Acoustic emission source positioning method and system based on grid search and simulated annealing

Abstract

The application discloses a kind of based on grid search and simulated annealing acoustic emission source positioning method, system, electronic equipment and storage medium, comprising the following steps: acoustic emission signal containing to time information is collected, acoustic emission signal is filtered and handled;According to the first arrival time of acoustic emission event of acoustic emission signal after filtering processing is picked up;Based on the first arrival time of picking up source location target function is constructed;Through the source location target function of construction, global search is carried out using grid search method, and preliminary source location is solved;The preliminary source location of solution is used as the initial iteration value of simulated annealing, and accurate source location is solved.The application utilizes the characteristics of global search of grid search method and the characteristics of simulated annealing method, such as making model jump out of local optimal solution, global convergence, improves the robustness of positioning algorithm, compared with existing acoustic emission source positioning method can simultaneously consider the calculation speed and stability of positioning algorithm, realize the rapid, efficient positioning of acoustic emission source.

Description

Technical Field

[0001] The present invention belongs to the technical field of acoustic emission source positioning methods, and specifically relates to an acoustic emission source positioning method, system, electronic equipment and storage medium based on grid search and simulated annealing, which are used to process acoustic emission signals containing arrival time information and perform source inversion positioning. Background Art

[0002] Acoustic emission (AE) refers to the rapid release of strain energy in the form of transient elastic waves when deformation or cracking occurs within a rock under external forces. As an acoustic method for studying rock damage and loss, AE technology infers the stress state and degree of damage by monitoring the AE signals generated by rocks under external loads. It is widely used in indoor rock experiments and provides an important theoretical basis for guiding engineering design and construction. Acoustic emission source location is a key step and key function of AE technology, and its accuracy directly impacts its effectiveness.

[0003] Currently, acoustic emission (AE) source localization is primarily based on the traditional time-of-day (MTD) method, which seeks the minimum residual between the theoretical and actual arrival times of an AE event at each station. This method treats the wave velocity within the rock as a constant and represents the propagation distance between the source and the sensor as a straight line. This method fails to fully account for the influence of rock anisotropy and internal irregularities. To further improve localization accuracy, a more complex wave velocity model is needed to recalculate the propagation path of elastic waves influenced by structures such as cavities and faults. This inevitably increases the computational complexity of the localization algorithm. Furthermore, with increasing sampling rates and the number of sensors in AE monitoring systems, the quality and processing difficulty of AE data are increasing, posing new challenges to traditional localization algorithms. Rapidly processing large amounts of data will inevitably become an unavoidable problem. Grid search and simulated annealing are currently commonly used methods for AE source localization. Simulated annealing can, to a certain extent, avoid the problem of the algorithm falling into local optima, but global optimization significantly increases the computational complexity and time, making it unsuitable for processing large amounts of AE data. The grid search method requires dividing the search area into a certain grid density. The finer the grid division, the higher the positioning accuracy, but it also requires a large amount of positioning calculations. Although the above methods have their own advantages, they struggle to balance computational efficiency and stability. They are not suitable for acoustic emission source localization with high computational complexity or large-scale acoustic emission data.

[0004] Therefore, it is necessary to improve the positioning algorithm and propose an acoustic emission source positioning algorithm that meets the requirements of computational efficiency and stability. Summary of the Invention

[0005] Aiming at the technical problem that the traditional acoustic emission positioning algorithm based on time difference cannot balance the calculation speed and stability,

[0006] The object of the present invention is to provide an acoustic emission source localization method, system, electronic device and storage medium based on grid search and simulated annealing, which can simultaneously take into account calculation speed and stability in the acoustic emission localization algorithm. By adopting the STA / LTA-AIC method to identify acoustic emission events and pick the initial arrival time, the picked arrival time will be used as the actual arrival to input the positioning objective function, and then the grid search method is used to obtain a preliminary result that is relatively close to the global optimal point. Finally, the output result of the grid search method is used as the initial value of the simulated annealing method to perform a local search, and the calculation nodes are continuously iterated to obtain the global optimal point, thereby realizing fast and efficient positioning of the acoustic emission source.

[0007] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0008] The first aspect of the present invention discloses a method for locating an acoustic emission source based on grid search and simulated annealing, comprising the following steps: S1, collecting an acoustic emission signal containing arrival time information and filtering the acoustic emission signal; S2, picking up the first arrival time of the acoustic emission event based on the filtered acoustic emission signal; S3, constructing a source location target function based on the picked first arrival time; S4, performing a global search using a grid search method based on the constructed source location target function to solve a preliminary source location; S5, using the solved preliminary source location as the initial iteration value of simulated annealing to solve the precise source location.

[0009] Based on the above-disclosed steps, the present invention improves the robustness of the positioning algorithm by utilizing the global search characteristics of the grid search method and the characteristics of the simulated annealing method that enable the model to jump out of the local optimal solution and global convergence. Compared with the existing acoustic emission source positioning method, the present invention takes into account both the calculation speed and stability of the positioning algorithm, and can meet the needs in the technical field of acoustic emission source positioning methods.

[0010] And by identifying the acoustic emission event and picking the first arrival time, the picked arrival time will be used as the actual arrival time to enter the positioning objective function, and then the grid search method is used to obtain a preliminary result that is relatively close to the global optimal point. Finally, the output result of the grid search method is used as the initial value of the simulated annealing method for local search, and the calculation nodes are continuously iterated to obtain the global optimal point, thereby realizing fast and efficient positioning of the acoustic emission source.

[0011] In the acoustic emission source localization method based on grid search and simulated annealing disclosed according to the first aspect of the present invention, in step S2, picking up the first arrival time of the acoustic emission event includes:

[0012] The long-short time window ratio method STA / LTA is used to identify acoustic emission events;

[0013] The AIC method is used to pick the first arrival time.

[0014] In a preferred embodiment, in step S2, the long-short time window ratio method STA / LTA is used to identify acoustic emission events, with the sudden change of the ratio of STA to LTA as the judgment condition, wherein the short-time window STA represents the amplitude or energy of the microseismic signal, and the long-time window LTA represents the amplitude or energy of the background noise. When the STA / LTA ratio exceeds a set threshold, it indicates that an acoustic emission event has occurred. The square of the amplitude is used as the characteristic function of the long-short time window ratio method STA / LTA, and the calculation formula is as follows:

[0015]

[0016] Where X(i′) represents the amplitude of the microseismic waveform, where i′ = 1, 2, …, N, STA(i′) and LTA(i′) are the short-time average and long-time average of the amplitude at time i′, respectively. Nsta and Nlta represent the short-time window width and long-time window width, respectively. The weighting coefficient α is the standard deviation σ of the signal amplitude in the short-time window. S and the standard deviation σ of the signal amplitude in the long time window L The ratio of is used to increase the sensitivity to signal changes, and R(i′) is the ratio of the weighted short-term average value to the long-term average value.

[0017] Furthermore, in step S2, after the long-short time window ratio method STA / LTA identifies an acoustic emission event, preferably, the AIC method is used to find the minimum value of the AIC function of the local waveform segment within a suitable time window. The time corresponding to the minimum value is the first arrival time. The calculation formula of the AIC function is as follows:

[0018] AIC(k)=klog{var(X[1,k])}+(Nk-1)log{var(x[k+1,N])}

[0019] Where N is the total number of sampling points, k represents the sliding variable, and var(x[1,k]) and var(x[k+1,N]) represent the variance of the samples in the two time periods, respectively.

[0020] In the present invention, in step S3, a source location objective function based on the sensor arrival time difference is constructed, and the acoustic emission source location based on the arrival time difference is achieved by searching for the minimum residual between the theoretical arrival time and the actual arrival time. Assuming that the acoustic emission event occurs at time t0, after Δt i The duration, at time t i To the i-th (i=1,2,…,n) sensor position, the residual θ between the theoretical arrival time and the actual arrival time of the i-th acoustic emission sensor is i It can be expressed as:

[0021] θ i =t i -Δti -t0

[0022] In order to simplify the calculation process, it is usually assumed that the propagation medium has a uniform velocity, so the elastic wave velocity is a constant value, which can be expressed as the equivalent wave velocity c. p Simple expression. Assume that the coordinates of the acoustic emission source are (x0, y0, z0), and the position coordinates of the i-th sensor are (x i ,y i ,z i ), l i is the distance between the i-th station and the earthquake source. According to the distance formula between two points, the calculation of the i-th sensor can be simplified to:

[0023]

[0024] In the case of only a single sensor, t0 cannot be obtained. In order to eliminate the influence of the earthquake occurrence time t0 on the positioning result, the relationship between multiple sensors can be used. The residual θ between the i-th sensor and the j-th sensor ij for:

[0025] θ ij =θ i -θ j =t i -Δt i -t0-(t j -Δt j -t0)=t i -t j -(Δt i -Δt j );

[0026] Under ideal conditions, the residual error θ between different sensors is ij = 0, but in practice, due to the influence of errors such as the first arrival time picking and theoretical arrival time calculation, θ ij Generally it is not 0. But compared with other points, the θ of the acoustic emission source is ij The absolute value of is the smallest, and the source location error is the smallest. Therefore, the realization of acoustic emission source location can be achieved by searching the acoustic emission monitoring area so that the following objective function reaches the minimum value. The objective function is:

[0027]

[0028] Where (x, y, z) is the coordinate of any node in the acoustic emission monitoring area, and the source point (x0, y0, z0) should make the objective function reach the minimum value; θ ij is the residual between the i-th acoustic emission sensor and the j-th acoustic emission sensor, c p is the equivalent wave velocity, l i is the distance between the i-th station and the earthquake source, lj is the distance between the jth station and the earthquake source, t i is the time from the acoustic emission event to the i-th acoustic emission sensor, t j is the time when the acoustic emission event reaches the jth acoustic emission sensor. i The first arrival time picked by AIC method can be obtained, Δt i It can be simply calculated using the equivalent wave velocity and the distance between two points. However, for rocks with structures such as pores and faults, the changes in the elastic wave propagation path need to be considered in order to accurately obtain the theoretical travel time of the elastic wave.

[0029] In the acoustic emission source localization method based on grid search and simulated annealing disclosed in the first aspect of the present invention, in step S4, a global search is performed using the grid search method to solve the preliminary source location, including:

[0030] 1) Model establishment: Based on the size of the rock specimen, the area where the acoustic emission event may occur is used as the search area. The search area is a three-dimensional space and is divided into a series of grid points. The density of the grid points determines the accuracy of positioning. When the search area is large and the grid division is fine, the computational complexity of the grid search is large. Therefore, the search area and grid point density of the grid search should not be too large. In three-dimensional space, the coordinates of any grid point can be expressed as:

[0031]

[0032] Where n x , n y , n z They are the number of grids on the X, Y, and Z axes, Δx, Δy, and Δz are the grid sizes on the three axes, and x min ,y min ,z min The minimum values ​​of the coordinates on the X, Y, and Z axes respectively;

[0033] 2) Propagation time calculation: For each grid point, the theoretical propagation time from that point to each sensor is calculated and compared with the actual monitored arrival time to obtain the time residual;

[0034] 3) Grid search: By constructing the earthquake source location objective function, calculate the objective function value of each grid point and find the grid point that minimizes the objective function value. This point is the preliminary earthquake source location (x g ,y g ,z g ).

[0035] In the acoustic emission source localization method based on grid search and simulated annealing disclosed in the first aspect of the present invention, in step S5, the solved preliminary source location is used as the initial iteration value of simulated annealing to solve the precise source location, including:

[0036] 1) Initialization parameters: set the initial temperature T and the end temperature T min , temperature drop function and number of iterations, where T is generally 100, T min Generally, it is set to 0.2, the temperature drop function is T = T·α, α is the temperature drop rate, and the value is 0<α<1. The number of iterations is generally 300;

[0037] 2) Select the initial solution: The initial source location estimated by grid search is used as the initial solution of simulated annealing (x g ,y g ,z g );

[0038] 3) Cooling: Lower the temperature T according to the set temperature drop function;

[0039] 4) Iteration process: Calculate the objective function value E at the current earthquake source position c , generate a new source position (x', y', z') by random perturbation at the current solution, and calculate the objective function E of the new source position n , update the current earthquake source position according to the following acceptance criteria: if E n <E c , accept the new solution; if E n >E c , then with a certain probability Accept the new solution, when the temperature T drops to T min Or when the set number of iterations is reached, the current solution is returned;

[0040] 5) Termination condition: Repeat the above steps until the termination condition is met, that is, the objective function value is less than 1×10 -6 When the final accurate earthquake source position (x s ,y s ,z s ) and the corresponding objective function value, otherwise, appropriately reduce the temperature drop rate or increase the number of iterations and perform re-iteration.

[0041] The second aspect of the present invention discloses an acoustic emission source localization system based on grid search and simulated annealing, comprising:

[0042] Acoustic emission signal acquisition and processing module, used to collect acoustic emission signals containing arrival information and perform filtering on the acoustic emission signals;

[0043] An acoustic emission event first arrival time picking module is used to pick up the first arrival time of the acoustic emission event based on the acoustic emission signal after filtering;

[0044] A source location objective function construction module is used to construct a source location objective function based on the picked first arrival times;

[0045] The module for solving the preliminary earthquake source position is used to solve the preliminary earthquake source position by performing a global search using the grid search method based on the constructed earthquake source position objective function;

[0046] The module for solving the precise earthquake source position is used to use the solved preliminary earthquake source position as the initial iteration value of simulated annealing to solve the precise earthquake source position.

[0047] The third aspect of the present invention discloses an electronic device, including a memory, a processor and a bus. The memory stores a computer program executable by the processor. When the electronic device is running, the memory and the processor communicate through the bus, and the processor executes the computer program to perform the acoustic emission source localization method based on grid search and simulated annealing disclosed in the first aspect above.

[0048] A fourth aspect of the present invention discloses a storage medium storing computer program instructions. When the computer program instructions are executed by a processor, the method for locating an acoustic emission source based on grid search and simulated annealing disclosed in the first aspect is executed.

[0049] The beneficial effects of the present invention compared to the prior art are as follows:

[0050] The present invention utilizes the global search characteristics of the grid search method and the characteristics of the simulated annealing method that enable the model to jump out of the local optimal solution and global convergence for the first time in the acoustic emission source localization method, thereby improving the robustness of the positioning algorithm. Compared with the acoustic emission source localization method in the prior art, the present invention can simultaneously take into account the calculation speed and stability of the positioning algorithm, and is significantly improved in terms of calculation speed and stability, meeting the needs in the technical field of acoustic emission source localization methods. Secondly, the present invention uses the STA / LTA-AIC method to identify acoustic emission events and pick the initial arrival time. The picked arrival time is used as the actual arrival time to enter the positioning objective function, and then uses the grid search method to obtain a preliminary result that is relatively close to the global optimal point. Finally, the output result of the grid search method is used as the initial value of the simulated annealing method to perform a local search, and the calculation nodes are continuously iterated to obtain the global optimal point, thereby avoiding the situation where the error of the simulated annealing method becomes larger due to improper initial value selection. The simulated annealing method has the characteristics of simple calculation and high efficiency, which improves the operating efficiency and accuracy in the iterative process, thereby realizing fast and efficient positioning of the acoustic emission source.

[0051] The following discloses in detail the acoustic emission source localization method, system, electronic device and storage medium based on grid search and simulated annealing of the present invention in conjunction with the embodiments shown in the accompanying drawings and the accompanying reference numerals. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A step diagram of an acoustic emission source localization method based on grid search and simulated annealing according to the present invention is shown;

[0053] Figure 2 The flowchart of the acoustic emission source localization system based on grid search and simulated annealing of the present invention is shown;

[0054] Figure 3 The present invention is a flowchart of specific steps for implementing the acoustic emission source localization system based on grid search and simulated annealing. DETAILED DESCRIPTION

[0055] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0056] It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other. It should also be noted that the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0057] In addition, the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0058] like Figure 1As shown, the present invention discloses a method for locating an acoustic emission source based on grid search and simulated annealing, comprising the following steps: S1, collecting an acoustic emission signal containing arrival time information and filtering the acoustic emission signal; S2, picking up the first arrival time of the acoustic emission event according to the filtered acoustic emission signal; S3, constructing a source location target function based on the picked first arrival time; S4, performing a global search using a grid search method based on the constructed source location target function to solve a preliminary source location; S5, using the solved preliminary source location as an initial iteration value of simulated annealing to solve the precise source location.

[0059] In the present invention, by identifying acoustic emission events and picking up the initial arrival time, the picked arrival time will be used as the actual arrival time to enter the positioning objective function, and then a grid search method is used to obtain a preliminary result that is relatively close to the global optimal point. Finally, the output result of the grid search method is used as the initial value of the simulated annealing method to perform a local search, and the calculation nodes are continuously iterated to obtain the global optimal point, thereby realizing fast and efficient positioning of the acoustic emission source; and the global search characteristics of the grid search method and the characteristics of the simulated annealing method that enable the model to jump out of the local optimal solution and have global convergence are utilized, thereby improving the robustness of the positioning algorithm. Compared with the existing acoustic emission source positioning method, the positioning algorithm takes into account both the calculation speed and stability of the positioning algorithm, thereby meeting the use requirements in the technical field of acoustic emission source positioning methods.

[0060] In the present invention, in step S1, the purpose of filtering the acoustic emission signal is to remove background noise, avoid interference with the acoustic emission source positioning algorithm, and affect the accuracy of the result.

[0061] In the embodiment of the present invention, in step S2, picking up the first arrival time of the acoustic emission event includes:

[0062] The long-short time window ratio method STA / LTA is used to identify acoustic emission events;

[0063] The AIC method is used to pick the first arrival time.

[0064] In a preferred embodiment, in step S2, the long-short time window ratio method STA / LTA is used to identify acoustic emission events, with the sudden change of the ratio of STA to LTA as the judgment condition, wherein the short-time window STA represents the amplitude or energy of the microseismic signal, and the long-time window LTA represents the amplitude or energy of the background noise. When the STA / LTA ratio exceeds a set threshold, it indicates that an acoustic emission event has occurred. The square of the amplitude is used as the characteristic function of the long-short time window ratio method STA / LTA, and the calculation formula is as follows:

[0065]

[0066] Where X(i′) represents the amplitude of the microseismic waveform, where i′ = 1, 2, …, N, STA(i′) and LTA(i′) are the short-time average and long-time average of the amplitude at time i′, respectively. Nsta and Nlta represent the short-time window width and long-time window width, respectively. The weighting coefficient α is the standard deviation σ of the signal amplitude in the short-time window. S and the standard deviation σ of the signal amplitude in the long time window L The ratio of is used to increase the sensitivity to signal changes, and R(i′) is the ratio of the weighted short-term average value to the long-term average value.

[0067] In a preferred embodiment, in step S2, after the AE event is identified using the long-short time window ratio method (STA / LTA), the AIC method is used to find the minimum value of the AIC function of the local waveform segment within the appropriate time window. The time corresponding to the minimum value is the first arrival time. The calculation formula of the AIC function is as follows:

[0068] AIC(k)=klog{var(X[1,k])}+(Nk-1)log{var(x[k+1,N])};

[0069] Where N is the total number of sampling points, k represents the sliding variable, and var(x[1,k]) and var(x[k+1,N]) represent the variance of the samples in the two time periods, respectively.

[0070] In the embodiment of the present invention, in step S3, a source location objective function based on the sensor arrival time difference is constructed, wherein the acoustic emission source location based on the arrival time difference is achieved by searching for the minimum residual between the theoretical arrival time and the actual arrival time. Assume that the acoustic emission event occurs at time t0, after Δt i The duration, at time t i To the i-th (i=1,2,…,n) sensor position, the residual θ between the theoretical arrival time and the actual arrival time of the i-th acoustic emission sensor is i It can be expressed as:

[0071] θ i =t i -Δt i -t0;

[0072] In order to simplify the calculation process, it is usually assumed that the propagation medium has a uniform velocity, so the elastic wave velocity is a constant value, which can be expressed as the equivalent wave velocity c. p Simple expression. Assume that the coordinates of the acoustic emission source are (x0, y0, z0), and the position coordinates of the i-th sensor are (x i ,y i ,z i ), l iis the distance between the i-th station and the source. According to the distance formula between two points, the calculated arrival time of the i-th sensor can be simplified as:

[0073]

[0074] In the case of only a single sensor, t0cannot be obtained. To eliminate the influence of the time of origin t0on the positioning result, the relationship between multiple sensors can be used, and the residual error θ ij is between the i-th sensor and the j-th sensor.

[0075] θ ij = θ i - θ j = t i - Δt i - t0- (t j - Δt j - t0) = t i - t j - (Δt i - Δt j )

[0076] In ideal conditions, the residual error θ ij between different sensors is 0, but in reality, it is affected by errors such as first arrival time picking and theoretical arrival time calculation, and θ ij is generally not 0. However, compared to other points, the absolute value of θ ij of the acoustic emission source is the smallest, and the source positioning error is the smallest. Therefore, the implementation of acoustic emission source positioning can be achieved by searching the acoustic emission monitoring area to make the following objective function reach the minimum value, and the objective function is:

[0077]

[0078] In the formula, x, y, and z are the coordinates of any node in the acoustic emission monitoring area, and the source point (x0, y0, z0) should make the objective function reach the minimum value. Among them, t i can be obtained by picking the first arrival time using the AIC method, and Δt i can be simply calculated using the equivalent wave speed and the distance between two points.

[0079] In a preferred embodiment, in step S4, a global search is performed using a grid search method to solve the preliminary source position. In this embodiment, first, in a given three-dimensional space region, all possible source positions are traversed according to a suitable grid density, the error of each position as a source with the monitored arrival time data is calculated, and the position with the smallest error is found as the preliminary estimate of the source. The specific steps are as follows:

[0080] 1) Model establishment: Based on the size of the rock specimen, the area where the acoustic emission event may occur is used as the search area. The search area is a three-dimensional space and is divided into a series of grid points. The density of the grid points determines the accuracy of positioning. When the search area is large and the grid division is fine, the computational complexity of the grid search is large. Therefore, the search area and grid point density of the grid search should not be too large. In three-dimensional space, the coordinates of any grid point can be expressed as:

[0081]

[0082] Where n x , n y , n z They are the number of grids on the X, Y, and Z axes, Δx, Δy, and Δz are the grid sizes on the three axes, and x min ,y min ,z min The minimum values ​​of the coordinates on the X, Y, and Z axes respectively;

[0083] 2) Propagation time calculation: For each grid point, the theoretical propagation time from that point to each sensor is calculated and compared with the actual monitored arrival time to obtain the time residual;

[0084] 3) Grid search: Based on the earthquake source location objective function constructed in step S3, calculate the objective function value of each grid point and find the grid point that minimizes the objective function value. This point is the preliminary earthquake source location (x g ,y g ,z g ).

[0085] In a preferred embodiment, in step S5, the solved preliminary earthquake source position is used as the initial iteration value of simulated annealing to solve the precise earthquake source position. In this embodiment, after step S4, a rough position close to the actual position is obtained, and the search range of the earthquake source point is controlled in a smaller area. Then, the positioning result obtained by grid search is used as the initial iteration value of simulated annealing, and the characteristics of simulated annealing method that jumps out of the local optimum and eventually tends to the global optimum are used for precise positioning. Simulated annealing is a heuristic global optimization algorithm that can continuously update the optimal solution with a certain probability during the optimization process, thereby jumping out of the local optimal solution and randomly searching for the global optimal solution of the objective function in the solution space. The specific steps are as follows:

[0086] 1) Initialization parameters: set the initial temperature T and the end temperature T min , temperature drop function and number of iterations. Among them, T is generally 100, T minGenerally, it is set to 0.2, the temperature drop function is T = T·α, α is the temperature drop rate, and the value is 0<α<1. The number of iterations is generally 300;

[0087] 2) Select the initial solution: Use the positioning result obtained by grid search as the initial solution of simulated annealing (x g ,y g ,z g );

[0088] 3) Cooling: Lower the temperature T according to the set temperature drop function;

[0089] 4) Iteration process: Calculate the objective function value E at the current earthquake source position c , generate a new source position (x', y', z') by random perturbation at the current solution, and calculate the objective function E of the new source position n ;

[0090] Update the current source location according to the following acceptance criteria: If E n <E c , accept the new solution; if E n >E c , then with a certain probability Accept the new solution. When the temperature T drops to T min Or when the set number of iterations is reached, the current solution is returned;

[0091] 5) Termination condition: Repeat the above steps until the termination condition is met, that is, the objective function value is less than 1×10 -6 When the final accurate earthquake source position (x s ,y s ,z s ) and the corresponding objective function value. Otherwise, appropriately reduce the temperature drop rate or increase the number of iterations and iterate again.

[0092] like Figure 2 As shown, the present invention also discloses an acoustic emission source positioning system based on grid search and simulated annealing, comprising:

[0093] Acoustic emission signal acquisition and processing module, used to collect acoustic emission signals containing arrival information and perform filtering on the acoustic emission signals;

[0094] An acoustic emission event first arrival time picking module is used to pick up the first arrival time of the acoustic emission event based on the acoustic emission signal after filtering;

[0095] A source location objective function construction module is used to construct a source location objective function based on the picked first arrival times;

[0096] The module for solving the preliminary earthquake source position is used to solve the preliminary earthquake source position by performing a global search using the grid search method based on the constructed earthquake source position objective function;

[0097] The module for solving the precise earthquake source position is used to use the solved preliminary earthquake source position as the initial iteration value of simulated annealing to solve the precise earthquake source position.

[0098] like Figure 3 As shown, when the acoustic emission source localization system based on grid search and simulated annealing of the present invention is implemented, the specific steps are as follows:

[0099] 1. Input the acoustic emission signal and perform filtering;

[0100] 2. Use the STA / LTA-ATC method to pick up the first arrival time of the acoustic emission event;

[0101] 3. Construct an objective function based on the time difference between sensors;

[0102] 4. Use the grid search method to perform a global search to solve the earthquake source location (x g ,y g ,z g );

[0103] 5. Set initialization parameters;

[0104] 6. Use the results obtained from the grid search as the initial solution for simulated annealing;

[0105] 7. Update the current optimal solution to obtain a more accurate earthquake source location (x s ,y s ,z s );

[0106] 8. Determine whether the termination condition is met. If so, output the result; if not, return to step 5 and continue iterating until the termination condition is met.

[0107] The present invention also discloses an electronic device, including a memory, a processor and a bus. The memory stores a computer program executable by the processor. When the electronic device is running, the memory and the processor communicate through the bus, and the processor executes the computer program to perform the acoustic emission source localization method based on grid search and simulated annealing disclosed in the first aspect of the present invention.

[0108] The present invention also discloses a storage medium storing computer program instructions. When the computer program instructions are executed by a processor, the acoustic emission source localization method based on grid search and simulated annealing disclosed in the first aspect of the invention is executed.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0110] In addition, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0111] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0112] The foregoing is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included within the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined or explained in subsequent figures.

[0113] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0114] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for locating acoustic emission sources based on grid search and simulated annealing, characterized in that: The steps include: S1, collecting acoustic emission signals containing arrival information and filtering the acoustic emission signals; S2, picking up the first arrival time of the acoustic emission event according to the filtered acoustic emission signal; S3, constructs the hypocenter location objective function based on the picked first arrival times; S4, using the constructed earthquake source location objective function to perform a global search using the grid search method to solve the preliminary earthquake source location; S5, using the solved preliminary earthquake source location as the initial iteration value of simulated annealing to solve the precise earthquake source location.

2. The acoustic emission source localization method based on grid search and simulated annealing according to claim 1, characterized in that: In step S2, picking up the first arrival time of the acoustic emission event includes: The long-short time window ratio method STA / LTA is used to identify acoustic emission events; The AIC method is used to pick the first arrival time.

3. The acoustic emission source localization method based on grid search and simulated annealing according to claim 2, characterized in that: In step S2, the long-short time window ratio method (STA / LTA) is used to identify acoustic emission events. The sudden change in the ratio of STA to LTA is used as the judgment condition. The short-time window STA represents the amplitude or energy of the microseismic signal, and the long-time window LTA represents the amplitude or energy of the background noise. When the STA / LTA ratio exceeds the set threshold, it indicates that an acoustic emission event has occurred. The square of the amplitude is used as the characteristic function of the long-short time window ratio method (STA / LTA). The calculation formula is as follows: Where X(i′) represents the amplitude of the microseismic waveform, where i′ = 1, 2, …, N, STA(i′) and LTA(i′) are the short-time average and long-time average of the amplitude at time i′, respectively. Nsta and Nlta represent the short-time window width and long-time window width, respectively. The weighting coefficient α is the standard deviation σ of the signal amplitude in the short-time window. S and the standard deviation σ of the signal amplitude in the long time window L The ratio of is used to increase the sensitivity to signal changes, and R(i′) is the ratio of the weighted short-term average value to the long-term average value.

4. The acoustic emission source localization method based on grid search and simulated annealing according to claim 3, characterized in that: In step S2, after the AE event is identified using the long-short time window ratio method (STA / LTA), the AIC method is used to find the minimum value of the AIC function of the local waveform segment within the appropriate time window. The time corresponding to the minimum value is the first arrival time. The calculation formula of the AIC function is as follows: AIC(k)=klog{var(X[1,k])}+(Nk-1)log{var(x[k+1,N])}; Where N is the total number of sampling points, k represents the sliding variable, and var(x[1,k]) and var(x[k+1,N]) represent the variance of the samples in the two time periods, respectively.

5. The acoustic emission source localization method based on grid search and simulated annealing according to claim 1, characterized in that: In step S3, the earthquake source location objective function is constructed, and the calculation formula is as follows: Where x, y, z are the coordinates of any node in the acoustic emission monitoring area, θ ij is the residual between the i-th acoustic emission sensor and the j-th acoustic emission sensor, c p is the equivalent wave velocity, l i is the distance between the i-th station and the earthquake source, l j is the distance between the jth station and the earthquake source, t i is the time from the acoustic emission event to the i-th acoustic emission sensor, t j is the time when the acoustic emission event arrives at the jth acoustic emission sensor.

6. The acoustic emission source localization method based on grid search and simulated annealing according to claim 5, characterized in that: In step S4, a global search is performed using a grid search method to solve the preliminary earthquake source location, including: Steps to establish the model: Based on the size of the rock specimen, the area where the acoustic emission event may occur is used as the search area. The search area is a three-dimensional space and is divided into a series of grid points. The density of the grid points determines the accuracy of positioning. In three-dimensional space, the coordinates of any grid point are expressed as: Where n x , n y , n z They are the number of grids on the X, Y, and Z axes, Δx, Δy, and Δz are the grid sizes on the three axes, and x min ,y min ,z min The minimum values ​​of the coordinates on the X, Y, and Z axes respectively; The steps of propagation time calculation are as follows: For each grid point, calculate the theoretical propagation time from that point to each sensor and compare the theoretical propagation time with the actual monitored arrival time to obtain the time residual; Grid search steps: By constructing the source location objective function, calculate the objective function value of each grid point, and find the grid point that minimizes the objective function value. This point is the preliminary source location estimated by the grid search.

7. The acoustic emission source localization method based on grid search and simulated annealing according to claim 6, characterized in that: In step S5, the obtained preliminary earthquake source location is used as the initial iteration value of simulated annealing to obtain the precise earthquake source location, including: Steps for initializing parameters: set initial temperature T, end temperature T min , temperature drop function and number of iterations, where T is 100, T min Take 0.2, the temperature drop function is T = T·α, α is the temperature drop rate, the value is 0<α<1, and the number of iterations is 300; Steps for selecting the initial solution: using the preliminary source location estimated by grid search as the initial solution for simulated annealing; Cooling steps: reduce the temperature T according to the set temperature drop function; Steps of the iterative process: Calculate the objective function value E at the current earthquake source position c , generate a new source position by random perturbation in the current solution, and calculate the objective function E of the new source position n , update the current earthquake source position according to the following acceptance criteria: if E n <E c , accept the new solution; if E n >E c , then with probability Accept the new solution, when the temperature T drops to T min Or when the set number of iterations is reached, the current solution is returned; Termination condition steps: Repeat the above steps until the termination condition is met, that is, the objective function value is less than 1×10 -6 When the temperature decreases, the final precise earthquake source position and the corresponding objective function value are output; otherwise, the temperature decrease rate is appropriately reduced or the number of iterations is increased to perform re-iteration.

8. A system for implementing the acoustic emission source localization method based on grid search and simulated annealing according to any one of claims 1 to 7, characterized in that: include: Acoustic emission signal acquisition and processing module, used to collect acoustic emission signals containing arrival information and perform filtering on the acoustic emission signals; An acoustic emission event first arrival time picking module is used to pick up the first arrival time of the acoustic emission event based on the acoustic emission signal after filtering; A source location objective function construction module is used to construct a source location objective function based on the picked first arrival times; The module for solving the preliminary earthquake source position is used to solve the preliminary earthquake source position by performing a global search using the grid search method based on the constructed earthquake source position objective function; The module for solving the precise earthquake source position is used to use the solved preliminary earthquake source position as the initial iteration value of simulated annealing to solve the precise earthquake source position.

9. An electronic device, characterized in that: The electronic device comprises a memory, a processor and a bus, wherein the memory stores a computer program executable by the processor. When the electronic device is running, the memory and the processor communicate via the bus, and the processor executes the computer program to perform the acoustic emission source localization method based on grid search and simulated annealing according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method for locating an acoustic emission source based on grid search and simulated annealing according to any one of claims 1 to 7 is executed.

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