to the seepage migration from many monitoring data. Characteristics such as seepage velocity, resistivity, and strain may directly reflect the dynamics and formation characteristics of the seepage, and these characteristics are preferentially selected for subsequent clustering analysis.

[0034] Step four, clustering analysis.

[0035] In the clustering analysis, run the K-means clustering algorithm at different K values through the calculate_sse function, calculate the SSE corresponding to each K value. SSE represents the sum of the squared errors of the data points to their respective cluster centers. Then use the plot_sse function to plot the curve of SSE changing with the K value, and the K value corresponding to the inflection point of the curve is the number of clusters.

[0036] The K-means clustering algorithm is usually run at different K values ranging from 1 to 10 or a larger range, and calculate the SSE (Sum of Squared Errors) corresponding to each K value. SSE represents the sum of the squared errors of the data points to their respective cluster centers. Then use the plot_sse function to plot the curve of SSE changing with the K value. The inflection point of the curve, that is, the point where the downward trend of SSE slows down, and the corresponding K value is usually the more appropriate number of clusters.

[0037] After determining the number of clusters as K, use the kmeans_clustering function. Input the selected feature data and the determined number of clusters K, and run the K-means clustering algorithm. This function divides the data points into K clusters, minimizing the sum of the distances from each data point to its cluster center. The clustering process updates the cluster centers iteratively until the convergence condition is met, such as the cluster centers no longer changing or changing less than the set threshold.

[0038] Step Five, analysis of clustering results.

[0039] Write the cluster_statistics function to perform statistical analysis on the data in each cluster. Calculate statistics such as the mean, median, and standard deviation of each feature. By comparing the differences in statistics between different clusters, observe the different performances of each feature in different clusters and determine the seepage migration pattern. For example, if the mean seepage velocity in a certain cluster is high and the resistivity is low, it may indicate that there are relatively active water flow channels in this area.

[0040] Use the cross_analysis function to perform correlation analysis between the clustering results and other monitoring data not used for clustering. For example, perform correlation analysis on the relationship between the seepage direction and water quality. Through cross-analysis, further understand other characteristics under different seepage migration patterns. For example, count the dominant seepage direction in each cluster and analyze the corresponding relationship between different seepage directions and clusters; compare the water quality data in different clusters and determine whether the water quality difference is related to the seepage migration pattern.

[0041] Step Six, verify the results.

[0042] Use the silhouette_score function to calculate the silhouette coefficient to evaluate the compactness and separation of the clustering, or use the leave_one_out_cross_validation function for leave-one-out cross-validation to test the stability of the clustering results; ensure that the clustering results can truly reflect the laws of coal mine floor water seepage and migration.

[0043] According to the different seepage migration patterns obtained from the clustering analysis, write the generate_prevention_strategy function to determine the corresponding coal mine water disaster prevention strategies and monitoring plans. Develop corresponding coal mine water disaster prevention strategies and monitoring plans. For areas with high-risk seepage patterns, strengthen the monitoring frequency and take targeted prevention and control measures to ensure the safe production of coal mines.

[0044] This device combines distributed optical fiber sensing technology and electrical monitoring methods to achieve efficient and real-time monitoring of the seepage process of confined water in the mine floor and the evolution of fractures, providing accurate data support and an early warning mechanism for mine safety production. The modular design of this device enables flexible adjustment of the configuration of each module according to the actual geological conditions and monitoring requirements of the mine floor. Both the optical fiber monitoring module and the electrical monitoring module can operate independently or work together. The number, layout method, and monitoring range of optical fiber sensors can be adjusted according to the fracture and seepage distribution to achieve optimized coverage and monitoring cost control. The layout density and scale of the electrode array can be flexibly changed according to the monitoring target to ensure the accuracy of monitoring.

[0045] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions, or substitutions made by those skilled in the art within the scope of the essence of the present invention should also fall within the protection scope of the present invention.

Claims

1. An experimental device for a coupled model of fissure evolution and seepage under confined water pressure, characterized in that, It includes an optical fiber monitoring module, an electrical method monitoring module, and a data acquisition and processing module. The optical fiber monitoring module includes an optical fiber sensor and an optical fiber demodulator. The optical fiber sensors are arranged along the fissures and seepage areas of the mine floor. The optical fiber sensors and the optical fiber demodulator are connected by an optical cable. The optical fiber monitoring module conducts high-precision dynamic monitoring of the fissure expansion and seepage path of the mine floor. The electrical method monitoring module includes an electrode array and a resistance measuring instrument. The electrode array is composed of multiple electrodes arranged. The electrodes are installed in the mine floor. The electrical method monitoring module collects the data of the resistivity of the rock mass in real time, generates a resistivity distribution map through data inversion calculation, and determines the path of the seepage water and its correlation with the fissure expansion. The data acquisition and processing module fuses the optical fiber monitoring data and the electrical method monitoring data to generate a multi-dimensional data model of the fissure expansion and seepage water flow of the mine floor.

2. The experimental device for the coupled model of fissure evolution and seepage in confined aquifers according to claim 1, wherein, The optical fiber sensors are linearly arranged or arranged in a king-shaped coverage in the area where fissures are concentrated; in the area where fissures are sparse, the optical fiber sensors are arranged at the positions of the paths where seepage is concentrated; the optical fiber sensors cover faults, goafs, and areas with sudden lithological changes.

3. An experimental device for a coupled model of fissure evolution and seepage under confined water according to claim 1, characterized in that, The electrodes are arranged in a straight line or in a grid pattern on the mine floor through drilling. Increasing the setting density of the electrodes improves the monitoring resolution; the resistivity parameters obtained by the electrical method monitoring module are used to judge the distribution of moisture in the rock mass, where the lower the resistivity, the higher the water content of the rock mass.

4. An experimental method for coupling the evolution of fractures and seepage in overlying confined aquifers, which uses the experimental device for coupling the evolution of fractures and seepage in overlying confined aquifers described in any one of claims 1 to 3, characterized in that, It includes: In the multi-dimensional data model of the fissure expansion and seepage water flow of the mine floor, the collect_data function is used to read and store data through the data acquisition function; the check_data_integrity function is used to check the integrity of the data; the standardize_data function is used to standardize all numerical data; the correlation_analysis function is used to calculate the correlation coefficient between each monitoring data and the seepage migration, and the characteristic parameters directly reflecting the dynamics and underlying characteristics of the seepage are determined in combination with the correlation coefficient, and the characteristic parameters are selected for cluster analysis.

5. A coupled experiment method for fracture evolution and seepage in confined water according to claim 4, characterized in that The parameters obtained by the data acquisition and processing module include strain, temperature, vibration, humidity, water quality, seepage velocity, seepage direction, resistivity, and voltage.

6. A coupled experiment method for crack evolution and seepage in confined water according to claim 4, characterized in that, During the process of checking the integrity of the data, after identifying the missing values, for the missing values, the fill_missing_values function is called and filled using the mean, median, or interpolation method; or through the delete_invalid_records function, they are deleted according to the actual situation, and at the same time, the identify_outliers function is used to set a threshold or use the box plot statistical method to identify and remove obvious outliers.

7. A coupled experimental method for fissure evolution and seepage in confined water according to claim 5, characterized in that In the cluster analysis, the calculate_sse function is used to run the K-means clustering algorithm at different K values, calculate the SSE corresponding to each K value. The SSE represents the sum of the squared errors of the data points to their respective cluster centers. Then, the plot_sse function is used to plot the curve of the SSE changing with the K value, and the K value corresponding to the inflection point of the curve is the number of clusters.

8. A coupled experimental method for fissure evolution and seepage in confined water according to claim 7, characterized in that, After the number of clusters is determined as K, use the kmeans_clustering function. Input the selected feature data and the determined number of clusters K, and run the K-means clustering algorithm. This function divides the data points into K clusters, minimizing the sum of the distances from each data point to its cluster center. The clustering process updates the cluster centers iteratively until the convergence condition is reached.

9. A method for coupling experiments on the evolution of fissures and seepage in confined aquifers according to claim 8, characterized in that Write the cluster_statistics function to perform statistical analysis on the data in each cluster, calculate statistics such as the mean, median, and standard deviation of each feature, and determine the seepage migration pattern by observing the differences in the performance of each feature in different clusters through comparing the statistics between different clusters. Use the cross_analysis function to perform correlation analysis between the clustering results and other monitoring data not used for clustering, and determine the characteristics under different seepage migration patterns through cross-analysis.

10. A coupled experiment method for fracture evolution and seepage in confined aquifers according to claim 9, characterized in that Use the silhouette_score function to calculate the silhouette coefficient to evaluate the compactness and separation of the clustering, or use the leave_one_out_cross_validation function for leave-one-out cross-validation to test the stability of the clustering results; according to the different seepage migration patterns obtained from the clustering analysis, write the generate_prevention_strategy function to determine the corresponding coal mine water disaster prevention strategies and monitoring plans.

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