A pressurized water over fracture evolution seepage coupling model experimental device and experimental method

Abstract

The application provides a pressure-bearing water fissure evolution seepage coupling model experiment device and experiment method, relates to the technical field of geotechnical engineering, and the experiment device comprises a fiber monitoring module, an electrical method monitoring module and a data acquisition and processing module, the fiber monitoring module comprises a fiber sensor and a fiber demodulator, high-precision dynamic monitoring of mine floor fissure expansion and seepage path is realized, the electrical method monitoring module comprises an electrode array and a resistance measuring instrument, and is used for determining the path of seepage water and the correlation of fissure expansion, the data acquisition and processing module fuses fiber monitoring data and electrical method monitoring data, generates a multidimensional data model of mine floor fissure expansion and seepage water flow, according to different seepage migration modes obtained through cluster analysis, corresponding coal mine water disaster prevention strategies and monitoring schemes are formulated. For the region with a high-risk seepage mode, the monitoring frequency is strengthened and targeted prevention measures are taken, so that the safety production of the coal mine can be ensured.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering technology, and in particular to an experimental apparatus and method for a coupled model of fracture evolution and seepage in confined water. Background Technology

[0002] Confined water seepage and fracture propagation in mine floors are significant contributing factors to mine water inrush accidents. Existing monitoring methods often rely on single technologies, such as temperature monitoring, resistivity monitoring, or deformation monitoring, which struggle to accurately locate and dynamically track fracture propagation and seepage paths, and their monitoring range and accuracy are limited. Therefore, a novel approach is needed to monitor mine water hazards, enabling multi-dimensional monitoring of seepage and fracture propagation in the mine floor, thus providing reliable technical support for safe mine production. Summary of the Invention

[0003] To achieve efficient and real-time monitoring of the seepage process and fracture evolution of confined water in mine floor, this invention provides an experimental device and method for a coupled model of fracture evolution and seepage in confined water, the specific technical solution of which is as follows.

[0004] An experimental device for a coupled model of fracture evolution and seepage in confined water includes an optical fiber monitoring module, an electrical resistivity monitoring module, and a data acquisition and processing module. The optical fiber monitoring module includes optical fiber sensors and an optical fiber demodulator. The optical fiber sensors are arranged along the fractures and seepage areas of the mine floor and are connected to the optical fiber demodulator via optical cables. The optical fiber monitoring module provides high-precision dynamic monitoring of fracture propagation and seepage paths in the mine floor. The electrical resistivity monitoring module includes an electrode array and a resistivity measuring instrument. The electrode array consists of multiple electrodes arranged in the mine floor. The electrodes are installed in the mine floor. The electrical resistivity monitoring module collects rock resistivity data in real time, generates a resistivity distribution map through data inversion calculation, and determines the path of seepage water and its correlation with fracture propagation. The data acquisition and processing module integrates the optical fiber monitoring data and the electrical resistivity monitoring data to generate a multi-dimensional data model of fracture propagation and seepage water flow in the mine floor.

[0005] Preferably, the fiber optic sensors are arranged linearly or covered in areas with concentrated fractures; in areas with sparse fractures, the fiber optic sensors are arranged at the path of concentrated seepage; the fiber optic sensors cover faults, goaf areas and areas of abrupt lithological changes.

[0006] Preferably, the electrodes are arranged in a straight line or grid pattern on the mine floor through boreholes to increase the electrode density and improve the monitoring resolution; the resistivity parameters obtained by the electrical resistivity monitoring module determine the distribution of water in the rock mass, where the lower the resistivity, the higher the water content of the rock mass.

[0007] A method for experimentally coupling seepage in the evolution of fractures above confined water, utilizing the aforementioned experimental apparatus for a model of fracture evolution and seepage coupling above confined water, includes:

[0008] In the multidimensional data model of mine floor fracture propagation and seepage flow, data is read and stored using the data acquisition function `collect_data`; the integrity of the data is checked using the `check_data_integrity` function; all numerical data are standardized using the `standardize_data` function; the correlation coefficient between each monitoring data point and seepage migration is calculated using the `correlation_analysis` function; the characteristic parameters that directly reflect the dynamics and underlying characteristics of seepage are determined based on the correlation coefficients; and cluster analysis is performed using the characteristic parameters.

[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 data integrity, after identifying missing values, the fill_missing_values ​​function is called to fill the missing values ​​using the mean, median, or interpolation method; or the delete_invalid_records function is used to delete them according to the actual situation, while the identify_outliers function is used to set a threshold or box plot statistical methods are used to identify and remove obvious outliers.

[0011] Preferably, in cluster analysis, the K-means clustering algorithm is run using the calculate_sse function at different K values ​​to calculate the SSE corresponding to each K value. SSE represents the sum of squared errors from the data point to its cluster center. Then, the plot_sse function is used to plot the curve of SSE as a function of K value. The K value corresponding to the inflection point of the curve is the number of clusters.

[0012] A further preferred approach is to use the kmeans_clustering function after determining the number of clusters as K, inputting the selected feature data and the determined number of clusters K, and running 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 iteratively updates the cluster centers until the convergence condition is met.

[0013] A further preferred approach is to write a cluster_statistics function to perform statistical analysis on the data in each cluster, calculate the mean, median, standard deviation and other statistics of each feature, and observe the different performance of each feature in different clusters by comparing the differences in statistics between different clusters, and determine the seepage transport pattern.

[0014] The cross_analysis function is used to perform correlation analysis between the clustering results and other monitoring data that were not used for clustering. Through cross analysis, the characteristics of different seepage transport patterns are determined.

[0015] A further preferred approach is to use the silhouette_score function to calculate the silhouette coefficient to evaluate the compactness and separation of the clusters, or to use the leave_one_out_cross_validation function to perform leave-one-out cross-validation to test the stability of the clustering results; based on the different seepage and transport patterns obtained from the clustering analysis, a generate_prevention_strategy function is written to determine the corresponding coal mine water hazard prevention strategies and monitoring schemes.

[0016] The beneficial effects of the experimental device and method for a coupled model of fracture evolution and seepage in confined water provided by this invention are as follows: the experimental device can flexibly adjust the configuration of each module according to the actual geological conditions and monitoring needs of the mine floor; both the fiber optic monitoring module and the electrical resistivity monitoring module can operate independently or work in concert; the device also combines the fiber optic monitoring module, the electrical resistivity monitoring module, and the data acquisition and processing module to capture the seepage path, fracture propagation, and rock deformation of the mine floor in real time, analyze various data, and generate early warning signals; the number, arrangement, and monitoring range of the fiber optic sensors can be adjusted according to the fracture and seepage distribution to achieve optimized coverage and control monitoring costs; the arrangement density and scale of the electrode array can be flexibly changed according to the monitoring target to ensure the accuracy of monitoring. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the detection device for the fiber optic monitoring module and the electrical resistivity monitoring module;

[0018] Figure 2 This is a schematic diagram of the electrical resistance monitoring module.

[0019] In the diagram: 1-Fiber optic demodulator; 2-Fiber optic sensor; 3-Data acquisition unit; 4-Electrode; 5-Resistance measuring instrument; 6-Data processing terminal; 7-Data cable. Detailed Implementation

[0020] Combination Figure 1 and Figure 2 As shown, the specific implementation of the experimental device and method for the coupled model of fracture evolution and seepage in confined water provided by the present invention will be described.

[0021] An experimental device for a coupled model of fracture evolution and seepage in confined water includes an optical fiber monitoring module, an electrical resistivity monitoring module, and a data acquisition and processing module. The optical fiber monitoring module includes optical fiber sensors and an optical fiber demodulator. The optical fiber sensors are arranged along the fractures and seepage areas of the mine floor and are connected to the optical fiber demodulator via optical cables. The optical fiber monitoring module provides high-precision dynamic monitoring of fracture propagation and seepage paths in the mine floor. The electrical resistivity monitoring module includes an electrode array and a resistivity measuring instrument. The electrode array consists of multiple electrodes arranged in the mine floor. The electrodes are installed in the mine floor. The electrical resistivity monitoring module collects rock resistivity data in real time, generates a resistivity distribution map through data inversion calculation, and determines the path of seepage water and its correlation with fracture propagation. The data acquisition and processing module integrates the optical fiber monitoring data and the electrical resistivity monitoring data to generate a multi-dimensional data model of fracture propagation and seepage water flow in the mine floor.

[0022] Fiber optic sensors are arranged linearly or with broad coverage in areas with concentrated fractures; in areas with sparse fractures, they are placed along the paths of concentrated seepage; and they cover faults, goafs, and areas of abrupt lithological changes. Specifically, fiber optic sensors can be deployed along fractures and seepage areas of the mine floor, connected to a fiber optic demodulator via optical cables, for real-time monitoring of temperature changes, strain states, and fracture propagation dynamics of the mine floor. Fiber optic sensors can capture various data from fracture and seepage areas using distributed fiber optic sensing technology. They can sense subtle strains caused by fracture propagation or compression in the mine floor in real time, determining the direction and rate of fracture propagation and the stability of the rock mass by measuring strain changes. Fiber optic sensors can locate seepage paths and water sources by detecting temperature changes caused by seepage water flow. Confined water flowing in fractures often causes localized temperature anomalies, which sensors can accurately locate. Fiber optic sensors can sense micro-vibration signals induced by water flow, providing dynamic information such as the intensity, flow rate, and range of water activity.

[0023] The fiber optic demodulator receives optical signals from fiber optic sensors and demodulates them into electrical signals. The demodulator supports multi-channel input and can process data from multiple fiber optic sensors simultaneously. The demodulated signal is transmitted to the data acquisition unit via a data line, providing reliable data support for subsequent data fusion and analysis.

[0024] Electrodes are arranged in a straight line or grid pattern on the mine floor through boreholes. Increasing the electrode density improves monitoring resolution. The resistivity parameters obtained by the electrical resistivity monitoring module determine the distribution of water in the rock mass; lower resistivity indicates higher water content. The resistivity meter is connected to the electrode array to collect rock resistivity data in real time. Algorithms are used to invert the data and generate resistivity distribution maps, showing the path of seepage water and its correlation with fracture propagation. This provides foundational data for subsequent data processing and is used to identify potential water hazard risk areas. Electrodes are arranged in a linear or grid pattern at different depths on the mine floor through boreholes. Electrode density is increased in key areas to improve data resolution and accuracy. For large-scale monitoring, electrodes can be arranged more sparsely to expand the monitoring coverage. The electrical resistivity monitoring module provides an important means for deep monitoring of fracture propagation and seepage water, effectively identifying dynamic changes in 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 via optical cable and to the resistance meter via wire, ensuring that the data from the fiber optic monitoring module and the electrical resistivity monitoring module are consistent in time and space, providing a guarantee for data fusion. Multi-source monitoring data is uniformly converted into a standardized format for easy subsequent analysis and storage. The data processing terminal is connected to the data acquisition unit via data cable and uses advanced fusion algorithms and machine learning methods to perform in-depth analysis of the data, combining fiber optic monitoring and electrical resistivity monitoring data to generate a multi-dimensional data model of mine floor fracture propagation and seepage flow.

[0026] A method for experimentally coupling seepage in the evolution of fractures above confined water, utilizing the aforementioned experimental apparatus for a model of fracture evolution and seepage coupling above confined water, includes:

[0027] Step 1: Data collection.

[0028] In the multidimensional data model of mine floor crack propagation and seepage flow, data is read and stored using 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 collected periodically from electrical resistivity monitoring equipment and fiber optic monitoring systems at the coal mine site at set reasonable time intervals. The functions used must ensure accurate connection to various monitoring devices.

[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 integrity of the data, after identifying missing values, call the `fill_missing_values` function to fill the missing values ​​using the mean, median, or interpolation method. Alternatively, use the `delete_invalid_records` function to delete the missing values ​​according to the actual situation. At the same time, use the `identify_outliers` function to set a threshold or use box plot statistical methods to identify and remove obvious outliers.

[0031] Then, the standardize_data function is used 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 cluster analysis and avoids the bias of the clustering results due to the difference in units.

[0032] Step 3, Feature Selection.

[0033] The correlation coefficient between each monitoring data point and seepage migration is calculated using the `correlation_analysis` function. Based on these correlation coefficients, characteristic parameters directly reflecting the dynamics of seepage and the underlying geological characteristics are determined, and these parameters are then used for cluster analysis. For example, based on experience with the seepage migration patterns of water in coal mine floors, features closely related to seepage migration are selected from numerous monitoring data points. Features such as seepage velocity, resistivity, and strain are likely to directly reflect the dynamics of seepage and formation characteristics, and these features are prioritized for subsequent cluster analysis.

[0034] Step 4: Cluster analysis.

[0035] In cluster analysis, the K-means clustering algorithm is run using the calculate_sse function at different K values ​​to calculate the SSE corresponding to each K value. SSE represents the sum of squared errors from a data point to its cluster center. Then, the plot_sse function is used to plot the curve of SSE as a function of K value. The K value corresponding to the inflection point of the curve is the number of clusters.

[0036] Different K values, typically ranging from 1 to 10 or larger, are used to run the K-means clustering algorithm to calculate the SSE (Sum of Squared Errors) for each K value. SSE represents the sum of squared errors from a data point to its cluster center. The `plot_sse` function is then used to plot the curve of SSE as a function of K. The inflection point of the curve, where the downward trend of SSE slows down, usually corresponds to a suitable K value for the number of clusters.

[0037] After determining the number of clusters to K, the kmeans_clustering function is used. The selected feature 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 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 when the cluster centers no longer change or the change is less than a set threshold.

[0038] Step 5: Clustering results analysis.

[0039] Write a cluster_statistics function to perform statistical analysis on the data in each cluster, calculate the mean, median, standard deviation and other statistics of each feature, and observe the different performance of each feature in different clusters by comparing the differences in statistics between different clusters, and determine the seepage transport pattern; for example, a cluster with a high mean seepage velocity and low resistivity may indicate that there are more active water flow channels in the area.

[0040] The `cross_analysis` function is used to perform correlation analysis between clustering results and other monitoring data not used for clustering, such as correlation analysis between seepage direction and water quality. Through cross-analysis, further insights can be gained into other characteristics of different seepage transport patterns. For example, the dominant seepage direction in each cluster can be identified, and the correspondence between different seepage directions and clusters can be analyzed. Water quality data in different clusters can be compared to determine whether water quality differences are related to seepage transport patterns.

[0041] Step six: Verify the results.

[0042] The silhouette coefficient is calculated using the silhouette_score function to evaluate the compactness and separation of the clusters, or the leave_one_out_cross_validation function is used to perform leave-one-out cross-validation to test the stability of the clustering results; this ensures that the clustering results can truly reflect the laws of water seepage and migration in the coal mine floor.

[0043] Based on the different seepage and transport patterns obtained from cluster analysis, the `generate_prevention_strategy` function was developed to determine the corresponding coal mine water hazard prevention strategies and monitoring plans. Appropriate coal mine water hazard prevention strategies and monitoring plans were formulated. For areas with high-risk seepage patterns, monitoring frequency was increased and targeted prevention and control measures were implemented to ensure safe coal mine production.

[0044] This device combines distributed fiber optic sensing technology and electrical resistivity tomography (IR) monitoring methods to achieve efficient, real-time monitoring of the seepage process of confined water and the evolution of fractures in the mine floor, providing precise data support and an early warning mechanism for safe mine production. The modular design of the device allows for flexible adjustment of the configuration of each module according to the actual geological conditions of the mine floor and monitoring needs. Both the fiber optic monitoring module and the IR monitoring module can operate independently or in conjunction. The number, arrangement, and monitoring range of the fiber optic sensors can be adjusted according to the distribution of fractures and seepage to achieve optimized coverage and control monitoring costs. The density and scale of the electrode array can be flexibly varied according to the monitoring target to ensure monitoring accuracy.

[0045] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for pressure-bearing water over fracture evolution seepage coupling experiment, using a pressure-bearing water over fracture evolution seepage coupling model experiment device, characterized in that, The device comprises a fiber monitoring module, an electrical method monitoring module and a data acquisition and processing module, the fiber monitoring module comprises a fiber sensor and a fiber demodulator, the fiber sensor is arranged along the fissure and seepage area of the mine floor, the fiber sensor and the fiber demodulator are connected through an optical cable, and the fiber monitoring module is used for high-precision dynamic monitoring of the fissure expansion and seepage path of the mine floor; The electrical method monitoring module comprises an electrode array and a resistance measuring instrument, the electrode array is composed of a plurality of electrodes arranged in an array, the electrodes are installed in the mine floor, the electrical method monitoring module is used for real-time acquisition of the data of the rock mass resistivity, and a resistivity distribution map is generated through data inversion calculation, so as to determine the path of the seepage water and the correlation between the seepage water and the fissure expansion; The data acquisition and processing module fuses the fiber monitoring data and the electrical method monitoring data, and generates a multi-dimensional data model of the fissure expansion and seepage water flow of the mine floor; the fiber sensor is arranged linearly or in a mesh in the area where the fissures are concentrated; In the area where the fissures are sparse, the fiber sensor is arranged at the position of the seepage concentration path; the fiber sensor covers the fault, the goaf and the area where the lithology changes suddenly; the electrodes are arranged in the mine floor through drilling and arranged in a straight line or in a grid, so as to increase the setting density of the electrodes and improve the resolution of the monitoring; The resistivity parameters obtained by the electrical method monitoring module are used for judging the distribution of the water in the rock mass, and the lower the resistivity is, the higher the water content in the rock mass is; The method comprises the following steps: In the multi-dimensional data model of the fissure expansion and seepage water flow of the mine floor, the data is read and stored through a data acquisition function collect_data; the integrity of the data is checked through a check_data_integrity function; all numerical data are standardized through a standardize_data function; the correlation coefficient of each monitoring data and the seepage migration is calculated through a correlation_analysis function, and the characteristic parameters directly reflecting the dynamics of the seepage and the characteristics of the bottom layer are determined according to the correlation coefficient, and the characteristic parameters are selected for cluster analysis; In the cluster analysis, the K-means clustering algorithm is run at different K values through a calculate_sse function, the SSE corresponding to each K value is calculated, the SSE represents the sum of squared errors of the data points to the cluster centers to which the data points belong, and then a plot_sse function is used to draw a curve of the change of the SSE with the K value, the K value corresponding to the inflection point of the curve is the cluster number; after the cluster number is determined as K, a kmeans_clustering function is used, the selected characteristic data and the determined cluster number K are input, and the K-means clustering algorithm is run, the function divides the data points into K clusters, so that the sum of the distances of each data point to the cluster center to which the data point belongs is minimized, and the cluster process updates the cluster center through iteration until the convergence condition is reached. The cluster_statistics function is written to perform statistical analysis on the data in each cluster, calculate the mean, median, and standard deviation of each feature, and observe the differences in the performance of each feature in different clusters by comparing the differences in statistical quantities between different clusters to determine the seepage migration mode; The cross_analysis function is used to associate the clustering results with other monitoring data that is not used for clustering, and through cross analysis, the characteristics under different seepage migration modes are determined.

2. The method according to claim 1, wherein, The parameters obtained by the data acquisition and processing module include strain, temperature, vibration, humidity, water quality, seepage velocity, seepage direction, resistivity, and voltage.

3. The method according to claim 1, wherein, In the process of checking the integrity of the data, after identifying missing values, the fill_missing_values function is called and the mean, median, or interpolation method is used to fill in the missing values; or through the delete_invalid_records function, the actual situation is deleted, 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.

4. The method according to claim 1, wherein, The silhouette_score function is used to calculate the silhouette coefficient to evaluate the compactness and separation degree of the clustering, or the leave_one_out_cross_validation function is used for leave-one-out cross-validation to test the stability of the clustering results; according to the different seepage migration modes obtained by clustering analysis, the generate_prevention_strategy function is written to determine the corresponding coal mine water disaster prevention strategy and monitoring scheme.

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