Rock burst hazard evaluation method based on "far-near" field precursor information

Abstract

The application provides a rock burst danger evaluation method based on "far-near" field precursor information, and relates to the technical field of mine safety. The method first establishes a rock burst "far-near" field comprehensive monitoring system, determines "far-near" field monitoring data evaluation indexes, and constructs a rock burst comprehensive evaluation index system; then, based on the rock burst comprehensive evaluation index system, a rock burst "far-near" field monitoring data group is constructed; the combination weight of the rock burst danger evaluation index is solved by using the "AHP method + variation coefficient method" based on game theory; finally, the rock burst danger grade is divided based on the weighted rank-sum ratio method. The method obtains the data monitored by the far-near field, realizes the rock burst early warning by using the combination weight and the weighted rank-sum ratio method, and considers the far-near field, so that the early warning is more accurate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mine safety, and particularly relates to a rock burst danger evaluation method based on far-near field precursor information. BACKGROUND

[0002] With the gradual depletion of shallow coal resources, deep coal mining has gradually become a development trend. The geological structure and stress environment faced by deep mining are more complex, leading to an increase in the danger of coal and rock dynamic disasters such as rock burst. At present, the rock burst monitoring method is mostly near-field monitoring within a certain range around the working face or roadway, without considering far-field monitoring.

[0003] Domestic scholars have proposed related rock burst danger prediction methods, such as the invention patents CN201911081711.5, CN202111558332.8, CN201610944309.5 and CN201510375710.7. However, the above patents all propose rock burst danger prediction methods from the aspects of microseisms, acoustic waves, etc. Rock burst is affected by the coupling of multiple factors, including coal seam impact tendency, high stress, faults and hard rock layers, etc. Single consideration of far-field data or near-field data lacks accuracy in predicting rock burst danger, and it is difficult to achieve accurate prediction of mine rock burst danger. The stress and displacement changes caused by tectonic activity and rock movement in the far-field region have an important influence on the occurrence of rock burst. Therefore, it is of great significance to propose a rock burst danger evaluation method based on far-near field precursor information, which provides a safety guarantee for rock burst mine production. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a rock burst danger evaluation method based on far-near field precursor information to solve the above technical problems of the prior art.

[0005] To solve the above technical problems, the technical solution adopted by the present application is: a rock burst danger evaluation method based on far-near field precursor information, comprising the following steps:

[0006] Step 1: Establish a rock burst far-near field comprehensive monitoring system;

[0007] The "far-near" field comprehensive monitoring system comprises a far-field monitoring system and a near-field monitoring system; the far-field monitoring system comprises a microseismic monitoring system, a hard rock anchor cable tension monitoring system and a fault anchor cable tension monitoring system; the microseismic monitoring system adopts ARAMIS seismometer to collect microseismic energy events occurring in the monitoring area and extract daily cumulative energy and daily cumulative frequency information; the hard rock anchor cable tension monitoring system and the fault anchor cable tension monitoring system both adopt GYM400 online monitoring system to extract hard rock anchor cable tension and fault anchor cable tension in the monitoring area;

[0008] The near-field monitoring system comprises a coal body stress monitoring system and a hydraulic support resistance monitoring system; the coal body stress monitoring system extracts coal deep hole stress and coal shallow hole stress maximum value information by arranging and installing borehole stress meters in the monitoring area; the hydraulic support resistance monitoring system extracts hydraulic support resistance information through hydraulic support monitoring sensors;

[0009] Step 2: Determine the "far-near" field monitoring data evaluation index, and construct an impact ground pressure comprehensive evaluation index system;

[0010] The impact ground pressure comprehensive evaluation index system comprises far-field evaluation indexes and near-field evaluation indexes; the far-field evaluation indexes are in turn daily cumulative energy, daily cumulative frequency, hard rock anchor cable tension maximum value and fault anchor cable tension maximum value; the near-field evaluation indexes are in turn daily deep hole maximum stress, daily shallow hole maximum stress and daily support resistance average value;

[0011] Step 3: Based on the impact ground pressure comprehensive evaluation index system, construct an impact ground pressure "far-near" field monitoring data group;

[0012] The evaluation index data in the impact ground pressure "far-near" field monitoring data group are normalized to eliminate the dimensional difference between the evaluation indexes:

[0013]

[0014] In the formula: g j is the jth evaluation index data after normalization; x ij is the jth evaluation index data corresponding to the ith monitoring data; the value range of i is 1~m, the value range of j is 1~n, m is the number of monitoring data, and n is the number of evaluation indexes.

[0015] Step 4: Solve the combination weight of the impact ground pressure risk evaluation index by using "AHP method+variation coefficient method" based on game theory;

[0016] Step 4.1: Solve the subjective weight of the impact ground pressure risk evaluation index by using AHP method;

[0017] Step 4.1.1: A comprehensive evaluation model of rock burst hazard is established according to the comprehensive evaluation index system of rock burst in step 2, which includes a target layer, a decision layer and an index layer; the target layer is used for rock burst hazard evaluation; the decision layer is far-field evaluation indexes and near-field evaluation indexes; the index layer is daily cumulative energy A1, daily cumulative frequency A2, maximum anchor cable tension of hard rock stratum A3 and maximum anchor cable tension of fault A4, daily maximum stress of deep hole A5, daily maximum stress of shallow hole A6 and daily average support resistance A7;

[0018] Step 4.1.2: A rock burst "far-near" field monitoring data judgment matrix is constructed according to the index layer data;

[0019] Step 4.1.3: Normalization processing is performed on the rock burst "far-near" field monitoring data judgment matrix, and the subjective weight of the rock burst "far-near" field evaluation indexes is solved by using the analytic hierarchy process;

[0020] Step 4.2: The objective weight of the rock burst hazard evaluation indexes is solved by using the coefficient of variation method;

[0021] Step 4.2.1: A rock burst "far-near" field monitoring data original matrix is constructed, normalized and a decision matrix is constructed;

[0022] Step 4.2.2: The coefficient of variation of each evaluation index is solved, and the objective weight of the rock burst "far-near" field evaluation indexes is determined;

[0023] Step 4.3: The combined weight of the subjective weight and the objective weight is solved by using the game theory;

[0024] Step 5: The rock burst hazard grade is divided based on the weighted rank-sum ratio method;

[0025] Step 5.1: The weighted rank-sum ratio WRSR of each index is calculated according to the weight of the rock burst "far-near" field evaluation indexes;

[0026]

[0027] In the formula, i=1, 2, …, n; j=1, 2, …, m; R ij is the rank of the ith row and jth column of the matrix R composed of each evaluation index data; W j is the combined weight of the jth evaluation index;

[0028] Step 5.2: The probability density Probit value is determined, and the regression equation WRSR=a+b×Probit is generated with the Probit value as the independent variable and the WRSR value as the dependent variable;

[0029] Step 5.3: Statistical test is performed on the regression equation;

[0030] Step 5.4: Calculate the WRSR critical value according to the regression equation to classify the rock burst danger.

[0031] The beneficial effects produced by the above technical solutions are that the rock burst danger evaluation method based on the far-near field precursor information provided by the application has the advantages that the rock burst is coupled by multiple factors, including the coal seam impact tendency, high stress, fault and hard rock layer and other influencing factors. Considering the far field data or near field data alone lacks accuracy in predicting the rock burst danger, and it is difficult to accurately predict the rock burst danger of the mine. The stress and displacement changes caused by the tectonic activity and rock movement in the far field area have an important influence on the occurrence of rock burst. Therefore, the rock burst danger evaluation method based on the far-near field precursor information has important significance for providing safety protection for the production of the rock burst mine. The background research analyzes the advantages of the method, that is, there is no means of considering far field monitoring at present, the method obtains the far-near field monitoring data, realizes the rock burst early warning by using the combination weight and weighted rank sum ratio method, provides a new early warning idea, and considers the far-near field to make the early warning more accurate. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 The flow chart of the rock burst danger evaluation method based on the far-near field precursor information provided by the embodiment of the application is shown in the figure.

[0033] Figure 2 The layout of the rock burst far-near field comprehensive monitoring system provided by the embodiment of the application is shown in the figure.

[0034] Figure 3 The layout of the hard rock layer and fault anchor cable tension monitoring system provided by the embodiment of the application is shown in the figure.

[0035] Figure 4 The structure diagram of the rock burst comprehensive evaluation index system provided by the embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0036] The specific embodiments of the application will be further described in detail below in combination with the drawings and embodiments. The following embodiments are used to illustrate the application, but not to limit the scope of the application.

[0037] In this embodiment, the rock burst danger evaluation method based on the far-near field precursor information includes the following steps as shown in the figure. Figure 1

[0038] Step 1: Establish a rock burst far-near field comprehensive monitoring system as shown in the figure. Figure 2

[0039] ​​The "far-near" field comprehensive monitoring system comprises a far field monitoring system and a near field monitoring system; the far field monitoring system comprises a microseismic monitoring system, a hard rock stratum anchor cable tension monitoring system and a fault anchor cable tension monitoring system; the microseismic monitoring system adopts ARAMIS seismic pick-up to collect microseismic energy events occurring in a monitoring area and extract daily cumulative energy and daily cumulative frequency information; the hard rock stratum anchor cable tension monitoring system and the fault anchor cable tension monitoring system both adopt GYM400 online monitoring system to extract hard rock stratum anchor cable tension and fault anchor cable tension in the monitoring area, and tension change reflects hard rock stratum migration and fault activation.

[0040] In the embodiment, the arrangement method of the hard rock stratum and fault anchor cable tension monitoring system is as follows: a special lengthened anchor cable is fixed in the hard rock stratum (fault) area by grouting anchoring, and a certain pre-tightening force is applied to the tension anchor cable, as shown in the figure. Figure 3 The anchor cable tension monitoring adopts Youluoka MCS-250 mine anchor cable dynamometer, and the dynamometer performs online data transmission through an underground monitoring substation and a ring network. With the continuous advancement of the working face, the hard rock stratum is destabilized and broken, and the fault is activated under the influence of mining disturbance. The displacement of the hard rock stratum (fault) movement is decomposed, and the displacement component in the radial direction of the tension anchor cable will reduce the tension value of the anchor cable, and the displacement component in other directions will increase the tension value of the anchor cable. According to the change of the tension value of the anchor cable, the possibility of rock burst can be predicted.

[0041] The near field monitoring system comprises a coal body stress monitoring system and a hydraulic support resistance monitoring system; the coal body stress monitoring system extracts coal body deep hole stress and coal body shallow hole stress maximum value information by arranging and installing borehole stress meters in the monitoring area; the hydraulic support resistance monitoring system extracts hydraulic support resistance information through a hydraulic support monitoring sensor;

[0042] In the embodiment, the coal body stress monitoring system arranges and installs borehole stress meters in the monitoring area, 2 monitoring points are arranged in each monitoring group, the buried depths are 12 m and 18 m respectively, and the spacing between the group monitoring points is not greater than 2 m. The coal body stress (deep) shallow hole stress maximum value information is extracted. The hydraulic support resistance monitoring system extracts hydraulic support resistance information through a hydraulic support monitoring sensor, and one KJ653 pressure substation is installed every 10 hydraulic supports (15 m).

[0043] Step 2: Determine the "far-near" field monitoring data evaluation index, and construct an impact ground pressure comprehensive evaluation index system;

[0044] The impact ground pressure comprehensive evaluation index system is as shown in the figure. Figure 4The evaluation indexes are shown, including a far-field evaluation index and a near-field evaluation index; the far-field evaluation index is in turn a daily cumulative energy, a daily cumulative frequency, a maximum hard rock anchor cable tension, and a maximum fault anchor cable tension; the near-field evaluation index is in turn a daily deep hole maximum stress, a daily shallow hole maximum stress, and a daily support resistance average value;

[0045] Step 3: Based on the comprehensive evaluation index system of rock burst, a rock burst “far-near” field monitoring data group is constructed;

[0046] The evaluation index data in the rock burst “far-near” field monitoring data group are normalized to eliminate the dimensional difference between the evaluation indexes:

[0047]

[0048] In the formula, g j is the jth normalized evaluation index data; x ij is the jth evaluation index data corresponding to the ith monitoring data; the value range of i is 1 to m, the value range of j is 1 to n, m is the number of monitoring data, and n is the number of evaluation indexes.

[0049] In this embodiment, the normalized rock burst evaluation index data are shown in Table 1.

[0050] Table 1: Normalized rock burst evaluation index data

[0051]

[0052]

[0053] Step 4: The combination weight of the rock burst hazard evaluation index is solved by using the “AHP method + variation coefficient method” based on game theory;

[0054] Step 4.1: The subjective weight of the rock burst hazard evaluation index is solved by using the AHP method;

[0055] Step 4.1.1: A rock burst hazard comprehensive evaluation model is established according to the comprehensive evaluation index system of rock burst in step 2, the model including a target layer, a decision layer, and an index layer; the target layer is used for rock burst hazard evaluation; the decision layer is the far-field evaluation index and the near-field evaluation index; and the index layer is a daily cumulative energy A1, a daily cumulative frequency A2, a maximum hard rock anchor cable tension A3, a maximum fault anchor cable tension A4, a daily deep hole maximum stress A5, a daily shallow hole maximum stress A6, and a daily support resistance average value A7;

[0056] Step 4.1.2: A rock burst “far-near” field monitoring data judgment matrix is constructed according to the index layer data;

[0057] In this embodiment, the "far-near" field monitoring data judgment matrix of rock burst is constructed according to the index layer data, as shown in Table 2.

[0058] Table 2 Rock burst "far-near" field monitoring data judgment matrix

[0059]

[0060] Step 4.1.3: Normalization processing is performed on the rock burst "far-near" field monitoring data judgment matrix, and the subjective weight of the rock burst "far-near" field evaluation index is solved by using the analytic hierarchy process;

[0061] In this embodiment, the judgment matrix is normalized to solve the subjective weight of the rock burst "far-near" field evaluation index, as shown in Table 3.

[0062] Table 3 Subjective weight of rock burst "far-near" field evaluation index solved by analytic hierarchy process

[0063]

[0064] Step 4.2: The objective weight of the rock burst risk evaluation index is solved by using the variation coefficient method;

[0065] Step 4.2.1: The rock burst "far-near" field monitoring data original matrix is constructed, normalized, and the decision matrix is constructed;

[0066] Step 4.2.2: The variation coefficient of each index is solved, and the objective weight of the rock burst "far-near" field evaluation index is determined;

[0067] In this embodiment, the objective weight of the rock burst "far-near" field evaluation index is determined, as shown in Table 4.

[0068] Table 4 Objective weight of rock burst "far-near" field evaluation index solved by variation coefficient method

[0069] Index name Average value Standard deviation CV coefficient Weight Daily cumulative energy 0.063 0.19 3.032 0.423 Daily cumulative frequency 0.451 0.288 0.639 0.089 Maximum anchor cable tension in hard rock stratum 0.479 0.385 0.805 0.112 Maximum anchor cable tension in fault 0.402 0.344 0.856 0.120 Daily maximum stress in deep hole 0.234 0.185 0.788 0.110 Daily maximum stress in shallow hole 0.541 0.32 0.592 0.083 Daily support resistance average value 0.668 0.299 0.448 0.063

[0070] Step 4.3: The combined weight is solved by using game theory;

[0071] Step 4.3.1: Assuming that L weight calculation methods are used to calculate the weight vector of n evaluation indexes, the weight vector u k :

[0072] u k =(u k1 ,u k2 ,...,u kn ),k=1,2,...,L

[0073] In this embodiment, L is 2, i.e. the index weight vector determined by AHP method is denoted as u1, and the index weight vector determined by coefficient of variation method is denoted as u2.

[0074] It is assumed that there is a linear relationship between the subjective weight and the objective weight of the impact ground pressure "far-near" field evaluation index, and a combined weight vector is constructed, as shown in the following formula:

[0075]

[0076] In the formula: u is a linear combination of the weight vectors solved by L methods; α k is a linear combination coefficient;

[0077] Step 4.3.2: According to the differential properties of the matrix, a weight optimal coefficient model is constructed to minimize the deviation of the subjective weight and the objective weight, and to achieve consistency or compromise between the two weights in game theory:

[0078]

[0079] In this embodiment, the constructed weight optimal coefficient model is shown in the following formula:

[0080]

[0081] Step 4.3.3: Solve the weight optimal coefficient model and perform normalization processing to determine the optimal coefficient, and then obtain the optimal combined weight vector;

[0082] The α k solved by solving the weight optimal coefficient model is normalized to obtain the optimal weight coefficient The optimal combined weight vector is U * The elements in the optimal combined weight vector are denoted as W j , i.e. U * = (W1, W2, …, W j , …, W n ).

[0083] In this embodiment, the subjective weight optimal coefficient and the objective weight optimal coefficient are determined, and the optimal combined weight of the impact ground pressure "far-near" field evaluation index is determined, as shown in Table 5.

[0084] Table 5 Optimal combined weight of impact ground pressure "far-near" field evaluation index

[0085]

[0086] Step 5: Based on the weighted rank sum ratio method, the impact ground pressure risk level is divided;

[0087] Step 5.1: According to the combined weight of the rock burst "far-near" field evaluation index, the weighted rank sum ratio WRSR of each index is calculated;

[0088]

[0089] wherein: i = 1, 2, ···, n; j = 1, 2, ···, m; R ij is the rank of the i-th row and the j-th column of the matrix R composed of each evaluation index data; W j is the comprehensive weight of the j-th evaluation index;

[0090] In this embodiment, according to the comprehensive weight of the rock burst "far-near" field evaluation index, the weighted rank sum ratio WRSR of each index is calculated, as shown in Table 6;

[0091] Table 6 WRSR value of each data set

[0092] Serial number WRSR Probit Serial number WRSR Probit 1 0.326 3.214 15 0.734 5.140 2 0.571 3.554 16 0.737 5.234 3 0.576 3.779 17 0.749 5.331 4 0.584 3.956 18 0.752 5.431 5 0.598 4.104 19 0.761 5.535 6 0.605 4.235 20 0.765 5.646 7 0.614 4.354 21 0.767 5.765 8 0.615 4.465 22 0.771 5.896 9 0.646 4.569 23 0.788 6.044 10 0.665 4.669 24 0.887 6.221 11 0.673 4.766 25 0.893 6.446 12 0.691 4.860 26 0.893 6.786 13 0.712 4.954 27 0.903 7.355 14 0.721 5.046

[0093] Step 5.2: Determine the probability density Probit value, and generate the regression equation WRSR = a + b x Probit with Probit value as the independent variable and WRSR value as the dependent variable.

[0094] In this embodiment, the linear regression fitting is performed with Probit value as the independent variable and WRSR value as the dependent variable, and the following regression equation is established:

[0095] WRSR = 0.099 + 0.119*Probit

[0096] Step 5.3: Statistical test is performed on the regression equation.

[0097] In this embodiment, the result analysis of F test can obtain that the significance P value is 0.000, the level presents significance, and the original hypothesis that the regression coefficient is 0 is rejected, and the fitting degree R 2 of the model is 0.9, and the model performs well, so the model basically meets the requirements. For the variable collinearity performance, all VIFs are less than 10, so the regression equation has no multiple collinearity problem, and the model is well constructed. See Table 7 for details.

[0098] Table 7 Effect test of regression equation

[0099]

[0100] Step 5.4: The rock burst hazard classification is performed according to the WRSR critical value calculated by the regression equation.

[0101] In the embodiment, the 27 groups of rock burst "far-near" field monitoring data are divided into four rock burst danger grades by using the Delphi method, as shown in Table 8.

[0102] Table 8 Classification of rock burst danger grades

[0103] Danger grade division Percentile critical value Probit WRSR critical value Serial number High impact danger <6.681 <3.5 <0.5148 3 Medium impact danger 6.681～ 3.5～ 0.5148～ 13～23 Weak impact danger 50.000～ 5～ 0.6931～ 1～2、4～12、25～26 No impact danger 93.319～ 6.5～ 0.8714～ 24、27

[0104] In the embodiment, the accuracy of the rock burst danger evaluation method based on "far-near" field precursor information is as high as 80% for the judgment of the rock burst field situation of a certain mine, and the evaluation effect is good.

[0105] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope defined by the claims of the present application.

Claims

1. A method for evaluating rock burst hazard based on "far-near" field precursor information, characterized in that: The method comprises the following steps: Step 1: establishing a rock burst "far-near" field comprehensive monitoring system; the "far-near" field comprehensive monitoring system comprises a far field monitoring system and a near field monitoring system; the far field monitoring system comprises a microseismic monitoring system, a hard rock stratum anchor cable tension monitoring system and a fault anchor cable tension monitoring system; the microseismic monitoring system adopts ARAMIS seismic pick-up to collect microseismic energy events occurring in a monitoring area, and extracts daily cumulative energy and daily cumulative frequency information; the hard rock stratum anchor cable tension monitoring system and the fault anchor cable tension monitoring system both adopt GYM400 online monitoring systems to extract hard rock stratum anchor cable tension and fault anchor cable tension in the monitoring area; the near field monitoring system comprises a coal body stress monitoring system and a hydraulic support resistance monitoring system; the coal body stress monitoring system extracts coal body deep hole stress and coal body shallow hole stress maximum value information by arranging and installing borehole stress meters in the monitoring area; the hydraulic support resistance monitoring system extracts hydraulic support resistance information through a hydraulic support monitoring sensor; Step 2: determining rock burst "far-near" field monitoring data evaluation indexes and constructing a rock burst comprehensive evaluation index system; the rock burst comprehensive evaluation index system comprises far field evaluation indexes and near field evaluation indexes; the far field evaluation indexes are daily cumulative energy, daily cumulative frequency, hard rock stratum anchor cable tension maximum value and fault anchor cable tension maximum value in sequence; the near field evaluation indexes are daily deep hole maximum stress, daily shallow hole maximum stress and daily support resistance average value in sequence; Step 3: constructing a rock burst "far-near" field monitoring data group based on the rock burst comprehensive evaluation index system; Step 4: solving the combination weight of the rock burst danger evaluation indexes by using the "AHP method + variation coefficient method" based on game theory; Step 5: performing rock burst danger grade division based on the weighted rank sum ratio method.

2. The "far-near" field precursor information-based rock burst hazard evaluation method according to claim 1, characterized in that: Step 3: performing normalization processing on the evaluation index data in the rock burst "far-near" field monitoring data group, so as to eliminate the dimensional difference between the evaluation indexes: ； wherein: g j is the normalized jth evaluation index data; x ij is the jth evaluation index data corresponding to the ith monitoring data; the value range of i is 1~m, the value range of j is 1~n, m is the number of monitoring data, and n is the number of evaluation indexes.

3. The "far-near" field precursor information-based rockburst hazard evaluation method according to claim 2, characterized by: the specific method of Step 4 is as follows: Step 4.1: solving the subjective weight of the rock burst danger evaluation indexes by using the AHP method; Step 4.1.1: establishing a rock burst danger comprehensive evaluation model according to the rock burst comprehensive evaluation index system in Step 2, the model comprising a target layer, a decision layer and an index layer; the target layer is used for rock burst danger evaluation; the decision layer comprises far field evaluation indexes and near field evaluation indexes; the index layer comprises daily cumulative energy A1, daily cumulative frequency A2, hard rock stratum anchor cable tension maximum value A3, fault anchor cable tension maximum value A4, daily deep hole maximum stress A5, daily shallow hole maximum stress A6 and daily support resistance average value A7; Step 4.1.2: constructing a rock burst "far-near" field monitoring data judgment matrix according to the index layer data; Step 4.1.3: performing normalization processing on the rock burst "far-near" field monitoring data judgment matrix, and solving the subjective weight of the rock burst "far-near" field evaluation indexes by using the analytic hierarchy process; Step 4.2: solving the objective weight of the rock burst danger evaluation indexes by using the variation coefficient method; Step 4.2.1: Constructing the original matrix of rock burst "far-near" field monitoring data, normalizing, and constructing the decision matrix; Step 4.2.2: Solving the variation coefficient of each evaluation index to determine the objective weight of rock burst "far-near" field evaluation index; Step 4.3: Solving the combination weight of subjective weight and objective weight by using game theory.

4. The "far-near" field precursor information-based rock burst hazard evaluation method according to claim 3, characterized in that: The specific method of step 5 is: Step 5.1: According to the weight of rock burst "far-near" field evaluation index, the weighted rank sum ratio WRSR of each index is calculated; ； wherein: i = 1, 2, ···, n; j = 1, 2, ···, m; R ij is the rank of the i-th row and the j-th column of the matrix R composed of each evaluation index data; W j is the combination weight of the j-th evaluation index; Step 5.2: Determine the probability density Probit value, take Probit value as independent variable, WRSR value as dependent variable, generate regression equation WRSR=a+b×Probit; Step 5.3: Statistical test is carried out for the regression equation; Step 5.4: According to the critical value of WRSR calculated by the regression equation, the rock burst risk classification is carried out.

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