Mine water sealing layer stability evaluation method based on micro-seismic monitoring

Abstract

The invention discloses a mine water sealing layer stability evaluation method based on micro-seismic monitoring, and belongs to the field of mine water sealing layers. The invention discloses a mine water sealing layer stability evaluation method based on micro-seismic monitoring. The method comprises the following steps: S1, identifying a mine water sealing layer area; s2, monitoring the mine water storage area in real time; s3, determining the spatial distribution density and the energy accumulation trend of the micro-seismic events; s4, analyzing the micro-seismic event clustering characteristic and the evolution law of water seal reservoir rock mass fracture; and S5, obtaining a stability evaluation result of the mine water sealing layer. The problem that cross-regional environmental disasters such as water inrush or surface collapse are easily caused in the prior art is solved, the accuracy and reliability of data are ensured, geological analysis and prediction can be more deeply carried out, damage to the environment and cost input are reduced, the activation rule of a hidden structure and the fracture depth of a bottom plate are effectively monitored, and the monitoring accuracy and reliability of the hidden structure are improved. Therefore, the water inrush risk can be predicted and forecasted.

Description

Technical Field

[0001] The present invention relates to the technical field of mine water sealing layers, and in particular to a method for evaluating the stability of mine water sealing layers based on microseismic monitoring. Background Art

[0002] The mine water storage layer refers to the use of water sealing technology to seal mine water in a specific stratum to prevent it from polluting the environment. The stability of the mine water storage layer is directly related to the sealing effect and the protection of the groundwater environment. Therefore, it is necessary to evaluate the stability of the mine water storage layer.

[0003] Chinese patent publication number CN118608309B discloses a method for determining the underground spatial stability of closed mines based on an integrated approach of air, space, ground, and rock data. This method uses synthetic aperture radar to obtain parameter information for large areas of the closed mine, and drone aerial surveys to obtain parameter information for smaller areas of the closed mine. Feature points are then deployed in key areas, and monitoring equipment is then deployed to obtain parameter information for the surface rock mass of the closed mine. Numerical simulations are used to obtain information about the underground rock formations within the closed mines. Hydrogeological and engineering data from the closed mines' production processes are then extracted and integrated into a database. This database is used to establish a closed mine stability assessment model based on multi-source air, space, ground, and rock data. This model is used to evaluate the stability of closed mines and provide scientific guidance for their safe reuse.

[0004] The above patent cannot monitor rock micro-fractures during actual use. If micro-fractures occur in the rock, it is easy to accelerate the connection between the aquifer and the mining space, thereby causing cross-regional environmental disasters such as water inrush or surface collapse. Therefore, it does not meet existing needs. We have proposed a mine water storage layer stability assessment method based on microseismic monitoring. Summary of the Invention

[0005] The purpose of the present invention is to provide a mine water storage layer stability assessment method based on microseismic monitoring, which can capture very small seismic wave signals, ensure the accuracy and reliability of the data, help to conduct more in-depth geological analysis and prediction, reduce environmental damage and cost investment, and microseismic monitoring technology can monitor rock microfractures, effectively monitor the activation laws of hidden structures and the depth of bottom plate fractures, thereby realizing the prediction and forecast of water inrush hazards, solving the problems raised in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for evaluating the stability of a mine water storage layer based on microseismic monitoring, comprising the following steps:

[0007] S1: using a data acquisition module to obtain geological structure data of the mine water storage layer area, and using the geological structure data to identify the mine water storage layer area;

[0008] S2: Deploy monitoring sensors in the mine water storage layer area and use the monitoring sensors to monitor the mine water storage area in real time;

[0009] S3: Using the determination and analysis module to perform preliminary analysis on the data monitored by the monitoring sensors to determine the spatial distribution density and energy accumulation trend of microseismic events;

[0010] S4: Based on the spatial distribution density and energy accumulation trend of microseismic events, analyze the clustering characteristics of microseismic events and the evolution law of rock mass rupture in the water-sealed storage layer;

[0011] S5: Use the model building module to establish a stability assessment model, combine the clustering characteristics of microseismic events with the evolution law of rock fracture in the water storage layer into the stability assessment model, and obtain the stability assessment results of the mine water storage layer.

[0012] Preferably, the data acquisition module includes:

[0013] A collection unit, used to collect hydrogeological data of the mine, including drilling data, core sample analysis results, water level observation data, and water quality sampling data;

[0014] The identification unit is used to analyze the geological structure of the mine using the collected data, and to identify the water storage layer area of ​​the mine based on the geological structure analysis.

[0015] Preferably, the step S3 specifically includes:

[0016] Real-time acquisition of waveform signals generated by rock mass fractures, filtering and denoising of effective data signals to eliminate environmental interference;

[0017] Set the initial energy threshold and dynamically adjust the energy threshold range based on the real-time noise level of the monitoring environment;

[0018] Based on the energy threshold, potential microseismic events are screened, a preliminary event list is generated, the specific location of the microseismic events in the water storage layer is determined, and high-density anomaly areas are identified, which are the spatial distribution density of microseismic events;

[0019] The energy release amount of a single event is counted, and a time series energy accumulation curve is drawn. The energy accumulation curve is then subjected to regression analysis to determine the energy release rate state, and the energy accumulation trend is obtained based on the determination results.

[0020] Preferably, the determination and analysis module specifically includes:

[0021] The threshold determination unit is used to collect waveform signals generated by rock mass fracture in real time, screen out potential microseismic events, and generate a preliminary event list;

[0022] The analysis unit is used to analyze the spatial distribution density of microseismic events, determine the energy release rate state, and obtain the energy accumulation trend.

[0023] Preferably, the threshold determination unit includes:

[0024] The processing unit is used to collect the waveform signal generated by rock mass fracture in real time, eliminate the environmental noise interference in the waveform signal, and retain the effective signal frequency band;

[0025] a generation unit, configured to dynamically adjust the energy threshold range, screen out potential microseismic events based on the energy threshold range, and generate a preliminary event list;

[0026] Preferably, the determination process of the threshold determination unit specifically includes:

[0027] Real-time acquisition of waveform signals generated by rock mass fracture, and conversion of waveform signals into data signals, recording time and space coordinates, energy release, and frequency parameters;

[0028] Screen valid data signals, filter and denoise them, and eliminate environmental interference;

[0029] Set the initial energy threshold to filter low-energy background noise and capture only valid microseismic events. Dynamically adjust the energy threshold range based on the real-time noise level of the monitoring environment to avoid missed detection or false detection.

[0030] Screen out potential microseismic events based on energy thresholds, generate a preliminary event list, and record basic parameters such as the trigger time and energy peak of each event;

[0031] Calculate the spatial coordinates of the event, determine the specific location of the microseismic event in the water storage layer, analyze the concentration of microseismic events in the horizontal and vertical directions, and identify high-density anomaly areas, which are the spatial distribution density of microseismic events;

[0032] Preferably, the analysis unit comprises:

[0033] The spatial distribution density analysis unit is used to analyze the aggregation degree of microseismic events in the horizontal and vertical directions to obtain the spatial distribution density of microseismic events;

[0034] The energy accumulation trend analysis unit is used to draw a time series energy accumulation curve, perform regression analysis on the energy accumulation curve, and obtain the energy accumulation trend.

[0035] Preferably, the analysis process of the analysis unit specifically includes:

[0036] Count the energy released by a single event, calculate the cumulative energy value per unit time, and use the cumulative energy value to draw a time series energy accumulation curve;

[0037] Regression analysis is performed on the energy accumulation curve to determine whether the energy release rate is exponentially increasing or in a stable state, and the energy accumulation trend is obtained based on the judgment results.

[0038] Preferably, the model building module includes:

[0039] The training set data acquisition unit is used to collect microseismic activity parameters and geomechanical parameters, and classify and label the collected data to obtain the original data training set;

[0040] The data set expansion unit is used to expand the original data training set and train the data expansion model to obtain the expanded data training set;

[0041] The stability assessment model training unit is used to input the expanded data training set into the stability assessment model for training to obtain a trained stability assessment model.

[0042] Preferably, the use of a model building module to establish a stability assessment model includes:

[0043] The number of microseismic events, spatial density, energy accumulation, apparent volume, impact deformation energy, microseismic activity, and geomechanical parameters of the mine water storage layer are collected, and the collected data are classified and labeled to obtain the original data training set;

[0044] Based on the generative adversarial network algorithm of Riemannian manifold feature diffusion, the original data training set is expanded and the data expansion model is trained to obtain the expanded data training set;

[0045] Based on the extreme learning machine algorithm of nonlinear system coupling learning, the expanded data training set is input into the stability assessment model for training to obtain the trained stability assessment model;

[0046] The particle swarm optimization algorithm is used to adjust the hyperparameters of the stability assessment model to obtain the optimized stability assessment model.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The present invention can capture very small seismic wave signals through microseismic monitoring, and can analyze information such as their position, magnitude and energy, so as to evaluate the stability of the mine water storage layer. By filtering and denoising the signals, the accuracy and reliability of the data are guaranteed, the subsequent positioning accuracy is ensured, and it is helpful to conduct more in-depth geological analysis and prediction. Through microseismic monitoring, data can be collected and analyzed in real time, abnormal situations can be discovered in time and early warnings can be issued. This real-time monitoring capability is of great significance for preventing geological disasters and ensuring mine safety. Compared with traditional geological exploration methods, microseismic monitoring does not require large-scale excavation or drilling, reducing environmental damage and cost investment. Microseismic monitoring technology can monitor rock microfractures, effectively monitor the activation laws of hidden structures and the depth of bottom plate ruptures, thereby realizing the prediction and forecast of water inrush hazards, avoiding microfractures in the rock mass that accelerate the connection between the aquifer and the mining space, and then leading to cross-regional environmental disasters such as water inrush or surface collapse. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Schematic diagram of the mine water storage layer stability assessment method based on microseismic monitoring of the present invention;

[0050] Figure 2 This is a module schematic diagram of the mine water storage layer stability assessment method based on microseismic monitoring of the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] In order to solve the problem that the existing technology cannot monitor the micro fractures in the rock mass during actual use, if the rock mass micro fractures occur, it is easy to accelerate the connection between the aquifer and the mining space, thereby causing cross-regional environmental disasters such as water inrush or surface collapse, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions:

[0053] A method for assessing the stability of a mine water storage layer based on microseismic monitoring comprises the following steps:

[0054] S1: Use the data acquisition module to obtain geological structure data of the mine water storage layer area, and use the geological structure data to identify the mine water storage layer area. The geological structure data includes rock layer structure, fault distribution and hydrogeological parameters, which serve as the basis for the deployment of microseismic monitoring equipment;

[0055] S2: Deploy monitoring sensors in the mine water storage layer area and use them to monitor the mine water storage area in real time. The monitoring sensors include: downhole sensors and surface sensors. The downhole sensors are used to capture low-frequency microseismic signals, and the surface sensors are used to capture vibration events covering a large area.

[0056] S3: Using the determination and analysis module to perform preliminary analysis on the data monitored by the monitoring sensors, the data monitored by the monitoring sensors are preliminarily analyzed through threshold analysis and waveform feature recognition to determine the spatial distribution density and energy accumulation trend of the microseismic events;

[0057] By setting dynamic thresholds based on energy amplitude or frequency characteristics, it is possible to distinguish between real microseismic signals and background noise interference, reducing the pressure on redundant data storage. Combined with characteristic parameters such as waveform duration and the proportion of high-frequency components, different earthquake source types such as rock fracture and mechanical vibration can be distinguished. By filtering and denoising the signals, the accuracy and reliability of the data are guaranteed, ensuring the subsequent positioning accuracy.

[0058] S4: Based on the spatial distribution density and energy accumulation trend of microseismic events, analyze the clustering characteristics of microseismic events and the evolution law of rock mass rupture in the water-sealed storage layer;

[0059] When evaluating the stability of a mine water storage layer, analyzing the spatial distribution density and energy accumulation trend of microseismic events to evaluate the stability of the water storage layer can effectively describe the process of the gestation, development and final instability of the water-conducting channel. By monitoring the spatial distribution density and energy accumulation trend of microseismic events, the stability of the mine water storage layer can be predicted and evaluated.

[0060] S5: Use the model building module to establish a stability assessment model, combine the clustering characteristics of microseismic events with the evolution law of rock fracture in the water-sealing layer into the stability assessment model, and obtain the stability assessment results of the mine water-sealing layer. Combine the microseismic activity parameters such as event frequency and energy release rate with geomechanical parameters to divide the stability levels into stable, potential risk, and high risk. Issue corresponding warning information according to the stability level, and locate the dangerous area based on the warning information.

[0061] Microseismic monitoring can capture very small seismic wave signals and analyze their location, magnitude, energy and other information to assess the stability of mine water storage layers. By filtering and denoising the signals, the accuracy and reliability of the data are guaranteed, ensuring the subsequent positioning accuracy, which helps to conduct more in-depth geological analysis and prediction. Microseismic monitoring can collect and analyze data in real time, detect abnormal situations in a timely manner and issue early warnings, and locate abnormal areas, so that personnel can deal with abnormal areas in the mine water storage layer based on the positioning. This real-time monitoring capability is of great significance for preventing geological disasters and ensuring mine safety. Compared with traditional geological exploration methods, microseismic monitoring does not require large-scale excavation or drilling, reducing environmental damage and cost investment. Microseismic monitoring technology can monitor rock microfractures, effectively monitor the activation patterns of hidden structures and the depth of floor ruptures, thereby realizing the prediction and forecast of water inrush hazards, avoiding the occurrence of rock microfractures that accelerate the connection between the aquifer and the mining space, and thus lead to cross-regional environmental disasters such as water inrush or surface collapse.

[0062] Data acquisition module, including:

[0063] A collection unit, used to collect hydrogeological data of the mine, including drilling data, core sample analysis results, water level observation data, and water quality sampling data;

[0064] The identification unit is used to analyze the geological structure of the mine using the collected data, and to identify the water storage layer area of ​​the mine based on the geological structure analysis.

[0065] Step S3 specifically includes:

[0066] Real-time acquisition of waveform signals generated by rock mass fractures, filtering and denoising of effective data signals to eliminate environmental interference;

[0067] Set the initial energy threshold and dynamically adjust the energy threshold range based on the real-time noise level of the monitoring environment to avoid missed detection or false detection;

[0068] Based on the energy threshold, potential microseismic events are screened, a preliminary event list is generated, the specific location of the microseismic events in the water storage layer is determined, and high-density anomaly areas are identified, which are the spatial distribution density of microseismic events;

[0069] The energy release amount of a single event is counted, and a time series energy accumulation curve is drawn. The energy accumulation curve is then subjected to regression analysis to determine the energy release rate state, and the energy accumulation trend is obtained based on the determination results.

[0070] Determine the analysis module, including:

[0071] The threshold determination unit is used to collect waveform signals generated by rock mass fracture in real time, screen out potential microseismic events, and generate a preliminary event list;

[0072] The analysis unit is used to analyze the spatial distribution density of microseismic events, determine the energy release rate state, and obtain the energy accumulation trend.

[0073] According to the changes in density and energy of microseismic events, rock fractures are divided into stable period, incubation period and unstable period. Focus is on the signal of the unstable period to judge whether the critical fracture threshold is reached. The stable period is a low-density and low-energy state, the incubation period is a state of density increase and slow energy accumulation, and the unstable period is a state of sudden density increase and rapid energy release.

[0074] A threshold determination unit, comprising:

[0075] The processing unit is used to collect the waveform signal generated by rock mass fracture in real time, eliminate the environmental noise interference in the waveform signal, and retain the effective signal frequency band;

[0076] a generation unit, configured to dynamically adjust the energy threshold range, screen out potential microseismic events based on the energy threshold range, and generate a preliminary event list;

[0077] The determination process of the threshold determination unit specifically includes:

[0078] Real-time acquisition of waveform signals generated by rock mass fracture, and conversion of waveform signals into data signals, recording time and space coordinates, energy release, and frequency parameters;

[0079] Screen valid data signals, filter and denoise them, and eliminate environmental interference;

[0080] Set the initial energy threshold to filter low-energy background noise and capture only valid microseismic events. Dynamically adjust the energy threshold range based on the real-time noise level of the monitoring environment to avoid missed detection or false detection.

[0081] Screen out potential microseismic events based on energy thresholds, generate a preliminary event list, and record basic parameters such as the trigger time and energy peak of each event;

[0082] Calculate the spatial coordinates of the event, determine the specific location of the microseismic event in the water storage layer, analyze the concentration of microseismic events in the horizontal and vertical directions, and identify high-density anomaly areas, which are the spatial distribution density of microseismic events;

[0083] Analysis unit, including:

[0084] The spatial distribution density analysis unit is used to analyze the aggregation degree of microseismic events in the horizontal and vertical directions to obtain the spatial distribution density of microseismic events;

[0085] The energy accumulation trend analysis unit is used to draw a time series energy accumulation curve, perform regression analysis on the energy accumulation curve, and obtain the energy accumulation trend.

[0086] The analysis process of the analysis unit specifically includes:

[0087] Count the energy released by a single event, calculate the cumulative energy value per unit time, and use the cumulative energy value to draw a time series energy accumulation curve;

[0088] Regression analysis is performed on the energy accumulation curve to determine whether the energy release rate is exponentially increasing or in a stable state, and the energy accumulation trend is obtained based on the judgment results.

[0089] Model building modules, including:

[0090] The training set data acquisition unit is used to collect microseismic activity parameters and geomechanical parameters, and classify and label the collected data to obtain the original data training set;

[0091] The data set expansion unit is used to expand the original data training set and train the data expansion model to obtain the expanded data training set;

[0092] The stability assessment model training unit is used to input the expanded data training set into the stability assessment model for training to obtain a trained stability assessment model.

[0093] Use the Model Building Module to build a stability assessment model, including:

[0094] The number of microseismic events, spatial density, energy accumulation, apparent volume, impact deformation energy, microseismic activity, and geomechanical parameters of the mine water storage layer are collected, and the collected data are classified and labeled to obtain the original data training set;

[0095] Based on the generative adversarial network algorithm of Riemannian manifold feature diffusion, the original data training set is expanded and the data expansion model is trained to obtain the expanded data training set;

[0096] Based on the extreme learning machine algorithm of nonlinear system coupling learning, the expanded data training set is input into the stability assessment model for training to obtain the trained stability assessment model;

[0097] The particle swarm optimization algorithm is used to adjust the hyperparameters of the stability evaluation model to obtain an optimized stability evaluation model, avoiding local optimality and improving prediction accuracy.

[0098] In summary, the present invention provides a method for evaluating the stability of a mine water storage layer based on microseismic monitoring. Through microseismic monitoring, very small seismic wave signals can be captured, and information such as their position, magnitude, and energy can be analyzed, thereby evaluating the stability of the mine water storage layer. By filtering and denoising the signals, the accuracy and reliability of the data are guaranteed, the subsequent positioning accuracy is ensured, and it is helpful to conduct more in-depth geological analysis and prediction. Through microseismic monitoring, data can be collected and analyzed in real time, abnormal situations can be discovered in time, early warnings can be issued, and abnormal areas can be located, so that staff can The real-time monitoring capability of processing abnormal areas of the mine water storage layer is of great significance for preventing geological disasters and ensuring mine safety. Compared with traditional geological exploration methods, microseismic monitoring does not require large-scale excavation or drilling, reducing damage to the environment and cost investment. Microseismic monitoring technology can monitor rock microfractures, effectively monitor the activation rules of hidden structures and the depth of bottom plate fractures, thereby realizing the prediction and forecast of water inrush hazards, avoiding microfractures in the rock mass that accelerate the connection between the aquifer and the mining space, and then leading to cross-regional environmental disasters such as water inrush or surface collapse.

[0099] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0100] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for evaluating the stability of a mine water storage layer based on microseismic monitoring, characterized in that: The following steps are involved: S1: using a data acquisition module to obtain geological structure data of the mine water storage layer area, and using the geological structure data to identify the mine water storage layer area; S2: Deploy monitoring sensors in the mine water storage layer area and use the monitoring sensors to monitor the mine water storage area in real time; S3: Using the determination and analysis module to perform preliminary analysis on the data monitored by the monitoring sensors to determine the spatial distribution density and energy accumulation trend of microseismic events; S4: Based on the spatial distribution density and energy accumulation trend of microseismic events, analyze the clustering characteristics of microseismic events and the evolution law of rock mass rupture in the water-sealed storage layer; S5: Use the model building module to establish a stability assessment model, combine the clustering characteristics of microseismic events with the evolution law of rock fracture in the water storage layer into the stability assessment model, and obtain the stability assessment results of the mine water storage layer.

2. The method for evaluating the stability of a mine water storage layer based on microseismic monitoring according to claim 1, characterized in that: The data acquisition module includes: A collection unit, used to collect hydrogeological data of the mine, including drilling data, core sample analysis results, water level observation data, and water quality sampling data; The identification unit is used to analyze the geological structure of the mine using the collected data, and to identify the water storage layer area of ​​the mine based on the geological structure analysis.

3. The method for evaluating the stability of a mine water storage layer based on microseismic monitoring according to claim 1, characterized in that: The step S3 specifically includes: Real-time acquisition of waveform signals generated by rock mass fractures, filtering and denoising of effective data signals to eliminate environmental interference; Set the initial energy threshold and dynamically adjust the energy threshold range based on the real-time noise level of the monitoring environment; Based on the energy threshold, potential microseismic events are screened, a preliminary event list is generated, the specific location of the microseismic events in the water storage layer is determined, and high-density anomaly areas are identified, which are the spatial distribution density of microseismic events; The energy release amount of a single event is counted, and a time series energy accumulation curve is drawn. The energy accumulation curve is then subjected to regression analysis to determine the energy release rate state, and the energy accumulation trend is obtained based on the determination results.

4. The method for evaluating the stability of a mine water storage layer based on microseismic monitoring according to claim 1, characterized in that: The determination and analysis module specifically includes: The threshold determination unit is used to collect waveform signals generated by rock mass fracture in real time, screen out potential microseismic events, and generate a preliminary event list; The analysis unit is used to analyze the spatial distribution density of microseismic events, determine the energy release rate state, and obtain the energy accumulation trend.

5. The method for evaluating the stability of a mine water storage layer based on microseismic monitoring according to claim 4 is characterized in that: The threshold determination unit includes: The processing unit is used to collect the waveform signal generated by rock mass fracture in real time, eliminate the environmental noise interference in the waveform signal, and retain the effective signal frequency band; The generation unit is used to dynamically adjust the energy threshold range, screen out potential microseismic events based on the energy threshold range, and generate a preliminary event list.

6. The method for assessing the stability of a mine water storage layer based on microseismic monitoring according to claim 5, characterized in that: The determination process of the threshold determination unit specifically includes: Real-time acquisition of waveform signals generated by rock mass fracture, and conversion of waveform signals into data signals, recording time and space coordinates, energy release, and frequency parameters; Screen valid data signals, filter and denoise them, and eliminate environmental interference; Set the initial energy threshold to filter low-energy background noise and capture only valid microseismic events. Dynamically adjust the energy threshold range based on the real-time noise level of the monitoring environment to avoid missed detection or false detection. Screen out potential microseismic events based on energy thresholds, generate a preliminary event list, and record the triggering time and energy peak of each event; Calculate the spatial coordinates of the event, determine the specific location of the microseismic event in the water storage layer, analyze the degree of aggregation of microseismic events in the horizontal and vertical directions, identify high-density abnormal areas, and obtain the spatial distribution density of microseismic events.

7. The method for assessing the stability of a mine water storage layer based on microseismic monitoring according to claim 4, characterized in that: The analysis unit comprises: The spatial distribution density analysis unit is used to analyze the aggregation degree of microseismic events in the horizontal and vertical directions to obtain the spatial distribution density of microseismic events; The energy accumulation trend analysis unit is used to draw a time series energy accumulation curve, perform regression analysis on the energy accumulation curve, and obtain the energy accumulation trend.

8. The method for assessing the stability of a mine water storage layer based on microseismic monitoring according to claim 7, characterized in that: The analysis process of the analysis unit specifically includes: Count the energy released by a single event, calculate the cumulative energy value per unit time, and use the cumulative energy value to draw a time series energy accumulation curve; Regression analysis is performed on the energy accumulation curve to determine whether the energy release rate is exponentially increasing or in a stable state, and the energy accumulation trend is obtained based on the judgment results.

9. The method for evaluating the stability of a mine water storage layer based on microseismic monitoring according to claim 1, characterized in that: The model building module includes: The training set data acquisition unit is used to collect microseismic activity parameters and geomechanical parameters, and classify and label the collected data to obtain the original data training set; The data set expansion unit is used to expand the original data training set and train the data expansion model to obtain the expanded data training set; The stability assessment model training unit is used to input the expanded data training set into the stability assessment model for training to obtain a trained stability assessment model.

10. The method for evaluating the stability of a mine water storage layer based on microseismic monitoring according to claim 9, characterized in that: The method of establishing a stability assessment model using a model building module includes: The number of microseismic events, spatial density, energy accumulation, apparent volume, impact deformation energy, microseismic activity, and geomechanical parameters of the mine water storage layer are collected, and the collected data are classified and labeled to obtain the original data training set; Based on the generative adversarial network algorithm of Riemannian manifold feature diffusion, the original data training set is expanded and the data expansion model is trained to obtain the expanded data training set; Based on the extreme learning machine algorithm of nonlinear system coupling learning, the expanded data training set is input into the stability assessment model for training to obtain the trained stability assessment model; The particle swarm optimization algorithm is used to adjust the hyperparameters of the stability assessment model to obtain the optimized stability assessment model.

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