A method for evaluating and improving the effect of well-ground combined microseismic monitoring

Abstract

A method for evaluating and improving the effect of well-ground combined microseismic monitoring determines two evaluation indicators, positioning capability and monitoring capability, for measuring the effect of well-ground combined microseismic monitoring. A positioning capability evaluation function D and a monitoring capability evaluation function Q are constructed. The coordinates of the coal mine well-ground combined microseismic network are substituted into the network to evaluate the monitoring effect, and the coordinates of several candidate ground sensor points are generated. Then, several well-ground combined network layout plans are randomly generated. By improving the NSGA-II multi-objective genetic algorithm, the generated network layout plans are used as the initial population for evaluation and evolution. According to the iterative results of the generated NSGA-II multi-objective genetic improved algorithm, a network layout plan with optimized well-ground combined microseismic monitoring effect is provided for the coal mine. The present invention scientifically and accurately evaluates the well-ground combined microseismic monitoring effect under the current network layout, improves the monitoring capability and positioning capability of the well-ground combined microseismic monitoring system, and provides a theoretical basis for the layout of a well-ground integrated microseismic monitoring network.

Description

Technical Field

[0001] The invention relates to a method for evaluating and improving the effect of combined well-ground microseismic monitoring, and belongs to the technical field of coal mine safety. Background Art

[0002] Rock burst disasters are a major hazard in coal mines, and accurate monitoring and early warning have long been a challenge for the industry. An increasing number of coal mines are integrating surface microseismic monitoring equipment with underground monitoring equipment to form integrated underground microseismic monitoring networks, aiming to improve the positioning capabilities of these systems.

[0003] Generally speaking, the stronger the microseismic positioning capability, the smaller the distance error between the theoretical and actual earthquake source locations, and the higher the positioning accuracy; the stronger the microseismic monitoring capability, the stronger the ability to pick up small-energy mine earthquakes, and the richer the microseismic signals collected by the system. In field applications, coal mines often focus on improving the positioning capability of microseismic monitoring systems, using them to determine the fracture locations of microseisms in high and low-lying coal and rock strata, while neglecting the improvement of monitoring capabilities. This results in the loss of a large amount of small-energy mine earthquake precursor information, affecting the accuracy of the final warning. In addition, there is currently much research on the layout location of underground sensors, but the installation location of ground sensors still relies on construction experience. There is a lack of layout theory based on improving monitoring and positioning capabilities. This greatly limits the advantages of the integrated well-ground microseismic monitoring system and cannot meet the needs of accurately and completely collecting microseismic signals, which has an adverse impact on the prevention and control of coal mine rock burst disasters. Summary of the Invention

[0004] In response to the problems existing in the above-mentioned prior art, the present invention provides a method for evaluating and improving the effect of combined well-ground microseismic monitoring. This method can scientifically and accurately evaluate the effect of combined well-ground microseismic monitoring under the current network layout, guide the optimal layout position of ground sensors based on the improved monitoring effect, improve the monitoring and positioning capabilities of the combined well-ground microseismic monitoring system, and provide a theoretical basis for the layout of a well-ground integrated microseismic monitoring network.

[0005] To achieve the above objectives, the present invention adopts a technical solution: a method for evaluating and improving the effect of combined well-ground microseismic monitoring, comprising the following steps:

[0006] (1) Determine the evaluation indicators of positioning capability and monitoring capability to measure the effect of well-ground joint microseismic monitoring;

[0007] (2) constructing a positioning capability evaluation function D and a monitoring capability evaluation function Q respectively according to the positioning capability and monitoring capability evaluation indicators determined in step (1);

[0008] (3) Substituting the positioning capability evaluation function D and the monitoring capability evaluation function Q constructed in step (2) into the coordinates of the coal mine ground-to-shaft microseismic network to evaluate the monitoring effect and generate the coordinates of multiple candidate points for deploying ground sensors;

[0009] (4) randomly generating several well-ground joint network layout plans based on the coordinates of the multiple candidate points for deploying ground sensors generated in step (3);

[0010] (5) By improving the NSGA-II multi-objective genetic algorithm, the well-ground joint network layout scheme generated in step (4) is used as the initial population for evaluation and evolution;

[0011] (6) Based on the iterative evolution results of the NSGA-II multi-objective genetic improvement algorithm generated in step (5), a network layout plan with optimized well-ground joint microseismic monitoring effect is provided for the coal mine.

[0012] Furthermore, in step (2), the positioning capability evaluation function D is:

[0013]

[0014] in,

[0015]

[0016]

[0017] Where W is a diagonal matrix, W w is the element on the diagonal matrix, T w is the distance from the wth station on the well-ground integrated microseismic monitoring network G to the inner point of the 3D grid evaluation model (X i ,Y j ,Z k ) propagation time; x1×y1×z1 is the number of grids in the three-dimensional grid evaluation model, V p is the P-wave velocity, and σ t are the variance of P-wave velocity and the variance of P-wave first arrival time, respectively; where w∈1,2,…,n; i∈1,2,…,x1, j∈1,2,…,y1, k∈1,2,…,z1;

[0018] The monitoring capability evaluation function Q is:

[0019]

[0020] in, is the inner point of the 3D grid model recorded by all surface and downhole sensors on the well-ground integrated microseismic monitoring network scheme G (X i ,Y j ,Z k ) can trigger the well-ground integrated microseismic monitoring network to record microseismic signals. i,j,k The fourth energy value after sorting from smallest to largest:

[0021]

[0022] Where, f is the ground or underground environmental background noise value three times, r is the ground or underground sensor distance point (X i ,Y j ,Z k ), α1 is the microseismic amplitude-to-energy coefficient, and α2 is the microseismic attenuation coefficient.

[0023] Furthermore, in step (3), when the positioning capability evaluation function D and the monitoring capability evaluation function Q are substituted into the coordinates of the coal mine ground-to-ground microseismic network to evaluate the monitoring effect, the sensitivity of the ground sensor and the underground sensor is set to 1500m, that is, when the point (X i ,Y j ,Z k ) When the distance between it and a sensor exceeds 1500m, the sensor will not participate in the point (X i ,Y j ,Z k ) monitoring effectiveness evaluation.

[0024] Furthermore, in the step (4), the randomly generated several well-ground joint network layout plans are as follows: any randomly generated well-ground joint network layout plan includes ground sensors and underground sensors, and the ground sensors are randomly selected from the coordinates of the candidate points, and the number is the same as the number of ground sensors in the original well-ground joint microseismic monitoring network of the coal mine, and the number and coordinates of the underground sensors are the same as the underground sensors in the original well-ground joint microseismic monitoring network of the coal mine.

[0025] Furthermore, in step (5), the method for improving the traditional NSGA-II multi-objective genetic algorithm is as follows: the traditional NSGA-II multi-objective genetic algorithm is improved by simulating binary crossover and polynomial mutation operations, and the new algorithm mutation operation selects a mixed use of adjacent gene mutation, gene insertion mutation, gene exchange mutation, three-point gene exchange mutation and two-point inversion mutation; the crossover operation selects a mixed use of partial mapping crossover, cyclic crossover operator, edge recombination crossover, linear sequential crossover, sequential crossover operator and uniform crossover; the evolutionary generation number of the improved multi-objective genetic algorithm should be no less than 100; the optimization objective function of the improved NSGA-II multi-objective genetic algorithm is:

[0026]

[0027] Furthermore, based on the iterative evolution results of the generated NSGA-II multi-objective genetic improved algorithm, three network layout schemes are provided for the coal mine after optimizing the effect of the well-ground joint microseismic monitoring. Specifically, according to the solution set of the iterative evolution results, if the coal mine tends to improve the monitoring capability, the well-ground joint microseismic monitoring network layout scheme with the smallest monitoring capability objective function value Q in the solution set is selected; if the coal mine tends to improve the positioning capability, the well-ground joint microseismic monitoring network layout scheme with the smallest positioning capability objective function value D in the solution set is selected; if the coal mine takes into account improving both positioning and monitoring capabilities, the well-ground joint microseismic monitoring network layout scheme closest to the origin in the solution set scatter diagram is selected.

[0028] Furthermore, the construction criteria of the three-dimensional evaluation grid model are as follows: determine the X, Y, and Z direction ranges [Xmin, Xmax], [Ymin, Ymax], and [Zmin, Zmax] according to the impact risk area, take the X direction spacing as dx, the Y direction spacing as dy, and the Z direction spacing as dz, and divide the grid into Three-dimensional evaluation grid model; where x1, y1, and z1 represent the number of grids in the X, Y, and Z directions, respectively.

[0029] The present invention determines two evaluation indicators, positioning capability and monitoring capability, to measure the effect of combined well-ground microseismic monitoring. It constructs a positioning capability evaluation function D and a monitoring capability evaluation function Q. The coordinates of the combined well-ground microseismic network of the coal mine are further substituted into the network to evaluate the monitoring effect, generate the coordinates of several candidate ground sensor points, and then randomly generate several combined well-ground network layout plans. By improving the NSGA-II multi-objective genetic algorithm, all generated network layout plans are used as the initial population for evaluation and evolution. Based on the iterative results of the generated NSGA-II multi-objective genetic algorithm, a network layout plan with optimized well-ground microseismic monitoring effect is provided for the coal mine. This method provides a criterion for evaluating and improving the effect of combined well-ground microseismic monitoring. It introduces dual evaluation indicators of positioning capability and monitoring capability and performs multi-objective optimization. It scientifically and accurately evaluates the combined well-ground microseismic monitoring effect under the current network layout, improves the monitoring capability and positioning capability of the combined well-ground microseismic monitoring system, and provides a theoretical basis for the layout of a well-ground integrated microseismic monitoring network. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a workflow diagram of the present invention;

[0031] Figure 2 is a graph of the optimal solution set after iterative evolution is completed in an embodiment of the present invention;

[0032] Figure 3 This is a schematic diagram of a mine selecting a station network layout plan from an optimal solution set according to its own tendency requirements in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The present invention is described in detail below through a specific embodiment in conjunction with the accompanying drawings.

[0034] like Figure 1 As shown, this embodiment includes the following steps:

[0035] (1) Determine the evaluation indicators of positioning capability and monitoring capability to measure the effect of well-ground combined microseismic monitoring. The principles are as follows: the stronger the positioning capability of the well-ground combined microseismic monitoring system, the smaller the distance error between the theoretical earthquake source position and the actual earthquake source position, and the higher the positioning accuracy; the stronger the monitoring capability, the stronger the ability to pick up small-energy mine earthquakes, and the richer the microseismic signals collected by the system;

[0036] (2) According to the positioning capability and monitoring capability evaluation indicators determined in step (1), a positioning capability evaluation function D and a monitoring capability evaluation function Q are respectively constructed. According to the impact hazard area of ​​the mine, [Xmin=19382410.16, Xmax=19382855.41], [Ymin=4320116.74, Ymax=4323157.40], and [Zmin=640, Zmax=790] are determined. The X-direction spacing dx=40, the Y-direction spacing dy=100, and the Z-direction spacing dz=50 are taken. Then, a three-dimensional grid evaluation model with a grid number of x1×y1×z1=12×31×4 can be constructed.

[0037] The mine ground sensor receives the signal and sets the wave speed V p Take 2900, the downhole sensor receives the signal and sets the wave speed V p Take 3700, P wave velocity variance Take 100, and read the variance σ when the P wave first arrives t Take 0.005 and substitute it into the positioning capability evaluation function D:

[0038]

[0039] in,

[0040]

[0041]

[0042] The ambient noise in the mine is NLu=1.5×10 -6 , the ground environmental noise is NLs=6×10 -8 , that is, the ground sensor requires the P wave initial peak amplitude f≥3×6.0×10 -8 m / s=1.8×10 -7m / s, the downhole sensor requires the peak amplitude of the P wave to be f≥3×1.5×10 -6 m / s=4.5×10 -6 m / s, microseismic amplitude-to-energy ratio coefficient α1=4.72274×10 -7 and the microseismic attenuation coefficient α2=0.0010618, and the monitoring capability evaluation function Q is constructed as follows:

[0043]

[0044] in, is the inner point of the 3D grid model recorded by all surface and downhole sensors on the well-ground integrated microseismic monitoring network scheme G (X i ,Y j ,Z k ) can trigger the well-ground integrated microseismic monitoring network to record microseismic signals. i,j,k The fourth energy value after sorting from smallest to largest:

[0045]

[0046] (3) Substitute the positioning capability evaluation function D and the monitoring capability evaluation function Q constructed in step (2) into the coordinates of the coal mine well-ground joint microseismic network to evaluate the monitoring effect:

[0047] The sensitivity of the surface sensor and the downhole sensor is set to 1500m, that is, when the midpoint (X i ,Y j ,Z k ) When the distance between it and a sensor exceeds 1500m, the sensor will not participate in the point (X i ,Y j ,Z k ) monitoring effectiveness evaluation;

[0048] The mine's existing underground combined microseismic monitoring system includes 9 ground sensors and 26 underground sensors. The specific coordinates of the ground sensors are:

[0049] serial number x / m y / m z / m 1 19382608.71 4322449.1 1391.67 2 19381865.17 4321677.21 1359.57 3 19383510.33 4321762.48 1390.2 4 19382666.62 4320947.98 1351.23 5 19381895.16 4320208.64 1340.84 6 19383448.73 4320226.14 1355.44 7 19381853.98 4323127.92 1405.79 8 19383491.11 4323183.41 1344.98 9 19380831.08 4321649.55 1338

[0050] The specific coordinates of the downhole sensor are:

[0051] serial number x / m y / m z / m serial number x / m y / m z / m S1 19383285 4321836 1400 S14 19382485 4321836 1400 S2 19383285 4322036 1400 S15 19382485 4322036 1400 S3 19383285 4322236 1400 S16 19382485 4322236 1400 S4 19383285 4322436 1400 S17 19382485 4322436 1400 S5 19383285 4322636 1400 S18 19382485 4322636 1400 S6 19383285 4322836 1400 S19 19382485 4322836 1400 S7 19383085 4321836 1400 S20 19382285 4321836 1400 S8 19383085 4322036 1400 S21 19382285 4322036 1400 S9 19383085 4322236 1400 S22 19382285 4322236 1400 S10 19383085 4322436 1400 S23 19382285 4322436 1400 S11 19383085 4322636 1400 S24 19382285 4322636 1400 S12 19383085 4322836 1400 S25 19382285 4322836 1400 S13 19382885 4321836 1400 S26 19383285 4323036 1400

[0052] Substituting the positioning capability evaluation function D constructed in step (2) into the function,

[0053]

[0054] Substituting the monitoring capability evaluation function Q constructed in step (2) into the function,

[0055]

[0056] Therefore, the current evaluation results of the mine's combined well-ground microseismic monitoring are as follows: positioning capability is 17.74m, monitoring capability is 662.34J, that is, the comprehensive positioning error is 17.74m, and the average microseismic energy that can be monitored by all grid evaluation nodes is 662.34J;

[0057] (4) Based on the coordinates of the multiple candidate points for ground sensor deployment generated in step (3), several well-ground joint network deployment plans are randomly generated:

[0058] In step (3), 420 ground candidate point coordinates are generated, numbered from 1 to 420, and then 200 well-ground joint network layout plans are randomly generated. Any randomly generated well-ground joint network layout plan includes ground sensors and downhole sensors. The ground sensors are randomly selected from the candidate point coordinates, and the number is the same as the number of ground sensors in the original well-ground joint microseismic monitoring network of the coal mine, which is 9. The number and coordinates of the downhole sensors are the same as the downhole sensors in the original well-ground joint microseismic monitoring network of the coal mine. The generated well-ground joint microseismic monitoring network layout plans are shown in the following table:

[0059]

[0060] (5) By improving the NSGA-II multi-objective genetic algorithm, the well-ground joint network layout plan generated in step (4) is used as the initial population for evaluation and evolution: the traditional NSGA-II multi-objective genetic algorithm simulates binary crossover and polynomial mutation operations, and the new algorithm mutation operation selects a mixed use of adjacent gene mutation, gene insertion mutation, gene exchange mutation, three-point gene exchange mutation and two-point inversion mutation; the crossover operation selects a mixed use of partial mapping crossover, cyclic crossover operator, edge recombination crossover, linear sequential crossover, sequential crossover operator and uniform crossover; the evolutionary generation number of the improved multi-objective genetic algorithm is set to 200, and the optimization objective function of the improved NSGA-II multi-objective genetic algorithm is:

[0061]

[0062] (6) According to the iterative evolution results of the NSGA-II multi-objective genetic improvement algorithm generated in step (5), a network layout plan with optimized well-ground joint microseismic monitoring effect is provided for the coal mine. The iterative evolution results of the NSGA-II multi-objective genetic improvement algorithm are as follows: Figure 2 shown.

[0063] like Figure 3As shown in the figure, based on the iterative solution set, if the coal mine tends to improve its monitoring capability, the well-ground combined microseismic monitoring network layout scheme with the smallest monitoring capability objective function value Q in the solution set is selected; if the coal mine tends to improve its positioning capability, the well-ground combined microseismic monitoring network layout scheme with the smallest positioning capability objective function value D in the solution set is selected; if the coal mine takes into account improving both positioning and monitoring capabilities, the well-ground combined microseismic monitoring network layout scheme closest to the origin in the solution set scatter diagram is selected. The ground sensor coordinates of the well-ground combined microseismic network layout schemes under the three tendency requirements provided for the coal mine are shown in the table below. It can be seen that the monitoring effect of the optimized well-ground combined microseismic monitoring network is significantly improved compared with the well-ground combined microseismic monitoring effect under the original coal mine network layout scheme:

[0064]

[0065]

Claims

1. A method for evaluating and improving the effect of well-ground combined microseismic monitoring, characterized in that: The steps include: (1) Determine the evaluation indicators of positioning capability and monitoring capability to measure the effect of well-ground joint microseismic monitoring; (2) constructing a positioning capability evaluation function D and a monitoring capability evaluation function Q respectively according to the positioning capability and monitoring capability evaluation indicators determined in step (1); (3) Substituting the positioning capability evaluation function D and the monitoring capability evaluation function Q constructed in step (2) into the coordinates of the coal mine ground-to-shaft joint microseismic network to evaluate the monitoring effect and generate the coordinates of multiple candidate points for deploying ground sensors; (4) randomly generating several well-ground joint network layout plans based on the coordinates of the multiple candidate points for deploying ground sensors generated in step (3); (5) By improving the NSGA-II multi-objective genetic algorithm, the well-ground joint network layout scheme generated in step (4) is used as the initial population for evaluation and evolution; (6) Based on the iterative evolution results of the NSGA-II multi-objective genetic improvement algorithm generated in step (5), a network layout plan with optimized well-ground joint microseismic monitoring effect is provided for the coal mine; In step (2), the positioning capability evaluation function D is: in, Where W is a diagonal matrix, W w is the element on the diagonal matrix, T w is the distance from the wth station on the well-ground integrated microseismic monitoring network G to the inner point of the 3D grid evaluation model (X i ,Y j ,Z k ) propagation time; x1×y1×z1 is the number of grids in the three-dimensional grid evaluation model, V p is the P-wave velocity, and σ t are the variance of P-wave velocity and the variance of P-wave first arrival time, respectively; where w∈1,2,…,n; i∈1,2,…,x1, j∈1,2,…,y1, k∈1,2,…,z1; The monitoring capability evaluation function Q is: in, is the inner point of the 3D grid model recorded by all surface and downhole sensors on the well-ground integrated microseismic monitoring network scheme G (X i ,Y j ,Z k ) can trigger the well-ground integrated microseismic monitoring network to record microseismic signals. i,j,k The fourth energy value after sorting from smallest to largest: Where, f is the ground or underground environmental background noise value three times, r is the ground or underground sensor distance point (X i ,Y j ,Z k ), α1 is the microseismic amplitude-to-energy ratio coefficient, and α2 is the microseismic attenuation coefficient; In the step (5), the improved method of the traditional NSGA-II multi-objective genetic algorithm is as follows: the simulated binary crossover and polynomial mutation operations in the traditional NSGA-II multi-objective genetic algorithm are improved, and the new algorithm mutation operation selects a mixed use of adjacent gene mutation, gene insertion mutation, gene exchange mutation, three-point gene exchange mutation and two-point inversion mutation; the crossover operation selects a mixed use of partial mapping crossover, cyclic crossover operator, edge recombination crossover, linear sequential crossover, sequential crossover operator and uniform crossover; the evolutionary generation number of the improved multi-objective genetic algorithm should be no less than 100; the optimization objective function of the improved NSGA-II multi-objective genetic algorithm is:

2. The method for evaluating and improving the effect of well-ground combined microseismic monitoring according to claim 1 is characterized in that: In the step (3), when the positioning capability evaluation function D and the monitoring capability evaluation function Q are substituted into the coordinates of the coal mine ground-to-ground combined microseismic network to evaluate the monitoring effect, the sensitivity of the ground sensor and the underground sensor is set to 1500m, that is, when the point (X i ,Y j ,Z k ) When the distance between it and a sensor exceeds 1500m, the sensor will not participate in the point (X i ,Y j ,Z k ) monitoring effectiveness evaluation.

3. The method for evaluating and improving the effect of well-ground combined microseismic monitoring according to claim 2 is characterized in that: In the step (4), the randomly generated several well-ground combined network layout plans are as follows: any randomly generated well-ground combined network layout plan includes ground sensors and underground sensors, and the ground sensors are randomly selected from the coordinates of the candidate points, and the number is the same as the number of ground sensors in the original well-ground combined microseismic monitoring network of the coal mine, and the number and coordinates of the underground sensors are the same as the underground sensors in the original well-ground combined microseismic monitoring network of the coal mine.

4. The method for evaluating and improving the effect of well-ground combined microseismic monitoring according to claim 3 is characterized in that: In the step (6), the network layout schemes after optimizing the well-ground microseismic monitoring effect provided to the coal mine according to the iterative evolution results of the generated NSGA-II multi-objective genetic improved algorithm include three types: according to the iterative evolution result solution set, if the coal mine tends to improve the monitoring capability, the well-ground microseismic monitoring network layout scheme with the smallest monitoring capability objective function value Q in the solution set is selected; if the coal mine tends to improve the positioning capability, the well-ground microseismic monitoring network layout scheme with the smallest positioning capability objective function value D in the solution set is selected; if the coal mine takes into account improving both the positioning capability and the monitoring capability, the well-ground microseismic monitoring network layout scheme closest to the origin in the solution set scatter diagram is selected.

5. The method for evaluating and improving the effect of well-ground combined microseismic monitoring according to claim 4 is characterized in that: The construction criteria of the three-dimensional evaluation grid model are: Determine the X, Y, and Z direction ranges [Xmin, Xmax], [Ymin, Ymax], and [Zmin, Zmax] according to the impact danger zone, take the X direction spacing as dx, the Y direction spacing as dy, and the Z direction spacing as dz, and divide the grid into the number of Three-dimensional evaluation grid model; where x1, y1, and z1 represent the number of grids in the X, Y, and Z directions, respectively.

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