Mine earthquake risk main control factor analysis method based on rock stratum macro-micro numerical simulation

Abstract

The invention discloses a mine earthquake risk main control factor analysis method based on rock stratum macro-microscopic numerical simulation. The method comprises the following steps: firstly, collecting a rock sample and testing to obtain microscopic rock mechanical parameters; secondly, constructing a standard sample model based on discrete element numerical analysis software, and performing heterogeneity parameter assignment on the standard sample model by utilizing a Weibull function in combination with fractal dimensions and mesoscopic rock mechanical parameters, so as to realize accurate matching of rock mass mesoscopic-macroscopic mechanical properties; the mesoscopic rock mechanical parameters are converted into macroscopic rock mechanical parameters for discrete element analysis according to the conversion relation between the rock quality indexes and the rock strength, and high-fidelity reconstruction of a discrete element macroscopic model is achieved; and subsequently performing an orthogonal test and range analysis to quantify the macroscopic heterogeneous main control factors, thereby realizing priority ranking of each macroscopic heterogeneous main control factor as a key disaster-causing factor. Simulation can be carried out under the condition of considering the rock stratum heterogeneity, the result fits the actual rock stratum condition, and data support is provided for subsequent mine earthquake risk prevention and control treatment on the rock stratum.

Description

Technical Field

[0001] This invention belongs to the field of coal mine safety technology, specifically a method for analyzing the main controlling factors of mine seismic risk based on macro- and micro numerical simulation of rock strata. Background Technology

[0002] Mine tremors, a typical dynamic disaster in mining operations, seriously threaten safe production. Their risk assessment and prevention are among the core issues for ensuring safe mining operations. Traditional mine tremor risk assessment methods are mostly based on the assumption of homogeneous rock masses, using simplified mechanical models for stability analysis. However, actual rock strata exhibit significant heterogeneity, with their mechanical parameters (such as elastic modulus and compressive strength) showing complex spatial distributions. This makes it difficult for homogeneous models to accurately reflect the failure and instability behavior of real rock masses.

[0003] In-depth research reveals that the heterogeneity of rock strata, its mechanical response characteristics to stress instability, and the complexity and severity of mine tremor development require comprehensive consideration at both macro and micro scales. At the micro level, differences in the microscopic properties of the rock mass (or rock body) result in significant variations in peak strength, fracture propagation, and failure modes at the time of failure. At the macro level, factors such as the thickness, occurrence, and phase transition of the roof strata lead to different fracture instability patterns, stress transfer, and energy release processes compared to those of overlying rock under uniformly thick roof conditions, as well as differences in mine tremor risk evolution.

[0004] However, existing technologies lack numerical analysis methods that can fully consider the macroscopic and microscopic heterogeneity of rock masses, accurately simulate the rock mass failure and instability process, and systematically analyze the main controlling factors of mine-induced seismic risks. This greatly limits the understanding of the actual mine-induced seismic formation mechanism and the accuracy of risk assessment. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a method for analyzing the main controlling factors of mine seismic risk based on macro- and micro-scale numerical simulation of rock strata. This method can perform macro- and micro-scale numerical simulations considering the heterogeneity of rock strata, determine the priority of the main controlling factors of mine seismic risk, and make the simulation closely match the actual rock strata conditions, thus ensuring the accuracy of the results and providing data support for subsequent mine seismic risk prevention and control of rock strata.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a method for analyzing the main controlling factors of mine seismic risk based on macro- and micro-numerical simulation of rock strata, comprising the following steps:

[0007] Step 1: Collect rock samples from the target rock layer and test the rock samples to obtain microscopic rock mechanical parameters;

[0008] Step 2: Construct a standard specimen model based on discrete element numerical analysis software, and use the Weibull function to assign heterogeneity parameters to the standard specimen model using the micromechanical parameters of the rock obtained in Step 1.

[0009] Step 3: Combining the rock quality index (RQD) with the rock mass strength conversion relationship, convert the microscopic rock mechanical parameters obtained in Step 1 into macroscopic rock mass mechanical parameters for discrete element analysis;

[0010] Step 4: Based on geological structural characteristics and historical mining and seismic data, screen out the main controlling factors of macroscopic heterogeneity, and then construct a multi-factor, multi-level orthogonal experimental parameter space;

[0011] Step 5: Based on the parameter space constructed in Step 4, use discrete element numerical analysis software to establish multiple sets of orthogonal excavation numerical models, and assign the macroscopic rock mechanics parameters obtained in Step 3 to the rock strata corresponding to the excavation numerical models.

[0012] Step 6: Establish mine tremor risk indicators based on the energy balance criteria of the mining process, and simulate the rock strata failure and instability process induced by full coal seam mining according to the excavation numerical model established in Step 5. Dynamically monitor the stress field and fracture evolution, and then calculate the mine tremor risk indicators corresponding to different levels.

[0013] Step 7: Quantify the weight of the main control factors on the mine tremor risk using orthogonal experimental range analysis on the mine tremor risk indicators obtained in Step 6, and finally determine the priority of each macroscopic heterogeneous main control factor as a key disaster-causing factor.

[0014] Furthermore, step one specifically involves: collecting rock samples from the target rock strata, analyzing the internal fracture network of the rock samples using CT scanning, and then obtaining micromechanical parameters of the rock samples through experimental testing, including density, elastic modulus, Poisson's ratio, normal stiffness, tangential stiffness, internal friction angle, cohesion, compressive strength, and tensile strength.

[0015] Furthermore, step two specifically involves:

[0016] ① Based on the CT scan results in step one, the fractal dimension is calculated using D = log(1 / r) / logN(r), where D is the fractal dimension and N(r) is the number of cubes with side length r required to cover the sample structure.

[0017] ② Based on the rock sample size, establish a standard sample model of equal size in the discrete element numerical analysis software (UDEC), assign non-homogeneity parameters to the measured micromechanical parameters of the rock in step one using the Weibull function, and calculate the fractal dimension of the standard sample model.

[0018] ③ Iteratively adjust the heterogeneity parameters until the fractal dimension of the standard sample model is consistent with the fractal dimension calculated in step ①.

[0019] Furthermore, step three specifically includes:

[0020] I. Based on the rock quality index conversion method E m / E r =10 0.186RQD-1.91 The elastic modulus of the microscopic rock mechanics parameters is converted into the elastic modulus of the macroscopic rock mass, where E is the elastic modulus of the rock mass. m and E r These are the elastic moduli of the rock mass and the standard sample, respectively, and RQD is the rock quality index.

[0021] II. Based on the rock mass strength conversion formula σ mc / σ c =(E m / E r ) n The compressive strength of the microscopic rock mechanics parameters is converted into the macroscopic rock mass compressive strength, where σ mc and σ c , respectively, are the compressive strengths of the rock mass and the standard sample, where n is a coefficient;

[0022] III. The tensile strength of the rock mass is determined by the formula σ. tc =0.1σ mc Calculate, where σ tc and σ mc These are the tensile strength and compressive strength of the rock mass, respectively;

[0023] IV. The bulk modulus, shear modulus, joint normal stiffness, and tangential stiffness of the macroscopic rock mass are calculated using formulas in discrete element numerical analysis software. The specific formulas are as follows:

[0024]

[0025] k s =0.4k n

[0026] In the formula, K is the volume model, G is the shear modulus, E is the elastic modulus, μ is Poisson's ratio, and k n k s These represent the joint surface normal and shear stiffness, ΔZ, respectively. min It represents the minimum width of the unit normal direction of the adjacent joint in the macroscopic rock mass.

[0027] Furthermore, the main macroscopic heterogeneous controlling factors in step four include the thickness of the overlying pinch-out layer, the vertical distance between the thick and hard rock layer and the coal seam, the spacing between the thick and hard rock layers, the uniform rock layer's relative curvature, and the pinch-out angle.

[0028] Furthermore, step six specifically includes:

[0029] S1. Based on the energy balance criterion, the energy evolution process during the mining process is obtained, specifically W. k =WU c -W s In the formula, W k W represents the system's radiated energy; W represents the work done by external forces and physical forces; U represents the energy radiated by the system. c This refers to the strain energy stored in unmined coal and rock masses;

[0030] S2. Based on the energy evolution process equation during the S1 mining process, W k Defined as an indicator for measuring mine tremor risk under different influencing factors, in UDEC, the system radiant energy W k It can be represented as

[0031]

[0032] In the formula, nt is the current calculation time step of the excavation numerical model, ngp is the number of grid nodes in the excavation numerical model, α is the damping coefficient, Δt is the time interval between two calculation steps, and m j For the mass of grid node j, u j For the speed of the grid nodes;

[0033] S3. Based on the excavation numerical model established in step five, excavate along the coal seam elevation direction, monitor the rock stress field and crack propagation, until the overlying rock periodically collapses.

[0034] S4. Based on the simulation results of step S3 and the mine tremor risk index defined in S2, calculate the mine tremor risk index corresponding to each macroscopic heterogeneous main control factor under different levels in the orthogonal experiment.

[0035] Furthermore, step seven specifically includes:

[0036] (i) Based on the orthogonal test results in step six, calculate the sum of mine tremor risk indicators under the same conditions for a certain macro-heterogeneous main control factor and the changing conditions of the other macro-heterogeneous main control factors. Then, obtain the sum of mine tremor risk indicators under different conditions where a certain macro-heterogeneous main control factor remains unchanged and the conditions of the other macro-heterogeneous main control factors change.

[0037] (ii) Repeating step (i) yields the sum of mine seismic risk indicators under different conditions where the macroscopic heterogeneous controlling factor remains constant and the conditions of the other macroscopic heterogeneous controlling factors change; according to formula R i =max(K) ij )-min(K ij Calculate the range of each macroscopic heterogeneous controlling factor, where R is the range of the macroscopic heterogeneous controlling factor. i K represents the range of the i-th macroscopic heterogeneous controlling factor.ij This represents the sum of mine tremor risk indicators under the condition that the i-th macro-heterogeneous controlling factor is at the j-th level, where max(K) ij ) represents the maximum sum of the mine tremor risk indicators under different level conditions for the i-th macroscopic heterogeneous controlling factor; min(K ij ) represents the minimum sum of the mine tremor risk indicators of the i-th macro-heterogeneous controlling factor under different level conditions;

[0038] (iii) The larger the range value of a certain macro-heterogeneous controlling factor, the higher the weight of the controlling factor in the impact of mine earthquakes; sort the range values ​​of each macro-heterogeneous controlling factor from large to small, and then determine the priority of each macro-heterogeneous controlling factor as a key disaster-causing factor according to the sorting.

[0039] Compared with existing technologies, this invention first collects rock samples and tests them to obtain microscopic rock mechanical parameters, and then calculates the fractal dimension. Next, a standard sample model is constructed based on discrete element numerical analysis software. The Weibull function, combined with the fractal dimension and microscopic rock mechanical parameters, is used to assign heterogeneous parameters to the standard sample model, achieving precise matching of the microscopic and macroscopic mechanical properties of the rock mass. The microscopic rock mechanical parameters are converted into macroscopic rock mass mechanical parameters for discrete element analysis through the conversion relationship between the rock quality index (RQD) and rock mass strength, achieving high-fidelity reconstruction of the macroscopic discrete element model. Subsequently, orthogonal experiments are conducted using multiple sets of orthogonal excavation models to calculate the seismic risk index corresponding to different levels of conditions. Finally, range analysis is performed on the seismic risk index to quantify the macroscopic heterogeneous controlling factors, enabling the priority ranking of each macroscopic heterogeneous controlling factor as a key disaster-causing factor. Therefore, this invention can perform macro- and micro-scale numerical simulations under the condition of heterogeneous rock strata, thereby determining the priority of the main controlling factors of mine seismic risk, making the simulation closely match the actual rock strata conditions, ensuring the accuracy of the results, and providing data support for subsequent mine seismic risk prevention and control of rock strata. Attached Figure Description

[0040] Figure 1 This is an overall flowchart of the present invention;

[0041] Figure 2 This is a diagram of the heterogeneous parameters for CT scans and numerical simulation calibration according to an embodiment of the present invention;

[0042] Figure 3 The heterogeneous UDEC numerical simulation sample is an embodiment of the present invention;

[0043] Figure 4 This describes the assignment of the bulk modulus of the sample block for different values ​​of m in this embodiment of the invention.

[0044] Where (a) is the bulk modulus when m = 5, and (b) is the bulk modulus when m = 10;

[0045] Figure 5 This is a failure diagram of the uniaxial compressive strength calibration specimen according to an embodiment of the present invention;

[0046] Figure 6 This is a failure diagram of the uniaxial tensile strength calibration specimen according to an embodiment of the present invention;

[0047] Figure 7 The diagram shows the UDEC numerical model in the orthogonal experiment of this invention. Detailed Implementation

[0048] The present invention will be further described below.

[0049] like Figure 1 As shown, the present invention includes the following steps:

[0050] Step 1: Collect rock samples from the target rock strata and prepare standard rock core samples with a diameter of 50 mm and a height of 100 mm. Analyze the internal fracture network of the rock samples using CT scanning. Figure 2 As shown, the density was then measured using vernier calipers and an electronic balance. The compressive strength, elastic modulus, and Poisson's ratio of the sample were tested using a uniaxial testing machine. The tensile strength of the sample was tested using the Brazilian splitting test. The cohesion and internal friction angle of the standard sample were measured using a triaxial testing machine, and the normal stiffness and tangential stiffness of the sample were obtained.

[0051] Step 2: Construct a standard specimen model using discrete element numerical analysis software. Utilize the Weibull function to assign heterogeneous parameters to the standard specimen model based on the micromechanical parameters of the rock obtained in Step 1. Specifically:

[0052] ① Based on the CT scan results in step one, the fractal dimension is calculated using D = log(1 / r) / logN(r), where D is the fractal dimension and N(r) is the number of cubes with side length r required to cover the sample structure.

[0053] ②, such as Figure 3 As shown, a heterogeneous numerical model with a diameter of 50 mm and a height of 100 mm was established in the discrete element numerical analysis software UDEC. A total of 3815 blocks and 2793 joints were created. Different heterogeneity values ​​(m=5, m=10, m=15, ...) were set from strong to weak. The Weibull function was used to assign heterogeneous parameters to the bulk modulus, shear modulus, joint cohesion, friction angle, and tensile strength of the blocks. Figure 4 Assign values ​​to the bulk modulus of the specimen block for different values ​​of m, thereby calculating the fractal dimension of the standard specimen model;

[0054] ③ Iteratively adjust the heterogeneity parameter m until the fractal dimension of the standard sample model is consistent with the fractal dimension calculated in step ①.

[0055] Step 3: Combining the rock quality index (RQD) with the rock mass strength conversion relationship, the microscopic rock mechanical parameters obtained in Step 1 are converted into macroscopic rock mass mechanical parameters for discrete element analysis, specifically as follows:

[0056] I. Based on the rock quality index conversion method E m / E r =10 0.186RQD-1.91 The elastic modulus of the microscopic rock mechanics parameters is converted into the elastic modulus of the macroscopic rock mass, where E is the elastic modulus of the rock mass. m and E r These are the elastic moduli of the rock mass and the standard sample, respectively, with RQD being the rock quality index; according to the rock mass strength conversion formula σ... mc / σ c =(E m / E r ) n The compressive strength of the microscopic rock mechanics parameters is converted into the macroscopic rock mass compressive strength, where σ mc and σ c The compressive strengths of the rock mass and the standard sample are respectively, where n is a coefficient. For different types, n takes values ​​of 0.56, 0.66, and 0.77; in this embodiment, the average value is 0.65. The tensile strength of the rock mass is obtained using the formula σ. tc =0.1σ mc Calculate, where σ tc and σ mc These are the tensile strength and compressive strength of the rock mass, respectively;

[0057] II. The bulk modulus, shear modulus, joint normal stiffness, and tangential stiffness of the macroscopic rock mass are calculated using formulas in the discrete element numerical analysis software UDEC. The specific formulas are as follows:

[0058]

[0059] k s =0.4k n

[0060] In the formula, K is the volume model, G is the shear modulus, E is the elastic modulus, μ is Poisson's ratio, and k n k s These represent the joint surface normal and shear stiffness, ΔZ, respectively. min This represents the minimum width of the element normal to adjacent joints in the macroscopic rock mass. The results of converting heterogeneous parameters into macroscopic rock mass mechanical parameters are shown in Table 1.

[0061] Table 1:

[0062]

[0063]

[0064] III. In the discrete element numerical analysis software UDEC, a standard initial uniaxial compression specimen model with a diameter of 50 mm and a height of 100 mm was established, comprising 3815 blocks and 2793 joints. Stress and displacement monitoring points were placed at the bottom of the specimen to study and analyze the fracture development characteristics during loading instability. The UDEC-Tri model was used to divide the specimen into blocks, with the blocks and structural surfaces set as isotropic elastic models and Coulomb slip models, respectively. The Weibull function was used to implement heterogeneous parameter assignment of macroscopic rock mass mechanics parameters. Uniaxial compression was achieved by fixing the lower steel plate and compressing the upper steel plate downwards at a loading rate of 0.05 m / s and a time step of 3 × 10⁻⁶ m / s. -7 m / s, the acceleration rate in the simulation is equivalent to 1.5 × 10 -8 m / step. According to Figure 5 Uniaxial compressive strength calibration specimen failure diagram and Figure 6 The failure diagram of the uniaxial tensile strength calibration specimen is used to determine whether the uniaxial compressive strength and uniaxial tensile strength calibration of the block are correct.

[0065] IV. Based on the successfully calibrated uniaxial compressive strength test, the calibrated elastic modulus and Poisson's ratio were obtained. The calibrated rock mass bulk modulus, shear modulus, joint normal stiffness, and tangential stiffness were then recalculated according to the formulas in the UDEC manual. Table 2 shows the calibrated model mechanical parameters.

[0066] Table 2:

[0067]

[0068] Step 4: Based on geological structural characteristics and historical seismic data, macroscopic heterogeneous controlling factors are screened out, and then a multi-factor, multi-level orthogonal experimental parameter space is constructed. The macroscopic heterogeneous controlling factors include the thickness of the overlying pinch-out layer of the coal seam, the vertical distance between the thick and hard rock layer and the coal seam, the spacing between the thick and hard rock layers, the uniform rock layer's relative curvature and pinch-out angle.

[0069] Based on the geological structure characteristics and historical seismic data of the Hongqinghe Coal Mine, this embodiment screens out the set of macroscopic heterogeneous controlling factors and forms an orthogonal experimental factor level table, as shown in Table 3:

[0070] Table 3:

[0071]

[0072] Step 5: Based on the parameter space constructed in Step 4, use discrete element numerical analysis software to establish multiple sets of orthogonal excavation numerical models, and assign the macroscopic rock mechanics parameters obtained in Step 3 to the rock strata corresponding to the excavation numerical models.

[0073] 1) Based on the parameter space in step four, design an orthogonal experimental scheme design table, as shown in Table 4.

[0074] Table 4:

[0075]

[0076] 2) such as Figure 7 As shown, a numerical model for excavation was established. The model is 1200m long and 550m high, simulating a coal seam with a burial depth of 600m and a thickness of 10m. The lateral pressure coefficient was set to 1.3. During excavation, 200m was left in front of and behind the working face to eliminate boundary effects.

[0077] Step Six: Establish mine seismic risk indicators based on the energy balance criterion of the mining process, and simulate the rock strata failure and instability process induced by full coal seam mining according to the excavation numerical model established in Step Five. Dynamically monitor the stress field and fracture evolution, and then calculate the mine seismic risk indicators corresponding to different levels of conditions, specifically:

[0078] S1. Based on the energy balance criterion, the energy evolution process during the mining process is obtained, specifically W. k =WU c -W s In the formula, W k W represents the system's radiated energy; W represents the work done by external forces and physical forces; U represents the energy radiated by the system. c This refers to the strain energy stored in unmined coal and rock masses;

[0079] S2. Based on the energy evolution process equation during the S1 mining process, W k Defined as an indicator for measuring mine tremor risk under different influencing factors, in UDEC, the system radiant energy W k It can be represented as

[0080]

[0081] In the formula, nt is the current calculation time step of the excavation numerical model, ngp is the number of grid nodes in the excavation numerical model, α is the damping coefficient, Δt is the time interval between two calculation steps, and m j For the mass of grid node j, u j For the speed of the grid nodes;

[0082] S3. Based on the excavation numerical model established in step five, excavate along the coal seam elevation direction, excavate 10m each time until equilibrium is reached, monitor the rock stress field and crack propagation, until the overlying rock periodically collapses.

[0083] S4. Based on the formula for the mine tremor risk index defined in S2, calculate the mine tremor risk assessment index W after each orthogonal experiment. kAs shown in Table 5, it was found that the mine seismic risk was highest when the pinch-out layer thickness was 50m, the vertical distance from the coal seam was 40m, the spacing between thick and hard rock layers was 10m, the relative curvature of the opposite bending was 0.00025 (radius of curvature was 4000m), and the pinch-out angle was 30°.

[0084] Table 5:

[0085]

[0086]

[0087] In the table, E10 represents 10 to the power of 10;

[0088] Step 7: The weights of the controlling factors on the mine tremor risk obtained in Step 6 are quantified using orthogonal experimental range analysis. Finally, the priority of each macroscopic heterogeneous controlling factor as a key disaster-causing factor is determined, specifically as follows:

[0089] (i) Based on the orthogonal test results in Table 4, calculate the sum of mine tremor risk indicators under the same conditions for a certain macro-heterogeneous main control factor and the changing conditions of the other macro-heterogeneous main control factors. Then, obtain the sum of mine tremor risk indicators under different conditions when a certain macro-heterogeneous main control factor remains unchanged and the conditions of the other macro-heterogeneous main control factors change.

[0090] (ii) Repeating step (i) yields the sum of mine seismic risk indicators under different conditions where the macroscopic heterogeneous main controlling factor remains constant and the conditions of the other macroscopic heterogeneous main controlling factors change, as shown in Table 6.

[0091] Table 6:

[0092]

[0093] Taking factor A as an example in Table 6, the value of A in K1 is the sum of the mine tremor risk indicators under the condition that factor A is the same in levels 1 to 3 of Table 5, which is 1.70E10 + 1.58E10 + 1.62E10 = 4.90E10; the other values ​​are calculated in sequence.

[0094] According to formula R i =max(K) ij )-min(K ij Calculate the range of each macroscopic heterogeneous controlling factor, where R is the range of the macroscopic heterogeneous controlling factor. i K represents the range of the i-th macroscopic heterogeneous controlling factor. ij This represents the sum of mine tremor risk indicators under the condition that the i-th macro-heterogeneous controlling factor is at the j-th level, where max(K) ij ) represents the maximum sum of the mine tremor risk indicators under different level conditions for the i-th macroscopic heterogeneous controlling factor; min(Kij () represents the minimum sum of the mine tremor risk indicators of the i-th macro-heterogeneous controlling factor under different level conditions; (taking factor A as an example,) as shown in Table 7:

[0095] Table 7:

[0096]

[0097] In Table 7, taking factor A as an example, the range R of factor A is obtained by selecting the maximum value of 5.31E10 and the minimum value of 4.90E10 of factor A at different levels K1 to K3 in Table 6, and the difference between the two is 4.10E9; the other factors are calculated in the same way.

[0098] (iii) The larger the range value of a certain macroscopic heterogeneity controlling factor, the higher the weight of that controlling factor in the impact of mine-induced seismic activity. The range values ​​of each macroscopic heterogeneity controlling factor are sorted from largest to smallest, and then the priority of each macroscopic heterogeneity controlling factor as a key disaster-causing factor is determined according to the sorting. In this embodiment, the pinch-out layer thickness > spacing between thick and hard rock layers > opposing bending curvature = pinch-out angle > vertical distance from the coal seam. This indicates that the stress concentration of the roof strata is more pronounced at the pinch-out point due to abrupt changes in lithology and stratum thickness, and the roof collapse and instability at the pinch-out point easily releases a large amount of elastic energy. Changes in the macroscopic heterogeneity of rock strata have a significant impact on mine-induced seismic risk. Therefore, in studying the occurrence mechanism and evolution law of mine-induced seismic activity, it is necessary to focus on the distribution characteristics of macroscopic heterogeneity of rock strata. During mine-induced seismic prevention, the "abrupt change points" of rock strata caused by macroscopic heterogeneity need to be given special attention.

[0099] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for analyzing the main controlling factors of mine seismic risk based on macro- and micro-scale numerical simulation of rock strata, characterized in that, The following steps are included: Step 1: Collect rock samples from the target rock layer and test the rock samples to obtain microscopic rock mechanical parameters; Step 2: Construct a standard specimen model based on discrete element numerical analysis software, and use the Weibull function to assign heterogeneity parameters to the standard specimen model using the micromechanical parameters of the rock obtained in Step 1. Step 3: Combining the conversion relationship between rock quality indicators and rock mass strength, convert the microscopic rock mechanical parameters obtained in Step 1 into macroscopic rock mass mechanical parameters for discrete element analysis; Step 4: Based on geological structural characteristics and historical mining and seismic data, screen out the main controlling factors of macroscopic heterogeneity, and then construct a multi-factor, multi-level orthogonal experimental parameter space; Step 5: Based on the parameter space constructed in Step 4, use discrete element numerical analysis software to establish multiple sets of orthogonal excavation numerical models, and assign the macroscopic rock mechanics parameters obtained in Step 3 to the rock strata corresponding to the excavation numerical models. Step 6: Establish mine tremor risk indicators based on the energy balance criteria of the mining process, and simulate the rock strata failure and instability process induced by full coal seam mining according to the excavation numerical model established in Step 5. Dynamically monitor the stress field and fracture evolution, and then calculate the mine tremor risk indicators corresponding to different levels. Step 7: Quantify the weight of the main control factors on the mine tremor risk using orthogonal experimental range analysis on the mine tremor risk indicators obtained in Step 6, and finally determine the priority of each macroscopic heterogeneous main control factor as a key disaster-causing factor.

2. The method for analyzing the main controlling factors of mine seismic risk based on macro- and micro-numerical simulation of rock strata as described in claim 1, characterized in that, Step one specifically involves: collecting rock samples from the target rock strata, analyzing the internal fracture network of the rock samples using CT scanning, and then obtaining microscopic rock mechanical parameters, including density, elastic modulus, Poisson's ratio, normal stiffness, tangential stiffness, internal friction angle, cohesion, compressive strength, and tensile strength, through experimental testing of the rock samples.

3. The method for analyzing the main controlling factors of mine seismic risk based on macro- and micro-numerical simulation of rock strata as described in claim 2, is characterized in that, Step two specifically involves: ① Based on the CT scan results in step one, the fractal dimension is calculated using D = log(1 / r) / logN(r), where D is the fractal dimension and N(r) is the number of cubes with side length r required to cover the sample structure. ② Based on the rock sample size, establish a standard sample model of equal size in the discrete element numerical analysis software, assign non-homogeneity parameters to the measured micromechanical parameters of the rock in step one using the Weibull function, and calculate the fractal dimension of the standard sample model. ③ Iteratively adjust the heterogeneity parameters until the fractal dimension of the standard sample model is consistent with the fractal dimension calculated in step ①.

4. The method for analyzing the main controlling factors of mine seismic risk based on macro- and micro-numerical simulation of rock strata as described in claim 2, is characterized in that, Step three specifically involves: I. Based on the rock quality index conversion method E m / E r =10 0.186RQD-1.91 The elastic modulus of the microscopic rock mechanics parameters is converted into the elastic modulus of the macroscopic rock mass, where E is the elastic modulus of the rock mass. m and E r These are the elastic moduli of the rock mass and the standard sample, respectively, and RQD is the rock quality index. II. Based on the rock mass strength conversion formula σ mc / σ c =(E m / E r ) n The compressive strength of the microscopic rock mechanics parameters is converted into the macroscopic rock mass compressive strength, where σ mc and σ c , respectively, are the compressive strengths of the rock mass and the standard sample, where n is a coefficient; III. The tensile strength of the rock mass is determined by the formula σ. tc =0.1σ mc Calculate, where σ tc and σ mc These are the tensile strength and compressive strength of the rock mass, respectively; IV. The bulk modulus, shear modulus, joint normal stiffness, and tangential stiffness of the macroscopic rock mass are calculated using formulas in discrete element numerical analysis software. The specific formulas are as follows: k s =0.4k n In the formula, K is the volume model, G is the shear modulus, E is the elastic modulus, μ is Poisson's ratio, and k n k s These represent the joint surface normal and shear stiffness, ΔZ, respectively. min It represents the minimum width of the unit normal direction of the adjacent joint in the macroscopic rock mass.

5. The method for analyzing the main controlling factors of mine seismic risk based on macro- and micro-numerical simulation of rock strata as described in claim 1, characterized in that, The main macroscopic heterogeneous controlling factors in step four include the thickness of the overlying pinch-out layer, the vertical distance between the thick and hard rock strata and the coal seam, the spacing between the thick and hard rock strata, and the curvature and pinch-out angle of the uniform rock strata.

6. The method for analyzing the main controlling factors of mine seismic risk based on macro- and micro-scale numerical simulation of rock strata as described in claim 1, characterized in that, Step six specifically involves: S1. Based on the energy balance criterion, the energy evolution process during the mining process is obtained, specifically W. k =WU c -W s In the formula, W k W represents the system's radiated energy; W represents the work done by external forces and physical forces; U represents the energy radiated by the system. c This refers to the strain energy stored in unmined coal and rock masses; S2. Based on the energy evolution process equation during the S1 mining process, W k Defined as an indicator for measuring mine tremor risk under different influencing factors, in UDEC, the system radiant energy W k Represented as In the formula, nt is the current calculation time step of the excavation numerical model, ngp is the number of grid nodes in the excavation numerical model, α is the damping coefficient, Δt is the time interval between two calculation steps, and m j For the mass of grid node j, u j For the speed of the grid nodes; S3. Based on the excavation numerical model established in step five, excavate along the coal seam elevation direction, monitor the rock stress field and crack propagation, until the overlying rock periodically collapses. S4. Based on the simulation results of step S3 and the mine tremor risk index defined in S2, calculate the mine tremor risk index corresponding to each macroscopic heterogeneous main control factor under different levels in the orthogonal experiment.

7. The method for analyzing the main controlling factors of mine seismic risk based on macro- and micro-numerical simulation of rock strata as described in claim 1, characterized in that, Step seven specifically involves: (i) Based on the orthogonal test results in step six, calculate the sum of mine tremor risk indicators under the same conditions for a certain macro-heterogeneous main control factor and the changing conditions of the other macro-heterogeneous main control factors. Then, obtain the sum of mine tremor risk indicators under different conditions where a certain macro-heterogeneous main control factor remains unchanged and the conditions of the other macro-heterogeneous main control factors change. (ii) According to formula R i =max(K) ij )-min(K ij Calculate the range of each macroscopic heterogeneous controlling factor, where R is the range of the macroscopic heterogeneous controlling factor. i K represents the range of the i-th macroscopic heterogeneous controlling factor. ij This represents the sum of mine tremor risk indicators under the condition that the i-th macro-heterogeneous controlling factor is at the j-th level, where max(K) ij ) represents the maximum sum of the mine tremor risk indicators under different level conditions for the i-th macroscopic heterogeneous controlling factor; min(K ij ) represents the minimum value of the sum of mine tremor risk indicators under different level conditions for the i-th macro-heterogeneous controlling factor; (iii) Sort the range values ​​of each macroscopic heterogeneous controlling factor from largest to smallest, and then determine the priority of each macroscopic heterogeneous controlling factor as a key disaster-causing factor according to the sorting.

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