CN115327617B - Rapid convergence and high-precision positioning method for micro-seismic source - Google Patents
Rapid convergence and high-precision positioning method for micro-seismic source Download PDFInfo
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Abstract
The invention provides a method for quickly converging and positioning a microseismic source with high precision, and belongs to the technical field of mine microseismic monitoring. The method for quickly converging and positioning the microseismic source comprises the steps of (1) arranging microseismic monitoring sensors in a mine monitoring area, and measuring coordinates of the microseismic monitoring sensors; (2) Performing two blasting tests in a monitoring area range, and determining the average wave speed of the rock mass in the monitoring area range; (3) When P waves are picked up by combining a time window energy ratio method and an AIC method, a microseismic source positioning objective function is established; (4) And calculating the coordinate position of the micro-seismic source by using a self-adaptive artificial bee colony algorithm. The invention has high positioning precision to the microseism and can promote the application of microseism monitoring technology in engineering practice.
Description
Technical Field
The invention relates to the technical field of mine microseism monitoring, in particular to a method for quickly converging and highly precisely positioning a microseism source.
Background
Along with the rapid development of the economic society, the demand of China for mineral resources is continuously increased, but the shallow mineral resources in China are gradually exhausted and the mining depth is increased at present, so that the problem of mine ground pressure is gradually highlighted, rock body dynamic disasters such as roof caving, pillar caving and the like occur in mining, and meanwhile, peripheral mine out-of-range mining activities exist, so that the stability of a rock structure system is influenced by the unknown mining operation. The microseismic monitoring technology is an important means for ensuring safe production in the mine exploitation process. The microseism monitoring technology is to utilize a sensor to receive elastic waves generated by mine rock mass fracture, analyze and obtain time, space, intensity, earthquake source mechanism and other information of a microseism event, monitor and early warn deep mine disasters, and become one of the known effective methods for monitoring the stability of mine rock in the mine exploitation tunneling process. Compared with the traditional rock mass monitoring technology, the microseismic monitoring technology can realize remote, full-area, real-time and dynamic monitoring, and has the advantages of analyzing the rock mass damage trend, unstable body position, early warning and the like.
At present, the main microseismic source positioning method is an inversion method. The method inverts the position of the seismic source by receiving the first arrival time of the seismic source through a seismic source detection detector, and the inversion method comprises non-heuristic and heuristic 2 methods. Non-heuristic methods mainly comprise Newton's method, quasi-Newton's method, gradient descent method and the like. Although the non-heuristic method can improve the positioning accuracy of the seismic source through a certain improvement, the problems of complex derivative solving, local searching, slow convergence speed and the like exist, and the positioning accuracy is poor due to the fact that the non-heuristic method is easy to position to an incorrect position. Therefore, heuristic methods are the main stream of research at present, and mainly comprise simulated annealing, simplex method, genetic algorithm, particle swarm optimization algorithm and the like. However, the searching capability is gradually reduced when iterative computation exists in the simulated annealing method, the simplex method, the genetic algorithm and the particle swarm optimization algorithm, the convergence rate is low, the computation precision is low, and the local optimization is easy to fall into. Therefore, a method for rapid convergence and high-precision positioning of microseismic sources is needed.
Disclosure of Invention
The invention aims to provide a method for quickly converging and positioning a microseism source, which can simply, quickly and effectively identify the microseism time and accurately pick up P waves, has high positioning precision on the microseism source, meets the requirements of the existing engineering, and promotes the microseism monitoring technology to better play a predictive and early warning role in engineering practice.
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
according to one aspect of the invention, a method for rapid convergence and high-precision positioning of a microseismic source is provided. The method for quickly converging and positioning the microseismic source comprises the following steps of:
(1) Installing a microseismic monitoring sensor:
installing n microseismic monitoring sensors in a rock mass area to be monitored, and measuringThe spatial coordinates of the ith microseismic monitoring sensor are (x i ，y i ，z i ) Wherein n is more than or equal to 4, and n microseismic monitoring sensors are distributed in a space reticular structure;
(2) Acquiring P wave arrival time;
(3) Establishing a microseism positioning objective function:
assuming isotropy and homogeneity of the rock mass, the wave speed of the P wave is kept unchanged, and establishing a travel time equation:
wherein:
x 0 、y 0 、z 0 is the coordinate of the micro-focus;
x i 、y i 、z i coordinates of the microseismic monitoring sensor;
v 0 monitoring the propagation speed from a sensor to a microseism for the microseism;
t i the time when the P wave propagates to the ith microseismic monitoring sensor to pick up the waveform;
t 0 the time of the micro-vibration source vibration generation is the time of the micro-vibration source vibration generation;
the theoretical moment of the k-th microseismic monitoring sensor picking up the P wave is assumed to be:
the theoretical to time difference of the kth and the u-th microseismic monitoring sensors is as follows:
Δt ku ＝t k -t u (3)
the microseismic localization objective function may be expressed as:
wherein:
ΔT ku the actual time difference of the monitoring of the kth and the nth microseismic monitoring sensors is that u is more than or equal to 1, k is more than or equal to 1, and k is not equal to u;
the minimum value calculated by the formula (4) is the coordinate value of the micro-focus;
(4) Honey source initialization
Initializing honey source according to formula (5), and calculating fitness value Fit (x) according to formula (6) i,d ) Wherein, the method comprises the steps of, wherein,
x i，d ＝L i +rand(0，1)(U i -L i ) (5)
wherein:
i is a honey source, wherein i=1, 2, …, NP;
d is the honey source dimension, where d = 1,2,3,4;
x i,d employment of bees for any of the honey sources i on dimension d;
L i l is the lower bound of the search space i ＝(x 0l ,y 0l ,z 0l ,v 0l ,)；
U i U, being the upper bound of the search space i ＝(x 0u ,y 0u ,z 0u ,v 0u ,)；
Fit(x i，d ) Is x i,d Is a fitness value of (a);
R(x i，d ) The objective function value is positioned for the microseism;
(5) Updating and searching for new honey sources:
employing bees to search for new honey sources in the vicinity of the honey source using equation (8) with an adaptive search strategy;
v i,d x id +rand(0,1)(x i，d -x j,d ) (7)
v i,d ＝x i,d +μ(e)(x i,d －x j,d )+(1－μ(e)rand(0,1))(V gbest,d －x i,d ) (8)
wherein:
x i,d employment of bees, x for any of the honey sources i on dimension d j,d Employing bees for any of the honey sources j on dimension d, where i+.j;
v i,d is a new honey source position;
V gbest,d an optimal honey source location for all currently employed bee individuals;
mu (e) is an adaptive coefficient, which determines the degree of dependence of the traditional honey source searching formula (7) on the circulation times, and e is the current circulation times;
maxcycles are the maximum number of cycles;
the formula (7) is a traditional honey source searching formula, and can be known from the formula (9): in the initial period of circulation, mu (e) is approximately equal to 1, and the formula (8) is mainly a traditional honey source searching formula (7), so that stronger exploring capability can be maintained, and along with searching, each honey source is gradually close, and the neighborhood range is gradually reduced; finally, comparing the adaptation values of the new and old honey sources by using a greedy algorithm, and selecting the best adaptation value as the best one;
(6) Probability of employment of bees as observation bees:
wherein:
x i for employment of bees in all dimensions in honey source i, x n Employment of bees for all dimensions in honey source n;
Fit(x i ) Is x i Is a fitness function value of (a);
NP is the number of sources of honey;
randomly generating a random number between 0 and 1, and comparing the random number with the probability calculated by the formula (10), when the generated random number is smaller than the probability P i When the method is used, the corresponding hiring bees are selected as observing bees, and neighborhood searching is carried out by using a formula (8);
(7) Stage of bee investigation
The honey source has a parameter three, when the new honey source is better than the current honey source, the honey source update is reserved, and trail=0; otherwise, reserving the current honey source, wherein trail=trail+1 so as to count the number of times that the current honey source is not updated;
when the trail value of a honey source exceeds a preset threshold limit, the honey source is discarded, a detection bee stage is started, and in the detection bee stage, a detection bee randomly searches for a new honey source to replace the discarded honey source by using the formula (5).
According to an embodiment of the invention, the P-wave arrival time of the microseismic event is picked up based on a time window energy ratio method and an AIC method;
and constructing a micro-focus positioning objective function based on the self-adaptive artificial bee colony algorithm.
According to one embodiment of the invention, in combination with monitoring the sensor coordinate position according to the picked P wave arrival time and the microseism, the microseism coordinate calculation is performed by using an adaptive artificial bee colony algorithm, so as to find the microseism source coordinate with the minimum travel time residual error in the three-dimensional space.
According to one embodiment of the present invention, in the bee detection stage, the fitness value is also calculated by the formula (6), and the obtained minimum fitness value is saved.
One embodiment of the present invention has the following advantages or benefits:
the method for quickly converging and positioning the microseism source can simply, quickly and effectively identify the microseism time and accurately pick up the P wave, has high positioning precision on the microseism source, meets the requirements of the existing engineering, and promotes the microseism monitoring technology to better play a predictive and early-warning role in engineering practice.
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The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.
FIG. 1 is a flow chart illustrating a method of microseismic source rapid convergence and high-precision positioning according to an exemplary embodiment.
Fig. 2 is a graph showing the results of P-wave arrival time picked up based on a time window energy ratio method and an AIC method according to an exemplary embodiment.
FIG. 3 is a schematic diagram illustrating a sensor mounting location according to an exemplary embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and thus detailed descriptions thereof will be omitted.
The terms "a," "an," "the," and "said" are used to indicate the presence of one or more elements/components/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. in addition to the listed elements/components/etc.
As shown in fig. 1 to 3, fig. 1 shows a flowchart of a method for rapid convergence and high-precision positioning of a microseismic source according to the present invention. Fig. 2 shows a graph of the P-wave arrival time picked up by the time window energy ratio method and the AIC method. FIG. 3 shows a schematic view of a sensor mounting location provided by the present invention.
The method for quickly converging and positioning the microseismic source comprises the following steps:
(1) Installing a microseismic monitoring sensor:
installing n microseismic monitoring sensors in a region to be monitored, measuring the space coordinates of all the microseismic monitoring sensors, wherein the coordinate of the ith microseismic monitoring sensor is (x i ，y i ，z i ) Wherein n is more than or equal to 4, and n microseismic monitoring sensors are distributed in a space reticular structure;
(2) Acquiring P wave arrival time;
(3) Establishing a microseism positioning objective function:
assuming isotropy and homogeneity of the rock mass, the wave speed of the P wave is kept unchanged, and establishing a travel time equation:
wherein:
x 0 、y 0 、z 0 is the coordinate of the micro-focus;
x i 、y i 、z i coordinates of the microseismic monitoring sensor;
v 0 monitoring the propagation speed from a sensor to a microseism for the microseism;
t i the time when the P wave propagates to the ith microseismic monitoring sensor to pick up the waveform;
t 0 the time of the micro-vibration source vibration generation is the time of the micro-vibration source vibration generation;
the theoretical moment of the k-th microseismic monitoring sensor picking up the P wave is assumed to be:
the theoretical to time difference of the kth and the u-th microseismic monitoring sensors is as follows:
Δt ku ＝t k -t u (3)
the microseismic localization objective function may be expressed as:
wherein:
ΔT ku the actual time difference of the monitoring of the kth and the nth microseismic monitoring sensors is that u is more than or equal to 1, k is more than or equal to 1, and k is not equal to u;
the minimum value calculated by the formula (4) is the coordinate value of the micro-focus;
(4) Honey source initialization
Initializing honey source according to formula (5), and calculating fitness value Fit (x) according to formula (6) i,d ) Wherein, the method comprises the steps of, wherein,
x i，d ＝L i +rand(0，1)(U i -L i ) (5)
wherein:
i is a honey source, wherein i=1, 2, …, NP;
d is the honey source dimension, where d = 1,2,3,4;
x i,d employment of bees for any of the honey sources i on dimension d;
L i l is the lower bound of the search space i ＝(x 0l ,y 0l ,z 0l ,v 0l ,)；
U i U, being the upper bound of the search space i ＝(x 0u ,y 0u ,z 0u ,v 0u ,)；
Fit(x i，d ) Is x i,d Is a fitness value of (a);
R(x i，d ) The objective function value is positioned for the microseism;
(5) Updating and searching for new honey sources:
employing bees to search for new honey sources in the vicinity of the honey source using equation (8) with an adaptive search strategy;
v i,d ＝X i，d +rand(0,1)(x i，d -x j,d ) (7)
v i,d ＝x i,d +μ(e)(x i,d －x j,d )+(1－μ(e))rand(0,1)(V gbest,d －x i,d ) (8)
wherein:
x i,d employment of bees, x for any of the honey sources i on dimension d j,d Employing bees for any of the honey sources j on dimension d, where i+.j;
v i,d is a new honey source position;
V gbest,d an optimal honey source location for all currently employed bee individuals;
mu (e) is an adaptive coefficient, which determines the degree of dependence of the traditional honey source searching formula (7) on the circulation times, and e is the current circulation times;
maxcycles are the maximum number of cycles;
the formula (7) is a traditional honey source searching formula, and can be known from the formula (9): in the initial period of circulation, mu (e) is approximately equal to 1, and the formula (8) is mainly a traditional honey source searching formula (7), so that stronger exploring capability can be maintained, and along with searching, each honey source is gradually close, and the neighborhood range is gradually reduced; finally, comparing the adaptation values of the new and old honey sources by using a greedy algorithm, and selecting the best adaptation value as the best one;
(6) Probability of employment of bees as observation bees:
wherein:
x i for employment of bees in all dimensions in honey source i, x n Employment of bees for all dimensions in honey source n;
Fit(x i ) Is x i Is a fitness function value of (a);
NP is the number of sources of honey;
randomly generating a random number between 0 and 1, and comparing the random number with the probability calculated by the formula (10), when the generated random number is smaller than the probability P i When the method is used, the corresponding hiring bees are selected as observing bees, and neighborhood searching is carried out by using a formula (8);
(7) Stage of bee investigation
The honey source has a parameter three, when the new honey source is better than the current honey source, the honey source update is reserved, and trail=0; otherwise, reserving the current honey source, wherein trail=trail+1 so as to count the number of times that the current honey source is not updated;
when the trail value of a honey source exceeds a preset threshold limit, the honey source is discarded, a detection bee stage is started, and in the detection bee stage, a detection bee randomly searches for a new honey source to replace the discarded honey source by using the formula (5).
In a preferred embodiment of the invention, P-wave arrival time picking of microseismic events is performed based on a time window energy ratio method and an AIC method; and constructing a micro-focus positioning objective function based on the self-adaptive artificial bee colony algorithm.
And (3) carrying out micro-seismic source coordinate calculation by utilizing a self-adaptive artificial bee colony algorithm according to the picked P wave arrival time and the sensor coordinate position so as to find the micro-seismic source coordinate with the minimum travel time residual error in the three-dimensional space.
In the bee detection stage, the fitness value is calculated through a formula (6), and the obtained minimum fitness value is stored.
The time window energy ratio method has the advantages of simplicity, directness, effectiveness and the like in the aspect of identifying microseism events, but the influence of the selection of the size of the time window on arrival time pickup is larger; the AIC method is an Akaike information rule, and can accurately pick up the arrival time of the microseism event, but cannot identify the microseism event. The P-wave arrival time of the microseism event is picked up by combining a time window energy ratio method and an AIC method, the advantages of the two methods can be combined, the arrival time can be rapidly identified, and the method has the characteristics of simplicity, directness, rapidness, accuracy and effectiveness.
In addition, the artificial bee colony algorithm simulates an actual bee honey collection mechanism to solve the engineering problem. Artificial bee colonies are generally classified into three categories: employment of bees, observation of bees and investigation of bees. The bees are employed and observed for the exploitation of honey sources, and the investigation bees avoid too few honey source types. The solution of the optimization problem and the corresponding function value are abstracted into the position of the honey source and the amount of nectar. The process of finding the optimal honey source is as follows: adopting bees to find honey sources and memorize, searching new honey sources nearby each honey source, selecting a better honey source according to the nectar amounts of the front honey source and the rear honey source, and marking; the employment bees release information proportional to the quality of the marked honey sources, and are used for recruiting observation bees, the observation bees select proper marked honey sources under a certain mechanism and search for new honey sources nearby, and compared with the marked honey sources, the more excellent honey sources are selected as the final marked honey sources of the cycle, and the best honey sources are searched for by repeated cycles. However, if the honey source is unchanged through a plurality of searches in the honey collection process, the corresponding employment bees become investigation bees, and a new honey source is searched randomly.
The artificial bee colony algorithm is introduced into the field of microseismic positioning, which is equivalent to searching a travel time residual minimum honey source in a three-dimensional space. The invention further improves the traditional honey source searching formula (7) to obtain a fast-convergence self-adaptive honey source searching formula (8), and constructs the self-adaptive artificial bee colony algorithm for improving the positioning precision and speed of the micro-focus.
In this example, the search space is the range of the monitored area, such as 200 x 200 regions, L (L) i Can be set to (0, 0), U i Can be set as (200, v) p )。v p The wave velocity of the P wave is obtained through acoustic testing, and an error of 5% and 10% is added to the P wave in a general searching stage. The upper and lower bounds are ranges of coordinates of the locations where the microseismic sources may occur. Fit (x) i )、Fit(x n ) Calculate fitness value Fit (x) with equation (6) i,d ) Meaning is the same, wherein x i Is a 4-dimensional vector, x i,d X represents i A value of d-th dimension in (c).
In order to make the objects and advantages achieved by the present invention more apparent, the present invention is further described in connection with the following examples and illustrations:
in this embodiment, the method is set in a uniform single medium model for positioning, and the size of the rock mass area of the area to be measured is 200m×200m, and the specific positioning steps of the microseism source are as follows:
(1) 8 microseismic monitoring sensors are arranged in the rock mass monitoring area, the numbers of the 8 microseismic monitoring sensors are S-1, S-2, S-3, S-4, S-5, S-6, S-7 and S-8 respectively, and the 8 microseismic monitoring sensors are distributed in a space reticular structure. The microseismic monitoring sensor coordinates and source location are shown in table 1.
Table 1 spatial coordinates of each sensor
And drilling two blastholes in the monitoring area by adopting the coordinates of a blasting mark microseismic source, wherein the coordinates of the two blastholes are M1 (80, 91,122) and M2 (75,84,102). The specific operation process is as follows: 200g of emulsion explosive is arranged at the hole bottom of the blasting hole, and the emulsion explosive is connected with the detonating cord and the high-voltage electrostatic detonator so as to realize blasting; the hole opening of the blast hole is properly plugged with field soil to reduce the energy dissipation. And taking M1 as a known explosion point, acquiring the P wave velocity of the region, and taking M2 as an unknown seismic source to simulate a micro-seismic source.
(2) And processing the collected blasting signals, and combining a time window energy ratio method and an AIC method to pick up P waves, wherein the pick-up is shown in figure 2. The result of picking up the P-wave arrival time of signals of 8 channels is shown in table 2, and the picked arrival time is stored in a database for the next calculation.
TABLE 2P wave arrival time of each microseismic monitoring sensor
(3) And calculating the coordinate of the micro-seismic source by utilizing an adaptive artificial bee colony algorithm according to the arrival time of the picked P wave and the known coordinate position of the sensor. Initializing honey sources according to formula (5), wherein boundary vectors are U respectively i 、L i And (3) respectively calculating an objective function value and a honey source fitness value through the formula (4) and the formula (6).
(4) And (3) carrying out honey source search in the neighborhood by utilizing a self-adaptive honey source search formula (8), storing the updated honey source in a database, calculating the fitness value of the new honey source, and judging the advantages and disadvantages of the updated new honey source and the current honey source. When the updated honey source is better than the current honey source, updating the honey source position, wherein trail=0; when the new honey source is inferior to the current honey source, trail=trail+1, and judging whether the Trail value exceeds a preset threshold limit, if so, selecting the corresponding employment bee as the investigation bee; if the fitness value of the new honey source is equal to the fitness value of the current honey source, the new honey source is not selected as a reconnaissance bee, and the honey source is selected as an observation bee according to the fifth step.
(5) Calculating the probability of selecting an employment bee as an observation bee through a formula (10), randomly generating a random number between 0 and 1, comparing the random number with the probability ratio calculated by the formula (10), selecting the corresponding employment bee as the observation bee when the generated random number is smaller than the probability Pi, and carrying out neighborhood search by utilizing a formula (8) to update a honey source; and storing the updated honey sources in a database, calculating the fitness value of the new honey sources, and judging the advantages and disadvantages of the updated new honey sources and the current honey sources according to the fitness value of the new honey sources and the current honey sources. When the updated new honey source is better than the current honey source, updating the honey source position, wherein trail=0; when the new honey source is inferior to the current honey source, trail=trail+1, judging whether the Trail value exceeds a preset threshold limit, and if so, converting the new honey source into the detection bees.
(6) The honey sources that are not updated for a plurality of times, that is, the honey sources whose trail value exceeds a predetermined threshold value, limit, are updated using formula (5), and a fitness value is calculated.
(7) The minimum fitness value is calculated and stored through the stages of employment, observation and reconnaissance of bees, and the global optimum value (calculated microseism coordinates) is obtained.
Finally, the calculated result is averaged for a plurality of times (75.159, 84.159, 101.964), and the coordinate distance between the theoretical microseism coordinates and the actual microseism coordinates located in this embodiment is:
in addition, in this embodiment, a nonlinear positioning method particle swarm algorithm and an artificial bee colony algorithm are further adopted to perform the calculation of microseism positioning, wherein maxcycle=100, and since both algorithms are in the prior art, the invention is not described in detail herein, and the microseism source coordinates calculated by the two algorithms are respectively as follows:
the theoretical microseismic source coordinates calculated by the particle swarm algorithm are: (57.391, 72.516, 109.336) which are located at a coordinate distance from the actual microseismic source coordinates:
the theoretical microseism coordinates calculated by the artificial bee colony algorithm are as follows: (68.476, 81.395, 103.454) which are located at a coordinate distance from the actual microseismic source coordinates:
as the number of iterations increases, the coordinate distances between the theoretical microseism coordinates and the actual microseism coordinates calculated by the adaptive artificial bee colony algorithm, the nonlinear positioning particle swarm algorithm and the artificial bee colony algorithm are as follows (see table 3 below):
TABLE 3 coordinate distance for different algorithms as the number of iterations increases
| Number of iterations | Self-adaptive artificial bee colony algorithm | Artificial bee colony algorithm | Particle swarm algorithm |
| 200 | 0.579m | 3.802m | 12.74m |
| 400 | 0.257m | 4.204m | 10.182m |
| 600 | 0.093m | 3.379m | 4.176m |
By comparing the theoretical microseism calculated by the self-adaptive artificial bee colony algorithm, the nonlinear positioning method particle swarm algorithm and the artificial bee colony algorithm with the actual microseism, the position of the theoretical microseism positioned by the self-adaptive artificial bee colony algorithm is closer to the actual position of the microseism, which also shows that the positioning method has higher precision.
In embodiments of the present invention, the term "plurality" refers to two or more, unless explicitly defined otherwise. The terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly attached, detachably attached, or integrally attached. The specific meaning of the above terms in the embodiments of the present invention will be understood by those of ordinary skill in the art according to specific circumstances.
In the description of the embodiments of the present invention, it should be understood that the directions or positional relationships indicated by the terms "upper", "lower", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience in describing the embodiments of the present invention and to simplify the description, and do not indicate or imply that the devices or units referred to must have a specific direction, be configured and operated in a specific direction, and thus should not be construed as limiting the embodiments of the present invention.
In the description of the present specification, the terms "one embodiment," "a preferred embodiment," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the embodiments of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above is only a preferred embodiment of the present invention and is not intended to limit the embodiment of the present invention, and various modifications and variations can be made to the embodiment of the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.
Claims (2)
1. The method for quickly converging and positioning the microseism source is characterized by comprising the following steps of:
(1) Installing a microseismic monitoring sensor:
installing n microseismic monitoring sensors in a rock mass area to be monitored, and measuring the space coordinates of all the microseismic monitoring sensors, wherein the coordinate of the ith microseismic monitoring sensor is (x i ，y i ，z i ) Wherein n is more than or equal to 4, and n microseismic monitoring sensors are distributed in a space reticular structure;
(2) Acquiring P wave arrival time;
(3) Establishing a microseism positioning objective function:
assuming isotropy and homogeneity of the rock mass, the wave speed of the P wave is kept unchanged, and establishing a travel time equation:
wherein:
x 0 、y 0 、z 0 is the coordinate of the micro-focus;
x i 、y i 、z i coordinates of the microseismic monitoring sensor;
v 0 monitoring the propagation speed from a sensor to a microseism for the microseism;
t i the time when the P wave propagates to the ith microseismic monitoring sensor to pick up the waveform;
t 0 the time of the micro-vibration source vibration generation is the time of the micro-vibration source vibration generation;
the theoretical moment of the k-th microseismic monitoring sensor picking up the P wave is assumed to be:
the theoretical to time difference of the kth and the u-th microseismic monitoring sensors is as follows:
Δt ku ＝t k -t u (3)
the microseismic localization objective function may be expressed as:
wherein:
ΔT ku the actual time difference of the monitoring of the kth and the nth microseismic monitoring sensors is that u is more than or equal to 1, k is more than or equal to 1, and k is not equal to u;
the minimum value calculated by the formula (4) is the coordinate value of the micro-focus;
(4) Honey source initialization
Initializing honey source according to formula (5), and calculating fitness value Fit (x) according to formula (6) i,d ) Wherein, the method comprises the steps of, wherein,
x i，d ＝L i +rand(0，1)(U i -L i ) (5)
wherein:
i is a honey source, wherein i=1, 2, …, NP;
d is the honey source dimension, where d = 1,2,3,4;
x i,d employment of bees for any of the honey sources i on dimension d;
L i l is the lower bound of the search space i ＝(x 0l ,y 0l ,z 0l ,v 0l ,)；
U i U, being the upper bound of the search space i ＝(x 0u ,y 0u ,z 0u ,v 0u ,)；
Fit(x i，d ) Is x i,d Is a fitness value of (a);
R(x i，d ) The objective function value is positioned for the microseism;
(5) Updating and searching for new honey sources:
employing bees to search for new honey sources in the vicinity of the honey source using equation (8) with an adaptive search strategy;
v i，d ＝x i，d +rand(0，1)(x i，d -x j，d ) (7)
v i,d ＝x i,d +μ(e)(x i,d －x j,d )+(1－μ(e))rand(0,1)(V gbest,d －x i,d ) (8)
wherein:
x i,d employment of bees, x for any of the honey sources i on dimension d j,d Employing bees for any of the honey sources j on dimension d, where i+.j;
v i,d is a new honey source position;
V gbest,d an optimal honey source location for all currently employed bee individuals;
mu (e) is an adaptive coefficient, which determines the degree of dependence of the traditional honey source searching formula (7) on the circulation times, and e is the current circulation times;
maxcycles are the maximum number of cycles;
the formula (7) is a traditional honey source searching formula, and can be known from the formula (9): in the initial period of circulation, mu (e) is approximately equal to 1, and the formula (8) is mainly a traditional honey source searching formula (7), so that stronger exploring capability can be maintained, and along with searching, each honey source is gradually close, and the neighborhood range is gradually reduced; finally, comparing the adaptation values of the new and old honey sources by using a greedy algorithm, and selecting the best adaptation value as the best one;
(6) Probability of employment of bees as observation bees:
wherein:
x i for employment of bees in all dimensions in honey source i, x n Employment of bees for all dimensions in honey source n;
Fit(x i ) Is x i Is a fitness function value of (a);
NP is the number of sources of honey;
randomly generating a random number between 0 and 1, and comparing the random number with the probability calculated by the formula (10), when the generated random number is smaller than the probability P i When the method is used, the corresponding hiring bees are selected as observing bees, and neighborhood searching is carried out by using a formula (8);
(7) Stage of bee investigation
The honey source has a parameter three, when the new honey source is better than the current honey source, the honey source update is reserved, and trail=0; otherwise, reserving the current honey source, wherein trail=trail+1 so as to count the number of times that the current honey source is not updated;
when the trail value of a honey source exceeds a preset threshold limit, discarding the honey source, starting a detection bee stage, and randomly searching a new honey source to replace the discarded honey source by the detection bee in the detection bee stage by using a formula (5);
the method is characterized in that P-wave arrival time of the microseism event is picked up based on a time window energy ratio method and an AIC method;
constructing a micro-focus positioning objective function based on a self-adaptive artificial bee colony algorithm;
and (3) carrying out microseism coordinate calculation by utilizing a self-adaptive artificial bee colony algorithm according to the picked P wave arrival time and microseism monitoring sensor coordinate position so as to find the microseism coordinate with the minimum travel time residual error in the three-dimensional space.
2. The method for rapid convergence and high-precision positioning of microseismic source according to claim 1, wherein the fitness value is calculated by equation (6) and the obtained minimum fitness value is stored during the bee detection stage.
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