Sensor network layout optimization method for mine microseismic monitoring and application thereof

Abstract

The application provides a sensor network layout optimization method for mine microseismic monitoring and application thereof, an optimization function of a sensor network layout of an unknown wave velocity system is constructed through a CRLB principle of optimal parameter estimation, and an encoding operator, a selection operator, a crossover operator, a mutation operator, an elite reservation strategy and a convergence criterion in an improved genetic algorithm are adopted to solve the optimization function, so that an optimal sensor network layout scheme is obtained. The application provides a sensor network optimization method based on CRLB parameter estimation and an improved genetic algorithm, provides an optimal network layout scheme of a target sensor, has generality, universality and higher calculation efficiency, and the result stability is significantly improved. The application solves the sensor network layout problem of the unknown wave velocity system in the mine microseismic monitoring work, provides reliable, timely and accurate source positioning information for the mine microseismic daily monitoring, improves the safety of mine operation, and has great practical application value and significance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mine microseismic monitoring, and particularly relates to a sensor network layout optimization method for mine microseismic monitoring and application thereof. BACKGROUND

[0002] The sensor array has a long-term impact on the daily monitoring project of mine microseismic, and the monitoring precision that can be achieved in a mine area depends largely on the sensor network layout used. A good sensor network is crucial for reliable and accurate source positioning. However, the prior art has less research and application on the sensor network layout. The invention patent (application number CN 201310379409.4) discloses a metal mine underground microseismic monitoring system, which comprises 16 microseismic signal collection devices, and the microseismic signal collection devices are respectively anchored in the underground monitoring holes in different directions on the sidewall of the underground roadway in the monitored section of the mine, so as to realize real-time online monitoring and analysis of the microseismic vibration of the underground mine rock. However, the microseismic signal collection devices are not effectively optimized and designed in the scheme, which will cause insufficient utilization of microseismic information, difficulty in ensuring the positioning precision of mine microseismic, and no possibility of high-precision disaster monitoring and early warning.

[0003] The design of the sensor network layout is one of the core content tasks of the daily monitoring of the mine microseismic positioning system. How to select a reasonable sensor network directly affects the upper limit of the mine microseismic source positioning precision, and seriously restricts the fine monitoring and early warning of mine disasters. In the arrangement of the microseismic monitoring system, the sensors in the monitoring area cannot be arranged at will, but there are often many candidate positions that meet the conditions; how to determine the optimal sensor network layout in these candidate positions has always been a key problem that puzzles engineering personnel. An unreasonable sensor network layout will make the control equation of source positioning approximately singular, and then cause the positioning system to be abnormally sensitive to input errors; the optimization or evaluation function of the existing sensor network layout method generally has a large deviation, does not consider the key monitoring area and the engineering actual situation, and is not suitable for the sensor network layout of the wave velocity unknown positioning system.

[0004] Therefore, it is necessary to design an improved sensor network layout optimization method for mine microseismic monitoring and application thereof to solve the above problems. SUMMARY

[0005] The application aims to provide a sensor network layout optimization method for mine microseismic monitoring and application thereof, utilize the CRLB principle of optimal parameter estimation to construct an optimization function of the sensor network layout of an unknown wave velocity system, and adopt an improved genetic algorithm to solve the optimization function to obtain an optimal sensor network layout scheme; the sensor network optimization method is more general, universal and has higher calculation efficiency, and provides reliable, timely and accurate source positioning for daily monitoring of mine microseismic.

[0006] To achieve the above application purposes, the application provides a sensor network layout optimization method for mine microseismic monitoring, including the following steps:

[0007] S1, utilize the CRLB principle of optimal parameter estimation to construct an optimization function of the sensor network layout of an unknown wave velocity system; the optimization function is:

[0008] wherein s is a sensor; vector u(x,y,z) T (u∈Ω u ) is a microseismic source coordinate; the subscripts i,j represent elements at the i-th column and j-th row of a matrix; it is assumed that all possible source positions jointly constitute a source domain Ω u , i.e. a monitoring area Ω u ; p(u) is a probability of the vector u appearing in the given monitoring area Ω u ; p(t i,0 |Δ) is a probability density function of the conditional of the time difference t i,0 , and the specific expression is E is a mathematical expectation solving symbol, the superscript T represents transposition of a matrix or vector, t i,0 is time difference data, t i,0 =t i -t0, represents a true value of t i,0 without error, R n is a MxM dimensional covariance matrix, and the specific expression is R n =σ 2 Q, M is the number of target sensors, σ is a standard deviation of the time difference measurement noise, σ is obtained according to prior experience or simplified calculation by assuming 1, is a medium wave velocity;

[0009] take the natural logarithm of the above formula of CRLB(u,s) and derive to obtain:

[0010] CRLB(u,s)=(σ 2 P(s,u) T Q -1P(s, u) -1 ,

[0011] where P is a M x 4 Jacobian matrix, the expression of matrix P(s, u) is:

[0012]

[0013] where, S i is a sensor coordinate, D i is the distance between the microseismic source coordinate u and the sensor S i , i = 0, 1, 2, …, M-1, M is the number of target sensors;

[0014] S2, the optimization function in step S1 is solved by using the improved genetic algorithm to obtain an optimal sensor network layout scheme.

[0015] As a further improvement of the present application, in step S2, the steps of the improved genetic algorithm include:

[0016] SS1, a coding operator: a group of initial populations is randomly generated, each individual in the population is coded by using binary 0 and 1; a gene site represents the position of a candidate sensor, if the ith gene of the chromosome is 0, it indicates that there is no sensor at the ith candidate position; if the ith gene of the chromosome is 1, it indicates that there is a sensor at the ith candidate position;

[0017] SS2, the individual fitness value is calculated by using the optimization function, and the gene values of all positions of the chromosome satisfy the formula: i …+b N i = M, b i is the gene value at the ith position of the chromosome;

[0018] SS3, it is judged whether the convergence criterion is met, if yes, the best individual is outputted and the iteration is terminated, otherwise, it is turned to step SS4; the convergence criterion is one of criterion one or criterion two, criterion one: if the most suitable individual in the population does not change for iter consecutive iterations, it is considered that the best individual is found, and the genetic process is automatically stopped; criterion two: when the difference between the maximum fitness fit max and the average fitness fit avg in a population of a certain generation is less than a threshold ε, i.e., |fit max -fit avg |≤ε, the iteration is terminated;

[0019] SS4, new individuals and a new generation of populations are generated by using improved crossover and mutation operators, and after being processed by using an elite reservation strategy, it is returned to step SS3.

[0020] As a further improvement of the present application, the improved genetic algorithm further comprises a selection operation: using a selection operator to select good individuals in the population and eliminate poor individuals; the selection operation is performed after step SS2 ends.

[0021] As a further improvement of the present application, the selection operation is specifically: according to the individual fitness, using truncation selection to eliminate individuals with lower fitness and select individuals with higher fitness.

[0022] As a further improvement of the present application, the truncation selection ratio of the selection operation is 70%, and the top 70% of relatively good individuals are retained to enter the next generation, and the remaining 30% are eliminated by truncation.

[0023] As a further improvement of the present application, in step SS4, generating new individuals by the improved crossover operator is specifically: using a uniform crossover algorithm to perform crossover operation of the chromosome, generating a set of random numbers whose length is equal to the encoding length of the chromosome, then recording the positions of the random numbers greater than 0.5 and generating a set ); the gene fragments corresponding to the positions of the parent chromosome probability values greater than 0.5 will be exchanged to create two new strings; assuming that the two parent chromosomes are P1 and P2, their gene positions need to satisfy l d (P1) =∑ l d (P2) , that is, the sum of the gene values of the two individuals at the position set l is equal, then the random number array is a valid exchange position identifier, otherwise a new random number array will be created again until the constraint condition is met.

[0024] As a further improvement of the present application, in step SS4, generating new individuals by the improved mutation operator is specifically: using an improved two-point mutation operator, the mutation operator randomly selects one from the gene positions with a value of 1 in the chromosome and mutates the binary code 1 at this position to 0; randomly select another gene from the gene with a value of 0 in the chromosome and mutate the binary code 0 at this position to 1 to produce offspring.

[0025] As a further improvement of the present application, in step SS4, the elite reservation strategy is specifically: replacing the worst individual in a population with an elite individual; let a(k) be the optimal individual in the kth generation population, and the next generation population is represented by A(k+1), if there is no individual in the population A(k+1) better than a(k), replace the worst individual in the population A(k+1) with the individual a(k).

[0026] The application of the sensor network layout optimization method for mine microseismic monitoring according to any one of the above, specifically: first, actual geological investigation is conducted on the mine area to be detected to obtain selectable sensor positions; then, the number M of target sensors is determined; finally, the sensor network layout optimization method is used to determine the most suitable M sensor positions from the selectable sensor positions to form the optimal sensor network layout.

[0027] The beneficial effects of the present application are:

[0028] 1. The present application provides a sensor network layout optimization method for mine microseismic monitoring, which uses the CRLB principle of optimal parameter estimation to construct an optimization function of the sensor network layout of an unknown wave velocity system to evaluate the robustness of a given sensor array, and uses an improved genetic algorithm to minimize the optimization function to obtain the best estimate of the sensor network; by constraining and improving the coding operator, selection operator, crossover operator, and mutation operator of the traditional genetic algorithm, a constrained elite genetic algorithm is proposed, which can efficiently and quickly calculate the optimal sensor network layout scheme even when there are many candidate sensor positions, avoiding the problem of local convergence or non-convergence of the calculation result; this sensor network optimization method solves the problem of sensor network layout for unknown wave velocity systems in mine microseismic monitoring, and takes into account the key monitoring areas and actual engineering conditions of the mine, obtaining the optimal sensor network layout scheme, which provides effective guidance for the layout of field microseismic sensors; and this method is more general and universal, and can provide reliable, timely, and accurate source positioning information for mine microseismic daily monitoring after being applied to mines, improving the safety of mine-related operations, and having great practical application value and significance. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 The flowchart of the sensor network layout optimization method for mine microseismic monitoring of the present application.

[0030] Figure 2 The schematic diagram of the three-dimensional system to be monitored and its selectable sensor positions in the embodiment of the present application.

[0031] Figure 3 The optimal sensor network layout diagram obtained by the embodiment under the condition of 5 sensors. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be described in detail below with reference to the drawings and specific embodiments.

[0033] It should be noted that, in order not to obscure the present application with unnecessary details, only the structures and / or processing steps closely related to the present application are shown in the drawings, and other details not closely related to the present application are omitted.

[0034] In addition, it should be noted that the term "comprise", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or device.

[0035] Please refer to Figure 1 The sensor network layout optimization method for mine microseismic monitoring comprises the following steps:

[0036] S1, using the CRLB principle of optimal parameter estimation to construct an optimization function of the sensor network layout of the unknown wave velocity system; the optimization function is:

[0037] Wherein, s is a sensor; vector u(x, y, z) T (u∈Ω u ) is a microseismic source coordinate; the subscripts i and j represent the elements at the i-th column and the j-th row of the matrix; it is assumed that all possible source locations together constitute a source domain Ω u , i.e. a monitoring area Ω u ; p(u) is the probability of the vector u appearing in the given monitoring area Ω u ; p(t i,0 |α) is the probability density function of the conditional α of the arrival time difference t i,0 , and the specific expression is E is the mathematical expectation solving symbol, the superscript T represents the transpose of the matrix or vector, t i,0 is the arrival time difference data, t i,0 =t i -t0, represents the true value of t i,0 without error, R n is a MxM-dimensional covariance matrix, and the specific expression is R n =σ 2 Q, M is the number of target sensors, σ is the standard deviation of the arrival time difference measurement noise, σ can be obtained according to prior experience or simplified calculation by assuming 1, is the medium wave velocity;

[0038] Taking the natural logarithm of the above formula CRLB(u, s) and taking the derivative, we get:

[0039] CRLB(u,s) = (σ 2 P(s,u) T Q -1 P(s,u)) -1 ,

[0040] where P is a M x 4 Jacobian matrix, and the expression of matrix P(s,u) is:

[0041]

[0042] where, S i is the sensor coordinate, D i is the distance between the microseismic source coordinate u and the sensor S i , i = 0, 1, 2, …, M-1, M is the number of target sensors;

[0043] S2, the optimization function in step S1 is solved by using the improved genetic algorithm to obtain the optimal sensor network layout scheme.

[0044] Specifically, the constraint elite genetic algorithm is proposed by improving the coding operator, selection operator, crossover operator and mutation operator of the traditional genetic algorithm, so that the optimal sensor network layout scheme can be efficiently and quickly calculated even when there are many candidate sensor positions, and the problem of local convergence or non-convergence of the calculation result is avoided, and the improved genetic algorithm has higher calculation efficiency. The steps of the improved genetic algorithm include:

[0045] SS1, coding operator: a group of initial population is randomly generated, and each individual in the population is coded by using binary 0 and 1; a gene site represents the position of a candidate sensor, if the ith gene of the chromosome is 0, it means that there is no sensor at the ith candidate position; if the ith gene of the chromosome is 1, it means that there is a sensor at the ith candidate position;

[0046] SS2, the individual fitness value is calculated by using the optimization function, and the gene values of all positions of the chromosome satisfy the formula: i …+b N = M, b i is the gene value at the ith position of the chromosome;

[0047] SS3, it is judged whether the convergence criterion is met, if yes, the best individual is output and the iteration is terminated, otherwise step SS4 is turned to; the convergence criterion is one of criterion one or criterion two, criterion one: if the most suitable individual in the population does not change for iter consecutive iterations, it is considered that the best individual is found, and the genetic process is automatically stopped; criterion two: the maximum fitness fit max of the individuals in a certain generation population is greater than the average fitness fitavg the difference between fit max -fit avg |≤ε, the iteration is terminated.

[0048] SS4, generating new individuals and a new generation of population by improved crossover operator and mutation operator, and adopting elite reservation strategy to process, returning to step SS3;

[0049] Specifically, in step SS2, the fitness value of the individual is calculated by using the optimization function: the optimization function is solved by using the sensor position represented by the individual gene information, combined with the medium wave velocity, the standard deviation σ of the measurement noise, and the probability distribution p(u) of the monitoring area, and the value obtained by solving is the fitness value of the individual.

[0050] Among them, the new individual generated by the improved crossover operator is: a uniform crossover algorithm is used to perform crossover operation on the chromosome to generate a set of random numbers, the length of which is equal to the coding length of the chromosome, then the positions of the random numbers greater than 0.5 are recorded and a set L is generated The gene fragments corresponding to the positions of the parent chromosome probability value greater than 0.5 will be exchanged to create two new strings; assuming that the two parent chromosomes are P1 and P2, their gene positions need to satisfy Σ l d (P1) =∑ l d (P2) , that is, the sum of the gene values of the two individuals at the position set L is equal, then the random number is a valid exchange position identifier, otherwise a new random number will be created again until the constraint condition is met.

[0051] The new individual generated by the improved mutation operator is: an improved double-point mutation operator is used, the mutation operator randomly selects one from the gene position with a value of 1 in the chromosome, and mutates the binary code 1 at this position to 0; another gene position is randomly selected from the gene with a value of 0 in the chromosome, and the binary code 0 at this position is mutated to 1, to generate offspring;

[0052] The elite reservation strategy is: the worst individual in a population is replaced by an elite individual; first, let a(k) be the optimal individual in the kth generation population, and the next generation population is represented by A(k+1), if there is no individual in the population A(k+1) better than a(k), then the worst individual in the population A(k+1) is replaced by the individual a(k).

[0053] In particular, the improved genetic algorithm further comprises a selection operation: using a selection operator to select good individuals in the population and eliminate poor individuals; the selection operation is performed after step SS2 ends; the selection operation is specifically: according to the individual fitness, using truncation selection to eliminate individuals with low fitness and select individuals with high fitness, the selection truncation ratio is 70%, the top 70% of relatively good individuals are reserved to enter the next generation, and the remaining 30% are eliminated by truncation.

[0054] In some specific embodiments, in step SS3, 200 iterations are taken as the termination condition.

[0055] The sensor network layout optimization method for mine microseismic monitoring first uses CRLB to provide the lower limit of the standard deviation of the source coordinate parameter estimation, and then uses it to evaluate the robustness of the given sensor array, minimizes the CRLB to obtain the optimization function of the sensor network layout, and minimizes the value of the optimization function to obtain the best estimation of the positioning accuracy; then the improved genetic algorithm is used to solve the optimization function to obtain the best sensor network layout scheme. The optimization algorithm combined with CRLB and the improved genetic algorithm provides the optimal network layout scheme of the target sensor, has higher calculation efficiency, and the result stability is very significant; and the method can comprehensively consider the mine key monitoring area and the actual engineering situation for network design, and has greater generalization and universality, and has great practical application value and significance.

[0056] An application of any one of the sensor network layout optimization methods for mine microseismic monitoring, the application of the sensor network layout optimization method for mine microseismic monitoring is specifically: first, actual geological investigation is performed on the mine to be detected to obtain selectable sensor positions; then the number of target sensors M is determined; finally, the sensor network layout optimization method is used to determine the most suitable M sensor positions from the selectable sensor positions to form the optimal sensor network layout.

[0057] Embodiment

[0058] The embodiment provides a sensor network layout optimization method and application for mine microseismic monitoring, assuming that a three-dimensional to-be-monitored region with a size of 100mm*100mm*100mm exists, there are selectable sensor positions, such as shown in (27), and the number of target sensors is 5; the sensor network layout optimization method is used to select the most suitable 5 sensor network layouts, including the following steps: Figure 2

[0059] S1, using the CRLB principle of optimal parameter estimation to construct an optimization function of the sensor network layout of the unknown wave velocity system;

[0060] ​S11, let vector u(x, y, z) T (u e Ω u ) represent the microseismic source coordinates, and all the microseismic sensor candidate locations together form a sensor domain Ω u , while the target sensors to be identified in the sensor domain are 27, denoted as s i (x i ,y i ,z i ), i = 0, 1, 2, …, 26; the distance between the selected sensor s i and the microseismic source coordinates u can be represented as:

[0061]

[0062] Without loss of generality, the first sensor s0is selected as the reference sensor. When v is the wave speed, the TDOA measurement t i between sensors s i,0 and s0is proportional to the distance difference, i.e.:

[0063]

[0064] where v is the wave speed; t i,0 is the time difference measurement between sensors s i and s0, i.e. t i,0 = t i - t0, t i and t0represent the time-of-arrival data received by the i-th and 0-th sensors, respectively. D i,0 is the distance difference between the microseismic source coordinates u and sensors s i and s0, i.e. D i,0 = D i - D0.

[0065] In real scenarios, the time difference data inevitably contains measurement noise. The time difference with error can be modeled as i = 1, 2, 3, …, 27

[0066] where {·} o represents the true value of {·}, and the measurement noise is denoted by n i,0 . It is assumed that the additional measurement noise D i,0 is a zero-mean Gaussian random process and is independent of the time difference and source coordinates.

[0067] If the standard deviation of the time difference measurement noise is σ, its M x M dimensional covariance matrix can be represented as:

[0068] R n = σ 2 Q, where

[0069] Thus, the time difference t i,0 The probability density function of the condition for α is:

[0070]

[0071] Wherein

[0072] S12, for any given source coordinates and sensor network layout, CRLB can provide the source coordinate parameter estimation standard deviation of the lower limit, the formula of CRLB of the parameter to be estimated is:

[0073]

[0074] S13, by taking the natural logarithm of the CRLB formula and derivation, the simplified formula of CRLB is obtained: CRLB(u,s)=(σ 2 P(s,u) T Q -1 P(s,u)) -1 ,

[0075] P is a Mx4 Jacobian matrix, when the wave velocity υ is unknown, the expression of matrix P is:

[0076]

[0077] Wherein,

[0078] S14, finally, the integral of the whole monitoring area microseismic source event is obtained, and the optimization function is:

[0079]

[0080] Wherein, the subscript i,j represents the element at the i-th column and the j-th row of the matrix;

[0081] S2, the optimization function in step S1 is solved by using the improved genetic algorithm, and the optimal sensor network layout scheme is obtained;

[0082] S21, coding operator: coding and initializing population of candidate solutions; a group of initial population is randomly generated, each individual in the population is coded by binary 0 and 1; a gene site represents the position of a candidate sensor, if the i-th gene of the chromosome is 0, it means that there is no sensor at the i-th candidate position; if the i-th gene of the chromosome is 1, it means that there is a sensor at the i-th candidate position;

[0083] S22, the individual fitness value is calculated by using the optimization function, and the gene value of all positions of the chromosome satisfies the formula: b1+b2+…+b i…+b N =M,b i This represents the gene value at the i-th position on the chromosome.

[0084] S23. Selection operation: Use the selection operator to select good individuals in the population and remove poor individuals; the selection operation is performed after step SS2; the selection operation is as follows: based on the individual fitness, use truncation selection to eliminate individuals with low fitness and select individuals with high fitness. The selection truncation ratio is 70%, and the top 70% of relatively excellent individuals are retained to enter the next generation, while the remaining 30% are truncated and eliminated.

[0085] S24. Determine if the convergence criterion is met. If it is, output the best individual and terminate the iteration; otherwise, proceed to step S24. The convergence criterion is either criterion one or criterion two. Criterion one: If the best individual in the population does not change after iterating iter times, the best individual is considered to have been found, and the genetic process automatically stops. Iter times is terminated after 200 iterations. Criterion two: The maximum fit of an individual in a certain generation of the population is determined by the maximum fit of that individual. max with average fit avg The difference between them is less than the threshold ε, i.e., |fit max -fit avg The iteration terminates when |≤ε;

[0086] S25. Generate new individuals and a new generation of population using improved crossover and mutation operators, and then process them using an elite retention strategy before returning to step S23.

[0087] This embodiment uses the sensor network layout optimization method based on CRLB and improved genetic algorithm of the present invention to calculate and obtain the optimal sensor network layout diagram with 5 sensors, as shown in the figure. Figure 3 As shown, this illustrates the feasibility of the technical solution provided by the present invention.

[0088] In summary, the application provides a sensor network layout optimization method for mine microseismic monitoring and its application, by using the CRLB principle of optimal parameter estimation to construct the optimization function of the sensor network layout of the unknown wave velocity system, to evaluate the robustness of the given sensor array, minimizing the above optimization function can obtain the best estimation of the sensor network; By improving the constraint of the encoding operator, selection operator, crossover operator and mutation operator of the traditional genetic algorithm, a constrained elite genetic algorithm is proposed, which can efficiently and quickly calculate the optimal sensor network layout scheme even when there are many candidate sensor positions, avoiding the problem of local convergence or non-convergence of the calculation result. The application provides the optimal network layout scheme of the target sensor through the joint optimization algorithm of CRLB and improved genetic algorithm, has higher calculation efficiency, and the result stability is very significant; the sensor network optimization method solves the sensor network layout problem of the wave velocity unknown system in the mine microseismic monitoring work, and considers the key monitoring area and the engineering actual situation of the mine, and obtains the optimal sensor network layout scheme, which provides guidance for the layout of the microseismic sensor in practice. The method can provide reliable, timely and accurate source positioning information for the daily monitoring of mine microseismic after being applied to the mine, can comprehensively consider the key monitoring area and the engineering actual situation of the mine for network design, is more general and universal, improves the safety of the mine related operation, and has great practical application value and significance.

[0089] The above examples are only used to illustrate the technical solutions of the application and not limit the application, although the application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the application.

Claims

1. A sensor network layout optimization method for mine microseismic monitoring, characterized in that, The method comprises the following steps: S1, using the principle of CRLB of optimal parameter estimation to construct an optimization function of sensor network layout of the unknown wave velocity system; the optimization function is: s∈Ω u ; where s is a sensor; vector u(x, y, z) T (u∈Ω u ) is the microseismic source coordinate; subscript i, i represents the element at the ith column and the ith row of the matrix; it is assumed that all possible source locations together constitute a source domain Ω u , i.e., the monitoring area Ω u ; p(u) is the probability of the occurrence of vector u in the given monitoring area Ω u ; p(t i,0 |α) is the probability density function of the conditional probability of the arrival time difference t i,0 to α, and the specific expression is E is the mathematical expectation solving symbol, the superscript T represents the transpose of the matrix or vector, t i,0 is the arrival time difference data, t i,0 =t i -t0, , t i,0 represents the true value without error, R n is the M×M dimensional covariance matrix, and the specific expression is R n =σ 2 Q, M is the number of target sensors, σ is the standard deviation of the arrival time difference measurement noise, υ is the medium wave velocity; Taking natural logarithm of the formula of the CRLB(u, s) and derivation, the following formula is obtained: CRLB(u,s) = (σ 2 P(s,u) T Q -1 P(s,u)) -1 , Wherein, P is a M*4 Jacobian matrix, and the expression of the matrix P(s, u) is: wherein, S i is the sensor coordinate, D i is the distance between the microseismic source coordinate u and the sensor S i M is the number of target sensors;​ S2, the optimization function in step S1 is solved by using the improved genetic algorithm to obtain an optimal sensor network layout scheme.

2. The sensor network layout optimization method for mine microseismic monitoring according to claim 1, characterized in that, In step S2, the steps of the improved genetic algorithm comprise: SS1, a coding operator: a group of initial populations are randomly generated, each individual in the population is coded by using binary 0 and 1; one gene bit represents one candidate sensor position, if the ith gene of the chromosome is 0, it means that there is no sensor at the ith candidate position; if the ith gene of the chromosome is 1, it means that there is a sensor at the ith candidate position; SS2, the individual fitness value is calculated by using an optimization function, and the gene values at all positions of the chromosome satisfy the formula: b1+b2+…+b i …+b N =M, b i is the gene value at the i-th position of the chromosome; SS3, judging whether the convergence criterion is satisfied, if yes, outputting the best individual and terminating the iteration, otherwise, turning to step SS4; the convergence criterion is one of criterion one or criterion two, criterion one: if the most suitable individual in the population does not change for consecutive iterations iter, it is considered that the best individual is found, and the genetic process is automatically stopped; criterion two: the difference between the maximum fitness fit max and the average fitness fit avg is less than a threshold ε, i.e., |fit max -fit avg |≤ε, the iteration is terminated; SS4, new individuals and a new generation of populations are generated by using the improved crossover operator and mutation operator, and after processing by using the elite reservation strategy, the step SS3 is returned.

3. The sensor network layout optimization method for mine microseismic monitoring according to claim 2, characterized in that, The improved genetic algorithm further comprises a selection operation: using a selection operator to select good individuals in the population and eliminate poor individuals; the selection operation is performed after step SS2 is completed.

4. The sensor network layout optimization method for mine microseismic monitoring according to claim 3, characterized in that, The selection operation is specifically: according to the fitness of the individual, low fitness individuals are eliminated by using truncation selection, and high fitness individuals are selected.

5. The sensor network layout optimization method for mine microseismic monitoring according to claim 2, characterized in that, In step SS4, a new individual is generated by the improved crossover operator, specifically: a uniform crossover algorithm is used to perform the crossover operation of the chromosome, a set of random numbers is generated, the length of which is equal to the coding length of the chromosome, then the positions of the random numbers greater than 0.5 are recorded and a set is generated The gene fragments corresponding to the positions of the parent chromosome probability values greater than 0.5 will be exchanged to create two new strings; assuming that the two parent chromosomes are P1 and P2, their gene positions need to satisfy l d (P1) =∑ l d (P2) , that is, the sum of the gene values of the two individuals at the position set l is equal, then the random number array is a valid exchange position identifier, otherwise a new random number array will be created again until the constraint condition is met.

6. The sensor network layout optimization method for mine microseismic monitoring according to claim 2, characterized in that, In step SS4, the new individual generated by the improved mutation operator is specifically: an improved double-point mutation operator is used, the mutation operator randomly selects one from the gene bit with a value of 1 in the chromosome, and mutates the binary code 1 at this position to 0; another gene bit is randomly selected from the gene with a value of 0 in the chromosome, and the binary code 0 at this position is mutated to 1, so as to generate offspring.

7. The sensor network layout optimization method for mine microseismic monitoring according to claim 2, characterized in that, In step SS4, the elite reservation strategy is specifically: the worst individual in a population is replaced by an elite individual; let a(k) be the optimal individual in the kth generation population, and the next generation population is represented by A(k+1), if there is no individual in the population A(k+1) better than a(k), the worst individual in the population A(k+1) is replaced by the individual a(k).

8. The sensor network layout optimization method for mine microseismic monitoring according to claim 4, characterized in that, The truncation selection ratio of the selection operation is 70%, the top 70% of relatively good individuals are reserved into the next generation, and the remaining 30% are truncated and eliminated.

9. Use of a method for optimizing a sensor network layout for mine microseismic monitoring according to any one of claims 1 to 8, characterized in that, The application of the sensor network layout optimization method for mine microseismic monitoring is specifically: first, the actual geological investigation of the mine area to be detected is performed to obtain the selectable sensor positions; then, the target number of sensors M is determined; finally, the sensor network layout optimization method is used to determine the most suitable M sensor positions from the selectable sensor positions to form the optimal sensor network layout.

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