Coal mine rock burst micro-seismic monitoring method and system

Abstract

The invention discloses a coal mine rock burst micro-seismic monitoring method and system. The method comprises the following steps: collecting multi-dimensional geological data to construct a three-dimensional geological structure model; performing monitoring area division on the target coal mine, and analyzing rock burst risk grades of different monitoring areas; screening an optimal layout scheme; receiving underground seismic wave signals of the target coal mine in real time; calculating a focus position and an intensity grade; evaluating the effectiveness of the optimal layout scheme, and optimizing the optimal layout scheme; according to the system, a data acquisition module comprises multiple groups of sensors; the geological data receiving module is used for receiving multi-dimensional geological data; the model construction module is used for constructing a three-dimensional geological structure model; the risk division module is used for determining a rock burst risk level; the scheme generation module is used for screening an optimal layout scheme; the data processing module is used for processing the signals; the micro-seismic analysis module is used for calculating a seismic source position and an intensity grade; and the feedback optimization module is used for optimizing the optimal layout scheme. According to the invention, accurate positioning of the seismic source and accurate calculation of the intensity can be realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent monitoring of coal mine rock burst, and in particular provides a method and system for microseismic monitoring of coal mine rock burst. Background Art

[0002] Coal mine rock burst is a violent dynamic phenomenon caused by the sudden release of rock stress during mining. It is characterized by strong suddenness and destructive power, and seriously threatens the safety of underground operations.

[0003] Existing microseismic monitoring technology has many limitations, including insufficient geological data integration capabilities, making it difficult to reflect the true impact of complex geological structures on seismic wave propagation, resulting in large monitoring blind spots; the lack of a dynamic optimization mechanism for sensor deployment, which is usually simply arranged at a fixed density, resulting in insufficient coverage or serious waste of resources, affecting the accuracy of earthquake source positioning; the poor match between risk assessment and actual earthquake source distribution, with traditional methods often using static indicators to divide risk areas, failing to effectively combine real-time seismic wave data for feedback adjustment, and prone to misjudgment; the separation of signal processing and earthquake source inversion, failing to fully utilize the physical parameters of the rock formation to correct the propagation path, resulting in large positioning errors. The above problems make it difficult for existing technologies to meet the needs of accurate monitoring of rock burst under complex geological conditions. In order to effectively solve these problems, it is urgent to provide a method and system for microseismic monitoring of coal mine rock burst. Summary of the Invention

[0004] In response to the problems existing in the above-mentioned prior art, the present invention provides a method and system for microseismic monitoring of coal mine rock burst. The method has a simple implementation process, low implementation cost, and high monitoring accuracy. It can accurately locate the earthquake source and calculate the earthquake source intensity. The system has a high degree of intelligence and reliable performance. It can efficiently and accurately obtain the earthquake source position and earthquake source intensity level, and can provide strong technical support for rock burst warning and prevention and control, and can ensure the safety of underground coal mine operations.

[0005] In order to achieve the above object, the present invention provides a method for monitoring microseismic rock burst in coal mines, comprising the following steps: Step 1: Collect multi-dimensional geological data of the target coal mine and integrate and process the multi-dimensional geological data using 3D modeling technology to construct a 3D geological structure model; Step 2: Divide the target coal mine into monitoring areas based on the 3D geological structure model, and use the analytic hierarchy process combined with the fuzzy comprehensive evaluation method to determine the rock burst risk level of different monitoring areas; Step 3: Based on the rock burst risk level of the monitoring area, multiple sensor deployment schemes are set. Monte Carlo simulation is used in conjunction with a 3D geological structure model to simulate the impact of various sensor deployment schemes on seismic signal reception, and the optimal deployment scheme is selected. Step 4: Install sensors in each monitoring area according to the optimal layout plan; receive downhole seismic wave signals in real time, pre-process the downhole seismic wave signals, and then extract characteristic parameters from the pre-processed downhole seismic wave signals; Step 5: Based on the characteristic parameters and combined with the 3D geological structure model, the propagation characteristics of the downhole seismic wave signal are analyzed to calculate the source location and intensity level; Step 6: Feedback the earthquake source location and intensity level to the three-dimensional geological structure model, evaluate the effectiveness of the optimal layout plan, and optimize the optimal layout plan based on the evaluation results.

[0006] Furthermore, in order to achieve accurate monitoring of rock burst microseismicity, in step one, the multi-dimensional geological data include coal seam parameters, rock layer 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 inclination, coal seam hardness and coal seam permeability; the rock layer distribution data include rock layer thickness, rock layer lithology and rock layer burial depth; the fault information includes fault strike, fault dip, fault inclination and fault drop; 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 value.

[0007] Furthermore, in order to construct a three-dimensional geological structure model that can accurately reflect the geological structure of the target area, in step 1, the process of constructing the three-dimensional 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: Importing the data in the structured database into the 3D modeling software, and performing grid processing based on the coal seam parameters and rock layer distribution data to generate an initial 3D geological framework of the layered entity; S13: Construct a spatial cutting surface based on fault information and joint development data, fuse the spatial cutting surface with the initial 3D geological framework through Boolean operations, and add fault and joint structures to obtain the basic model; S14: Combining the spatial morphological 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.

[0008] Furthermore, in order to accurately classify the rock burst risk level of the monitoring area, in step 2, the rock burst risk level of different monitoring areas is determined as follows: S21: Constructing evaluation indicators that affect the risk of rock burst in coal mines, including geological structure complexity, stress concentration risk, and historical rock burst conditions; S22: Use the analytic hierarchy process to construct a judgment matrix, and calculate the relative importance weight of each evaluation indicator through consistency test; S23: Use the fuzzy comprehensive evaluation method to determine the membership function of each evaluation indicator, quantify the evaluation indicators of each monitoring area, and obtain the comprehensive risk score of each monitoring area through weighted summation; S24: Determine the rock burst risk level of each monitoring area based on the comprehensive risk score, where the rock burst risk levels include high risk, medium risk, and low risk.

[0009] Furthermore, in order to obtain the optimal layout plan more efficiently and accurately through simulation, and to effectively reduce investment costs, in step three, the screening process of the optimal layout plan is as follows: S31: Setting basic parameters for sensor deployment according to the rock burst risk level of the monitoring area, the basic parameters including sensor density in high-risk areas, deployment intervals in medium- and low-risk areas, and effective sensor monitoring radius; S32: Based on the 3D geological structure model, generate multiple deployment schemes with different sensor numbers and spatial distributions in each monitoring area; S33: using a Monte Carlo simulation method to randomly generate earthquake source samples, simulating the seismic wave propagation path in combination with rock formation propagation velocity parameters, and calculating screening indicators for each layout scheme, wherein the screening indicators include signal coverage, signal-to-noise ratio, and positioning error; S34: Use the weighted scoring method to comprehensively rank the screening indicators and obtain the optimal layout plan.

[0010] Furthermore, in order to efficiently remove noise data from the signal, effectively reduce the amount of calculation required for calculating the source location and intensity level, and ensure the accuracy of the calculation results, in step 4, the process of preprocessing the downhole seismic wave signal and extracting characteristic parameters is as follows: S41: using the wavelet transform method to denoise the downhole seismic signal and remove the interference noise; S42: Segment the continuous signal using a sliding window method and identify independent shock wave events using energy thresholds; S43: extracting the frequency characteristics, waveform parameters, type of seismic wave, propagation angle and vibration intensity data of each seismic wave event as characteristic parameters, and identifying effective seismic waves.

[0011] Furthermore, in order to obtain the calculation results efficiently and accurately, in step 5, the calculation process of the earthquake source location and intensity level is as follows: S51: Based on the arrival time difference and propagation angle of the shock waves in the characteristic parameters, the possible location range of the earthquake source is preliminarily calculated using the time difference positioning method; S52: calling the rock stratum wave velocity parameters of the corresponding area in the three-dimensional geological structure model, correcting the possible position range, and obtaining the earthquake source coordinates; S53: An attenuation model is constructed using the vibration intensity data in the characteristic parameters, and the intensity level is calculated based on the focal depth. At the same time, the calculation error is reduced by integrating multiple sets of sensor data and applying cross-validation technology.

[0012] Furthermore, in order to ensure the effectiveness of the optimal layout plan, in step 6, the effectiveness evaluation process of the optimal layout plan is as follows: S161: Count the matching degree between the earthquake source location and the rock burst risk level of the monitoring area according to the preset period, 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 earthquake 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 the source signal, count the proportion of sources with signal loss or excessive signal-to-noise ratio, and evaluate the effectiveness of the deployment plan coverage.

[0013] Furthermore, in order to ensure the reliability and accuracy of long-term monitoring through dynamic adaptive adjustment, in step 6, the process of optimizing the optimal layout plan based on the evaluation results is as follows: S261: For monitoring areas with low signal reception rates, the spatial orientation and deployment depth of sensors are adjusted based on the distribution of faults and rock interfaces in the 3D geological structure model. Furthermore, based on the revised evaluation index weights, the number of sensors is increased in high-risk areas and areas with dense earthquake sources to expand the monitoring coverage density and obtain an optimized deployment plan. S262: The optimized deployment plan is imported 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.

[0014] The present invention provides a method for monitoring coal mine rock burst microseismic pressure. First, through multi-dimensional geological data integration and three-dimensional modeling, a refined representation of the coal mine's geological structure is achieved. This method can reflect the true impact of complex geological structures on seismic wave propagation, provide a precise spatial carrier for subsequent seismic wave propagation analysis, and effectively reduce monitoring errors caused by missing geological information. Next, a hierarchical analysis method and a fuzzy comprehensive evaluation method are used to divide risk zones. Evaluation indicators are dynamically corrected using real-time source data, significantly improving the matching degree between risk levels and actual source distribution, providing a scientific basis for sensor deployment. Furthermore, a sensor deployment optimization mechanism based on Monte Carlo simulations selects the optimal solution by comparing multiple solutions. This ensures dense coverage in high-risk areas while reducing redundant deployment in low-risk areas. This effectively avoids the inadequate coverage and severe waste of sensor resources that can occur with traditional deployment methods that rely on manual experience. While ensuring monitoring accuracy, it also effectively balances the investment cost of monitoring resources. Finally, an inversion method that effectively integrates seismic wave signal preprocessing with a three-dimensional geological model is used. Rock stratum wave velocity parameters are used to correct the propagation path. Combined with multi-sensor cross-validation, this method significantly improves the accuracy of source location and the reliability of intensity level calculation. Finally, through the closed-loop mechanism of the earthquake source data feedback optimization model and layout plan, dynamic adaptive adjustment of the monitoring process was achieved, the long-term monitoring efficiency was continuously improved, the monitoring error was significantly reduced, the accuracy of monitoring was ensured, and strong support was provided for the early warning and prevention and control decisions of coal mine rock burst, which significantly enhanced the safety of underground operations.

[0015] This method has a simple implementation process, low implementation cost, and high monitoring accuracy, and can achieve accurate positioning of the earthquake source and accurate calculation of the earthquake source intensity.

[0016] The present invention also provides a coal mine rock burst microseismic monitoring system for implementing a coal mine rock burst microseismic monitoring method, comprising a data acquisition module, a processor and a memory; The data acquisition module comprises multiple sets of sensors, each of which is arranged in multiple monitoring areas in the target coal mine. Multiple sensors in each set of sensors are buried in different positions in the corresponding monitoring area, and are used 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 solution 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 of the target coal mine and send it to the model building module; The model building module is used to integrate and process the multi-dimensional geological data of the coal mine using three-dimensional modeling technology to build a three-dimensional geological structure model; The risk division module is used to divide the target coal mine into monitoring areas according to the three-dimensional geological structure model, and determine the rock burst risk level of different monitoring areas based on the hierarchical analysis method combined with the fuzzy comprehensive evaluation method; The scheme generation module is used to simulate the impact of various preset sensor layout schemes on seismic signal reception using a Monte Carlo simulation method combined with a three-dimensional geological structure model, and screen out the optimal layout scheme; The data processing module is used to perform denoising on the downhole seismic wave signal and to extract characteristic parameters from the denoised signal; The microseismic analysis module is used to analyze the propagation characteristics of downhole seismic wave signals based on characteristic parameters and in combination with a three-dimensional geological structure model, and calculate the source location and intensity level; The feedback optimization module is used to feed back the earthquake source location and intensity level to the three-dimensional geological structure model, and evaluate the effectiveness of the optimal layout plan. At the same time, the optimal layout plan is optimized according to the evaluation results; The memory is used for storing and reading data by the processor.

[0017] In the present invention, multiple sensors in the data acquisition module are buried at different locations in different monitoring areas, which can comprehensively collect the underground seismic wave signals of the target coal mine, providing a data basis for the accurate calculation of the source location and intensity level. Through the setting of the geological data receiving module, a data transmission interface can be provided for the model construction module, and multi-dimensional geological data can be easily collected and sent to the model construction module. Through the setting of the model construction module, a three-dimensional geological structure model that can accurately characterize the geological structure of the target coal mine can be efficiently and accurately constructed based on the multi-dimensional geological data, which can provide an accurate spatial carrier for seismic wave propagation and provide reliable technical support for the subsequent calculation process of the microseismic analysis module. Through the setting of the risk division module, the weight of each factor can be reasonably determined by the hierarchical analysis method. At the same time, the fuzzy comprehensive evaluation method can be effectively combined to deal with the ambiguity and uncertainty in the risk estimation, realize the quantitative analysis of the risk, and significantly improve the matching degree between the risk level and the actual source distribution. Thus, a detailed risk assessment result can be obtained, ensuring that the obtained risk level is consistent with the actual rock burst occurrence. By setting up the solution generation module, the optimal solution can be obtained from multiple solution comparison and screening through simulation. While ensuring dense coverage of high-risk areas, redundant deployment in low-risk areas is reduced, balancing monitoring accuracy and cost. By setting up the data processing module, noise data in the signal can be effectively removed. At the same time, the characteristic parameters required for the calculation of the source location and intensity level can be extracted, which is conducive to reducing the amount of calculation of the subsequent microseismic analysis module. By setting up the microseismic analysis module, the signal processing and source inversion processes can be fully integrated, greatly improving the source location accuracy and the reliability of the intensity level calculation. By setting up the feedback optimization module, dynamic adaptive adjustment of the monitoring system can be achieved through feedback optimization, continuously improving the long-term monitoring efficiency, and ensuring the accuracy of monitoring. By setting up the memory, real-time storage and reading of historical data can be facilitated.

[0018] The system is highly intelligent and reliable in performance. It can obtain the earthquake source location and intensity level efficiently and accurately, providing strong technical support for rock burst warning and prevention, and ensuring the safety of underground operations in coal mines. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of the monitoring method of the present invention; Figure 2 This is a flow chart for screening the optimal layout scheme in the present invention; Figure 3 is a flow chart for calculating the earthquake source location and intensity level in the present invention; Figure 4 It is a flow chart of the effectiveness evaluation of the optimal layout scheme in the present invention; Figure 5It is a principle block diagram of the monitoring system in the present invention. DETAILED DESCRIPTION

[0020] The present invention will be further described below with reference to the accompanying drawings.

[0021] like Figure 1 As shown, the present invention provides a method for monitoring microseismic rock burst in coal mines, comprising the following steps: Step 1: Collect multi-dimensional geological data of the target coal mine and integrate and process the multi-dimensional geological data using 3D modeling technology to construct a 3D geological structure model; In order to achieve accurate monitoring of impact ground pressure microseismicity, the multi-dimensional geological data includes coal seam parameters, rock layer 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 inclination, coal seam hardness and coal seam permeability; the rock layer distribution data includes rock layer thickness, rock layer lithology and rock layer burial depth; the fault information includes fault direction, fault dip, fault inclination and fault drop; the joint development data includes joint density, joint direction and joint opening; the mine pressure monitoring data includes support resistance, surrounding rock deformation and stress value.

[0022] In order to construct a three-dimensional geological structure model that can accurately reflect the geological structure of the target area, the process of constructing the three-dimensional 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 (such as coal seam parameters, rock layer distribution data, etc.); for coal seam parameters, the coal seam thickness, inclination and other data of different detection points need to be associated with the corresponding downhole coordinates (X, Y, Z three-dimensional coordinates); for fault information, the spatial coordinate range of each fault and the spatial changes of parameters such as strike and dip 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.

[0023] S12: Import the data in the structured database into 3D modeling software (such as Surpac, Micromine, etc.), and perform gridding processing based on the coal seam parameters and rock layer distribution data to generate an initial 3D geological framework of the layered entity; specifically, first generate the top and bottom 3D curved surfaces of the coal seam based on the elevation data of the coal seam roof and floor; then, based on the rock layer distribution data, generate the top and bottom curved surfaces of each rock layer according to the lithology, and form a layered entity by stretching the curved surface to construct an initial 3D geological framework that reflects the spatial distribution of the coal seam and each rock layer.

[0024] S13: Construct spatial cutting surfaces based on fault information and joint development data. For faults, based on parameters such as strike, dip, and inclination, a fault plane is generated in three-dimensional space as the cutting surface. The fault plane must extend to the affected coal seams and rock formations. For joints, based on data such as joint density and strike, multiple joint planes are generated at regular intervals as cutting surfaces. Boolean operations are used to merge the spatial cutting surface with the initial three-dimensional geological framework, segmenting the portion of the framework that is traversed by the cutting surface. Fault and joint structures are then added to the model to obtain a basic model that reflects the geological structural fracture zone. S14: Based on the spatial morphological data of historical mining records (including the location, scope, and mining depth of the mined working face, the direction and cross-sectional dimensions of the tunnel, etc.) and the spatiotemporal distribution characteristics of the mine pressure monitoring data (including the trend of changes in support resistance in different time periods and locations, and the accumulation of surrounding rock deformation), 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 the rock strata in the corresponding area in the model is adjusted; based on the changes in the mechanical properties of the rock mass reflected by the mine pressure monitoring data, the physical and mechanical parameters of the relevant rock strata in the model (including elastic modulus, Poisson's ratio, etc.) are corrected, and finally a three-dimensional geological structure model that can accurately reflect the current geological conditions and stress characteristics of the target coal mine is obtained.

[0025] Step 2: Divide the target coal mine into monitoring areas based on the 3D geological structure model, and use the analytic hierarchy process combined with the fuzzy comprehensive evaluation method to determine the rock burst risk level of different monitoring areas; Using the three-dimensional geological structure model as the spatial carrier, combined with the underground mining layout of the coal mine (such as the distribution of the mining face and the tunneling tunnel) and the geological structural characteristics (such as the spatial range of the fault zone and the joint-dense area), the grid division method is used to divide the underground area of ​​the coal mine into several basic monitoring units. The side length of each unit is set to 5-10 meters (which can be adjusted according to the actual scale of the coal mine and the geological complexity) to ensure that the geological parameters within each unit (such as coal seam thickness, rock lithology, stress distribution, etc.) are uniform; then, through the spatial topological relationship of the three-dimensional model, adjacent basic monitoring units with similar geological characteristics are merged to form a monitoring area of ​​100-500 square meters. At the same time, it is ensured that each monitoring area contains a complete geological structural unit (such as the influence range of a fault, a joint-dense zone) and a mining engineering unit (such as a certain area of ​​a working face), and each monitoring area is given a unique spatial code to associate the coordinate information and geological parameters in the three-dimensional geological structure model.

[0026] In order to accurately classify the rock burst risk level in the monitoring area, the analytic hierarchy process combined with the fuzzy comprehensive evaluation method is used to determine the rock burst risk level in different monitoring areas. The process is as follows: S21: Construct evaluation indicators that affect the risk of rock burst in coal mines, including the complexity of geological structure, stress concentration risk and historical rock burst; among them, the complexity of geological structure mainly considers the number and scale of faults in the monitoring area (a drop greater than 5 meters is a large fault), joint density (greater than 5 joints / square meter is dense) and the degree of rock fragmentation (a fracture zone thickness exceeding 30% is high fragmentation); stress concentration risk is based on the mine pressure monitoring data in the three-dimensional geological structure model, covering the ratio of current stress value to rock compressive strength (greater than 0.8 is a high stress ratio), stress gradient change rate (a monthly increase rate exceeding 10% is a high gradient) and mining stress superposition area (such as the superposition area of ​​working face advance support pressure and fault stress); historical rock burst includes the number of rock bursts in the monitoring area in the past five years (3 times or more is high frequency), the maximum impact intensity (a magnitude greater than 2.0 is a strong impact) and the time of the most recent occurrence (within 1 year is a recent occurrence).

[0027] S22: A judgment matrix is ​​constructed using the analytic hierarchy process (AHP) and the relative importance weight of each evaluation indicator is calculated through consistency test. Specifically, 5-7 experts in the fields of coal mine geology, mining engineering and rock burst prevention are first invited to compare the indicators in pairs according to the degree of influence of each indicator on the occurrence of rock burst (using a 1-9 scale: 1 indicates equal importance, 3 indicates slightly more important, 5 indicates obviously more important, 7 indicates strongly important, 9 indicates extremely important, and vice versa), to form a judgment matrix. The maximum eigenvalue and corresponding eigenvector of the judgment matrix are then calculated, and 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., a weight of 0.4 for geological structure complexity, a weight of 0.4 for stress concentration risk, and a weight of 0.2 for historical rock burst).

[0028] 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, and 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 / m2 and the proportion of rock fracture zones is less than 10%, the membership is 0 (corresponding to a score of 0-30 points); if there is 1 large fault, the joint density is 2-5 / m2 and the proportion of fracture zones is 10%-30%, the membership is 0.5 (corresponding to a score of 30-70 points); if there are 2 or more large faults, the joint density is greater than 5 / m2 and the proportion of fracture zones exceeds 30%, the membership is 1 (corresponding to a score of 70-100 points). Similarly, corresponding membership functions and scoring intervals are set for stress concentration risks and historical rock burst conditions. Subsequently, the membership of each indicator is determined and the corresponding score is assigned based on the actual geological data and three-dimensional model parameters of the monitoring area. The score of each indicator is then multiplied by the weight and summed to obtain a comprehensive risk score (the full score is 100 points).

[0029] S24: Determine the rock burst risk level for each monitoring area based on the comprehensive risk score, which includes high risk, medium risk, and low risk. Specifically, a score of 70 or higher is set as a high-risk area. This area has complex geological structures, obvious stress concentration, and may have historical rock burst records, and requires key monitoring; a score of 30-70 is set as a medium-risk area. This area has certain abnormalities in geological structure or stress state and requires routine monitoring; a score of <30 is set as a low-risk area. This area has stable geological conditions, uniform stress distribution, and no historical rock burst records, and the monitoring frequency can be reduced. At the same time, the risk level information is associated with the three-dimensional geological structure model and visualized in different colors (red for high risk, yellow for medium risk, and green for low risk).

[0030] Step 3: Based on the rock burst risk level of the monitoring area, multiple sensor deployment schemes are set. Monte Carlo simulation is used in conjunction with a 3D geological structure model to simulate the impact of various sensor deployment schemes on seismic signal reception, and the optimal deployment scheme is selected. like Figure 2 As shown in the figure, in order to obtain the optimal layout plan more efficiently and accurately through simulation, and at the same time, to effectively reduce the investment cost, the screening process of the optimal layout plan is as follows: S31: The basic parameters for sensor deployment are set according to the rock burst risk level of the monitoring area. The basic parameters include the sensor density in high-risk areas, the deployment interval in medium- and low-risk areas, and the effective monitoring radius of the sensor. The high-risk area adopts a high-density deployment standard, with the sensor density set at 1 per 500 square meters and the deployment interval controlled within the range of 20-30 meters to ensure intensive capture of seismic wave signals. The medium-risk area adopts a conventional density deployment, with 1 sensor deployed per 1,000 square meters and a deployment interval of 30-50 meters. The low-risk area adopts a sparse density deployment, with 1 sensor deployed per 2,000 square meters and a deployment interval of 50-80 meters. At the same time, combined with the physical and mechanical properties of the rock formations 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 formations (such as sandstone), the effective monitoring radius is set to 80-100 meters; in softer rock formations (such as shale), the effective monitoring radius is set to 50-70 meters; 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.

[0031] S32: Based on the 3D geological structure model, multiple sensor deployment schemes with varying numbers and spatial distributions are generated within each monitoring area. Specifically, using the spatial coordinates of the 3D geological structure model as a reference, the sensor deployment boundaries are delineated within each monitoring area (avoiding obstacles such as tunnel support structures and equipment foundations). For high-risk areas, three to five different deployment schemes are generated: the basic scheme fully deploys sensors at a set density; Optimization Scheme 1 increases the number of sensors by 10%; Optimization Scheme 2 increases the number of sensors by 50% at key locations such as faults and dense joints; and Optimization Scheme 3 attempts a non-uniform distribution (adjusting density based on areas of stress concentration). For medium- and low-risk areas, two to three schemes are generated, each including a uniformly spaced deployment scheme, a directional deployment scheme along geological tectonic lines, and a deployment scheme dynamically adjusted based on mining progress. Each sensor deployment scheme is marked with specific coordinates in the 3D model and associated with rock formation parameters (such as lithology and wave velocity) at the location, forming a visual deployment model.

[0032] S33: Monte Carlo simulation is used to randomly generate source samples. The propagation paths of seismic waves are simulated using rock formation propagation velocity parameters. Screening metrics for each placement scheme are calculated, including signal coverage, signal-to-noise ratio, and positioning error. Specifically, within a 3D geological structure model, 1,000-2,000 virtual source samples are randomly generated based on the probability distribution of seismic sources in different risk zones (60% of samples are in high-risk zones, 30% in medium-risk zones, and 10% in low-risk zones). Each sample includes the source coordinates, magnitude (0.5-3.0), and the time of occurrence. Ray tracing is used to simulate the propagation paths of seismic waves from each virtual source to each sensor, based on the P- and S-wave propagation velocity parameters of each rock formation in the 3D model (e.g., P-wave velocity of 3,500-5,000 m / s for sandstone and 1,500-2,500 m / s for coal seams). Arrival time, propagation attenuation coefficient, and signal distortion due to geological structures (e.g., reflection and refraction at faults) are calculated. Based on the simulation results, the screening indicators of each scheme were calculated: the signal coverage rate is the proportion of source samples that can receive effective signals (amplitude exceeds the noise threshold) to the total samples; the 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 is taken); the positioning error is the spatial distance between the source position inverted by sensor data and the actual coordinates of the virtual source (the root mean square error of all samples is taken).

[0033] S34: A weighted scoring method was used to comprehensively rank the screening indicators to determine the optimal deployment plan. Specifically, weights were first determined for each screening indicator: signal coverage was assigned a weight of 0.4 (prioritizing effective capture of the earthquake source), signal-to-noise ratio was assigned a weight of 0.3 (ensuring signal quality to improve positioning accuracy), and positioning error was assigned a weight of 0.3 (directly reflecting monitoring accuracy). The indicator values ​​for each plan were then normalized (normalized to the interval [0, 100]). Signal coverage and signal-to-noise ratio were treated as positive indicators (higher values ​​yield higher scores), while positioning error was treated as negative indicators (lower values ​​yield higher scores). Finally, a comprehensive score was calculated for each plan based on the weights (comprehensive score = signal coverage score × 0.4 + signal-to-noise ratio score × 0.3 + positioning error score × 0.3). The plan with the highest comprehensive score was selected as the optimal deployment plan. If multiple plans had similar scores (a difference of less than 5 points), the positioning error indicators for high-risk areas were further compared. The plan with the highest monitoring accuracy for the high-risk area was selected as the final optimal deployment plan. The sensor coordinates, number, and distribution characteristics of this plan were then exported to the 3D geological structure model.

[0034] Step 4: Install sensors in each monitoring area according to the optimal layout plan. Specifically, according to the sensor coordinates marked in the 3D geological structure model, drill holes or fix the sensors on brackets at the corresponding locations underground to ensure that the orientation of the sensor probe matches the rock formation trend (for example, adjust the probe angle to 45° near the fault zone to enhance the receiving sensitivity to obliquely propagating seismic waves). After installation, perform signal calibration and verify the sensor response performance through artificial excitation (such as hammering the coal seam) to ensure that the sampling frequency (set to 1000-2000Hz) and dynamic range (≥120dB) of all sensors are consistent, forming a seismic signal receiving network covering the entire monitoring area, receiving seismic wave signals from the target coal mine in real time, and transmitting the signals to the ground processing system in the form of a time series.

[0035] The downhole seismic wave signal is received in real time, pre-processed, and then characteristic parameters are extracted from the pre-processed downhole seismic wave signal. Specifically, in order to efficiently remove noise data from the signal, effectively reduce the amount of calculation in the source location and intensity level calculation process, and ensure the accuracy of the calculation results, the process of pre-processing the downhole seismic wave signal and extracting characteristic parameters is as follows: S41: First, analyze the noise types in the original signal, including mechanical vibrations generated by mining operations, electromagnetic interference from motor equipment, and cable transmission noise. Next, use wavelet transform to denoise the downhole seismic signal and remove interference noise. S42: Use the sliding window method to segment the continuous signal, and identify independent shock wave events through energy thresholds; specifically, set the window length to 1-2 seconds, the window sliding step to 0.5 seconds, and calculate the root mean square energy of the signal in each window; determine the energy threshold based on historical data statistics (3-5 times the normal background noise energy), and when the energy of three consecutive windows exceeds the threshold, it is determined as the starting point of the shock wave event; when the window energy falls below the threshold and lasts for two windows, it is determined as the end point of the event, thereby segmenting the continuous signal into independent shock wave event segments, each segment containing the complete occurrence process of the shock wave.

[0036] S43: Extract the frequency characteristics, waveform parameters, wave type, propagation angle, and vibration intensity data of each seismic event as characteristic parameters, and identify valid seismic waves. Specifically, Fourier transform the segmented seismic event segments to obtain the spectral distribution, and extract features such as the dominant frequency and frequency bandwidth. Rock burst seismic waves typically have a dominant frequency between 10 and 100 Hz and a wide bandwidth; whereas, the dominant frequencies of interference signals such as blasting and mechanical vibration are often concentrated in specific frequency bands. Waveform parameters are also extracted, including peak amplitude, rise time, and duration. Rock burst seismic waves have a short rise time (typically <0.5 seconds) and a long duration (1-5 seconds), and their peak amplitude decays regularly with distance. These features are compared with a pre-set rock burst seismic wave feature library (matching is calculated using cosine similarity). If the match is ≥70%, the seismic wave is identified as a valid seismic wave; otherwise, it is marked as an interference signal and discarded.

[0037] After the above preprocessing, the characteristic parameters of the effective shock wave are extracted: the type of shock wave (differentiated according to the main frequency and waveform characteristics, such as coal rock impact, roof fracture, etc.), the propagation angle (the angle between the shock wave propagation direction and the horizontal plane is calculated by the time difference and spatial coordinates of the signals received by multiple sensors, and the triangulation positioning method is used to calculate the angle), and the vibration intensity (the peak amplitude is converted into magnitude).

[0038] Step 5: Based on the characteristic parameters and combined with the 3D geological structure model, the propagation characteristics of the downhole seismic wave signal are analyzed to calculate the source location and intensity level; like Figure 3 As shown, in order to obtain the calculation results efficiently and accurately, the calculation process of the earthquake source location and intensity level is as follows: S51: Based on the characteristic parameters of the seismic wave arrival time difference and propagation angle, the time difference positioning method is used to preliminarily calculate the possible location range of the earthquake source. Specifically, the arrival time of the first wave of the seismic wave recorded by each sensor (the arrival time of the P wave) is first extracted. Using one sensor as the reference (usually the sensor with the strongest signal), the arrival time difference between the remaining sensors and the reference sensor is calculated. Based on the propagation law of seismic waves in a homogeneous medium, a set of equations is established, as shown in formula (1). (1); Where (x, y, z) is the coordinate of the earthquake source, (x i ,y i ,z i ) and (x j ,y j ,z j ) are the coordinates of sensor i and sensor j respectively, v is the preset average wave velocity, represents the arrival time difference between sensors i and j.

[0039] By solving this nonlinear system of equations, we obtain the initial three-dimensional coordinate range of the earthquake source. At the same time, we construct spatial angle constraints based on the propagation angles calculated by each sensor, narrowing the range of possible locations and forming an initial positioning cube.

[0040] S52: The rock velocity parameters for the corresponding area in the 3D geological structure model are used to correct the possible location range and obtain the earthquake source coordinates. Specifically, the rock distribution data within the initial positioning range is extracted from the 3D geological structure model, including the P-wave velocity of each rock layer, the spatial position of rock interface, and the distribution of faults. A layered ray tracing method is used to simulate the propagation path of seismic waves in heterogeneous media. The refraction angle of the seismic wave passing through the rock interface is calculated based on Snell's law. The theoretical arrival times of different sensors are corrected by factoring in the reflection / scattering effects of faults on the seismic wave. The corrected time differences are re-substituted into the positioning equations and solved iteratively using the least squares method (≥5 iterations until the error is less than 0.1 ms) to obtain more accurate earthquake source coordinates (x0, y0, z0). If the earthquake source is located in a fault zone or densely jointed area, further geological structural factors (such as a velocity reduction factor of 0.7-0.9 for the fault fracture zone) are required to fine-tune the coordinates to ensure that the positioning results are consistent with the geological characteristics of the 3D geological structure model.

[0041] S53: The attenuation model is constructed using the vibration intensity data in the characteristic parameters, and the intensity level is calculated in combination with the focal depth. At the same time, the calculation error is reduced by integrating multiple sets of sensor data and applying cross-validation techniques. Specifically, the effective peak amplitude of the shock wave recorded by each sensor is first collected. Combined with the straight-line distance between the sensor and the focal point, the amplitude attenuation model is constructed using a power function fitting, as shown in Formula (2). (2); Where 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 1.5-2.5), and α is the medium absorption coefficient (related to the lithology, α for coal seams = 0.05-0.15 / km, α for sandstones = 0.02-0.08 / km).

[0042] Model parameters were determined through least-squares fitting, and the initial amplitude A0 at the earthquake source was inferred. Combined with the focal depth h, A0 was converted to Richter magnitude. To verify accuracy, a cross-validation method was used, removing the data from one sensor at a time and recalculating the magnitude using the remaining sensors. If the standard deviation of all results was ≤0.2, the magnitude level was considered valid. Otherwise, the sensor with the largest deviation was removed and the data was refitted. The final magnitude level of the earthquake source was determined and correlated with parameters such as the lithology and stress state of the earthquake source location in the 3D geological structure model to form a complete earthquake source information report.

[0043] Step 6: Feedback the earthquake source location and intensity level to the three-dimensional geological structure model, evaluate the effectiveness of the optimal layout plan, and optimize the optimal layout plan based on the evaluation results.

[0044] like Figure 4 As shown, in order to ensure the effectiveness of the optimal layout plan, the effectiveness evaluation process of the optimal layout plan is as follows: S161: Calculate the degree of match between the earthquake source locations and the rock burst risk level of the monitoring area at a preset interval (e.g., weekly), and calculate the distribution ratio of actual earthquake sources in high-risk, medium-risk, and low-risk monitoring areas. Specifically, first spatially overlay the coordinates of all earthquake source locations with the risk zoning layers in the 3D geological structure model, and calculate the proportion of the number of actual earthquake sources in high-risk areas to the total number of earthquake sources (theoretically, it should be ≥60%), the proportion in medium-risk areas (should be 20%-40%), and the proportion in low-risk areas (should be ≤20%). Quantify the degree of match by calculating the deviation between the actual distribution ratio and the theoretical risk ratio.

[0045] S162: If the matching degree is lower than the preset threshold, the membership function of the evaluation index is modified in combination with the earthquake 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; If the matching degree falls below a preset threshold (e.g., 0.7), the risk assessment model is considered biased and requires correction. For example, if high-intensity earthquake sources (magnitude ≥ 2.0) frequently occur in a low-risk area, the weight of the stress concentration risk indicator is increased (e.g., from 0.4 to 0.5), and its membership function is revised: stress value / compressive strength > 0.6 is now considered high stress (the original standard was > 0.8), expanding the membership range of the high-stress range. Experts are invited to reconstruct the judgment matrix, and after consistency testing, the weights of each indicator are updated to ensure that the risk classification is more consistent with the actual earthquake source distribution characteristics.

[0046] S163: Analyze the sensor reception rate of source signals and calculate the percentage of sources experiencing signal loss or excessive signal-to-noise ratios to assess the effectiveness of the deployment plan. Specifically, for each source, check whether it is effectively captured by at least three sensors (signal-to-noise ratio ≥ 10dB). If a source is only received by two or fewer sensors, it is considered signal loss; if the signal-to-noise ratio of the received signal is less than 10dB, the signal quality is considered substandard. Calculate the proportion of these two types of sources relative to the total number of sources (the preset qualification standard is ≤5%). If the proportion exceeds 10%, the sensor deployment in this area is determined to have coverage blind spots or severe signal attenuation, requiring targeted optimization.

[0047] In order to ensure the reliability and accuracy of long-term monitoring through dynamic adaptive adjustment, the process of optimizing the optimal layout plan based on the evaluation results is as follows: S261: For monitoring areas with low signal reception, the sensor's spatial orientation and placement depth are adjusted based on the distribution of faults and rock interfaces in the 3D geological structure model. For example, near a fault fracture zone, the sensor probe's orientation is adjusted to a 30° angle with the fault strike to enhance reception of refracted waves. Above and below the rock interface, the sensor's placement depth is increased by 1-2 meters into the intact rock layer to reduce interfacial reflection interference. The adjusted signal propagation path is simulated using the 3D model to verify the effectiveness of the adjustment.

[0048] At the same time, based on the revised evaluation indicator weights, the number of sensors was increased in high-risk areas and areas with dense earthquake sources, expanding monitoring coverage density and resulting in an optimized deployment plan. In 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 a 10-meter x 10-meter grid with dense earthquake sources. For localized areas within medium-risk zones with frequent earthquake sources (e.g., areas with ≥3 earthquakes with a magnitude greater than 1.0 per month), deployment was intensified according to high-risk zone standards. The locations of the new sensors were required to avoid obstacles such as tunnel supports and waterlogged areas marked in the 3D model to ensure installation feasibility.

[0049] S262: The optimized layout plan is validated in a Monte Carlo simulation system. A virtual sample consistent with the actual earthquake source distribution is generated (60% in high-risk areas, 30% in medium-risk areas, and 10% in low-risk areas). Metrics such as signal coverage (must be ≥95%), average positioning error (must be ≤5 meters), and signal-to-noise ratio (SNR) in high-risk areas (must be ≥15dB) are calculated. If these metrics are not met, the system returns to adjust the sensor location or quantity (for example, adding one or two sensors in areas where positioning error exceeds the standard). The simulation is repeated until the signal coverage, average positioning error, and SNR in high-risk areas meet the preset standards. Finally, the optimized layout plan is updated in the 3D geological structure model, and a list of sensor installation coordinates and adjustment instructions are generated to guide the secondary deployment of downhole sensors, forming a closed-loop monitoring-assessment-optimization mechanism.

[0050] The present invention provides a method for monitoring coal mine rock burst microseismic pressure. First, through multi-dimensional geological data integration and three-dimensional modeling, a refined representation of the coal mine's geological structure is achieved. This method can reflect the true impact of complex geological structures on seismic wave propagation, provide a precise spatial carrier for subsequent seismic wave propagation analysis, and effectively reduce monitoring errors caused by missing geological information. Next, a hierarchical analysis method and a fuzzy comprehensive evaluation method are used to divide risk zones. Evaluation indicators are dynamically corrected using real-time source data, significantly improving the matching degree between risk levels and actual source distribution, providing a scientific basis for sensor deployment. Furthermore, a sensor deployment optimization mechanism based on Monte Carlo simulations selects the optimal solution by comparing multiple solutions. This ensures dense coverage in high-risk areas while reducing redundant deployment in low-risk areas. This effectively avoids the inadequate coverage and severe waste of sensor resources that can occur with traditional deployment methods that rely on manual experience. While ensuring monitoring accuracy, it also effectively balances the investment cost of monitoring resources. Finally, an inversion method that effectively integrates seismic wave signal preprocessing with a three-dimensional geological model is used. Rock stratum wave velocity parameters are used to correct the propagation path. Combined with multi-sensor cross-validation, this method significantly improves the accuracy of source location and the reliability of intensity level calculation. Finally, through the closed-loop mechanism of the earthquake source data feedback optimization model and layout plan, dynamic adaptive adjustment of the monitoring process was achieved, the long-term monitoring efficiency was continuously improved, the monitoring error was significantly reduced, the accuracy of monitoring was ensured, and strong support was provided for the early warning and prevention and control decisions of coal mine rock burst, which significantly enhanced the safety of underground operations.

[0051] This method has a simple implementation process, low implementation cost, and high monitoring accuracy, and can achieve accurate positioning of the earthquake source and accurate calculation of the earthquake source intensity.

[0052] like Figure 5 As shown, based on the same inventive concept, the present invention also provides a coal mine rock burst microseismic monitoring system for implementing a coal mine rock burst microseismic monitoring method, including a data acquisition module, a processor and a memory; The data acquisition module comprises multiple sets of sensors, each of which is arranged in multiple monitoring areas in the target coal mine. Multiple sensors in each set of sensors are buried in different positions in the corresponding monitoring area, and are used 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 solution 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 of the target coal mine and send it to the model building module; The model building module is used to integrate and process the multi-dimensional geological data of the coal mine using three-dimensional modeling technology to build a three-dimensional geological structure model; The risk division module is used to divide the target coal mine into monitoring areas according to the three-dimensional geological structure model, and determine the rock burst risk level of different monitoring areas based on the hierarchical analysis method combined with the fuzzy comprehensive evaluation method; The scheme generation module is used to simulate the impact of various preset sensor layout schemes on seismic signal reception using a Monte Carlo simulation method combined with a three-dimensional geological structure model, and screen out the optimal layout scheme; The data processing module is used to receive the underground seismic wave signal of the target coal mine in real time, perform denoising on the underground seismic wave signal, and extract characteristic parameters from the denoised signal; The microseismic analysis module is used to analyze the propagation characteristics of downhole seismic wave signals based on characteristic parameters and in combination with a three-dimensional geological structure model, and calculate the source location and intensity level; The feedback optimization module is used to feed back the earthquake source location and intensity level to the three-dimensional geological structure model, and evaluate the effectiveness of the optimal layout plan. At the same time, the optimal layout plan is optimized according to the evaluation results; The memory is used for storing and reading data by the processor.

[0053] As a preference, the sensor is a microseismic sensor, and the processor is an industrial computer.

[0054] In the present invention, multiple sensors in the data acquisition module are buried at different locations in different monitoring areas, which can comprehensively collect the underground seismic wave signals of the target coal mine, providing a data basis for the accurate calculation of the source location and intensity level. Through the setting of the geological data receiving module, a data transmission interface can be provided for the model construction module, and multi-dimensional geological data can be easily collected and sent to the model construction module. Through the setting of the model construction module, a three-dimensional geological structure model that can accurately characterize the geological structure of the target coal mine can be efficiently and accurately constructed based on the multi-dimensional geological data, which can provide an accurate spatial carrier for seismic wave propagation and provide reliable technical support for the subsequent calculation process of the microseismic analysis module. Through the setting of the risk division module, the weight of each factor can be reasonably determined by the hierarchical analysis method. At the same time, the fuzzy comprehensive evaluation method can be effectively combined to deal with the ambiguity and uncertainty in the risk estimation, realize the quantitative analysis of the risk, and significantly improve the matching degree between the risk level and the actual source distribution. Thus, a detailed risk assessment result can be obtained, ensuring that the obtained risk level is consistent with the actual rock burst occurrence. By setting up the solution generation module, the optimal solution can be obtained from multiple solution comparison and screening through simulation. While ensuring dense coverage of high-risk areas, redundant deployment in low-risk areas is reduced, balancing monitoring accuracy and cost. By setting up the data processing module, noise data in the signal can be effectively removed. At the same time, the characteristic parameters required for the calculation of the source location and intensity level can be extracted, which is conducive to reducing the amount of calculation of the subsequent microseismic analysis module. By setting up the microseismic analysis module, the signal processing and source inversion processes can be fully integrated, greatly improving the source location accuracy and the reliability of the intensity level calculation. By setting up the feedback optimization module, dynamic adaptive adjustment of the monitoring system can be achieved through feedback optimization, continuously improving the long-term monitoring efficiency, and ensuring the accuracy of monitoring. By setting up the memory, real-time storage and reading of historical data can be facilitated.

[0055] The system is highly intelligent and reliable in performance. It can obtain the earthquake source location and intensity level efficiently and accurately, providing strong technical support for rock burst warning and prevention, and ensuring the safety of underground operations in coal mines.

Claims

1. A method for monitoring microseismic rock burst in coal mines, characterized in that: The following steps are involved: Step 1: Collect multi-dimensional geological data of the target coal mine and integrate and process the multi-dimensional geological data using 3D modeling technology to construct a 3D geological structure model; Step 2: Divide the target coal mine into monitoring areas based on the 3D geological structure model, and use the analytic hierarchy process combined with the fuzzy comprehensive evaluation method to determine the rock burst risk level of different monitoring areas; Step 3: Based on the rock burst risk level of the monitoring area, multiple sensor deployment schemes are set. Monte Carlo simulation is used in conjunction with a 3D geological structure model to simulate the impact of various sensor deployment schemes on seismic signal reception, and the optimal deployment scheme is selected. Step 4: Install sensors in each monitoring area according to the optimal layout plan; receive downhole seismic wave signals in real time, pre-process the downhole seismic wave signals, and then extract characteristic parameters from the pre-processed downhole seismic wave signals; Step 5: Based on the characteristic parameters and combined with the 3D geological structure model, the propagation characteristics of the downhole seismic wave signal are analyzed to calculate the source location and intensity level; Step 6: Feedback the earthquake source location and intensity level to the three-dimensional geological structure model, evaluate the effectiveness of the optimal layout plan, and optimize the optimal layout plan based on the evaluation results.

2. A method for monitoring microseismic rock burst in coal mines according to claim 1, characterized in that: In step one, the multi-dimensional geological data includes coal seam parameters, rock layer 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 inclination, coal seam hardness and coal seam permeability; the rock layer distribution data includes rock layer thickness, rock layer lithology and rock layer burial depth; the fault information includes fault strike, fault dip, fault inclination and fault drop; 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 value.

3. A method for monitoring microseismic rock burst in coal mines according to claim 2, characterized in that: In step 1, the process of constructing a 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: Importing the data in the structured database into the 3D modeling software, and performing grid processing based on the coal seam parameters and rock layer distribution data to generate an initial 3D geological framework of the layered entity; S13: Construct a spatial cutting surface based on fault information and joint development data, fuse the spatial cutting surface with the initial 3D geological framework through Boolean operations, and add fault and joint structures to obtain the basic model; S14: Combining the spatial morphological 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.

4. A method for monitoring microseismic rock burst in coal mines according to claim 3, characterized in that: In step 2, the rock burst risk level of different monitoring areas is determined as follows: S21: Constructing evaluation indicators that affect the risk of rock burst in coal mines, including geological structure complexity, stress concentration risk, and historical rock burst conditions; S22: Use the analytic hierarchy process to construct a judgment matrix, and calculate the relative importance weight of each evaluation indicator through consistency test; S23: Use the fuzzy comprehensive evaluation method to determine the membership function of each evaluation indicator, quantify the evaluation indicators of each monitoring area, and obtain the comprehensive risk score of each monitoring area through weighted summation; S24: Determine the rock burst risk level of each monitoring area based on the comprehensive risk score, where the rock burst risk levels include high risk, medium risk, and low risk.

5. A method for monitoring microseismic rock burst in coal mines according to claim 4, characterized in that: In step three, the screening process for the optimal layout plan is as follows: S31: Setting basic parameters for sensor deployment according to the rock burst risk level of the monitoring area, the basic parameters including sensor density in high-risk areas, deployment intervals in medium- and low-risk areas, and effective sensor monitoring radius; S32: Based on the 3D geological structure model, generate multiple deployment schemes with different sensor numbers and spatial distributions in each monitoring area; S33: using a Monte Carlo simulation method to randomly generate earthquake source samples, simulating the seismic wave propagation path in combination with rock formation propagation velocity parameters, and calculating screening indicators for each layout scheme, wherein the screening indicators include signal coverage, signal-to-noise ratio, and positioning error; S34: Use the weighted scoring method to comprehensively rank the screening indicators and obtain the optimal layout plan.

6. A method for monitoring microseismic rock burst in coal mines according to claim 5, characterized in that: In step 4, the process of preprocessing the downhole seismic signal and extracting characteristic parameters is as follows: S41: using the wavelet transform method to denoise the downhole seismic signal and remove the interference noise; S42: Segment the continuous signal using a sliding window method and identify independent shock wave events using energy thresholds; S43: extracting the frequency characteristics, waveform parameters, type of seismic wave, propagation angle and vibration intensity data of each seismic wave event as characteristic parameters, and identifying effective seismic waves.

7. A method for monitoring microseismic rock burst in coal mines according to claim 6, characterized in that: In step 5, the calculation process of the earthquake source location and intensity level is as follows: S51: Based on the arrival time difference and propagation angle of the shock waves in the characteristic parameters, the possible location range of the earthquake source is preliminarily calculated using the time difference positioning method; S52: calling the rock stratum wave velocity parameters of the corresponding area in the three-dimensional geological structure model, correcting the possible position range, and obtaining the earthquake source coordinates; S53: An attenuation model is constructed using the vibration intensity data in the characteristic parameters, and the intensity level is calculated based on the focal depth. At the same time, the calculation error is reduced by integrating multiple sets of sensor data and applying cross-validation technology.

8. A method for monitoring microseismic rock burst in coal mines according to claim 7, characterized in that: In step 6, the effectiveness evaluation process of the optimal layout plan is as follows: S161: Count the matching degree between the earthquake source location and the rock burst risk level of the monitoring area according to the preset period, 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 earthquake 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 the source signal, count the proportion of sources with signal loss or excessive signal-to-noise ratio, and evaluate the effectiveness of the deployment plan coverage.

9. A method for monitoring microseismic rock burst in coal mines according to claim 8, characterized in that: In step six, the process of optimizing the optimal layout plan based on the evaluation results is as follows: S261: For monitoring areas with low signal reception rates, the spatial orientation and deployment depth of sensors are adjusted based on the distribution of faults and rock interfaces in the 3D geological structure model. Furthermore, based on the revised evaluation index weights, the number of sensors is increased in high-risk areas and areas with dense earthquake sources to expand the monitoring coverage density and obtain an optimized deployment plan. S262: The optimized deployment plan is imported 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.

10. A coal mine rock burst microseismic monitoring system, used to implement the coal mine rock burst microseismic monitoring method according to any one of claims 1 to 9, characterized in that: including a data acquisition module, a processor and a memory; The data acquisition module comprises multiple sets of sensors, each of which is arranged in multiple monitoring areas in the target coal mine. Multiple sensors in each set of sensors are buried in different positions in the corresponding monitoring area, and are used 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 solution 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 of the target coal mine and send it to the model building module; The model building module is used to integrate and process the multi-dimensional geological data of the coal mine using three-dimensional modeling technology to build a three-dimensional geological structure model; The risk division module is used to divide the target coal mine into monitoring areas according to the three-dimensional geological structure model, and determine the rock burst risk level of different monitoring areas based on the hierarchical analysis method combined with the fuzzy comprehensive evaluation method; The scheme generation module is used to simulate the impact of various preset sensor layout schemes on seismic signal reception using a Monte Carlo simulation method combined with a three-dimensional geological structure model, and screen out the optimal layout scheme; The data processing module is used to perform denoising on the downhole seismic wave signal and to extract characteristic parameters from the denoised signal; The microseismic analysis module is used to analyze the propagation characteristics of downhole seismic wave signals based on characteristic parameters and in combination with a three-dimensional geological structure model, and calculate the source location and intensity level; The feedback optimization module is used to feed back the earthquake source location and intensity level to the three-dimensional geological structure model, and evaluate the effectiveness of the optimal layout plan. At the same time, the optimal layout plan is optimized according to the evaluation results; The memory is used for storing and reading data by the processor.

Images