Rock burst working face overburden fracture line position state intelligent identification method

Abstract

The application provides a microseismic monitoring rock burst working face overburden fracture line position intelligent identification method, and relates to the technical field of rock burst. After normal mining of the working face, the inclination length of the whole working face is selected as a statistical interval, and a "unit length" is determined. Then, a fitting curve of the working face overburden fracture line is determined, the microseismic event cumulative frequency and cumulative energy of the microseismic event in the inclination dimension during the working face advancing and mining process are determined, and then the microseismic cumulative frequency curve and cumulative energy curve of the working face inclination dimension are determined. Then, a working face overburden fracture line position prediction model is established. Finally, the working face overburden fracture line position prediction model is used to predict the overburden fracture line position of the new working face. The method combines the working face overburden fracture height with the frequency and energy of the microseismic signal, quantitatively characterizes the working face overburden fracture height, and determines the rock burst working face overburden fracture line position.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of rock burst, in particular to a microseismic monitoring rock burst working face overburden fracture line position intelligent identification method. BACKGROUND

[0002] After the coal mining face is mined, the rock stratum in a certain height range of the roof is unloaded and broken in the vertical direction, so that the overburden stratum is broken and moved, and the ground subsides, the damage degree and the crack distribution gradually decrease from bottom to top, showing the characteristics of gradient damage, which can cause damage to buildings in the ground subsidence area and easily cause safety problems and property loss problems, so determining the position of the fracture line is an urgent problem to be solved in green mining and ecological protection of coal mines and safe mining of rock burst coal seams.

[0003] At present, there are few methods for determining the position of the fracture line of the mine working face, mainly theoretical analysis, numerical simulation calculation and field drilling monitoring. However, these methods have the following problems, resulting in poor actual engineering application effect: (1) The theoretical analysis and numerical simulation calculation method often simplifies the field a lot, which is quite different from the actual complex geological occurrence conditions and mechanical properties, and cannot well correspond to the actual situation; (2) The field drilling method takes samples at limited positions, the information is not complete, and the cost is high, which consumes a lot of time, manpower and equipment, and is also dangerous, and in deep coal mines, underground drilling detection is limited by environmental factors such as stratum lithology, ground temperature and underground water, and the cost is high, so the underground drilling method is not suitable for deep mines.

[0004] At present, microseismic monitoring systems are arranged in rock burst mines in China, which can effectively monitor the microseismic events representing rock stratum rupture during the mining process of the working face. However, there is no intelligent method for determining the position of the fracture line of the working face based on the microseismic monitoring technology. SUMMARY

[0005] The technical problem to be solved by the present application is to solve the shortcomings of the prior art, and to provide a microseismic monitoring rock burst working face overburden fracture line position intelligent identification method, which determines the position of the working face fracture line by using a large amount of microseismic signal data.

[0006] To solve the above technical problems, the technical scheme adopted by the present application is: a microseismic monitoring rock burst working face overburden fracture line position intelligent identification method, comprising the following steps:

[0007] Step 1: Determine the "unit length" of the inclination dimension of the working face;

[0008] After the working face is normally mined, the inclination length of the entire working face is selected as the statistical interval L, and the "unit length" Dl is determined;

[0009] Step 2: Determine the fitting curve of the working face overburden fracture line;

[0010] Step 2.1: Establish a coordinate system for the working face;

[0011] A rectangular coordinate system is established with the lower left corner of the cross section in the working face tendency dimension along the advancing direction as the origin, the horizontal axis representing the working face tendency length, and the vertical axis representing the working face overburden height;

[0012] Step 2.2: Select the horizontal coordinate position of the midpoint of each "unit length" to determine the overburden fracture height at that point through drilling;

[0013] Set the statistical interval to have N "unit lengths", then obtain N+1 drilling points on the fracture line, and the drilling point coordinates are represented as (x n ,y n ), where x n represents the horizontal coordinate of the nth drilling point, and y n represents the vertical coordinate of the nth drilling point;

[0014] Step 2.3: Determine the parametric representation formula of the working face overburden fracture line through cubic spline interpolation;

[0015] Step 2.3.1: Take N "unit lengths" as the interpolation interval, where the nth interpolation interval is represented as [x n ,x n+1 ], and take N+1 drilling points as the data points for cubic spline interpolation;

[0016] Step 2.3.2: Fit the curve of the working face overburden fracture line;

[0017] Fit a cubic polynomial of the overburden fracture line for each interpolation interval, and the cubic polynomial of the overburden fracture line fitted for the nth interpolation interval is represented as:

[0018] S n (x)=a n x 3 +b n x 2 +c n x+d n

[0019] In the formula, S n (x) represents the cubic polynomial fitted for the nth interpolation interval; a n , b n , c n , d n represent the coefficients to be solved; and x represents the horizontal coordinate of the working face tendency dimension;

[0020] Step 2.3.3: Solve the coefficients an , b n , c n , d n , construct the overall equation set of the cubic interpolation curve of the overburden fracture line, and obtain the parameterized expression of the cubic interpolation curve of the overburden fracture line;

[0021] Step 2.3.3.1: Determine the boundary condition of the working face overburden fracture line interpolation curve;

[0022] The boundary condition of the working face overburden fracture line interpolation curve is that the second derivative value of the working face overburden fracture line interpolation curve at the two endpoints of the statistical interval is zero, which is expressed as:

[0023] S''(x0) = 0

[0024] S''(x n ) = 0

[0025] In the formula, S''(x0) is the second derivative value of the cubic polynomial S(x) of the working face overburden fracture line position at the borehole point x0; S''(x n ) is the second derivative value of the cubic polynomial S(x) of the working face overburden fracture line position at the borehole point x n ;

[0026] Step 2.3.3.2: Determine the interpolation condition of the working face overburden fracture line interpolation curve;

[0027] The interpolation condition of the working face overburden fracture line interpolation curve is that the function value of the working face overburden fracture line interpolation curve at the two endpoints of the interpolation interval is the ordinate corresponding to the abscissa of the point on the fracture line, which is expressed as:

[0028] S n (x n ) = y n

[0029] S n (x n+1 ) = y n+1

[0030] In the formula, S n (x n ) is the function value of the cubic polynomial of the nth interpolation interval at the borehole point x n , and S n (x n+1 ) is the function value of the cubic polynomial of the nth interpolation interval at the borehole point x n+1 ;

[0031] Step 2.3.3.3: Construct the equation set of the working face overburden fracture line interpolation curve of each interpolation interval;

[0032] The boundary conditions and interpolation conditions of the working face overburden fracture line interpolation curve are brought into the cubic polynomial expression of the interpolation interval fitting overburden fracture line, and the linear equation group of the working face overburden fracture line interpolation curve of each interpolation interval is obtained, which is expressed as:

[0033]

[0034] Step 2.3.3.4: Construct the overall equation group of the cubic interpolation curve of the overburden fracture line;

[0035] The equation group of each interpolation interval is combined into an overall equation group to obtain the parameterized expression of the cubic interpolation curve of the overburden fracture line:

[0036]

[0037] Step 3: Determine the microseismic event cumulative frequency and cumulative energy of the microseismic event in the inclination dimension during the working face advancing process;

[0038] Step 3.1: Establish the coordinate system of the microseismic event cumulative frequency and cumulative energy;

[0039] A rectangular coordinate system is established with the lower left corner of the working face inclination dimension section along the advancing direction as the origin, the horizontal axis representing the working face inclination length, and the vertical axis representing the microseismic event cumulative frequency and cumulative energy of the working face;

[0040] Step 3.2: Determine the microseismic cumulative frequency and microseismic cumulative energy in each "unit length" in the statistical interval L, and take N "unit lengths" as interpolation intervals to determine the microseismic cumulative frequency and cumulative energy of the interpolation interval;

[0041] In the working face mining process, the microseismic event cumulative frequency f i and the cumulative energy E i in each "unit length" Δl in the statistical interval L are determined. n , the microseismic event cumulative energy is represented as E n , and in the form of coordinates, it is represented as (x n , f n ), (x n , E n ).

[0042] Step 4: Determine the microseismic cumulative frequency curve and cumulative energy curve in the working face inclination dimension;

[0043] Step 4.1: Fit the working face inclination microseismic cumulative frequency curve;

[0044] Step 4.1.1: fitting a microseismic cumulative frequency cubic polynomial for each interpolation interval, the microseismic cumulative frequency cubic polynomial fitted for the nth interpolation interval is expressed as:

[0045]

[0046] wherein, is the microseismic cumulative frequency cubic polynomial of the nth interpolation interval; is the coefficient to be solved; x is the horizontal coordinate of the overburden fracture line;

[0047] Step 4.1.2: solving the coefficient constructing the overall equation group of the microseismic cumulative frequency interpolation curve of the working face tendency, obtaining the parameterized expression of the microseismic cumulative frequency interpolation curve of the working face tendency;

[0048] Step 4.1.2.1: determining the boundary condition of the microseismic cumulative frequency interpolation curve of the working face tendency;

[0049] The boundary condition of the microseismic cumulative frequency interpolation curve of the working face tendency is that the second derivative value of the microseismic cumulative frequency interpolation curve of the working face tendency at the two endpoints of the statistical interval is zero, which is expressed as:

[0050] S f ″ (x0)=0

[0051] S f″ (x n )=0

[0052] In the formula, S f″ (x0) is the second derivative value of the microseismic cumulative frequency interpolation curve of the working face tendency at point x0; S f″ (x n ) is the second derivative value of the microseismic cumulative frequency interpolation curve of the working face tendency at point x n

[0053] Step 4.1.2.2: determining the interpolation condition of the microseismic cumulative frequency interpolation curve of the working face tendency;

[0054] The interpolation condition of the microseismic cumulative frequency interpolation curve of the working face tendency is that the function value of the microseismic cumulative frequency interpolation curve of the working face tendency at the two endpoints of the interpolation interval is the vertical coordinate corresponding to the horizontal coordinate of the point on the fracture line, which is expressed as:

[0055]

[0056] In the formula, the function value of the microseismic cumulative frequency interpolation curve of the working face tendency at x n ​​the function value of the working face tendency microseismic cumulative frequency interpolation curve of the nth interpolation interval at x n+1 ;

[0057] Step 4.1.2.3: Constructing the working face tendency microseismic cumulative frequency interpolation curve equation group of each interpolation interval;

[0058] The boundary conditions and interpolation conditions of the working face tendency microseismic cumulative frequency interpolation curve are brought into the cubic polynomial expression of the working face tendency microseismic cumulative frequency interpolation curve, and the linear equation group of the working face tendency microseismic cumulative frequency interpolation curve of each interpolation interval is obtained, which is expressed as:

[0059]

[0060] Step 4.1.2.4: Constructing the overall equation group of the working face tendency microseismic cumulative frequency interpolation curve;

[0061] The equation group of each interpolation interval of the working face tendency microseismic cumulative frequency interpolation curve is combined into an overall equation group, and the parameterized expression of the working face tendency microseismic cumulative frequency interpolation curve is obtained:

[0062]

[0063] Step 4.2: Fitting the working face tendency microseismic cumulative energy;

[0064] Step 4.2.1: Fitting a microseismic cumulative energy cubic polynomial for each interpolation interval, and the microseismic cumulative energy cubic polynomial fitted for the nth interpolation interval is expressed as:

[0065]

[0066] wherein, is the nth interpolation interval microseismic cumulative energy cubic polynomial; is the coefficient to be solved; x is the horizontal coordinate of the overburden fracture line;

[0067] Step 4.2.2: Solving the coefficient An overall equation group of the working face tendency microseismic cumulative energy interpolation curve is constructed, and a parameterized expression of the working face tendency microseismic cumulative energy interpolation curve is obtained;

[0068] Step 4.2.2.1: Determining the boundary conditions of the working face tendency microseismic cumulative energy interpolation curve;

[0069] The boundary conditions of the working face tendency microseismic cumulative energy interpolation curve are the second derivative values of the working face tendency microseismic cumulative energy interpolation curve at the two endpoints of the statistical interval, which are expressed as:

[0070] S E″(x0) = 0

[0071] S E″ (x n ) = 0

[0072] In the formula, S E″ (x0) is the second derivative value of the microseismic cumulative energy interpolation curve of the working face tendency at the borehole point x0; S E″ (x n ) is the second derivative value of the microseismic cumulative energy interpolation curve of the working face tendency at the borehole point x n ;

[0073] Step 4.2.2.2: Determine the interpolation condition of the microseismic cumulative energy interpolation curve of the working face tendency;

[0074] The interpolation condition of the microseismic cumulative energy interpolation curve of the working face tendency is that the function value of the microseismic cumulative energy interpolation curve of the working face tendency at the two end points of the interpolation interval is the ordinate corresponding to the abscissa of the point on the fracture line, which is expressed as:

[0075]

[0076] In the formula, The function value of the microseismic cumulative energy interpolation curve of the working face tendency at x n of the nth interpolation interval, The function value of the microseismic cumulative energy interpolation curve of the working face tendency at x n+1 of the nth interpolation interval;

[0077] Step 4.2.2.3: Construct the equation set of the microseismic cumulative energy interpolation curve of the working face tendency;

[0078] The boundary condition and the interpolation condition of the microseismic cumulative energy interpolation curve of the working face tendency are brought into the cubic polynomial expression of the microseismic cumulative energy interpolation curve of the working face tendency, to obtain the linear equation set of the microseismic cumulative energy interpolation curve of the working face tendency in each interpolation interval, which is expressed as:

[0079]

[0080] Step 4.2.2.4: Construct the overall equation set of the microseismic cumulative frequency interpolation curve of the working face tendency;

[0081] Combine the equation set of each interpolation interval into an overall equation set to obtain the parameterized expression of the microseismic cumulative frequency interpolation curve of the working face tendency:

[0082]

[0083] Step 5: Establish a working face overburden fracture line position prediction model;

[0084] Step 5.1: determining training samples;

[0085] M borehole points in the statistical interval L are selected as training samples, the abscissa of the training samples is represented as x m , m = 1, 2, …, M, the corresponding overburden fracture height of the working face is represented as S(x m ), the microseismic cumulative frequency is represented as S f (x m ), and the microseismic cumulative energy is represented as S E (x m );

[0086] Step 5.2: designing a neural network model architecture for predicting the position of the overburden fracture line of the working face;

[0087] Step 5.2.1: determining the basic structure of the neural network for predicting the position of the overburden fracture line of the working face;

[0088] Step 5.2.1.1: determining the input layer size and feature vector of the neural network for predicting the position of the overburden fracture line of the working face;

[0089] The feature vector input by the input layer of the neural network is represented as:

[0090] X m = [S f (x m ), S E (x m ), Δx m , w, h, D]

[0091]

[0092] In the formula, represents the input layer of the neural network; X m represents the feature vector of the mth training sample; Δx m represents the distance from the mth training sample to the midpoint of the working face in the inclination dimension; w represents the width of the working face; h represents the height of the working face; and D represents the depth of the working face;

[0093] Step 5.2.1.2: designing the intermediate layer structure of the neural network for predicting the position of the overburden fracture line of the working face;

[0094] Step 5.2.1.2.1: determining the number of layers of the intermediate layer of the neural network;

[0095] The neural network has a total of L layers, and the intermediate layer includes L-1 layers;

[0096] Step 5.2.1.2.2: determining the number of neurons in the middle of each layer of the neural network;

[0097] The number of neurons in each layer of the intermediate layer is determined according to the "empirical formula", and the calculation formula is:

[0098]

[0099] Wherein, N h l represents the number of neurons in the lth layer of the neural network; N i l represents the number of input neurons in the lth layer of the neural network; N o l represents the number of output neurons in the lth layer of the neural network; M represents the number of training samples; β represents an arbitrary variable value that can be self-taken, and the range can be taken as 2-10;

[0100] Step 5.2.1.2.3: Determine the weight and bias of the intermediate layer of the neural network;

[0101] In the neural network, the intermediate layer calculates the process from input to output through forward propagation, and the output of each layer is the result obtained by linear transformation of the output of the previous layer and then through the activation function transformation, which is expressed as:

[0102]

[0103] In the formula, W l represents the weight matrix of the lth layer of the neural network; b l represents the bias vector of the lth layer of the neural network; represents the output result of the mth training sample through the l-1th layer of the neural network; represents the activation output of the mth training sample in the l-1th layer of the neural network after linear transformation; represents the result obtained after the activation function σ transformation;

[0104] Step 5.2.1.3: Determine the size of the output layer of the neural network;

[0105] The output layer of the neural network is the predicted working face fracture height corresponding to the mth "unit length" of the working face, which is expressed as:

[0106]

[0107] In the formula, H m represents the predicted working face fracture height corresponding to the mth "unit length" of the working face;

[0108] Step 5.2.2: Initialize the neural network model parameters;

[0109] Step 5.2.2.1: Calculate the loss function value;

[0110] The mean square error is selected as the loss function, and the calculation formula is:

[0111]

[0112] In the formula, Loss represents the loss value;

[0113] Step 5.2.2.2: Calculate the gradient;

[0114]

[0115] wherein, represents the gradient of the loss function Loss with respect to the weight matrix W l of the lth layer of neural network; represents the gradient of the loss function Loss with respect to the bias vector b l of the lth layer of neural network;

[0116] Step 5.2.2.3: Iteratively update the neural network model parameters;

[0117] The weight matrix and the bias vector are updated using the gradient descent method, and the calculation formula is:

[0118]

[0119] wherein, t represents the iteration number, and a represents the correction coefficient, which is used to control the step size in updating the weight matrix W l of the lth layer of neural network and the bias vector b l of the lth layer of neural network;

[0120] Step 5.2.2.4: Repeat steps 5.2.2.2 and 5.2.2.3 until the condition for stopping iteration is met, as shown in the following formula:

[0121]

[0122] In the formula, represents the infinite norm of ; and represents the infinite norm of ; and e1, e2 represent the set threshold value;

[0123] Step 6: Use the working face overburden fracture line position prediction model to predict the new working face overburden fracture line position;

[0124] Step 6.1: Divide Q "unit lengths" in the tendency dimension of the working face to be predicted, wherein the midpoint coordinate of the qth unit length is represented as x q ;

[0125] Step 6.2: Collecting working face data;

[0126] Step 6.2.1: Determining physical information of working face;

[0127] Determine the length of the inclination, the height, and the buried depth of the working face, respectively represented as x pred , h pred , D pred ;

[0128] Determine the distance Δx of the measuring point from the midpoint of the length of the inclination of the working face m pred ;

[0129] Step 6.2.2: Collecting microseismic data during the working face mining process;

[0130] Collect the microseismic signals during the working face mining process using the microseismic monitoring equipment, and determine the microseismic cumulative frequency and the microseismic cumulative energy of the qth "unit length" in the inclination dimension during the working face mining process, respectively represented as ;

[0131] Step 6.3: Predicting the fracture line position of the working face using the working face overburden fracture line position prediction model;

[0132] Input the data collected in step 6.2 into the neural network model of the working face overburden fracture line position prediction model, and the input data is represented as:

[0133]

[0134] wherein, is the input data of the mth sample;

[0135] Obtain the fracture line position of the working face to be predicted, as shown in the following formula:

[0136]

[0137] In the formula, represents the fracture height corresponding to the midpoint of the qth "unit length".

[0138] The beneficial effects produced by the above technical solution are that the intelligent identification method for the overburden fracture line position of the rock burst working face provided by the present application proposes a data-driven method, uses a large amount of microseismic signal data to solve the problem of determining the working face fracture line position which is difficult to solve by analytical method, combines the working face overburden fracture height with the frequency and energy of the microseismic signal, quantitatively represents the working face overburden fracture height, and thus determines the overburden fracture line position of the rock burst working face, providing scientific and powerful guidance for actual engineering. BRIEF DESCRIPTION OF DRAWINGS

[0139] Figure 1 A flow chart of the intelligent identification method for the position of the overburden fracture line of the rock stratum of the rock burst working face provided by the embodiment of the present application is shown in the figure;

[0140] Figure 2 A schematic diagram of the overburden fracture of the working face provided by the embodiment of the present application is shown in the figure;

[0141] Figure 3 A fitting curve diagram of the overburden fracture line of the working face provided by the embodiment of the present application is shown in the figure;

[0142] Figure 4 A fitting curve diagram of the cumulative frequency of the working face microseism provided by the embodiment of the present application is shown in the figure;

[0143] Figure 5 A fitting curve diagram of the cumulative energy of the working face microseism provided by the embodiment of the present application is shown in the figure;

[0144] Figure 6 A structure schematic diagram of the position prediction model of the overburden fracture line of the working face provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0145] The specific embodiments of the present application will be further described in detail below in combination with the drawings and examples. The following examples are used to illustrate the present application, but not to limit the scope of the present application.

[0146] The present embodiment is directed to a N115 working face of a certain mine, the length of the working face in the tendency dimension is 200m, the height is 4m, the working face has been mined for 350m, and the rock burst has occurred, the mine has installed a microseismic monitoring system, and the intelligent identification method for the position of the overburden fracture line of the rock stratum of the rock burst working face provided by the present application is used to determine the position of the overburden fracture line of the working face.

[0147] In the present embodiment, the intelligent identification method for the position of the overburden fracture line of the rock stratum of the rock burst working face provided by the present application, as shown in Figure 1 、 2 , includes the following steps:

[0148] Step 1: Determine the "unit length" of the working face in the tendency dimension;

[0149] After the working face is normally mined, the length of the working face in the tendency dimension is selected as the statistical interval L, and the "unit length" Dl is determined;

[0150] In the present embodiment, the length of the working face in the tendency dimension is selected as the statistical interval L as 200m, and the "unit length" Dl is determined as 20m;

[0151] Step 2: Determine the fitting curve of the overburden fracture line of the working face;

[0152] Step 2.1: Establish a coordinate system for the working face;

[0153] A rectangular coordinate system is established with the lower left corner of the cross section along the advancing direction of the working face as the origin, the horizontal axis representing the length of the working face in the tendency direction, and the vertical axis representing the height of the overburden rock of the working face;

[0154] Step 2.2: Select the horizontal coordinate position of the midpoint of each "unit length" to determine the height of the overburden rock fracture at that point through drilling;

[0155] Set the statistical interval to have N "unit lengths", then obtain N+1 drilling points on the fracture lines, and the drilling point coordinates are represented as (x n ,y n ), where x n represents the horizontal coordinate of the nth drilling point, and y n represents the vertical coordinate of the nth drilling point;

[0156] In this embodiment, the statistical interval is set to have 10 "unit lengths", then 11 drilling points on the fracture lines are obtained, and the drilling point coordinates are (0, 0), (20, 8), (40, 17), (60, 27), (80, 38), (100, 37), (120, 39), (140, 29), (160, 18), (180, 7), and (200, 0);

[0157] Step 2.3: Determine the parametric representation formula of the overburden rock fracture line of the working face through cubic spline interpolation;

[0158] Step 2.3.1: Take N "unit lengths" as the interpolation interval, where the nth interpolation interval is represented as [x n ,x n+1 ], and take N+1 drilling points as the data points for cubic spline interpolation;

[0159] Step 2.3.2: Fit the curve of the overburden rock fracture line;

[0160] Fit a cubic polynomial of the overburden rock fracture line for each interpolation interval, and the cubic polynomial of the overburden rock fracture line fitted for the nth interpolation interval is represented as:

[0161] S n (x)=a n x 3 +b n x 2 +c n x+d n

[0162] In the formula, S n (x) represents the cubic polynomial fitted for the nth interpolation interval; a n , bn , c n , d n denotes the coefficient to be solved; x denotes the horizontal coordinate of the working face tendency dimension;

[0163] Step 2.3.3: solving the coefficient a n , b n , c n , d n , constructing the overall equation group of the overburden fracture line cubic interpolation curve, and obtaining the parameterized expression of the overburden fracture line cubic interpolation curve;

[0164] Step 2.3.3.1: determining the boundary condition of the working face overburden fracture line interpolation curve;

[0165] The boundary condition of the working face overburden fracture line interpolation curve is that the second derivative value of the working face overburden fracture line interpolation curve at the two endpoints of the statistical interval is zero, which is expressed as:

[0166] S''(x0) = 0

[0167] S''(x n ) = 0

[0168] In the formula, S''(x0) is the second derivative value of the working face overburden fracture line position cubic polynomial S(x) at the drilling point x0; S''(x n ) is the second derivative value of the working face overburden fracture line position cubic polynomial S(x) at the drilling point x n ;

[0169] Step 2.3.3.2: determining the interpolation condition of the working face overburden fracture line interpolation curve;

[0170] The interpolation condition of the working face overburden fracture line interpolation curve is that the function value of the working face overburden fracture line interpolation curve at the two endpoints of the interpolation interval is the ordinate corresponding to the abscissa of the point on the fracture line, which is expressed as:

[0171] S n (x n ) = y n

[0172] S n (x n+1 ) = y n+1

[0173] In the formula, S n (x n ) is the function value of the cubic polynomial of the nth interpolation interval at the drilling point x n , S n (x n+1 ) is the function value of the cubic polynomial of the nth interpolation interval at the drilling point x n+1The function value at the point;

[0174] Step 2.3.3.3: Construct the equation group of the interpolation curve of the working face overburden fracture line in each interpolation interval;

[0175] The boundary conditions and interpolation conditions of the interpolation curve of the working face overburden fracture line are brought into the cubic polynomial expression of the overburden fracture line fitting in the interpolation interval to obtain the linear equation group of the interpolation curve of the working face overburden fracture line in each interpolation interval, which is expressed as:

[0176]

[0177] Step 2.3.3.4: Construct the overall equation group of the cubic interpolation curve of the overburden fracture line;

[0178] The equation group of each interpolation interval is combined into an overall equation group to obtain the parameterized expression of the cubic interpolation curve of the overburden fracture line:

[0179]

[0180] In this embodiment, the working face overburden fracture is as shown in Figure 2 , and the constructed working face overburden fracture line fitting curve is as shown in Figure 3 .

[0181] This embodiment selects the seventh microseismic cumulative frequency interpolation interval as an example for detailed calculation and description, and the interpolation interval is [x7, x8]. The cubic polynomial of the interpolation interval is set as

[0182] S3(x) = a3x 3 +b3x 2 +c3x+d3

[0183] The following solves the coefficients a3, b3, c3, and d3,

[0184] The boundary conditions are solved, and the second-order derivatives at both ends of the interpolation interval are zero, that is,

[0185] S″(x0) = 0

[0186] S″(x 11 ) = 0

[0187] That is, S″(x0) = 6a3x0+2b3 = 0, S″(x 11 ) = 6a3x 11 +2b3 = 0

[0188] The interpolation condition in the third interpolation interval is

[0189] S3(x3) = y3

[0190] S3(x4) = y4

[0191] S3(x3) = a3x3 3 +b3x3 2 +c3x3+d3 = y3, S3(x4) = a3x4 3 +b3x4 2 +c3x4+d3 = y4

[0192] Similarly, the calculated fracture line interpolation curve is:

[0193]

[0194] Step 3: Determine the microseismic cumulative frequency and cumulative energy of the microseismic event in the inclination dimension during the working face advancing process;

[0195] Step 3.1: Establish the coordinate system of the microseismic event cumulative frequency and cumulative energy;

[0196] Taking the lower left corner of the working face inclination dimension section along the advancing direction as the origin, a rectangular coordinate system is established, the horizontal axis represents the working face inclination length, and the vertical axis represents the microseismic cumulative frequency and the microseismic cumulative energy of the working face;

[0197] Step 3.2: Determine the microseismic cumulative frequency and microseismic cumulative energy in each "unit length" in the statistical interval L, and take N "unit lengths" as the interpolation interval to determine the microseismic cumulative frequency and cumulative energy of the interpolation interval;

[0198] In the working face mining process, the microseismic event cumulative frequency f i and the cumulative energy E i in each "unit length" Δl = 20m in the statistical interval L are determined. n , the microseismic event cumulative energy is represented as E n , which is represented in the form of coordinates as (x n , f n ), (x n , E n );

[0199] In this embodiment, the microseismic event cumulative frequency is (0, 30), (20, 57), (40, 112), (60, 99), (80, 98), (100, 105), (120, 102), (140, 98), (160, 111), (180, 49), (200, 27) respectively;

[0200] The cumulative energy of the microseismic events is respectively (0, 2205.69), (20, 26996.22), (40, 61845.86), (60, 76674.30), (80, 229916.79), (100, 108252.23), (120, 126769.05), (140, 127858.20), (160, 147339.96), (180, 162280.65), (200, 14338.40);

[0201] Step 4: determining the microseismic cumulative frequency curve and the cumulative energy curve of the working face tendency dimension;

[0202] Step 4.1: fitting the microseismic cumulative frequency curve of the working face tendency;

[0203] Step 4.1.1: fitting a microseismic cumulative frequency cubic polynomial for each interpolation interval, and the microseismic cumulative frequency cubic polynomial fitted for the nth interpolation interval is expressed as:

[0204]

[0205] wherein, is the microseismic cumulative frequency cubic polynomial of the nth interpolation interval; is a coefficient to be solved; x is the horizontal coordinate of the overburden fracture line;

[0206] Step 4.1.2: solving the coefficient constructing the overall equation group of the microseismic cumulative frequency interpolation curve of the working face tendency to obtain the parameterized expression of the microseismic cumulative frequency interpolation curve of the working face tendency;

[0207] Step 4.1.2.1: determining the boundary condition of the microseismic cumulative frequency interpolation curve of the working face tendency;

[0208] The boundary condition of the microseismic cumulative frequency interpolation curve of the working face tendency is that the second derivative value of the microseismic cumulative frequency interpolation curve of the working face tendency at the two endpoints of the statistical interval is zero, which is expressed as:

[0209] S f″ (x0)=0

[0210] S f″ (x n )=0

[0211] In the formula, S f″ (x0) is the second derivative value of the microseismic cumulative frequency interpolation curve of the working face tendency at point x0; S f″ (x n ) is the second derivative value of the microseismic cumulative frequency interpolation curve of the working face tendency at point x n ; and

[0212] Step 4.1.2.2: Determine the interpolation condition of the microseismic cumulative frequency interpolation curve of the working face inclination;

[0213] The interpolation condition of the microseismic cumulative frequency interpolation curve of the working face inclination is that the function value of the microseismic cumulative frequency interpolation curve of the working face inclination at the two endpoints of the interpolation interval is the ordinate corresponding to the abscissa of the point on the fracture line, which is expressed as:

[0214]

[0215] In the formula, The function value of the microseismic cumulative frequency interpolation curve of the working face inclination at x n of the nth interpolation interval, The function value of the microseismic cumulative frequency interpolation curve of the working face inclination at x n+1 of the nth interpolation interval;

[0216] Step 4.1.2.3: Construct the equation set of the microseismic cumulative frequency interpolation curve of the working face inclination in each interpolation interval;

[0217] The boundary condition and the interpolation condition of the microseismic cumulative frequency interpolation curve of the working face inclination are brought into the cubic polynomial expression of the microseismic cumulative frequency interpolation curve of the working face inclination to obtain the linear equation set of the microseismic cumulative frequency interpolation curve of the working face inclination in each interpolation interval, which is expressed as:

[0218]

[0219] Step 4.1.2.4: Construct the overall equation set of the microseismic cumulative frequency interpolation curve of the working face inclination;

[0220] The equation set of each interpolation interval of the microseismic cumulative frequency interpolation curve of the working face inclination is combined into an overall equation set to obtain the parameterized expression of the microseismic cumulative frequency interpolation curve of the working face inclination:

[0221]

[0222] In this embodiment, the expression of the microseismic cumulative frequency interpolation curve of the working face inclination is:

[0223]

[0224] Step 4.2: Fit the microseismic cumulative energy of the working face inclination;

[0225] Step 4.2.1: Fit a microseismic cumulative energy cubic polynomial for each interpolation interval, and the microseismic cumulative energy cubic polynomial fitted for the nth interpolation interval is expressed as:

[0226]

[0227] wherein, is the third-degree polynomial of the nth interpolation interval microseismic cumulative energy; is the coefficient to be solved; x is the horizontal coordinate of the overburden fracture line;

[0228] Step 4.2.2: solving the coefficient The overall equation group of the microseismic cumulative energy interpolation curve of the working face inclination is constructed to obtain the parameterized expression of the microseismic cumulative frequency interpolation curve of the working face inclination;

[0229] Step 4.2.2.1: determining the boundary condition of the microseismic cumulative energy interpolation curve of the working face inclination;

[0230] The boundary condition of the microseismic cumulative energy interpolation curve of the working face inclination is that the second derivative value of the microseismic cumulative energy interpolation curve of the working face inclination at the two endpoints of the statistical interval is zero, which is expressed as:

[0231] S E″ (x0)=0

[0232] S E″ (x n )=0

[0233] In the formula, S E″ (x0) is the second derivative value of the microseismic cumulative energy interpolation curve of the working face inclination at the borehole point x0; S E″ (x n ) is the second derivative value of the microseismic cumulative energy interpolation curve of the working face inclination at the borehole point x n ;

[0234] Step 4.2.2.2: determining the interpolation condition of the microseismic cumulative energy interpolation curve of the working face inclination;

[0235] The interpolation condition of the microseismic cumulative energy interpolation curve of the working face inclination is that the function value of the microseismic cumulative energy interpolation curve of the working face inclination at the two endpoints of the interpolation interval is the vertical coordinate corresponding to the horizontal coordinate of the point on the fracture line, which is expressed as:

[0236]

[0237] In the formula, the function value of the microseismic cumulative energy interpolation curve of the working face inclination at x n , the function value of the microseismic cumulative energy interpolation curve of the working face inclination at x n+1 ;

[0238] Step 4.2.2.3: constructing the equation group of the microseismic cumulative energy interpolation curve of the working face inclination; ​

[0239] The boundary condition and the interpolation condition of the working face tendency microseismic cumulative energy interpolation curve are brought into the cubic polynomial expression of the working face tendency microseismic cumulative energy interpolation curve to obtain a linear equation group of the working face tendency microseismic cumulative energy interpolation curve in each interpolation interval, which is expressed as:

[0240]

[0241] Step 4.2.2.4: Construct the overall equation group of the working face tendency microseismic cumulative frequency interpolation curve;

[0242] The equation group of each interpolation interval is combined into an overall equation group to obtain a parameterized expression of the working face tendency microseismic cumulative frequency interpolation curve:

[0243]

[0244] In this embodiment, the expression of the working face tendency microseismic cumulative frequency interpolation curve is:

[0245]

[0246] The constructed working face tendency microseismic cumulative frequency fitting curve is shown in FIG. 2, and the cumulative energy fitting curve is shown in FIG. 3. Figure 4 Figure 5

[0247] Step 5: Establish a working face overburden fracture line position prediction model;

[0248] Step 5.1: Determine training samples;

[0249] M borehole points are selected in the statistical interval L as training samples, the horizontal coordinates of the training samples are expressed as x m , m = 1, 2, …, M, the corresponding working face overburden fracture height is expressed as S(x m ), the microseismic cumulative frequency is expressed as S f (x m ), and the microseismic cumulative energy is expressed as S E (x m ).

[0250] In this embodiment, 100 points are selected in the statistical interval L = 200 m as training samples, as shown in Table 1:

[0251] Table 1 Training sample points

[0252]

[0253]

[0254] ​​

[0255] Step 5.2: design the neural network model architecture for predicting the position of the working face overburden fracture line;

[0256] Step 5.2.1: determine the basic structure of the neural network for predicting the position of the working face overburden fracture line;

[0257] Step 5.2.1.1: determine the input layer size and feature vector of the neural network for predicting the position of the working face overburden fracture line;

[0258] In this embodiment, the size of the neural network input layer is 6, and the input feature vector is represented as:

[0259] X m = [S f (x m ), S E (x m ), Δx m , w, h, D]

[0260]

[0261] In the formula, represents the input layer of the neural network; X m represents the feature vector of the mth training sample; Δx m represents the distance from the mth training sample to the midpoint of the working face inclination dimension; w represents the width of the working face; h represents the height of the working face; and D represents the working face depth;

[0262] Take the 10th training sample as an example:

[0263]

[0264] Step 5.2.1.2: design the intermediate layer structure of the neural network for predicting the position of the working face overburden fracture line;

[0265] Step 5.2.1.2.1: determine the number of layers of the neural network intermediate layer;

[0266] Set the neural network to have L layers, and the intermediate layer includes L-1 layers;

[0267] In this embodiment, the neural network is set to have 4 layers, and the intermediate layer includes 3 layers;

[0268] Step 5.2.1.2.2: determine the number of neurons in each layer of the neural network;

[0269] According to the "empirical formula", the number of neurons in each layer of the intermediate layer is determined, and the calculation formula is:

[0270]

[0271] wherein, N h l represents the number of neurons of the lth layer of the neural network; N i l represents the number of input neurons of the lth layer of the neural network; N o l represents the number of output layer neurons of the lth layer of the neural network; M represents the number of training samples; β represents an arbitrary variable value that can be self-taken, and the range can be taken as 2-10;

[0272] In this embodiment, the number of neurons of the first layer is calculated as 2, the number of neurons of the second layer is calculated as 3, and the number of neurons of the third layer is calculated as 3; β is taken as 5;

[0273] Step 5.2.1.2.3: Determine the weights and biases of the intermediate layers of the neural network;

[0274] In the neural network, the intermediate layer calculates the process from the input to the output through the forward propagation, and the output of each layer is the result obtained by linearly transforming the output of the previous layer and then transforming it through the activation function, which is expressed as:

[0275]

[0276] In the formula, W l represents the weight matrix of the lth layer of the neural network; b l represents the bias vector of the lth layer of the neural network; represents the output result of the mth training sample through the l-1th layer of the neural network; represents the result obtained by linearly transforming the mth training sample in the activation function of the l-1th layer of the neural network; represents the result obtained by transforming through the activation function σ;

[0277] Step 5.2.1.3: Determine the size of the output layer of the neural network:

[0278] The output layer of the neural network is the predicted working face fracture height corresponding to the mth "unit length" of the working face, which is expressed as:

[0279]

[0280] In the formula, H m represents the predicted working face fracture height corresponding to the mth "unit length" of the working face;

[0281] In this embodiment, the neural network structure for predicting the working face fracture line position is as shown in Figure 6 .

[0282] Step 5.2.2: Initialize the neural network model parameters;

[0283] In this embodiment, the initialized weight matrix is:

[0284]

[0285] W 4 =[-0.1801 0.2394 -0.1503]

[0286] b 1 =[-0.1145 -0.3952]

[0287] b 2 =[0.2532 -0.2307 -0.3764]

[0288] b 3 =[0.4225 0.0416 0.3694]

[0289] b 4 =[-0.5364]

[0290] Step 5.2.2.1: Calculate the loss function value;

[0291] The mean square error is selected as the loss function, and the calculation formula is:

[0292]

[0293] Where, Loss represents the loss value;

[0294] Step 5.2.2.2: Calculate the gradient;

[0295]

[0296] in, Represents the loss function Loss for the l-th layer neural network weight matrix W l The gradient, Represents the loss function Loss for the l-th layer neural network bias vector b l gradient;

[0297] Step 5.2.2.3: Iteratively update the neural network model parameters;

[0298] Use the gradient descent method to update the weight matrix and bias vector. The calculation formula is:

[0299]

[0300] Among them, t represents the number of iterations, α represents the correction coefficient, which is used to control the update of the weight matrix W of the lth layer neural network. lThe first layer neural network bias vector b l In this embodiment, the step size in the process is set to 0.01, and

[0301] Step 5.2.2.4: Steps 5.2.2.2 and 5.2.2.3 are repeatedly executed until the condition for stopping iteration is met, as shown in the following formula:

[0302]

[0303] In the formula, represents the infinite norm of represents the infinite norm of ; and ε1, ε2 represent the set threshold value.

[0304] In this embodiment, v1=0.02, ε2=0.02, the iteration number t=20000, and the calculated weight matrix and bias vector are as follows:

[0305]

[0306] Step 6: The working face overburden fracture line position prediction model is used to predict the new working face overburden fracture line position.

[0307] Step 6.1: Divide the working face to be predicted into Q "unit lengths" in the tendency dimension, wherein the midpoint coordinate of the qth "unit length" is represented as x q .

[0308] Step 6.2: Collect working face data.

[0309] Step 6.2.1: Determine the physical information of the working face.

[0310] Determine the tendency length, height, and depth of the working face, represented as x pred , h pred , D pred , respectively.

[0311] Determine the distance Δx m pred of the measuring point from the midpoint of the tendency length of the working face.

[0312] Step 6.2.2: Collect microseismic data during the working face mining process.

[0313] Use the microseismic monitoring equipment to collect the microseismic signals during the working face mining process, and determine the microseismic cumulative frequency and microseismic cumulative energy of the qth "unit length" in the tendency dimension during the working face mining process, represented as .

[0314] ​Step 6.3: predicting the fracture line position of the working face to be predicted by using the working face overburden fracture line position prediction model;

[0315] The data collected in step 6.2 is input into the neural network model of the working face overburden fracture line position prediction model, and the input data is represented as:

[0316]

[0317] wherein, is the input data of the mth sample;

[0318] The fracture line position of the working face to be predicted is obtained, as shown in the following formula:

[0319]

[0320] In the formula, represents the fracture height corresponding to the midpoint of the qth "unit length".

[0321] In this embodiment, the length x of the working face is determined pred = 200, the height h pred = 4, the buried depth D pred = 800, the cumulative frequency of microseismic events Cumulative energy of microseismic events Distance Δx of the midpoint of the working face inclination dimension m = 2.31;

[0322] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and 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: 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 intelligently identifying the position of overburden fracture lines in a rock burst working face using microseismic monitoring, characterized by: The following steps are involved: Step 1: Determine the "unit length" of the working surface inclination dimension; After the working face is mined normally, the inclination length of the entire working face is selected as the statistical interval , and determine the "unit length" ; Step 2: Determine the fitting curve of the overburden fracture line of the working face; Step 3: Determine the cumulative frequency and cumulative energy of microseismic events in the dip dimension during the mining process of the working face; Step 4: Determine the microseismic cumulative frequency curve and cumulative energy curve in the working face inclination dimension; Step 5: Establish a prediction model for the location of the overburden fracture line at the working face; The neural network for predicting the position of the overburden fracture line of the working face has a total of Layer, the input feature vector includes the distance from the training sample to the midpoint of the working face in the dip dimension, the width of the working face, the height of the working face, the buried depth of the working face, and the cumulative frequency and cumulative energy of microseismic events in the dip dimension during the working face mining process; The middle layer includes Layer, determine the number of neurons in each layer of the middle layer according to the "empirical formula"; In a neural network, the middle layer calculates the process from input to output through forward propagation. The output of each layer is the result of a linear transformation of the output of the previous layer and then the transformation of the activation function. The output layer of the neural network is the predicted working face The height of the overburden fracture at the working face corresponding to a "unit length"; The neural network selects mean square error as the loss function, calculates the gradient of the loss function with respect to the weight matrix and the bias vector, and iteratively updates the weight matrix and the bias vector; Step 6: Use the working face overburden fracture line position prediction model to predict the position of the new working face overburden fracture line.

2. The method for intelligently identifying the position of overburden fracture lines in a rock burst working face using microseismic monitoring according to claim 1 is characterized by: The specific method of step 2 is: Step 2.1: Establish a coordinate system for the working surface; With the lower left corner of the working face dip dimension section along the advancing direction as the origin, a rectangular coordinate system is established, with the horizontal axis representing the dip length of the working face and the vertical axis representing the overburden height of the working face; Step 2.2: Select the horizontal coordinate position of the midpoint of each "unit length" and determine the height of the overburden fracture at that point on the working face by drilling; Set the statistical interval to "unit length", we get The drilling points on the fault line, the coordinates of the drilling points are expressed as ,in, Indicates the The horizontal coordinate of the drilling point, Indicates the The vertical coordinate of the drilling point; Step 2.3: Determine the parameterized characterization formula of the overburden fault line of the working face through cubic spline interpolation.

3. The method for intelligently identifying the position of overburden fracture lines in a rock burst working face using microseismic monitoring according to claim 2 is characterized by: The specific method of step 2.3 is: Step 2.3.1: "Unit length" as the interpolation interval, where the The interpolation interval is expressed as ,Will Drilling points are used as data points for cubic spline interpolation; Step 2.3.2: Fit the curve of the overburden fracture line of the working face; For each interpolation interval, a cubic polynomial of the overburden fault line is fitted. The cubic polynomial of the overburden fault line fitted by the interpolation interval is expressed as: ； Where, Indicates the The cubic polynomial fitted to the interpolation interval; 、 、 、 represents the coefficient to be determined; The horizontal axis represents the inclination dimension of the working face; Step 2.3.3: Solve for the coefficients 、 、 、 , the overall equation group of the cubic interpolation curve of the overburden fault line is constructed, and the parameterized expression of the cubic interpolation curve of the overburden fault line is obtained.

4. The method for intelligently identifying the position of overburden fracture lines in a rock burst working face using microseismic monitoring according to claim 3 is characterized by: The specific method of step 2.3.3 is: Step 2.3.3.1: Determine the boundary conditions of the interpolation curve of the overburden fracture line of the working face; The boundary condition of the interpolation curve of the overburden fracture line of the working face is that the second-order derivative value of the interpolation curve of the overburden fracture line of the working face at the two end points of the statistical interval is zero; Step 2.3.3.2: Determine the interpolation conditions of the interpolation curve of the overburden fracture line of the working face; The interpolation condition of the interpolation curve of the overburden fracture line of the working face is that the function value of the interpolation curve of the overburden fracture line of the working face at the two end points of the interpolation interval is the vertical coordinate corresponding to the horizontal coordinate of the point on the fracture line; Step 2.3.3.3: Construct the equation set for the interpolation curve of the overburden fracture line in each interpolation interval; Substitute the boundary conditions and interpolation conditions of the interpolation curve of the overburden fracture line of the working face into the cubic polynomial expression of the overburden fracture line fitted in the interpolation interval to obtain the linear equation group of the interpolation curve of the overburden fracture line of the working face in each interpolation interval; Step 2.3.3.4: Construct the overall equation system of the cubic interpolation curve of the overburden fault line; The equations of each interpolation interval are combined into an overall equation system to obtain the parameterized expression of the cubic interpolation curve of the overburden fault line.

5. The method for intelligently identifying the position of overburden fracture lines in a rock burst working face using microseismic monitoring according to claim 4 is characterized by: The specific method of step 3 is: Step 3.1: Establish a coordinate system for the cumulative frequency and cumulative energy of microseismic events; Taking the lower left corner of the working face dip dimension section along the advancing direction as the origin, a rectangular coordinate system is established, with the horizontal axis representing the dip length of the working face and the vertical axis representing the cumulative frequency and cumulative energy of microseismic events on the working face. Step 3.2: Determine the statistical interval The cumulative frequency and cumulative energy of microseismic events in each unit length are calculated and The "unit length" is used as the interpolation interval to determine the cumulative frequency and cumulative energy of microseismic events in the interpolation interval; During the mining process at the working face, determine the statistical interval Each "unit length" The cumulative frequency of microseismic events in and accumulated energy ,Will "Unit length" as the interpolation interval, where the The cumulative frequency of microseismic events in the interpolation interval is expressed as , the cumulative energy of microseismic events is expressed as , expressed in coordinate form as 、 .

6. The method for intelligently identifying the position of overburden fracture lines in a rock burst working face using microseismic monitoring according to claim 5 is characterized by: The specific method of step 4 is: Step 4.1: Fit the cumulative frequency curve of microseismic inclination of the working face; Step 4.1.1: Fit a cubic polynomial of microseismic cumulative frequency to each interpolation interval. The cubic polynomial of the cumulative frequency of microseismicity fitted in the interpolation interval is expressed as: ； in, For the Cumulative frequency cubic polynomial of microseismicity in interpolation intervals; 、 、 、 is the coefficient to be determined; is the abscissa of the overburden fault line; Step 4.1.2: Solve for the coefficients 、 、 、 , construct the overall equation group of the interpolation curve of the cumulative frequency of microseismic inclination of the working face, and obtain the parameterized expression of the interpolation curve of the cumulative frequency of microseismic inclination of the working face; Step 4.2: Fit the microseismic cumulative energy curve of the working face; Step 4.2.1: Fit a cubic polynomial of microseismic cumulative energy to each interpolation interval. The cubic polynomial of microseismic cumulative energy fitted in the interpolation interval is expressed as: ； in, For the Cumulative microseismic energy cubic polynomial of interpolation interval; 、 、 、 is the coefficient to be determined; is the abscissa of the overburden fault line; Step 4.2.2: Solve for the coefficients 、 、 、 , the overall equation group of the interpolation curve of the microseismic cumulative energy of the working face inclination is constructed, and the parameterized expression of the interpolation curve of the microseismic cumulative energy of the working face inclination is obtained.

7. The method for intelligently identifying the position of overburden fracture lines in a rock burst working face using microseismic monitoring according to claim 6 is characterized by: The specific method of step 4.1.2 is: Step 4.1.2.1: Determine the boundary conditions of the interpolation curve of the cumulative frequency of microseismic events along the working face; The boundary condition of the working face inclination microseismic cumulative frequency interpolation curve is that the second-order derivative value of the working face inclination microseismic cumulative frequency interpolation curve at the two end points of the statistical interval is zero; Step 4.1.2.2: Determine the interpolation conditions for the interpolation curve of the cumulative frequency of microseismic events along the working face; The interpolation condition of the working face inclination microseismic cumulative frequency interpolation curve is that the function value of the working face inclination microseismic cumulative frequency interpolation curve at the two end points of the interpolation interval is the vertical coordinate corresponding to the horizontal coordinate of the point on the fault line; Step 4.1.2.3: Construct the interpolation curve equations for the cumulative frequency of microseismic inclination of the working face in each interpolation interval; Substitute the boundary conditions and interpolation conditions of the working face inclination microseismic cumulative frequency interpolation curve into the cubic polynomial expression of the working face inclination microseismic cumulative frequency interpolation curve to obtain the linear equation group of the working face inclination microseismic cumulative frequency interpolation curve in each interpolation interval; Step 4.1.2.4: Construct the overall equation system of the interpolation curve of the cumulative frequency of microseismic events in the working face; The equation group of each interpolation interval of the interpolation curve of the cumulative frequency of microseismic inclination of the working face is combined into an overall equation group to obtain the parameterized expression of the interpolation curve of the cumulative frequency of microseismic inclination of the working face.

8. The method for intelligently identifying the position of overburden fracture lines in a rock burst working face using microseismic monitoring according to claim 7 is characterized by: The specific method of step 4.2.2 is: Step 4.2.2.1: Determine the boundary conditions of the interpolation curve of the microseismic cumulative energy of the working face; The boundary condition of the working face inclination microseismic cumulative energy interpolation curve is that the second-order derivative value of the working face inclination microseismic cumulative energy interpolation curve at the two end points of the statistical interval is zero; Step 4.2.2.2: Determine the interpolation conditions of the working face dip microseismic cumulative energy interpolation curve; The interpolation condition of the working face dip microseismic cumulative energy interpolation curve is that the function value of the working face dip microseismic cumulative energy interpolation curve at the two end points of the interpolation interval is the vertical coordinate corresponding to the horizontal coordinate of the point on the fault line; Step 4.2.2.3: Construct the equation group for the interpolation curve of microseismic cumulative energy of the working face; Substitute the boundary conditions and interpolation conditions of the working face inclination microseismic cumulative energy interpolation curve into the cubic polynomial expression of the working face inclination microseismic cumulative energy interpolation curve to obtain the linear equation group of the working face inclination microseismic cumulative energy interpolation curve in each interpolation interval; Step 4.2.2.4: Construct the overall equation system of the interpolation curve of the microseismic cumulative energy of the working face; The equations of each interpolation interval are combined into an overall equation system to obtain the parameterized expression of the interpolation curve of the microseismic cumulative energy of the working face dip.

9. The method for intelligently identifying the position of overburden fracture lines in a rock burst working face using microseismic monitoring according to claim 8, characterized in that: The specific method of step 6 is: Step 6.1: Divide the working face to be predicted into inclination dimensions "unit lengths", where The coordinates of the midpoint of a unit length are expressed as ; Step 6.2: Collect working face data; Step 6.2.1: Determine the physical information of the working surface; Determine the inclined length, height and buried depth of the working face, which are respectively , , ; Determine the distance between the measuring point and the midpoint of the working surface's inclination length ; Step 6.2.2: Collect microseismic data during the mining process; The microseismic monitoring equipment is used to collect the microseismic signals during the mining process of the working face to determine the first The cumulative frequency and cumulative energy of microseismicity per unit length are expressed as 、 express; Step 6.3: Use the working face overburden fracture line position prediction model to predict the fracture line position of the working face to be predicted; The data collected in step 6.2 is input into the neural network model of the working face overburden fracture line position prediction model to obtain the fracture line position of the working face to be predicted.

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