Rock burst event positioning method based on optimization algorithm

Abstract

The invention relates to the technical field of geophysics, in particular to a rockburst event positioning method based on an optimization algorithm. According to the method, a time difference positioning method of a hyperbolic positioning method is adopted as an objective function of the method, so that the positioning reliability is improved. Meanwhile, the method is improved on the basis of a mouse swarm optimization algorithm, and a Levy flight strategy is introduced to enhance the local search capability and the global optimization capability of the algorithm, so that local optimum can be effectively avoided. Compared with a wolf pack optimization algorithm and a mouse group optimization algorithm, the method has the advantages that positioning errors are effectively reduced, positioning precision is improved, iteration distance statistics is reduced, and a theoretical foundation is laid for subsequent rock mass stability evaluation.

Description

Technical Field

[0001] The invention relates to the field of geophysics, and in particular to a rockburst event positioning method based on an optimization algorithm. Background Art

[0002] In recent years, with the increasing demand for geological disaster monitoring, intelligent monitoring has developed rapidly. In the process of geological disaster monitoring, rockburst event positioning is one of the key steps. At present, in actual monitoring, the positioning method is mainly conventional, and there are still problems such as inaccurate positioning and inability to judge the credibility of the positioning results, which increases the difficulty of monitoring. Therefore, improving the rockburst event positioning method and improving the positioning accuracy are of great research significance for the development and promotion of geological disaster monitoring.

[0003] At present, the mainstream method for locating rockburst events is still the conventional method, including the least squares method, the Geiger method, the double difference method, and the particle swarm algorithm. In the article "Research on the double-difference microseismic source positioning method and its application" published by Wang Yu in 2020, a double-difference positioning method was proposed. The relative distance between the observed travel time of the perforation and the event and the theoretical calculated travel time difference was calculated. The results show that this method can reduce the influence of complex surface on the accuracy of source positioning and effectively overcome the problem of near-surface low-velocity layer velocity modeling and static correction. In the article "Research on the Microseismic Source Positioning Method Based on Improved Particle Swarm Optimization" published by Da Shujin in 2021, the improved particle swarm optimization algorithm was introduced. The improved particle swarm optimization algorithm is based on the particle swarm optimization algorithm. The improvement has been made in two aspects: first, a nonlinear decreasing mechanism is adopted to ensure the effectiveness of the algorithm; second, a dynamic balance between local search capability and global search capability is achieved; third, the flight step length is dynamically adjusted based on the average evaluation value. The step length factor. This can effectively improve the optimization ability.

[0004] However, these methods all have certain errors, the reason is that conventional methods have limitations to varying degrees: the double difference method may not be able to find the optimal solution; the particle swarm algorithm is prone to fall into the local optimum. Therefore, the existing methods cannot solve this problem well, and it is difficult to effectively improve the accuracy of positioning.

[0005] The Chinese patent with publication number CN112051548A discloses a rockburst monitoring and positioning method, device and system, which includes the following steps: obtaining sound signals from various directions in the tunnel; using array positioning technology to determine the distance and direction of the obtained sound signals, and removing the noise signals in the sound signals whose distance and direction are non-rockburst areas; using autocorrelation technology to perform noise reduction processing on the noise signals removed from the non-rockburst areas to remove environmental noise interference; using acoustic spectrum analysis technology to perform spectrum analysis on the sound signals after removing environmental noise interference, and obtaining amplitude spectrum data of the sound signals; when spectrum data matching the spectrum feature data before the rockburst appears in the amplitude spectrum data, the distance and direction of the sound signal corresponding to the spectrum data are obtained and rockburst warning is issued. Real-time acoustic wave monitoring of rockburst phenomena in tunnels is realized, noise interference is reduced, and the accuracy of acoustic wave warning is improved.

[0006] However, the positioning method of this patent is traditional and does not use new algorithms to locate rock burst events. The accuracy of locating rock burst events is poor, and it is impossible to judge the credibility of the positioning results.

[0007] Therefore, a more accurate method for locating rockburst events is needed today. Summary of the invention

[0008] The purpose of the present invention is to overcome the problems of inaccurate positioning and inability to judge the credibility of positioning results in the prior art, and to provide a rockburst event positioning method based on an optimization algorithm.

[0009] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0010] A rockburst event location method based on an optimization algorithm comprises the following steps:

[0011] S1: Acquire the rockburst event data to be located, perform preprocessing, and output time analysis data;

[0012] S2: Analyze the time analysis data according to the time difference positioning principle, establish the objective function of the rockburst event to be located, and generate rockburst data that meets the conditions;

[0013] S3: introducing the Levy flight strategy into the objective function to update the rockburst data;

[0014] S4: optimizing the updated rockburst data by improving the rat swarm optimization algorithm, and outputting the obtained optimal solution as positioning information corresponding to the rockburst event data to be located.

[0015] As a preferred embodiment of the present invention, S1 comprises the following steps:

[0016] S11: acquiring the rockburst event data to be located through pre-arranged geophones; the arrangement of the geophones meets the following rules;

[0017] The detectors are arranged in an array, with n*n detectors arranged in a rectangular grid at equal intervals for monitoring;

[0018] S12: Preprocess the rockburst event data to be located and output time analysis data; the preprocessing is to convert the original electrical signal in the rockburst event data to be located into binary SAC format data; the time analysis data includes the time information when each detector receives the rockburst point.

[0019] As a preferred solution of the present invention, the objective function of the rockburst event to be located in S2 is expressed as:

[0020]

[0021] Δt n,m =t n -t m ,

[0022] Where n, m are detector numbers, n, m = 1, 2, 3…, z, z is the total number of detectors; Δt n,m is the P wave measurement time difference between detector n and detector m; ΔT n T m is the time difference between the detector and the rockburst event position in the theoretical model; (x n ,y n ,z n ) and (x m ,y m ,z m ) represent the nth and mth geophone positions, respectively; x0, y0, z0) represent the rockburst event position; V0 is the waveform velocity; t n ,t m are the P-wave measurement time differences of detector n and detector m respectively.

[0023] As a preferred embodiment of the present invention, the expression of the Levy flight strategy calculation formula in S3 is:

[0024] Y i (t+1)=Y i (t)+m⊕Levy(γ),

[0025] m=random(size(i_position)),

[0026] Levy(γ)=0.01(u*|v| γ *(Y i (t)-Yibest )),

[0027] Among them, Y i =fitness, Y i (t) = fitness(t) represents Y i The objective function value at the tth generation, Y i (t+1) represents the current optimal objective function value of the t+1 generation; m represents the random number of the position of the i mouse; ⊕ represents point-to-point multiplication; Levy(γ) represents the Levy flight search path; i_position represents the position of the i mouse; the value range of γ is 1<γ<3; Y ibest represents the historical optimal position of mouse i; u and v obey the normal distribution shown in the following formula:

[0028] u~N(0,σ u 2 ), v~N(0,σ v 2 ),

[0029] And σ u 2 and σ v 2 The value of satisfies the following formula:

[0030]

[0031] σ v 2 =1.

[0032] As a preferred embodiment of the present invention, S4 comprises the following steps:

[0033] S41: the rockburst position in the known best objective function value is updated by replacing the best objective function value obtained so far with other objective function values;

[0034] S42: Determine the optimal position by continuously updating and iterating in the search space:

[0035] When all the rockburst locations in the rockburst point have completed an optimization search, the optimal point in the current rockburst location is selected for comparison with the original optimal point, and the better solution is retained as the new optimal point;

[0036] S43: When all points in the search space have been optimized, the current optimal point is output as the optimal solution as the positioning information corresponding to the rockburst event data to be located.

[0037] As a preferred solution of the present invention, in S41, the position update formula is expressed as:

[0038]

[0039] in, Defined as a random initial position, is the current optimal position, is the global optimal solution, A represents the movement behavior of the rockburst point in the search space, and its expression is:

[0040]

[0041] t=0,1,2,…,Max Iteration ,

[0042] Among them, R is the parameter of the control variable A, Max Iteration is the maximum number of iterations, t is the current number of iterations, and parameters R and C are random numbers between [1,5] and [0,2] respectively.

[0043] As a preferred solution of the present invention, the expression for updating iteration in S42 is:

[0044]

[0045] in, Indicates the next location of the rockburst point after update. is the current optimal position, It is the global optimal solution.

[0046] A rockburst event locating device based on an optimization algorithm comprises at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any of the methods described above.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The present invention adopts a time difference positioning method of a hyperbolic positioning method as the objective function of the present method, thereby improving the reliability of positioning. At the same time, the present invention is improved on the basis of the rat swarm optimization algorithm, and the Levy flight strategy is introduced to enhance the local search ability and global optimization ability of the algorithm, which can effectively avoid falling into the local optimum. Compared with the wolf pack optimization algorithm and the rat swarm optimization algorithm, the method of the present invention effectively reduces the positioning error, improves the positioning accuracy, and reduces the iterative distance statistics, thereby laying a theoretical foundation for the subsequent rock mass stability evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A rockburst event positioning method based on an optimization algorithm according to an embodiment of the present invention;

[0050] Figure 2 It is the root mean square error diagram calculated by different algorithms for fifteen rockburst points of the horizontal terrain model.

[0051] Figure 3 This is the result of iterative distance calculation for fifteen rockburst points in the horizontal model.

[0052] Figure 4 This is a structural schematic diagram of a rockburst event locating device based on an optimization algorithm, which utilizes a rockburst event locating method based on an optimization algorithm described in Example 1, described in Example 3 of the present invention. DETAILED DESCRIPTION

[0053] The present invention is further described in detail below in conjunction with test examples and specific implementation methods. However, this should not be understood as the scope of the above subject matter of the present invention being limited to the following embodiments, and all technologies realized based on the content of the present invention belong to the scope of the present invention.

[0054] Example 1

[0055] like Figure 1 As shown, a rockburst event location method based on an optimization algorithm comprises the following steps:

[0056] S1: Obtain the rockburst event data to be located, perform preprocessing, and output time analysis data.

[0057] S2: Analyze the time analysis data according to the time difference positioning principle to establish the objective function of the rock burst event to be located.

[0058] S3: Update the objective function according to the Levy flight strategy.

[0059] S4: optimizing and solving the updated objective function by improving the rat swarm optimization algorithm, and outputting the obtained optimal solution as positioning information corresponding to the rockburst event data to be located.

[0060] Example 2

[0061] This embodiment is a specific implementation scheme of a rockburst event location method based on an optimization algorithm described in Example 1, comprising the following steps:

[0062] S1: Obtain the rockburst event data to be located, perform preprocessing, and output time analysis data.

[0063] S11: acquiring the rockburst event data to be located through pre-arranged geophones; the arrangement of the geophones meets the following rules;

[0064] The detectors are arranged in an array. Specifically, nine detectors are arranged in a rectangular 3x3 grid at equal intervals and monitored.

[0065] The detector layout coordinates of the horizontal terrain model are shown in the following table:

[0066] Table 1 Detector coordinate parameters of horizontal terrain model

[0067]

[0068] Furthermore, the rockburst point coordinate parameters of the horizontal terrain model simulated and set in this embodiment are shown in the following table:

[0069] Table 2 Coordinate parameters of rockburst points in horizontal terrain model

[0070] Rockburst point number X / m Y / m Z / m 1 110 90 60 2 122 120 80 3 145 75 30 4 155 165 65 5 80 65 60 6 55 30 40 7 125 135 70 8 160 80 25 9 45 40 65 10 100 25 35 11 50 150 90 12 150 170 75 13 190 70 20 14 55 95 62 15 130 85 30

[0071] S12: Preprocess the rockburst event data to be located and output time analysis data; the time analysis data includes time information when each detector receives the rockburst point.

[0072] S2: Analyze the time analysis data according to the time difference positioning principle to establish the objective function of the rock burst event to be located.

[0073] Set the coordinates of the i-th detector to τ(x i ,y i , z i ), the coordinates of the jth rockburst event are ∈(xj, y j , z j ), where i, j = 1, 2, 3, ... z, and the detector and the rockburst event are required to be located in the same rock layer. The P-wave measurement time difference between the i-th detector and the rockburst event is:

[0074]

[0075] Where Δ represents the arrival time difference of the P wave received by the i-th detector, and v is the known rock formation velocity.

[0076] The P-wave measurement time difference between different detectors is:

[0077] Δt n,m =t n -t m ,

[0078] Among them, t n ,t m are the P-wave measurement time differences of detector n and detector m respectively.

[0079] Secondly, by knowing the waveform velocity V0, according to the difference method (TD), the time difference between the detector and the rockburst event position in the theoretical model is calculated as:

[0080]

[0081] Where n, m are detector numbers, n, m = 1, 2, 3 ..., z, z is the total number of detectors, (x n ,y n ,z n ) and (x m ,y m ,z m ) represent the nth and mth geophone locations, respectively, and (x0, y0, z0) represents the location of the rockburst event.

[0082] Finally, the difference between the measured time difference and the calculated time difference between the detectors is used as the objective function. That is, the following formula:

[0083]

[0084] Among them, fitness is the objective function value, that is, the generated rockburst data that meets the conditions. According to the solution of the above formula, when ΔT n,m 2 When the minimum value is taken, that is, (x j ,y j ,z j ) value is the location point of the unknown underground rockburst event.

[0085] S3: Update the objective function according to the Levy flight strategy.

[0086] The rockburst data generated by S2 that meets the conditions, namely the fitness data, is input into the Levy flight strategy for data update. The calculation formula of the Levy flight strategy is:

[0087] Y i (t+1)=Y i (t)+m⊕Levy(γ),

[0088] m=random(size(i_position)),

[0089] Levy(γ)=0.01(u*|v| γ *(Y i (t)-Y ibest )),

[0090] Among them, Y i =fitness, Y i (t) = fitness(t) represents Y iThe objective function value at the tth generation, Y i (t+1) represents the current optimal objective function value of the t+1 generation; m represents the random number of the position of the i mouse; ⊕ represents point-to-point multiplication; Levy(γ) represents the Levy flight search path; i_position represents the position of the i mouse; the value range of γ is 1<γ<3, and in this embodiment, it is 1.5; Y ibest represents the historical optimal position of mouse i; u and v obey the normal distribution shown in the following formula:

[0091] u~N(0,σ u 2 ), v~N(0,σ v 2 ),

[0092] And σ u 2 and σ v 2 The value of satisfies the following formula:

[0093]

[0094] σ v 2 =1.

[0095] S4: Optimizing the updated objective function by improving the rat swarm optimization algorithm, and outputting the obtained optimal solution as the positioning information corresponding to the rockburst event data to be located. That is, the rockburst data updated in S3 is iteratively calculated by the rat swarm optimization algorithm, and finally the data of the positioning point is obtained.

[0096] S41: the rockburst position in the known best objective function value is updated by replacing the best objective function value obtained so far with other objective function values;

[0097] The position update formula is:

[0098]

[0099] in, Defined as a random initial position, is the current optimal position, is the global optimal solution, A represents the movement behavior of the rockburst point in the search space, and its expression is:

[0100]

[0101] t=0,1,2,…,Max Iteration ,

[0102] Among them, R is the parameter of the control variable A, Max Iterationis the maximum number of iterations, t is the current number of iterations, and parameters R and C are random numbers between [1,5] and [0,2] respectively.

[0103] S42: Determine the optimal position by continuously updating and iterating in the search space:

[0104] When all the rockburst locations in the rockburst point have completed an optimization search, the optimal point in the current rockburst location is selected for comparison with the original optimal point, and the better solution is retained as the new optimal point;

[0105] The expression for the update iteration is:

[0106]

[0107] in, Indicates the next location of the rockburst point after update. is the current optimal position, It is the global optimal solution.

[0108] S43: When all points in the search space have been optimized, the current optimal point is output as the optimal solution as the positioning information corresponding to the rockburst event data to be located.

[0109] like Figure 2 As shown in the figure, the wolf pack optimization algorithm, the rat swarm optimization algorithm and the present method are used to calculate the root mean square error of fifteen rock burst points of the horizontal terrain model respectively. Ten calculations are performed for each rock burst point, and the average value is taken as the positioning error result. From the positioning error results of different algorithms, it can be seen that the positioning error of this method is smaller than that of the other two algorithms, and the positioning error results are relatively stable, which further shows that the positioning accuracy of this method is higher than that of the other two algorithms. At the same time, it shows that the algorithm has good robustness.

[0110] like Figure 3 As shown in Figure 1, the results of iterative distance calculations for the fifteen rockburst points of the horizontal model are shown. It can be seen that the iterative distance value of this method is smaller than that of the other two algorithms, indicating that the convergence of this method is better than that of the other two algorithms, further verifying the superiority of the performance of this algorithm.

[0111] Example 3

[0112] like Figure 4As shown, a rockburst event locating device based on an optimization algorithm includes at least one processor, a memory connected to the at least one processor, and at least one input / output interface connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the rockburst event locating method based on the optimization algorithm described in the above embodiment. The input / output interface may include a display, a keyboard, a mouse, and a USB interface for inputting and outputting data.

[0113] Those skilled in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM), magnetic disks or optical disks, etc. Various media that can store program codes.

[0114] When the above-mentioned integrated unit of the present invention is implemented in the form of a software functional unit and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0115] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A rockburst location method based on an optimization algorithm, characterized in that: The following steps are involved: S1: Acquire the rockburst event data to be located, perform preprocessing, and output time analysis data; S2: Analyze the time analysis data according to the time difference positioning principle, establish the objective function of the rockburst event to be located, and generate rockburst data that meets the conditions; S3: introducing the Levy flight strategy into the objective function to update the rockburst data; S4: optimizing the updated rockburst data by improving the rat swarm optimization algorithm, and outputting the obtained optimal solution as positioning information corresponding to the rockburst event data to be located.

2. A rockburst location method based on optimization algorithm according to claim 1, characterized in that: The S1 comprises the following steps: S11: Acquire the rockburst event data to be located through pre-arranged geophones; the arrangement of the geophones meets the following rules: The detectors are arranged in an array, with n*n detectors arranged in a rectangular grid at equal intervals for monitoring; S12: preprocessing the rockburst event data to be located and outputting time analysis data; The preprocessing is to convert the original electrical signal in the rockburst event data to be located into binary SAC format data; the time analysis data includes the time information when each detector receives the rockburst point.

3. A rockburst event location method based on optimization algorithm according to claim 1, characterized in that: The objective function of the rockburst event to be located in S2 is expressed as: Where n, m are detector numbers, n, m = 1, 2, 3…, z, z is the total number of detectors; Δt n,m is the P wave measurement time difference between detector n and detector m; ΔT n T m is the time difference between the detector and the rockburst event position in the theoretical model; (x n ,y n ,z n ) and (x m ,y m ,z m ) represent the nth and mth geophone positions respectively, (x0, y0, z0) represent the rockburst event position, V0 is the waveform velocity; t n ,t m are the P-wave measurement time differences of detector n and detector m respectively.

4. A rockburst event location method based on optimization algorithm according to claim 3, characterized in that: The expression of the Levy flight strategy calculation formula in S3 is: m=random(size(i_position)), Levy(γ)=0.01(u*|v| γ *(Y i (t)-Y ibest )), Among them, Y i =fitness, Y i (t) = fitness(t) represents Y i The objective function value at the tth generation, Y i (t+1) represents the current optimal objective function value of the t+1 generation; m represents the random number of the position of the i mouse; ⊕ represents point-to-point multiplication; Levy(γ) represents the Levy flight search path; i_position represents the position of the i mouse; the value range of γ is 1<γ<3; Y ibest represents the historical optimal position of mouse i; u and v obey the normal distribution shown in the following formula: u~N(0,σ u 2 ),v~N(0,σ v 2 ), And σ u 2 and σ v 2 The value of satisfies the following formula: s v 2 =1.

5. A rockburst event location method based on optimization algorithm according to claim 4, characterized in that: The S4 comprises the following steps: S41: the rockburst position in the known best objective function value is updated by replacing the best objective function value obtained so far with other objective function values; S42: Determine the optimal position by continuously updating and iterating in the search space: When all the rockburst locations in the rockburst point have completed an optimization search, the optimal point in the current rockburst location is selected for comparison with the original optimal point, and the better solution is retained as the new optimal point; S43: When all points in the search space have been optimized, the current optimal point is output as the optimal solution as the positioning information corresponding to the rockburst event data to be located.

6. A rockburst event location method based on optimization algorithm according to claim 5, characterized in that: In S41, the position update formula is expressed as: in, Defined as a random initial position, is the current optimal position, is the global optimal solution, A represents the movement behavior of the rockburst point in the search space, and its expression is: t=0,1,2,…,Max Iteration , Among them, R is the parameter of the control variable A, Max Iteration is the maximum number of iterations, t is the current number of iterations, and parameters R and C are random numbers between [1,5] and [0,2] respectively.

7. A rockburst event location method based on optimization algorithm according to claim 6, characterized in that: The expression for updating iteration in S42 is: in, Indicates the next location of the rockburst point after update. is the current optimal position, It is the global optimal solution.

8. A rockburst location device based on an optimization algorithm, characterized in that: It includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in any one of claims 1 to 7.

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