A coal mine rock burst microseismic monitoring method and system

Abstract

A coal mine rock burst microseismic monitoring method and system, the method comprises the following steps: collecting multidimensional geological data to construct a three-dimensional geological structure model; dividing the monitoring area of the target coal mine, analyzing the rock burst risk level of different monitoring areas; screening the optimal layout scheme; receiving the underground seismic wave signal of the target coal mine in real time; calculating the position and intensity level of the seismic source; evaluating the effectiveness of the optimal layout scheme and optimizing the optimal layout scheme; the system: the data acquisition module is a plurality of sensors; the geological data receiving module is used for receiving multidimensional geological data; the model construction module is used for constructing a three-dimensional geological structure model; the risk division module is used for determining the rock burst risk level; the scheme generation module is used for screening the optimal layout scheme; the data processing module is used for processing the signal; the microseismic analysis module is used for calculating the position and intensity level of the seismic source; the feedback optimization module is used for optimizing the optimal layout scheme. The present application can realize accurate positioning of the seismic source and accurate calculation of the intensity.

Description

Technical Field

[0001] This invention belongs to the field of intelligent monitoring technology for rockbursts in coal mines, specifically a method and system for microseismic monitoring of rockbursts in coal mines. Background Technology

[0002] Coal mine rock bursts are violent dynamic phenomena caused by the sudden release of rock mass stress during mining operations. They are characterized by their suddenness and destructive power, seriously threatening the safety of underground operations.

[0003] Existing microseismic monitoring technologies suffer from numerous limitations, including insufficient geological data integration capabilities, making it difficult to reflect the true impact of complex geological structures on seismic wave propagation and resulting in significant monitoring blind spots; a lack of dynamic optimization mechanisms for sensor deployment, often relying on fixed density arrangements that lead to insufficient coverage or severe resource waste, affecting the accuracy of seismic source location; low matching degree between risk assessment and actual seismic source distribution, with traditional methods often using static indicators to delineate risk zones without effectively integrating real-time seismic wave data for feedback adjustments, easily leading to misjudgments; and disconnect between signal processing and source inversion, failing to fully utilize rock strata physical parameters to correct propagation paths, resulting in large location errors. These problems make existing technologies insufficient to meet the needs of accurate monitoring of rockbursts under complex geological conditions. To effectively address these issues, there is an urgent need for a microseismic monitoring method and system for coal mine rockbursts. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method and system for monitoring microseismic events of rockburst in coal mines. This method is simple to implement, has low implementation cost, and high monitoring accuracy. It can accurately locate the seismic source and accurately calculate the source intensity. The system is highly intelligent and reliable, and can efficiently and accurately obtain the source location and source intensity level. It can provide strong technical support for rockburst early warning and prevention, and ensure the safety of underground coal mine operations.

[0005] To achieve the above objectives, the present invention provides a method for microseismic monitoring of rockburst in coal mines, comprising the following steps:

[0006] Step 1: Collect multi-dimensional geological data from the target coal mine, and use 3D modeling technology to integrate and process the multi-dimensional geological data to construct a 3D geological structure model;

[0007] Step 2: Divide the target coal mine into monitoring zones based on the three-dimensional geological structure model, and determine the rockburst risk level of different monitoring zones using the analytic hierarchy process combined with the fuzzy comprehensive evaluation method;

[0008] Step 3: Based on the rockburst risk level of the monitoring area, set up multiple sensor deployment schemes, use Monte Carlo simulation method combined with three-dimensional geological structure model to simulate the impact of multiple sensor deployment schemes on seismic wave signal reception, and select the optimal deployment scheme.

[0009] Step 4: Install sensors in each monitoring area according to the optimal deployment plan; receive downhole seismic wave signals in real time, preprocess the downhole seismic wave signals, and then extract characteristic parameters from the preprocessed downhole seismic wave signals;

[0010] Step 5: Based on the characteristic parameters and combined with the three-dimensional geological structure model, analyze the propagation characteristics of the downhole seismic wave signal, and calculate the source location and intensity level;

[0011] Step 6: Feed the source location and intensity level back to the three-dimensional geological structure model, evaluate the effectiveness of the optimal deployment scheme, and optimize the optimal deployment scheme based on the evaluation results.

[0012] Furthermore, to achieve accurate monitoring of rockburst micro-seismic activity, in step one, the multi-dimensional geological data includes coal seam parameters, rock strata distribution data, fault information, joint development data, historical mining records, and mine pressure monitoring data. The coal seam parameters include coal seam thickness, coal seam dip angle, coal seam hardness, and coal seam permeability. The rock strata distribution data includes rock strata thickness, rock strata lithology, and rock strata burial depth. The fault information includes fault strike, fault dip, fault dip angle, and fault displacement. The joint development data includes joint density, joint strike, and joint opening. The mine pressure monitoring data includes support resistance, surrounding rock deformation, and stress values.

[0013] Furthermore, in order to construct a three-dimensional geological structure model that accurately reflects the geological structure of the target area, the process of constructing the three-dimensional geological structure model in step one is as follows:

[0014] S11: Classify and organize multi-dimensional geological data, and establish a structured database containing spatial coordinate indexes according to data types;

[0015] S12: Import the data from the structured database into the 3D modeling software, and perform meshing based on coal seam parameters and rock strata distribution data to generate the initial 3D geological framework of the layered entity;

[0016] S13: Construct a spatial cutting surface based on fault information and joint development data. Use Boolean operations to fuse the spatial cutting surface with the initial three-dimensional geological framework, and add fault and joint structures to obtain the basic model.

[0017] S14: Combining the spatial morphology data of historical mining records and the spatiotemporal distribution characteristics of mine pressure monitoring data, the basic model is corrected for deviations to obtain a three-dimensional geological structure model.

[0018] Furthermore, in order to achieve accurate classification of rockburst risk levels in the monitoring areas, the process for determining the rockburst risk levels in different monitoring areas in step two is as follows:

[0019] S21: Construct evaluation indicators that affect the risk of rockbursts in coal mines. The evaluation indicators include the complexity of geological structures, stress concentration risk, and historical rockburst conditions.

[0020] S22: The analytic hierarchy process (AHP) is used to construct the judgment matrix, and the relative importance weight of each evaluation indicator is calculated through a consistency test.

[0021] S23: The fuzzy comprehensive evaluation method is used to determine the membership function of each evaluation indicator, the evaluation indicators of each monitoring area are quantitatively scored, and the comprehensive risk score of each monitoring area is obtained by weighted summation.

[0022] S24: Determine the rockburst risk level of each monitoring area based on the comprehensive risk score. The rockburst risk level includes high risk, medium risk and low risk.

[0023] Furthermore, in order to obtain the optimal deployment scheme more efficiently and accurately through simulation, and at the same time, to effectively reduce investment costs, the selection process for the optimal deployment scheme in step three is as follows:

[0024] S31: Set the basic parameters for sensor deployment according to the rockburst risk level of the monitoring area. The basic parameters include sensor density in high-risk areas, deployment interval in medium and low-risk areas, and effective monitoring radius of the sensors.

[0025] S32: Based on a three-dimensional geological structure model, generate multiple deployment schemes in each monitoring area, including different numbers and spatial distributions of sensors;

[0026] S33: The Monte Carlo simulation method is used to randomly generate earthquake source samples, and the propagation path of the seismic wave is simulated by combining the rock layer propagation velocity parameters. The screening index for each deployment scheme is calculated. The screening index includes signal coverage, signal-to-noise ratio and positioning error.

[0027] S34: Use a weighted scoring method to comprehensively rank the screening indicators and obtain the optimal deployment scheme.

[0028] Furthermore, in order to efficiently remove noise data from the signal, and to effectively reduce the computational load in calculating the source location and intensity level while ensuring the accuracy of the calculation results, the process of preprocessing the downhole seismic wave signal and extracting characteristic parameters in step four is as follows:

[0029] S41: Wavelet transform is used to denoise the downhole seismic signal and remove interference noise;

[0030] S42: The sliding window method is used to segment continuous signals, and independent seismic events are identified by energy thresholds;

[0031] S43: Extract the frequency characteristics, waveform parameters, seismic wave type, propagation angle, and vibration intensity data of each seismic wave event as feature parameters, and identify the effective seismic waves.

[0032] Furthermore, in order to obtain the calculation results efficiently and accurately, the calculation process for the earthquake source location and intensity level in step five is as follows:

[0033] S51: Based on the time difference of arrival and propagation angle of the seismic waves in the characteristic parameters, the possible location range of the seismic source is initially calculated using the time difference positioning method;

[0034] S52: Call the rock wave velocity parameters of the corresponding area in the three-dimensional geological structure model, correct the possible location range, and obtain the source coordinates;

[0035] S53: An attenuation model is constructed using vibration intensity data from characteristic parameters, and the intensity level is calculated by combining the source depth. At the same time, calculation errors are reduced by integrating multiple sets of sensor data and applying cross-validation technology.

[0036] Furthermore, to ensure the effectiveness of the optimal deployment scheme, the effectiveness evaluation process for the optimal deployment scheme in step six is ​​as follows:

[0037] S161: Calculate the matching degree between the earthquake source location and the rockburst risk level of the monitoring area according to the preset cycle, and calculate the distribution ratio of the actual earthquake source in the high-risk, medium-risk and low-risk monitoring areas;

[0038] S162: If the matching degree is lower than the preset threshold, the membership function of the evaluation index is modified in combination with the source intensity level, and the relative importance weight of each evaluation index is recalculated until the matching degree is not lower than the preset threshold.

[0039] S163: Analyze the sensor's reception rate of seismic source signals, count the proportion of seismic sources with signal loss or excessive signal-to-noise ratio, and evaluate the coverage effectiveness of the deployment scheme.

[0040] Furthermore, in order to ensure the reliability and accuracy of long-term monitoring through dynamic adaptive adjustment, the optimization process of the optimal deployment scheme based on the evaluation results in step six is ​​as follows:

[0041] S261: For monitoring areas with low signal reception, the spatial orientation and deployment depth of the sensors are adjusted by combining the distribution of faults and rock layer interfaces in the three-dimensional geological structure model. At the same time, according to the corrected evaluation index weights, the number of sensors is increased in high-risk areas and areas with dense seismic sources to expand the monitoring coverage density and obtain an optimized deployment scheme.

[0042] S262: Import the optimized deployment scheme into the Monte Carlo simulation system for verification until the signal coverage, average positioning error and high-risk signal-to-noise ratio meet the preset standards.

[0043] This invention provides a method for monitoring microseismic events related to rockbursts in coal mines. First, by integrating multi-dimensional geological data and creating a 3D model, a refined characterization of the coal mine's geological structure is achieved. This reflects the true impact of complex geological structures on seismic wave propagation, providing a precise spatial basis for subsequent seismic wave propagation analysis and effectively reducing monitoring errors caused by missing geological information. Next, the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method are used to delineate risk zones. Combined with real-time seismic source data, evaluation indicators are dynamically corrected, significantly improving the matching degree between risk levels and actual seismic source distribution, providing a scientific basis for sensor deployment. Furthermore, a sensor deployment optimization mechanism based on Monte Carlo simulation is used. By comparing multiple schemes and selecting the optimal one, dense coverage in high-risk areas is ensured while redundant deployment in low-risk areas is reduced. This effectively avoids the insufficient coverage or significant waste of sensor resources that often occurs with traditional methods relying on manual experience. This approach effectively balances the cost of monitoring resources while ensuring monitoring accuracy. Finally, an inversion method that effectively integrates seismic signal preprocessing with a 3D geological model is effectively integrated. The propagation path is corrected using rock stratum wave velocity parameters, and cross-validation with multiple sensors significantly improves the accuracy of seismic source location and the reliability of intensity level calculation. Finally, through the closed-loop mechanism of source data feedback optimization model and deployment scheme, dynamic adaptive adjustment of the monitoring process was realized, which continuously improved the long-term monitoring efficiency, significantly reduced monitoring errors, ensured the accuracy of monitoring, provided strong support for early warning and prevention decisions of coal mine rockbursts, and significantly enhanced the safety of underground operations.

[0044] This method is simple to implement, has low implementation costs, and high monitoring accuracy. It can achieve precise location of the earthquake source and accurate calculation of the source intensity.

[0045] The present invention also provides a coal mine rockburst microseismic monitoring system for implementing a coal mine rockburst microseismic monitoring method, comprising a data acquisition module, a processor and a memory;

[0046] The data acquisition module consists of multiple sets of sensors, which are respectively arranged in multiple monitoring areas underground in the target coal mine. Multiple sensors in each set are buried at different locations in the corresponding monitoring area to collect underground seismic wave signals of the target coal mine in real time and send them to the processor.

[0047] The processor includes a geological data receiving module, a model building module, a risk classification module, a scheme generation module, a data processing module, a microseismic analysis module, and a feedback optimization module.

[0048] The geological data receiving module is used to receive multi-dimensional geological data from the target coal mine and send it to the model building module;

[0049] The model building module is used to integrate and process multi-dimensional geological data from underground coal mines using three-dimensional modeling technology to construct a three-dimensional geological structure model.

[0050] The risk classification module is used to divide the target coal mine into monitoring areas based on the three-dimensional geological structure model, and to determine the rockburst risk level of different monitoring areas based on the analytic hierarchy process combined with the fuzzy comprehensive evaluation method.

[0051] The scheme generation module is used to simulate the impact of various preset sensor deployment schemes on seismic signal reception using Monte Carlo simulation combined with a three-dimensional geological structure model, and to select the optimal deployment scheme.

[0052] The data processing module is used to denoise downhole seismic signals and extract feature parameters from the denoised signals.

[0053] The microseismic analysis module is used to analyze the propagation characteristics of downhole seismic wave signals based on characteristic parameters and a three-dimensional geological structure model, and to calculate the source location and intensity level.

[0054] The feedback optimization module is used to feed back the source location and intensity level to the three-dimensional geological structure model, evaluate the effectiveness of the optimal deployment scheme, and optimize the optimal deployment scheme based on the evaluation results.

[0055] The memory is used by the processor to store and retrieve data.

[0056] In this invention, multiple sensors in the data acquisition module are buried at different locations in different monitoring areas, enabling comprehensive acquisition of underground seismic wave signals from the target coal mine. This provides a data foundation for the accurate calculation of the seismic source location and intensity level. The geological data receiving module provides a data transmission interface for the model building module, facilitating the collection and transmission of multi-dimensional geological data. The model building module, based on multi-dimensional geological data, efficiently and accurately constructs a three-dimensional geological structure model that finely characterizes the geological structure of the target coal mine. This provides a precise spatial carrier for seismic wave propagation and offers reliable technical support for subsequent calculations in the microseismic analysis module. The risk classification module uses the analytic hierarchy process (AHP) to rationally determine the weights of various factors. Simultaneously, it effectively combines fuzzy comprehensive evaluation methods to address the fuzziness and uncertainty in risk estimation, achieving quantitative risk analysis and significantly improving the matching degree between the risk level and the actual seismic source distribution. This results in a refined risk assessment, ensuring that the obtained risk level matches the actual occurrence of rockbursts. The scheme generation module allows for the selection of the optimal solution through simulation and comparison of multiple schemes. This ensures dense coverage of high-risk areas while reducing redundant deployment in low-risk areas, balancing monitoring accuracy and cost. The data processing module effectively removes noise from the signal and extracts the characteristic parameters needed for calculating the seismic source location and intensity level, reducing the computational load of the subsequent microseismic analysis module. The microseismic analysis module fully integrates signal processing and source inversion, significantly improving the accuracy of source location and the reliability of intensity level calculations. The feedback optimization module enables dynamic adaptive adjustment of the monitoring system, continuously improving long-term monitoring effectiveness and ensuring monitoring accuracy. The storage module facilitates real-time storage and retrieval of historical data.

[0057] The system is highly intelligent and reliable, and can efficiently and accurately obtain the location and intensity of the seismic source. It can provide strong technical support for early warning and prevention of rockbursts and ensure the safety of underground coal mine operations. Attached Figure Description

[0058] Figure 1 This is a flowchart of the monitoring method in this invention;

[0059] Figure 2 This is a flowchart of the selection process for the optimal deployment scheme in this invention;

[0060] Figure 3 This is a flowchart of the process for calculating the earthquake source location and intensity level in this invention;

[0061] Figure 4This is a flowchart of the effectiveness evaluation of the optimal deployment scheme in this invention;

[0062] Figure 5 This is a block diagram of the monitoring system in this invention. Detailed Implementation

[0063] The invention will now be further described with reference to the accompanying drawings.

[0064] like Figure 1 As shown, the present invention provides a method for monitoring microseismic events of rockburst in coal mines, comprising the following steps:

[0065] Step 1: Collect multi-dimensional geological data from the target coal mine, and use 3D modeling technology to integrate and process the multi-dimensional geological data to construct a 3D geological structure model;

[0066] To achieve accurate monitoring of microseismic events related to rockburst, the multi-dimensional geological data includes coal seam parameters, strata distribution data, fault information, joint development data, historical mining records, and mine pressure monitoring data. The coal seam parameters include coal seam thickness, dip angle, hardness, and permeability. The strata distribution data includes strata thickness, lithology, and burial depth. The fault information includes fault strike, dip direction, dip angle, and displacement. The joint development data includes joint density, strike, and opening. The mine pressure monitoring data includes support resistance, surrounding rock deformation, and stress values.

[0067] To construct a three-dimensional geological structure model that accurately reflects the geological structure of the target area, the process of constructing the three-dimensional geological structure model is as follows:

[0068] S11: Classify and organize multi-dimensional geological data, and establish a structured database with spatial coordinate indexes according to data type (such as coal seam parameters, rock strata distribution data, etc.); for coal seam parameters, the thickness, dip angle, and other data of coal seams at different detection points need to be associated with the corresponding underground coordinates (X, Y, Z three-dimensional coordinates); for fault information, the spatial coordinate range and spatial changes of parameters such as strike and dip of each fault need to be recorded; for mine pressure monitoring data, it needs to be stored according to time series and monitoring point coordinates to ensure that the data can be quickly retrieved and called through spatial coordinates.

[0069] S12: Import data from the structured database into 3D modeling software (such as Surpac, Micromine, etc.), and perform meshing based on coal seam parameters and rock strata distribution data to generate an initial 3D geological framework of layered entities. Specifically, first, based on the elevation data of the top and bottom plates of the coal seam, generate 3D curved surfaces of the top and bottom plates of the coal seam; then, based on the rock strata distribution data, generate the top and bottom plates of each rock stratum according to lithology, and form layered entities through surface stretching to construct the initial 3D geological framework, reflecting the spatial distribution morphology of the coal seam and each rock stratum.

[0070] S13: Based on fault information and joint development data, spatial cutting surfaces are constructed. For faults, fault planes are generated in three-dimensional space as cutting surfaces according to parameters such as strike, dip, and dip angle. The fault planes need to extend to the affected coal seams and rock strata. For joints, multiple joint planes are generated at certain intervals as cutting surfaces according to data such as joint density and strike. Boolean operations are used to integrate the spatial cutting surfaces with the initial three-dimensional geological framework. That is, the parts of the framework that are cut through by the cutting surfaces are segmented, and fault and joint structures are added to the model to obtain a basic model that can reflect the geological fracture zone.

[0071] S14: Combining spatial morphological data from historical mining records (including the location, extent, and depth of mined working faces, the orientation and cross-sectional dimensions of roadways, etc.) and the spatiotemporal distribution characteristics of mine pressure monitoring data (including the changing trends of support resistance at different times and locations, and the cumulative deformation of surrounding rock), the basic model is corrected for deviations. For example, based on the displacement data of coal seams and rock strata caused by the redistribution of surrounding rock stress after mining, the spatial position of rock strata in the corresponding areas of the model is adjusted; based on the changes in rock mass mechanical properties reflected by mine pressure monitoring data, the physical and mechanical parameters of relevant rock strata in the model (including elastic modulus, Poisson's ratio, etc.) are corrected, ultimately resulting in a three-dimensional geological structure model that accurately reflects the current geological conditions and stress characteristics of the target coal mine.

[0072] Step 2: Divide the target coal mine into monitoring zones based on the three-dimensional geological structure model, and determine the rockburst risk level of different monitoring zones using the analytic hierarchy process combined with the fuzzy comprehensive evaluation method;

[0073] Using a three-dimensional geological structure model as the spatial carrier, and combining the underground mining layout of the coal mine (such as the distribution of longwall faces and tunnels) and geological structural features (such as the spatial range of fault zones and densely jointed areas), the underground coal mine area is divided into several basic monitoring units using a grid division method. The side length of each unit is set to 5-10 meters (which can be adjusted according to the actual scale and geological complexity of the coal mine) to ensure the uniformity of geological parameters (such as coal seam thickness, lithology, stress distribution, etc.) within each unit. Subsequently, through the spatial topological relationship of the three-dimensional model, adjacent basic monitoring units with similar geological features are merged to form monitoring areas with an area of ​​100-500 square meters. At the same time, each monitoring area is guaranteed to contain a complete geological structural unit (such as the influence range of a fault or a densely jointed zone) and a mining engineering unit (such as a certain area of ​​a working face). Each monitoring area is assigned a unique spatial code and associated with the coordinate information and geological parameters in the three-dimensional geological structure model.

[0074] To achieve accurate classification of rockburst risk levels in monitoring areas, the following process was used to determine the rockburst risk levels for different monitoring areas using the analytic hierarchy process combined with fuzzy comprehensive evaluation:

[0075] S21: Construct evaluation indicators affecting the risk of rockbursts in coal mines. These indicators include the complexity of geological structures, stress concentration risk, and historical rockburst data. The complexity of geological structures mainly considers the number and scale of faults (a drop greater than 5 meters is considered a large fault), joint density (greater than 5 joints / square meter is considered dense), and the degree of rock fragmentation (fractured zones accounting for more than 30% of the total thickness are considered highly fragmented). Stress concentration risk is based on mine pressure monitoring data from a three-dimensional geological structure model, covering the ratio of current stress value to rock mass compressive strength (a higher ratio is considered high stress ratio), stress gradient change rate (a monthly increase rate greater than 10% is considered high gradient), and areas of overlapping mining stress (such as areas of overlapping working face support pressure and fault stress). Historical rockburst data includes the number of rockbursts that occurred in the monitoring area in the past 5 years (3 or more times is considered high frequency), the maximum impact intensity (magnitude greater than 2.0 is considered strong impact), and the time of the most recent occurrence (within one year is considered recent).

[0076] S22: A judgment matrix is ​​constructed using the analytic hierarchy process (AHP), and the relative importance weight of each evaluation indicator is calculated through a consistency test. Specifically, firstly, 5-7 experts in coal mine geology, mining engineering, and rockburst prevention are invited to conduct pairwise comparisons of the indicators based on their influence on rockburst occurrence (using a 1-9 scale: 1 indicates equal importance, 3 indicates slightly important, 5 indicates significantly important, 7 indicates strongly important, and 9 indicates extremely important; otherwise, the reciprocal is used). This forms the judgment matrix. Then, the maximum eigenvalue and corresponding eigenvector of the judgment matrix are calculated. The consistency ratio CR = CI / RI is calculated using the consistency index CI and the random consistency index RI. When CR < 0.1, the judgment matrix passes the consistency test; otherwise, the matrix needs to be readjusted. Finally, the eigenvectors that pass the test are normalized to obtain the weights of each indicator (e.g., geological structural complexity weight 0.4, stress concentration risk weight 0.4, historical rockburst situation weight 0.2).

[0077] S23: The fuzzy comprehensive evaluation method is used to determine the membership function of each evaluation indicator, and the evaluation indicators of each monitoring area are quantitatively scored. The comprehensive risk score of each monitoring area is obtained by weighted summation. Specifically, the membership function is set according to the complexity of the geological structure: if there are no large faults in the monitoring area, the joint density is less than 2 joints / square meter and the proportion of fractured rock strata is less than 10%, the membership degree is 0 (corresponding score 0-30 points); if there is 1 large fault, the joint density is 2-5 joints / square meter and the proportion of fractured rock strata is 10%-30%, the membership degree is 0.5 (corresponding score 30-70 points); if there are 2 or more large faults, the joint density is greater than 5 joints / square meter and the proportion of fractured rock strata is more than 30%, the membership degree is 1 (corresponding score 70-100 points). Similarly, membership functions and scoring intervals are set for stress concentration risk and historical rockburst conditions. Then, based on the actual geological data and three-dimensional model parameters of the monitoring area, the membership degree of each indicator is determined and scored accordingly. The scores of each indicator are multiplied by their weights and summed to obtain the comprehensive risk score (out of 100).

[0078] S24: Determine the rockburst risk level of each monitoring area based on the comprehensive risk score. The rockburst risk level includes high risk, medium risk, and low risk. Specifically, a score ≥70 is defined as a high-risk area, which has complex geological structure, obvious stress concentration, and may have historical rockburst records, requiring key monitoring; a score of 30-70 is defined as a medium-risk area, where there are certain anomalies in geological structure or stress state, requiring routine monitoring; a score <30 is defined as a low-risk area, where geological conditions are stable, stress distribution is uniform, and there are no historical rockburst records, allowing for a reduction in monitoring frequency. At the same time, the risk level information is linked to a three-dimensional geological structure model and visualized using different colors (red for high risk, yellow for medium risk, and green for low risk).

[0079] Step 3: Based on the rockburst risk level of the monitoring area, set up multiple sensor deployment schemes, use Monte Carlo simulation method combined with three-dimensional geological structure model to simulate the impact of multiple sensor deployment schemes on seismic wave signal reception, and select the optimal deployment scheme.

[0080] like Figure 2 As shown, in order to obtain the optimal deployment scheme more efficiently and accurately through simulation, and at the same time, to effectively reduce investment costs, the selection process for the optimal deployment scheme is as follows:

[0081] S31: Set the basic parameters for sensor deployment based on the rockburst risk level of the monitoring area. The basic parameters include sensor density in high-risk areas, deployment interval in medium- and low-risk areas, and effective monitoring radius of the sensors. Among them, high-risk areas adopt a high-density deployment standard, with a sensor density of one per 500 square meters and a deployment interval controlled within the range of 20-30 meters to ensure dense capture of seismic wave signals. Medium-risk areas adopt a conventional density deployment, with one sensor deployed per 1000 square meters and a deployment interval of 30-50 meters. Low-risk areas adopt a sparse density deployment, with one sensor deployed per 2000 square meters and a deployment interval of 50-80 meters. Simultaneously, by combining the physical and mechanical properties of the rock strata in the three-dimensional geological structure model (such as rock density and elastic modulus), the effective monitoring radius of the sensor is determined: in hard rock strata (such as sandstone), the effective monitoring radius is set to 80-100 meters; in softer rock strata (such as shale), the effective monitoring radius is set to 50-70 meters; and in fractured zones or coal seams, the effective monitoring radius is set to 30-50 meters, ensuring that the sensor can effectively receive seismic wave signals under different geological conditions.

[0082] S32: Based on a 3D geological structure model, multiple deployment schemes with varying numbers and spatial distributions of sensors are generated for each monitoring area. Specifically, firstly, using the spatial coordinates of the 3D geological structure model as a reference, the boundary range for sensor deployment is delineated within each monitoring area (avoiding obstacles such as roadway support structures and equipment foundations). For high-risk areas, 3-5 deployment schemes with different numbers of sensors are generated: the basic scheme deploys sensors at a set density; optimization scheme 1 increases the number of sensors by 10% on top of the basic scheme; optimization scheme 2 densifies the sensors by 50% at key locations such as faults and densely jointed zones; and optimization scheme 3 attempts a non-uniform distribution (adjusting the density according to stress concentration areas). For medium- and low-risk areas, 2-3 schemes are generated for each, including a uniformly spaced deployment scheme, a directional deployment scheme along geological structural lines, and a deployment scheme dynamically adjusted based on mining progress. For each scheme, the specific coordinates of the sensors are marked in the 3D model and associated with the rock strata parameters (such as lithology and wave velocity) at the location, forming a visualized deployment scheme model.

[0083] S33: Using Monte Carlo simulation, random source samples are generated. Combined with rock stratum propagation velocity parameters, the seismic wave propagation path is simulated, and screening criteria for each deployment scheme are calculated. These criteria include signal coverage, signal-to-noise ratio, and positioning error. Specifically, in the three-dimensional geological structure model, 1000-2000 virtual source samples are randomly generated according to the source probability distribution of different risk zones (60% for high-risk zones, 30% for medium-risk zones, and 10% for low-risk zones). Each sample includes source coordinates, magnitude (0.5-3.0), and time of origin. Based on the P-wave and S-wave propagation velocity parameters of each rock stratum in the three-dimensional model (e.g., P-wave velocity of sandstone 3500-5000 m / s, P-wave velocity of coal seam 1500-2500 m / s), ray tracing is used to simulate the propagation path of the seismic wave from each virtual source to each sensor. The arrival time, propagation attenuation coefficient, and signal distortion caused by geological structures (e.g., fault reflection and refraction) are calculated. Based on the simulation results, the screening criteria for each scheme were calculated: signal coverage rate is the proportion of source samples that can receive effective signals (amplitude exceeding the noise threshold) to the total sample; signal-to-noise ratio is the ratio of the effective amplitude of the signal received by the sensor to the noise amplitude (the average value of all sensors); positioning error is the spatial distance between the source location inverted from the sensor data and the actual coordinates of the virtual source (the root mean square error of all samples).

[0084] S34: A weighted scoring method is used to comprehensively rank the screening indicators to obtain the optimal deployment scheme. Specifically, firstly, the weights of each screening indicator are determined: signal coverage weight 0.4 (prioritizing effective capture of the seismic source), signal-to-noise ratio weight 0.3 (ensuring signal quality to improve positioning accuracy), and positioning error weight 0.3 (directly reflecting monitoring accuracy). Then, the indicator values ​​of each scheme are standardized (normalized according to the [0,100] interval), where signal coverage and signal-to-noise ratio are treated as positive indicators (higher values, higher scores), and positioning error is treated as a negative indicator (lower values, higher scores). Finally, the comprehensive score of each scheme is calculated according to the weights (comprehensive score = signal coverage score × 0.4 + signal-to-noise ratio score × 0.3 + positioning error score × 0.3), and the scheme with the highest comprehensive score is selected as the optimal deployment scheme. If multiple schemes have similar scores (difference less than 5 points), the positioning error index of high-risk areas is further compared, and the scheme with higher monitoring accuracy in high-risk areas is selected as the final optimal deployment scheme. The sensor coordinates, quantity, and distribution characteristics of this scheme are then exported to the three-dimensional geological structure model.

[0085] Step 4: Install sensors in each monitoring area according to the optimal deployment plan. This includes drilling or fixing the sensors in the corresponding locations underground based on the sensor coordinates marked in the 3D geological structure model, ensuring that the sensor probe orientation matches the rock strata strike (e.g., adjusting the probe angle to 45° near fault zones to enhance the receiving sensitivity of obliquely propagating seismic waves). After installation, perform signal calibration and verify the sensor response performance through artificial excitation (e.g., hammering the coal seam). Ensure that the sampling frequency (set to 1000-2000Hz) and dynamic range (≥120dB) of all sensors are consistent, forming a seismic wave signal receiving network covering the entire monitoring area. This network receives seismic wave signals from the target coal mine in real time and transmits the signals to the ground processing system in time-series format.

[0086] The downhole seismic wave signal is received in real time, preprocessed, and then feature parameters are extracted from the preprocessed signal. Specifically, in order to efficiently remove noise data from the signal, effectively reduce the computational load of calculating the source location and intensity level, and ensure the accuracy of the calculation results, the process of preprocessing the downhole seismic wave signal and extracting feature parameters is as follows:

[0087] S41: First, the noise types in the original signal are analyzed, including mechanical vibrations generated by mining operations, electromagnetic interference from motor equipment, and cable transmission noise. Next, wavelet transform is used to denoise the downhole seismic signal, removing interfering noise.

[0088] S42: The continuous signal is segmented using a sliding window method, and independent seismic events are identified by an energy threshold. Specifically, the window length is set to 1-2 seconds, the window sliding step is 0.5 seconds, and the root mean square energy of the signal in each window is calculated. The energy threshold (3-5 times the normal background noise energy) is determined based on historical data statistics. When the energy of 3 consecutive windows exceeds the threshold, it is determined to be the starting point of the seismic event. When the window energy falls below the threshold and continues for 2 windows, it is determined to be the ending point of the event. Thus, the continuous signal is segmented into independent seismic event segments, each segment containing the complete occurrence process of the seismic wave.

[0089] S43: Extract the frequency characteristics, waveform parameters, seismic wave type, propagation angle, and vibration intensity data of each seismic event as feature parameters, and identify valid seismic waves. Specifically, perform Fourier transform on the segmented seismic event fragments to obtain the spectral distribution, and extract features such as the dominant frequency and frequency bandwidth: the dominant frequency of rockburst seismic waves is usually between 10-100Hz, and the bandwidth is relatively wide; while the dominant frequencies of interference signals such as blasting and mechanical vibration are mostly concentrated in specific frequency bands. At the same time, extract waveform parameters, including peak amplitude, rise time, and duration: the rise time of rockburst seismic waves is relatively short (usually <0.5 seconds), the duration is relatively long (1-5 seconds), and the peak amplitude decreases regularly with distance. Compare these features with a preset rockburst seismic wave feature library (using cosine similarity to calculate the matching degree). When the matching degree is ≥70%, it is determined to be a valid seismic wave; otherwise, it is marked as an interference signal and discarded.

[0090] After the above preprocessing, the characteristic parameters of the effective seismic waves are extracted: seismic wave type (distinguished by the dominant frequency and waveform characteristics, such as coal and rock impact, roof fracture, etc.), propagation angle (calculated by the triangulation method using the time difference and spatial coordinates of signals received by multiple sensors), and vibration intensity (converting the peak amplitude into magnitude).

[0091] Step 5: Based on the characteristic parameters and combined with the three-dimensional geological structure model, analyze the propagation characteristics of the downhole seismic wave signal, and calculate the source location and intensity level;

[0092] like Figure 3 As shown, in order to obtain the calculation results efficiently and accurately, the calculation process for the focal location and intensity level is as follows:

[0093] S51: Based on the arrival time difference and propagation angle of the seismic waves in the characteristic parameters, the possible location range of the seismic source is initially calculated using the time difference positioning method; specifically, firstly, the arrival time of the first wave (the arrival time of the P wave) recorded by each sensor is extracted, and one sensor is used as the reference (usually the sensor with the strongest signal is selected) to calculate the arrival time difference between the other sensors and the reference sensor. According to the propagation law of the seismic waves in the homogeneous medium, a set of equations is established, as shown in formula (1);

[0094] (1);

[0095] In the formula, (x,y,z) are the coordinates of the earthquake source, (x... i ,y i ,z i ) and (x j ,y j ,z j ) represent the coordinates of sensor i and sensor j, respectively, and v is the preset average wave velocity. This represents the time difference of arrival between sensors i and j.

[0096] By solving this set of nonlinear equations, the preliminary three-dimensional coordinate range of the earthquake source is obtained. Simultaneously, spatial angular constraints are constructed by combining the propagation angles calculated from each sensor, narrowing down the possible location range and forming an initial positioning cube.

[0097] S52: The rock strata wave velocity parameters of the corresponding region in the 3D geological structure model are used to correct the possible location range and obtain the source coordinates. Specifically, rock strata distribution data within the initial positioning range are extracted from the 3D geological structure model, including the P-wave velocity of each rock stratum, the spatial location of rock strata interfaces, and fault distribution. A layered ray tracing method is used to simulate the propagation path of seismic waves in a non-homogeneous medium: the refraction angle of the seismic wave passing through the rock strata interface is calculated according to Snell's law, and the theoretical arrival time of different sensors is corrected by combining the reflection / scattering effect of faults on the seismic waves. The corrected time difference is then substituted back into the positioning equations and solved iteratively using the least squares method (iterations ≥ 5 times, until the error is less than 0.1ms) to obtain more accurate source coordinates (x0, y0, z0). If the source is located in a fault zone or a densely jointed area, a geological structure influence coefficient (such as a wave velocity reduction coefficient of 0.7-0.9 for fault fracture zones) needs to be further introduced to fine-tune the coordinates, ensuring that the positioning results are consistent with the geological characteristics of the 3D geological structure model.

[0098] S53: An attenuation model is constructed using the vibration intensity data in the characteristic parameters, and the intensity level is calculated by combining the source depth. At the same time, the calculation error is reduced by integrating multiple sets of sensor data and applying cross-validation technology. Specifically, the effective peak amplitude of the seismic wave recorded by each sensor is collected first, and the amplitude attenuation model is constructed by fitting the power function with the straight distance between the sensor and the source, as shown in formula (2).

[0099] (2);

[0100] In the formula, A represents the effective peak amplitude of the seismic wave recorded by each sensor, k is the source intensity coefficient, R represents the straight-line distance between the sensor and the source, n is the geometric attenuation index (usually taken as 1.5-2.5), and α is the medium absorption coefficient (related to lithology, α=0.05-0.15 / km for coal seams, α=0.02-0.08 / km for sandstone).

[0101] The model parameters were determined by least-squares fitting, and the initial amplitude A0 at the epicenter was derived. Combined with the epicenter depth h, A0 was converted to the Richter magnitude. To verify accuracy, cross-validation was used: data from one sensor was discarded at a time, and the magnitude was recalculated using the remaining sensors. If the standard deviation of all results was ≤0.2, the intensity level was considered valid; otherwise, the sensor data with the largest deviation was discarded, and the data was refitted to finally determine the epicenter intensity level. This information was then correlated with lithology, stress state, and other parameters at the epicenter location in the 3D geological structure model to form a complete epicenter information report.

[0102] Step 6: Feed the source location and intensity level back to the three-dimensional geological structure model, evaluate the effectiveness of the optimal deployment scheme, and optimize the optimal deployment scheme based on the evaluation results.

[0103] like Figure 4 As shown, to ensure the effectiveness of the optimal deployment scheme, the effectiveness evaluation process of the optimal deployment scheme is as follows:

[0104] S161: At a preset cycle (e.g., weekly), statistically analyze the matching degree between earthquake source locations and the rockburst risk levels of the monitoring area, and calculate the distribution ratio of actual earthquake sources in high-risk, medium-risk, and low-risk monitoring areas. Specifically, first, spatially overlay all earthquake source location coordinates with the risk zoning layer in the three-dimensional geological structure model, and statistically analyze the proportion of actual earthquake sources occurring in high-risk areas to the total number of earthquake sources (theoretically ≥60%), the proportion in medium-risk areas (20%-40%), and the proportion in low-risk areas (≤20%). The matching degree is quantified by calculating the deviation between the actual distribution ratio and the theoretical risk ratio.

[0105] S162: If the matching degree is lower than the preset threshold, the membership function of the evaluation index is modified in combination with the source intensity level, and the relative importance weight of each evaluation index is recalculated until the matching degree is not lower than the preset threshold.

[0106] If the matching degree is lower than a preset threshold (e.g., 0.7), the risk assessment model is considered to have a bias and needs to be corrected. For example, if high-intensity earthquakes (magnitude ≥ 2.0) frequently occur in low-risk areas, the weight of the stress concentration risk index is increased (e.g., from 0.4 to 0.5), and its membership function is corrected: stress value / compressive strength > 0.6 is judged as high stress (the original standard was > 0.8), thus expanding the membership range of the high-stress interval. Experts are invited to reconstruct the judgment matrix, and after passing the consistency test, the weights of each index are updated to make the risk level classification more consistent with the actual earthquake source distribution characteristics.

[0107] S163: Analyze the sensor reception rate of seismic source signals, and statistically analyze the proportion of seismic sources with signal loss or excessive signal-to-noise ratio (SNR) to evaluate the coverage effectiveness of the deployment scheme. Specifically, for each seismic source, check whether it is effectively captured by at least 3 sensors (SNR ≥ 10dB): if a seismic source is received by only 2 or fewer sensors, it is considered a signal loss; if the SNR of the received signal is < 10dB, it is considered a substandard signal quality. Calculate the proportion of these two types of seismic sources to the total number of seismic sources (the preset acceptable standard is ≤ 5%). If the proportion exceeds 10%, it is determined that the sensor deployment in that area has coverage blind spots or severe signal attenuation problems, requiring targeted optimization.

[0108] To ensure the reliability and accuracy of long-term monitoring through dynamic adaptive adjustments, the process of optimizing the optimal deployment scheme based on the evaluation results is as follows:

[0109] S261: For monitoring areas with low signal reception, the spatial orientation and deployment depth of sensors are adjusted based on the distribution of faults and rock strata interfaces in the 3D geological structure model. For example, near fault fracture zones, the sensor probes are oriented at a 30° angle to the fault strike to enhance the reception of refracted waves; above and below rock strata interfaces, the sensor deployment depth is increased by 1-2 meters into the intact rock strata to reduce interface reflection interference. The adjusted signal propagation path is simulated using a 3D model to verify the adjustment effect.

[0110] Meanwhile, based on the revised evaluation index weights, the number of sensors was increased in high-risk areas and areas with dense seismic sources to expand the monitoring coverage density, resulting in an optimized deployment scheme. For high-risk areas, the sensor density was increased from one per 500 square meters to one per 300 square meters, with a focus on adding sensors within 10m x 10m grids with dense seismic sources. For localized areas with frequent seismic sources within medium-risk areas (such as areas experiencing ≥3 earthquakes of magnitude > 1.0 per month), the deployment density was increased according to the high-risk area standards. The locations of newly added sensors must avoid obstacles such as roadway supports and waterlogged areas marked in the 3D model to ensure installation feasibility.

[0111] S262: Import the optimized deployment scheme into the Monte Carlo simulation system for verification. Simulate and generate virtual samples consistent with the actual seismic source distribution characteristics (60% high-risk area, 30% medium-risk area, and 10% low-risk area). Calculate indicators such as signal coverage (≥95%), average positioning error (≤5 meters), and signal-to-noise ratio in high-risk areas (≥15dB). If the standards are not met, return to adjust the sensor positions or number (e.g., add 1-2 sensors in areas with excessive positioning errors), and repeat the simulation verification until the signal coverage, average positioning error, and signal-to-noise ratio in high-risk areas meet the preset standards. Finally, update the optimized deployment scheme to the 3D geological structure model, generate a sensor installation coordinate list and adjustment instructions, guide the secondary deployment of downhole sensors, and form a closed-loop mechanism of monitoring-evaluation-optimization.

[0112] This invention provides a method for monitoring microseismic events related to rockbursts in coal mines. First, by integrating multi-dimensional geological data and creating a 3D model, a refined characterization of the coal mine's geological structure is achieved. This reflects the true impact of complex geological structures on seismic wave propagation, providing a precise spatial basis for subsequent seismic wave propagation analysis and effectively reducing monitoring errors caused by missing geological information. Next, the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method are used to delineate risk zones. Combined with real-time seismic source data, evaluation indicators are dynamically corrected, significantly improving the matching degree between risk levels and actual seismic source distribution, providing a scientific basis for sensor deployment. Furthermore, a sensor deployment optimization mechanism based on Monte Carlo simulation is used. By comparing multiple schemes and selecting the optimal one, dense coverage in high-risk areas is ensured while redundant deployment in low-risk areas is reduced. This effectively avoids the insufficient coverage or significant waste of sensor resources that often occurs with traditional methods relying on manual experience. This approach effectively balances the cost of monitoring resources while ensuring monitoring accuracy. Finally, an inversion method that effectively integrates seismic signal preprocessing with a 3D geological model is effectively integrated. The propagation path is corrected using rock stratum wave velocity parameters, and cross-validation with multiple sensors significantly improves the accuracy of seismic source location and the reliability of intensity level calculation. Finally, through the closed-loop mechanism of source data feedback optimization model and deployment scheme, dynamic adaptive adjustment of the monitoring process was realized, which continuously improved the long-term monitoring efficiency, significantly reduced monitoring errors, ensured the accuracy of monitoring, provided strong support for early warning and prevention decisions of coal mine rockbursts, and significantly enhanced the safety of underground operations.

[0113] This method is simple to implement, has low implementation costs, and high monitoring accuracy. It can achieve precise location of the earthquake source and accurate calculation of the source intensity.

[0114] like Figure 5 As shown, based on the same inventive concept, the present invention also provides a coal mine rockburst microseismic monitoring system for implementing a coal mine rockburst microseismic monitoring method, including a data acquisition module, a processor and a memory;

[0115] The data acquisition module consists of multiple sets of sensors, which are respectively arranged in multiple monitoring areas underground in the target coal mine. Multiple sensors in each set are buried at different locations in the corresponding monitoring area to collect underground seismic wave signals of the target coal mine in real time and send them to the processor.

[0116] The processor includes a geological data receiving module, a model building module, a risk classification module, a scheme generation module, a data processing module, a microseismic analysis module, and a feedback optimization module.

[0117] The geological data receiving module is used to receive multi-dimensional geological data from the target coal mine and send it to the model building module;

[0118] The model building module is used to integrate and process multi-dimensional geological data from underground coal mines using three-dimensional modeling technology to construct a three-dimensional geological structure model.

[0119] The risk classification module is used to divide the target coal mine into monitoring areas based on the three-dimensional geological structure model, and to determine the rockburst risk level of different monitoring areas based on the analytic hierarchy process combined with the fuzzy comprehensive evaluation method.

[0120] The scheme generation module is used to simulate the impact of various preset sensor deployment schemes on seismic signal reception using Monte Carlo simulation combined with a three-dimensional geological structure model, and to select the optimal deployment scheme.

[0121] The data processing module is used to receive underground seismic wave signals from the target coal mine in real time, perform noise reduction processing on the underground seismic wave signals, and extract feature parameters from the noise-reduced signals.

[0122] The microseismic analysis module is used to analyze the propagation characteristics of downhole seismic wave signals based on characteristic parameters and a three-dimensional geological structure model, and to calculate the source location and intensity level.

[0123] The feedback optimization module is used to feed back the source location and intensity level to the three-dimensional geological structure model, evaluate the effectiveness of the optimal deployment scheme, and optimize the optimal deployment scheme based on the evaluation results.

[0124] The memory is used by the processor to store and retrieve data.

[0125] As a preferred embodiment, the sensor is a micro-vibration sensor, and the processor is an industrial computer.

[0126] In this invention, multiple sensors in the data acquisition module are buried at different locations in different monitoring areas, enabling comprehensive acquisition of underground seismic wave signals from the target coal mine. This provides a data foundation for the accurate calculation of the seismic source location and intensity level. The geological data receiving module provides a data transmission interface for the model building module, facilitating the collection and transmission of multi-dimensional geological data. The model building module, based on multi-dimensional geological data, efficiently and accurately constructs a three-dimensional geological structure model that finely characterizes the geological structure of the target coal mine. This provides a precise spatial carrier for seismic wave propagation and offers reliable technical support for subsequent calculations in the microseismic analysis module. The risk classification module uses the analytic hierarchy process (AHP) to rationally determine the weights of various factors. Simultaneously, it effectively combines fuzzy comprehensive evaluation methods to address the fuzziness and uncertainty in risk estimation, achieving quantitative risk analysis and significantly improving the matching degree between the risk level and the actual seismic source distribution. This results in a refined risk assessment, ensuring that the obtained risk level matches the actual occurrence of rockbursts. The scheme generation module allows for the selection of the optimal solution through simulation and comparison of multiple schemes. This ensures dense coverage of high-risk areas while reducing redundant deployment in low-risk areas, balancing monitoring accuracy and cost. The data processing module effectively removes noise from the signal and extracts the characteristic parameters needed for calculating the seismic source location and intensity level, reducing the computational load of the subsequent microseismic analysis module. The microseismic analysis module fully integrates signal processing and source inversion, significantly improving the accuracy of source location and the reliability of intensity level calculations. The feedback optimization module enables dynamic adaptive adjustment of the monitoring system, continuously improving long-term monitoring effectiveness and ensuring monitoring accuracy. The storage module facilitates real-time storage and retrieval of historical data.

[0127] The system is highly intelligent and reliable, and can efficiently and accurately obtain the location and intensity of the seismic source. It can provide strong technical support for early warning and prevention of rockbursts and ensure the safety of underground coal mine operations.

Claims

1. A method for monitoring microseismic events related to rockburst in coal mines, characterized in that, Includes the following steps: Step 1: Collect multi-dimensional geological data from the target coal mine, and integrate and process this data using 3D modeling technology to construct a 3D geological structure model. The process of constructing the 3D geological structure model is as follows: S11: Classify and organize multi-dimensional geological data, and establish a structured database containing spatial coordinate indexes according to data types; S12: Import the data from the structured database into the 3D modeling software, and perform meshing based on coal seam parameters and rock strata distribution data to generate the initial 3D geological framework of the layered entity; S13: Construct a spatial cutting surface based on fault information and joint development data. Use Boolean operations to fuse the spatial cutting surface with the initial three-dimensional geological framework, and add fault and joint structures to obtain the basic model. S14: Combining the spatial morphology data of historical mining records and the spatiotemporal distribution characteristics of mining pressure monitoring data, the basic model is corrected for deviations to obtain a three-dimensional geological structure model; Step 2: Divide the target coal mine into monitoring zones based on the three-dimensional geological structure model, and determine the rockburst risk level of different monitoring zones using the analytic hierarchy process combined with the fuzzy comprehensive evaluation method; Step 3: Based on the rockburst risk level of the monitoring area, set up multiple sensor deployment schemes, use Monte Carlo simulation method combined with three-dimensional geological structure model to simulate the impact of multiple sensor deployment schemes on seismic wave signal reception, and select the optimal deployment scheme. Step 4: Install sensors in each monitoring area according to the optimal deployment plan; receive downhole seismic wave signals in real time, preprocess the downhole seismic wave signals, and then extract characteristic parameters from the preprocessed downhole seismic wave signals; Step 5: Based on the characteristic parameters and combined with the three-dimensional geological structure model, analyze the propagation characteristics of the downhole seismic wave signal, and calculate the source location and intensity level; Step 6: Feed the source location and intensity level back to the three-dimensional geological structure model, evaluate the effectiveness of the optimal deployment scheme, and optimize the optimal deployment scheme based on the evaluation results.

2. The method for monitoring microseismic rockburst in coal mines according to claim 1, characterized in that, In step one, the multi-dimensional geological data includes coal seam parameters, strata distribution data, fault information, joint development data, historical mining records, and mine pressure monitoring data; the coal seam parameters include coal seam thickness, coal seam dip angle, coal seam hardness, and coal seam permeability; the strata distribution data includes strata thickness, strata lithology, and strata burial depth; the fault information includes fault strike, fault dip, fault dip angle, and fault displacement; the joint development data includes joint density, joint strike, and joint opening; and the mine pressure monitoring data includes support resistance, surrounding rock deformation, and stress values.

3. The method for monitoring microseismic rockburst in coal mines according to claim 2, characterized in that, In step two, the process for determining the rockburst risk level in different monitoring areas is as follows: S21: Construct evaluation indicators that affect the risk of rockbursts in coal mines. The evaluation indicators include the complexity of geological structures, stress concentration risk, and historical rockburst conditions. S22: The analytic hierarchy process (AHP) is used to construct the judgment matrix, and the relative importance weight of each evaluation indicator is calculated through a consistency test. S23: The fuzzy comprehensive evaluation method is used to determine the membership function of each evaluation indicator, the evaluation indicators of each monitoring area are quantitatively scored, and the comprehensive risk score of each monitoring area is obtained by weighted summation. S24: Determine the rockburst risk level of each monitoring area based on the comprehensive risk score. The rockburst risk level includes high risk, medium risk and low risk.

4. The method for monitoring microseismic rockburst in coal mines according to claim 3, characterized in that, In step three, the selection process for the optimal deployment scheme is as follows: S31: Set the basic parameters for sensor deployment according to the rockburst risk level of the monitoring area. The basic parameters include sensor density in high-risk areas, deployment interval in medium and low-risk areas, and effective monitoring radius of the sensors. S32: Based on a three-dimensional geological structure model, generate multiple deployment schemes in each monitoring area, including different numbers and spatial distributions of sensors; S33: The Monte Carlo simulation method is used to randomly generate earthquake source samples, and the propagation path of the seismic wave is simulated by combining the rock layer propagation velocity parameters. The screening index for each deployment scheme is calculated. The screening index includes signal coverage, signal-to-noise ratio and positioning error. S34: Use a weighted scoring method to comprehensively rank the screening indicators and obtain the optimal deployment scheme.

5. The method for monitoring microseismic rockburst in coal mines according to claim 4, characterized in that, In step four, the process of preprocessing the downhole seismic wave signal and extracting characteristic parameters is as follows: S41: Wavelet transform is used to denoise the downhole seismic signal and remove interference noise; S42: The sliding window method is used to segment continuous signals, and independent seismic events are identified by energy thresholds; S43: Extract the frequency characteristics, waveform parameters, seismic wave type, propagation angle, and vibration intensity data of each seismic wave event as feature parameters, and identify the effective seismic waves.

6. The method for microseismic monitoring of rockburst in coal mines according to claim 5, characterized in that, In step five, the calculation process for the focal location and intensity level is as follows: S51: Based on the time difference of arrival and propagation angle of the seismic waves in the characteristic parameters, the possible location range of the seismic source is initially calculated using the time difference positioning method; S52: Call the rock wave velocity parameters of the corresponding area in the three-dimensional geological structure model, correct the possible location range, and obtain the source coordinates; S53: An attenuation model is constructed using vibration intensity data from characteristic parameters, and the intensity level is calculated by combining the source depth. At the same time, calculation errors are reduced by integrating multiple sets of sensor data and applying cross-validation technology.

7. The method for microseismic monitoring of rockburst in coal mines according to claim 6, characterized in that, In step six, the effectiveness evaluation process of the optimal deployment scheme is as follows: S161: Calculate the matching degree between the earthquake source location and the rockburst risk level of the monitoring area according to the preset cycle, and calculate the distribution ratio of the actual earthquake source in the high-risk, medium-risk and low-risk monitoring areas; S162: If the matching degree is lower than the preset threshold, the membership function of the evaluation index is modified in combination with the source intensity level, and the relative importance weight of each evaluation index is recalculated until the matching degree is not lower than the preset threshold. S163: Analyze the sensor's reception rate of seismic source signals, count the proportion of seismic sources with signal loss or excessive signal-to-noise ratio, and evaluate the coverage effectiveness of the deployment scheme.

8. The method for monitoring microseismic rockburst in coal mines according to claim 7, characterized in that, In step six, the process of optimizing the optimal deployment scheme based on the evaluation results is as follows: S261: For monitoring areas with low signal reception, the spatial orientation and deployment depth of the sensors are adjusted by combining the distribution of faults and rock layer interfaces in the three-dimensional geological structure model. At the same time, according to the corrected evaluation index weights, the number of sensors is increased in high-risk areas and areas with dense seismic sources to expand the monitoring coverage density and obtain an optimized deployment scheme. S262: Import the optimized deployment scheme into the Monte Carlo simulation system for verification until the signal coverage, average positioning error and high-risk signal-to-noise ratio meet the preset standards.

9. A microseismic monitoring system for coal mine rockburst, used to implement the microseismic monitoring method for coal mine rockburst as described in any one of claims 1 to 8, characterized in that, Includes a data acquisition module, processor, and memory; The data acquisition module consists of multiple sets of sensors, which are respectively arranged in multiple monitoring areas underground in the target coal mine. Multiple sensors in each set are buried at different locations in the corresponding monitoring area to collect underground seismic wave signals of the target coal mine in real time and send them to the processor. The processor includes a geological data receiving module, a model building module, a risk classification module, a scheme generation module, a data processing module, a microseismic analysis module, and a feedback optimization module. The geological data receiving module is used to receive multi-dimensional geological data from the target coal mine and send it to the model building module; The model building module is used to integrate and process multi-dimensional geological data from underground coal mines using three-dimensional modeling technology to construct a three-dimensional geological structure model. The risk classification module is used to divide the target coal mine into monitoring areas based on the three-dimensional geological structure model, and to determine the rockburst risk level of different monitoring areas based on the analytic hierarchy process combined with the fuzzy comprehensive evaluation method. The scheme generation module is used to simulate the impact of various preset sensor deployment schemes on seismic signal reception using Monte Carlo simulation combined with a three-dimensional geological structure model, and to select the optimal deployment scheme. The data processing module is used to denoise downhole seismic signals and extract feature parameters from the denoised signals. The microseismic analysis module is used to analyze the propagation characteristics of downhole seismic wave signals based on characteristic parameters and a three-dimensional geological structure model, and to calculate the source location and intensity level. The feedback optimization module is used to feed back the source location and intensity level to the three-dimensional geological structure model, evaluate the effectiveness of the optimal deployment scheme, and optimize the optimal deployment scheme based on the evaluation results. The memory is used by the processor to store and retrieve data.

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