CN117991349A - Microseism positioning method based on improved ant lion optimization algorithm - Google Patents
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Abstract
The invention belongs to the technical field of microseism positioning, in particular to a microseism positioning method based on an improved ant lion optimization algorithm, which comprises the steps of filtering seismic data acquired by a microseism monitoring system; the time of arrival of the seismic data after the filtering treatment is picked up by an energy ratio method to obtain first arrival time; and optimizing the objective function by adopting an improved ant-lion optimization algorithm according to the first arrival time to obtain the positioning position of the micro-seismic source. The method has the advantages that the data of the seismic waves are detected, the information of the microseism sources can be comprehensively analyzed, the depth can be obtained, the horizontal distance from the microseism source to the detector can be measured, reliable data are provided for positioning of the microseism sources, and the microseism sources can be rapidly and accurately positioned to solve the problem of out-of-range exploitation.
Description
Technical Field
The invention belongs to the technical field of microseism positioning, and particularly relates to a microseism positioning method based on an improved ant lion optimization algorithm.
Background
As the intensity and depth of coal mining increases, the number of rock burst mines and the degree of risk of impact increases significantly. In coal mine operation, the out-of-range mining of the coal mining machine is a serious problem, and the out-of-range mining of the coal field refers to the behavior of mining coal mine resources beyond the area range, and the behavior can cause accidents such as coal mine gas explosion, water burst flooding and the like. The information of the mine microseismic space position is significant in ensuring safe production and improving production efficiency, and is a key for accurately preventing and controlling rock burst disasters. The positioning of the microseismic source is a very complex work influenced by various factors, and the microseismic and the time of the occurrence of the microseismic source in a three-dimensional space can be determined through the positioning of the microseismic source. The positioning of the microseismic source is a core element of the microseismic monitoring technology, and the accuracy of the positioning of the microseismic source is related to the application effect of the microseismic technology.
The method for positioning the microseism source based on the arrival time can be applied to positioning of earthquake, mine microseism or acoustic emission microseism. A large number of classical microseismic source positioning methods were proposed before 2000, the Geiger iterative positioning algorithm proposed by Geiger in 1912, the Inglada linear non-iterative microseismic source positioning method proposed by Inglada in 1928, and the double difference positioning methods proposed by Waldhauser and Ellsworth in 2000 are all proposed for positioning the seismic microseismic source. Beginning in the 60 s of the 20 th century, with the development of microseismic monitoring technology, microseismic source positioning methods for microseismic monitoring have also been proposed, such as the USBM microseismic source positioning method proposed by the united states mining agency (USBM) researchers in the early 70 s of the 20 th century. The last 80 s of the 20 th century simplex method was introduced into microseismic origin positioning. So far, most of the positioning methods are still applied to positioning of the mine microseismic sources.
The positioning of a microseismic source is always the most important problem in coal mining operation, and is the core of coal mine safety monitoring. The most critical of these is the accuracy problem of positioning. The reasons for the influence on the accuracy mainly come from a plurality of aspects, namely firstly, the accuracy of the system is derived from hardware, then the accuracy of a positioning algorithm, and finally, geological data can generate errors such as: formations, wave velocities, etc.
Disclosure of Invention
The invention aims to solve the technical problem of providing a microseism positioning method based on an improved ant lion optimization algorithm, which can realize quick and accurate positioning of a microseism source so as to solve the problem of out-of-range exploitation.
The present invention has been achieved in such a way that,
A microseismic positioning method based on an improved ant lion optimization algorithm comprises the following steps:
filtering the seismic data collected by the microseism monitoring system;
The time of arrival of the seismic data after the filtering treatment is picked up by an energy ratio method to obtain first arrival time;
Optimizing an objective function by adopting an improved ant-lion optimization algorithm according to the first arrival time to obtain a micro-focus positioning position, wherein the objective function is as follows:
，
Wherein N is the number of all first arrival times obtained by the microseismic monitoring system, and the three-dimensional coordinate of one detector for receiving the seismic data is ， />， />First arrival time is/>The three-dimensional coordinates of another random detector are/>， ， />First arrival time is/>The position of the microseismic source is O (/ >)， />， />) P-wave velocity is/>P wave velocity is replaced by average velocity of the whole underground operation area, and the earthquake moment is/>Let/>，/>，
，
I, j=1, 2, …, N and i+.j, k is 1,2;
，
Wherein, Is the velocity in the subsurface uniform media model.
Further, the underground uniform medium model is established by the following steps:
Arranging M detectors, simultaneously establishing a three-dimensional rectangular coordinate system, recording the three-dimensional coordinates of the detectors, and recording the real-time coordinates of blasting points by simulating the work of a coal mining machine;
Detecting the propagation distance between the coordinates of the coal mining machine and Y detectors in the coal mining machine, wherein Y is smaller than M, detecting the first arrival time of the vibration wave reaching the detectors at the same time, fitting the absolute value of the propagation distance and the first arrival time difference to obtain the average speed of the vibration wave on the current monitoring working surface, popularizing the average speed of the whole underground operation area to obtain the average speed of the whole underground operation area according to the known geological data, dividing the underground operation area according to grids, and taking weighted average of the wave speeds when the grids span multiple strata to form an underground uniform medium model:
，
Wherein m is the number of grids; n is the number of layers occupied in the grid; l is the length of the grid; Is the thickness of the ith layer in the grid; Is the wave velocity of the i-th layer.
Further, the average speed of the entire underground work area is: Wherein/> For the average velocity of the entire subsurface region,/>For the speed of the i-th layer, t i is the vertical travel time of the i-th layer, and n is the number of layers.
Further, positioning analysis is carried out according to the average speed of the whole underground operation area and the first arrival time of the remaining detectors, the specific place of the micro-seismic source is reversely deduced to serve as the calculated position of the test vibration source according to the time difference of arrival of the micro-seismic source at the detectors, and the calculated position is compared with the actual position of the micro-seismic source to determine an error range.
Further, the microseismic source location is corrected according to the error range.
Further, according to the first arrival time, an improved ant lion optimization algorithm is adopted to optimize the objective function, and the method specifically comprises the following steps:
(1) Data initialization, determining the number of ants and ant lions and the dimension of variables, randomly initializing the positions of the ants and the ant lions in a feasible domain, and calculating the adaptability of the ants and the ant lions;
(2) Determining elite ant lion, and selecting the ant lion with the minimum adaptability in the ant lion population after initialization as the elite ant lion;
(3) Selecting one ant-lion for each ant through roulette, and updating according to ant-lion positions ，/>，/>，/>Of (1), wherein/>Is the minimum value of all variables at the t-th iteration,/>Vector representing the maximum of all variables at the t-th iteration,/>Is the minimum value of all variables at the ith iteration,/>The method comprises the steps that the method is the maximum value of all variables in the ith iteration, the ants randomly walk around the ant lion and elite ant lion, and finally the positions of the ants are updated by taking the average value of the maximum value and the minimum value of the same iteration;
(4) Re-calculating the adaptation value of ants and ant lions after each iteration, and updating the ant lion position according to the position and adaptation of the ants, wherein the position of the minimum adaptation value is the position of a new elite ant lion;
(5) Judging whether the maximum iteration times are reached, if so, outputting a result and ending the iteration, otherwise, repeating the step (3).
Further, the minimum value of all variables at the t-th iterationAnd vector/>, which contains the maximum of all variables at the t-th iterationUpdating according to the following formula:
，
Wherein: Is a proportionality coefficient; - For maximum iteration number,/>And/>Is two scaling factors for adjusting the accuracy level of the trap.
Compared with the prior art, the invention has the beneficial effects that:
According to the invention, the micro-vibration positioning is performed by using the improved ant-lion optimization algorithm, so that the method has higher running speed and higher precision compared with the original ant-lion optimization algorithm. Firstly, carrying out self-adaptive adjustment on a boundary when ants walk around an ant lion, improving the development capability of the algorithm and preventing the algorithm from sinking into a local extremum, then adding a preferred condition into a roulette selection strategy, accelerating the convergence rate of the algorithm, finally introducing a self-adaptive proportionality coefficient, effectively improving the exploration and development capability of the algorithm, and carrying out theoretical analysis to prove that the time complexity of the improved ant lion optimization algorithm is the same as that of the ant lion optimization algorithm; according to the related experimental results, the method not only improves the convergence speed and convergence precision of the algorithm, but also improves the stability of the algorithm under high dimensionality.
The invention controls the positioning error in a specified range through the seismic position comparison of theoretical calculation and actual points. The underground geological structure is subjected to networking layering, a uniform medium model is adopted, and the propagation speed of vibration waves on the current monitoring working surface is averaged, so that the calculation process is simpler, and meanwhile, the requirement that the calculation result is within the error allowable range can be met. The data of the earthquake waves are detected, the information of the microseism sources can be comprehensively analyzed, the depth can be obtained, the horizontal distance from the microseism source to the detector can be measured, and reliable data are provided for positioning of the microseism sources.
Drawings
FIG. 1 is a schematic diagram of a system architecture employed in the method of the present invention;
FIG. 2 is a schematic flow chart of the method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 2, the embodiment of the invention provides a microseismic positioning method based on an improved ant lion optimization algorithm, which comprises the following steps:
filtering the seismic data collected by the microseism monitoring system;
The time of arrival of the seismic data after the filtering treatment is picked up by an energy ratio method to obtain first arrival time;
optimizing an objective function by adopting an improved ant lion optimization algorithm according to the first arrival time to obtain a microseismic source position, wherein the objective function is as follows:
wherein N is the number of all first arrival times obtained by the microseismic monitoring system, and the three-dimensional coordinate of one detector for receiving the seismic data is ，/>， />First arrival time is/>The three-dimensional coordinates of another random detector are/>，/>，First arrival time is/>The position of the microseismic source is O (/ >)，/>，/>) P-wave velocity is/>Replaced by the average velocity of the whole underground operation area, the earthquake moment is/>Let/>，/>，
，
I, j=1, 2, …, N and i+.j, k is 1,2;
，
Wherein, Is the velocity in the subsurface uniform media model.
The method for establishing the underground uniform medium model comprises the following steps:
arranging M detectors, simultaneously establishing a three-dimensional rectangular coordinate system, recording the three-dimensional coordinates of the detectors, simultaneously carrying out artificial microseismic blasting by simulating the work of a coal mining machine, and recording the real-time coordinates of blasting points; here, M is 5 or more. The number of detectors is at least 5, so that enough data are needed to operate in the subsequent iterative algorithm, and enough iterative equations can be formed to obtain a final positioning result. As an optimization method, the error range of the three-dimensional coordinates of the microseismic source is that the error in the X-axis direction and the Y-axis direction is less than +/-15 meters, and the error in the Z-axis direction is less than +/-20 meters. Under the condition that the precision of microseismic positioning meets engineering requirements, a proper error range is set, the accuracy of a result is ensured, and the working complexity is also reduced.
Detecting the propagation distance between the coordinates of the coal mining machine and Y detectors in the coal mining machine, wherein Y is smaller than M, detecting the first arrival time of the vibration wave reaching the detectors at the same time, fitting the absolute value of the propagation distance and the first arrival time difference to obtain the average speed of the vibration wave on the current monitoring working surface, popularizing the average speed to the whole experimental area to obtain the average speed of the whole underground operation area, dividing the underground operation area according to the known geological data, and weighting and averaging the wave speeds to form an underground uniform medium model when the grids span a plurality of strata:
，
Wherein the method comprises the steps of Is the velocity in the underground uniform medium model, m is the grid number; n is the number of layers occupied in the grid; l is the length of the grid; /(I)Is the thickness of the ith layer in the grid; /(I)Is the wave velocity of the i-th layer.
Due to the differences in geologic structures. The depth of the stratum and the corresponding wave speed are known, and the propagation speed of the vibration wave in different strata is different, so that the underground stratum is subjected to networking layering, and the position of the vibration source can be calculated in a targeted manner.
And a uniform medium model is adopted to average the propagation speed of the vibration wave on the current monitoring working surface, so that the calculation process is simpler, and the requirement that the calculation result is within the error allowable range can be met. The data of the earthquake waves are detected, the information of the microseism sources can be comprehensively analyzed, the depth can be obtained, the horizontal distance from the microseism source to the detector can be measured, and reliable data are provided for positioning of the microseism sources.
The average speed of the entire underground working area is: Wherein/> For the average velocity of the entire subsurface region,/>For the speed of the i-th layer, t i is the vertical travel time of the i-th layer, and n is the number of layers.
And (3) carrying out positioning analysis according to the average speed of the whole underground operation area and the first arrival time of the remaining detectors, reversely pushing out the specific place of the microseismic source as the calculated position of the test vibroseis source according to the time difference of arrival of the microseismic at the detectors, and comparing the calculated position with the actual position of the microseismic source to determine an error range.
And correcting the calculated micro-seismic source position according to the error range.
And importing the three-dimensional coordinates of the calculated positions of the microseismic sources into a three-dimensional seismic model, and displaying the positions of the detectors and the microseismic sources respectively in different colors.
In this embodiment, the first arrival time pickup method is a new energy ratio method based on MER (modified energy ratio) method and STA/LTA (long short time window energy ratio) method, and the energy ratio formula of MER method:
，
In the method, in the process of the invention, Index of measuring point,/>For the length of the energy collection window around the measuring point,/>For measuring point/>Seismic record values at. The ability of the energy ratio attribute to detect the arrival of an earthquake at a starting point in the presence of random noise can be improved by the improvement. The improvement formula:
，/> A function taking absolute value is shown.
The STA/LTA method is proposed by Stevenson and is applied to the discrimination of the arrival time of the first arrival of an earthquake. The basic principle is as follows: the change of the characteristics of signal amplitude, frequency and the like is reflected by the ratio of STA (Short-TERMAVERAGE) to LTA (Long-TERM AVERAGE); when the seismic signal arrives, the STA/LTA value has a sudden change, and when the ratio is larger than a certain set threshold value R, the STA/LTA value is judged to be a valid signal. STA/LTA formula:
，
Wherein, I=1, 2, …, N, representing data in a short time window; /(I)J=1, 2, …, M, representing data over a long time window; m and N represent the number of samples in the long and short time windows, respectively. If R is greater than the set threshold, then the signal is considered to be a microseismic valid signal.
Comparison of STA/LTA and MER time selection methods shows that both techniques can handle microseismic data with high signal-to-noise ratio well. However, when the signal-to-noise ratio is 3.5 and lower, the MER method is more accurate and a more consistent arrival time can be obtained. For very noisy seismic data with signal to noise ratios as low as 1.5, it is critical for the trace and time window to select reliable arrival times. By analyzing the arrival time of the initially applied microwave pick-up, the position and length of the appropriate window is obtained.
In the common shot gather, let t be the first arrival time of each station R i (i=1, 2, …, n) of a shot arrangement, then the first arrival time difference of two adjacent stationsThe method comprises the following steps:
，
Wherein the method comprises the steps of For/>First arrival time of individual site,/>For/>The first arrival time of each site, n is the total number of sites;
The average value is as follows:
，
The standard deviation is:
，
And (3) making:
，i = 1,2,…,n ；
The first arrival pickup criteria are:
，
Wherein, The error level control parameter is properly selected according to the quality of the first arrival curve, and is usually 1-2. Points satisfying this formula are regarded as outliers. If the first arrival curve is approximately linear, the reference first arrival time can be generally expressed as:
，
For/> First arrival time of each site.
If the first arrival curve is the conventional curve, spline interpolation can be carried out according to the first arrival time of the adjacent site to obtain the first arrival time of the site. The above stations are all referred to as detectors.
According to the embodiment of the invention, an improved ant lion optimization algorithm is adopted to optimize the objective function according to the first arrival time so as to obtain the micro-seismic source position.
The method specifically comprises the following steps:
(1) Data initialization, determining the number of ants and ant lions and the dimension of variables, randomly initializing the positions of the ants and the ant lions in a feasible domain, and calculating the adaptability of the ants and the ant lions;
(2) Determining elite ant lion, and selecting the ant lion with the minimum adaptability in the ant lion population after initialization as the elite ant lion;
(3) Selecting one ant-lion for each ant through roulette, and updating according to ant-lion positions ，/>，/>，/>Of (1), wherein/>Is the minimum value of all variables at the t-th iteration,/>Vector representing the maximum of all variables at the t-th iteration,/>Is the minimum value of all variables at the ith iteration,/>The method comprises the steps that the method is the maximum value of all variables in the ith iteration, the ants randomly walk around the ant lion and elite ant lion, and finally the positions of the ants are updated by taking the average value of the maximum value and the minimum value of the same iteration;
(4) Re-calculating the adaptation value of ants and ant lions after each iteration, and updating the ant lion position according to the position and adaptation of the ants, wherein the position of the minimum adaptation value is the position of a new elite ant lion;
(5) Judging whether the maximum iteration times are reached, if so, outputting a result and ending the iteration, otherwise, repeating the step (3).
In the improved ant lion optimizing algorithm, a hunting mechanism for hunting ants is simulated to realize global optimization. The ant lion digs a funnel-shaped trap in sandy soil by utilizing the huge jaw before hunting, and the ant lion is hidden at the bottom of the trap to wait for hunting. Once the randomly walked ant falls into the trap, the ant lion rapidly predates it, and then repaires the trap to wait for the next hunting. The interaction between ants and ant lions is realized through numerical simulation, so that the problem is optimized, the random walk of the ants is introduced to realize global search, and the diversity of the population and the optimizing performance of an algorithm are ensured through a roulette strategy and an elite strategy. The ant lion is equivalent to the solution of the optimization problem, and the updating and the preservation of the approximate optimal solution are realized by hunting ants with high fitness. Comprising the following steps:
Random walk of ants:
The process of ants randomly walking in nature to find food can be seen as a process of each search agent searching for a feasible region. The process of random walk can be expressed mathematically as:
，
Wherein: a step number set for ants to walk randomly; /(I) To calculate the sum,/>For the number of steps walked randomly (taking the maximum number of iterations), r (t) is a random function defined as
，
Wherein: rand is a random number of [0,1 ].
The location of ants cannot be updated directly with the above formula because of the boundaries of the feasible regions. To ensure ants walk randomly within the feasible region, they are normalized according to the following formula
，
Wherein the method comprises the steps ofIs the minimum value of the ith variable random walk,/>For the maximum value of the ith variable random walk,/>Is the minimum value of the ith variable at the t-th iteration,/>Maximum value of the ith variable at the t-th iteration;
influence of ant lion on random walk of ants:
traps made by ant lions affect the path of ant random walks, and for mathematical modeling of this hypothesis, we propose
，
In the method, in the process of the invention,Is the minimum value of all variables at the t-th iteration,/>Vector representing the maximum of all variables at the t-th iteration,/>Is the minimum value of all variables at the ith iteration,/>Maximum value of all variables at the ith iteration,/>Representing the position of the selected jth ant colony at the t-th iteration.
Through the roulette strategy, a specific ant is selected to be prey on by the ant lion, each ant can only be prey on by one ant lion, and the probability that the ant lion with higher adaptability captures the ant is higher. In addition, once ants fall into traps made by ant lions, the ants lions throw sand towards the edges of the traps to prevent the ants from escaping. At this time, the range of the ant random walk is drastically reduced. Minimum value of all variables at t-th iterationAnd vector/>, which contains the maximum of all variables at the t-th iterationUpdating according to the following formula:
，
Wherein: is a proportionality coefficient; /(I) For maximum iteration number,/>And/>Is two scaling factors for adjusting the accuracy level of the trap.
When the adaptability of ant is smaller than that of ant lion, it is considered that the ant lion captures it, and the ant lion can update its position according to the position of ant
，
Where t represents the current iteration,Represents the position of the selected jth ant at the t-th iteration,/>Representing the position of the ith ant at the t-th iteration, f is the fitness function.
After each iteration, the ant lion with the minimum adaptation value is selected as the elite ant lion. Introducing a dynamic weight coefficient based on the iteration number into an ant position updating formula:
，
Wherein: Random walk value of ants selected for wheel at t-th iteration; - For/>Random walk of elite on iteration,/>Represents the/>The ants at the/>Location at iteration,/>And/>Is a weight coefficient, and T is the maximum number of iterations.
Example 1: the system of measurement employed in this embodiment is shown in fig. 1: the system comprises a central processing system 1, a wireless communication system 2 and a data storage system 3 which are respectively connected with the processing system; the wireless communication system is used for carrying out microseismic data information interaction with the outside; the data storage system is used for storing microseismic data and computer executable operation instructions; the central processing system is used for analyzing and processing the microseism data according to the computer executable operation instruction, calculating to obtain microseism source coordinates of the microseism, and importing the microseism source coordinates into the three-dimensional earthquake model.
The microseismic positioning method based on the improved ant lion optimization algorithm adopted by the system comprises the following steps:
The method comprises the steps of performing filtering processing on seismic data acquired by a microseism monitoring system, and performing filtering processing on the obtained original seismic data: the seismic data acquired by the detectors may have certain natural noise and artificial noise, and the seismic data is filtered by a filtering method to obtain clean seismic data;
The time of arrival of the seismic data after the filtering treatment is picked up by an energy ratio method to obtain first arrival time;
optimizing an objective function by adopting an improved ant lion optimization algorithm according to the first arrival time to obtain a microseismic source position, wherein the objective function is as follows:
，
wherein N is the number of all first arrival times obtained by the microseismic monitoring system, and the three-dimensional coordinate of one detector for receiving the seismic data is ， />，/>First arrival time is/>The three-dimensional coordinates of another random detector are/>， />， First arrival time is/>The position of the microseismic source is O (/ >)， />， />) P-wave velocity is/>The average velocity P wave velocity of the whole underground operation area is used, and the earthquake time is/>Let/>，/>，
，
I, j=1, 2, …, N and i+.j, k is 1,2;
，
Wherein, Is the velocity in the subsurface uniform media model.
Wherein the average speed of the whole underground operation area is obtained by a manual test modeAnd establishing an underground uniform medium model to obtain the speed/>, in the underground uniform medium model。
The method for manual test comprises the following steps:
1) The method comprises the steps of carrying out microseismic data point acquisition by arranging a detector, simultaneously establishing a three-dimensional rectangular coordinate system, recording the three-dimensional coordinate of the detector, carrying out artificial microseismic blasting, and recording the real-time coordinate of a blasting point;
2) Carrying out a manual test, detecting the propagation distance between the coordinates of the coal mining machine and a plurality of detectors in the coal mining machine, detecting the time when a vibration wave arrives at the detectors, obtaining the average speed of the vibration wave on a current monitoring working surface by fitting the absolute value of the propagation distance and the time difference of the first arrival time, popularizing the method to obtain the average speed of the whole underground operation area in the whole experimental area, dividing the underground operation area according to known geological data and carrying out grid division, and carrying out weighted average on the wave speed when the grid spans a plurality of strata to form a uniform underground medium model:
，
Wherein the method comprises the steps of Is the velocity in the underground uniform medium model, m is the grid number; n is the number of layers occupied in the grid; l is the length of the grid; /(I)Is the thickness of the ith layer in the grid; /(I)Is the wave velocity of the i-th layer.
The average speed of the entire underground working area is: Wherein/> For the average velocity of the entire subsurface region,/>For the speed of the i-th layer, t i is the vertical travel time of the i-th layer, and n is the number of layers.
In the manual test method, positioning analysis can be performed according to the average speed of the whole underground operation area and the first arrival time of the remaining detectors, the specific place of the microseismic source is reversely deduced to serve as the calculated position of the test vibroseis source according to the time difference of arrival of the microseismic at the detectors, and the calculated position is compared with the actual position of the microseismic source to determine an error range.
The position of the microseismic source position can be corrected through the error range:
the corrected three-dimensional coordinates of the microseismic source are led into a three-dimensional seismic model, and the positions of the detectors and the microseismic source are respectively displayed in different colors.
In this embodiment, 4 underground test points are selected for blasting, and 15 detectors are set to receive signals.
Through manual testing, a specific position is selected in the pit for explosion test, the error of the method is corrected by comparing the calculated value of the micro-seismic source with the actual value, the positioning error is controlled within an acceptable range, the micro-seismic information is detected by using a plurality of detectors, then the three-dimensional coordinates of the micro-seismic source are calculated according to an iterative algorithm, and the three-dimensional coordinates are imported into a three-dimensional seismic model for simulation display, so that the micro-seismic source of the underground micro-seismic and related information can be displayed, and the real-time inspection of staff is facilitated. The detector array arrangement is in a parallel arrangement.
The specific implementation process of this embodiment is as follows:
Firstly, performing a manual test, namely installing 4 blasting test microseismic sources at a specific underground position by a worker, performing ignition blasting, receiving detection signals by using 15 detectors, detecting the propagation distance between the center coordinates of the test microseismic sources and a plurality of detectors, and simultaneously detecting the first arrival time of vibration waves to the plurality of detectors;
Step two, P wave identification is used for judging signal travel time, geometrical position relations of 5 detectors and blastholes are used, the propagation distance and absolute value of the first arrival time difference are fitted, underground geological structures are layered according to geological data such as coal mine ground three-dimensional seismic exploration, well logging and the like, and average speed of the whole underground operation area is obtained by utilizing an average speed formula ，
，
Wherein,Is the number of layers; /(I)Is/>Wave velocity of the layer; /(I)Is/>Vertical travel time of the layer;
thirdly, carrying out positioning analysis according to the calculated average speed of the whole underground operation area and the first arrival time of the remaining 5 detectors, and reversely pushing out the specific location of the microseismic source according to the time difference of arrival of the microseismic to the detectors according to a preset calculation method so as to determine the spatial position of the microseismic source;
Calculating to obtain the calculated position of the test vibration source, comparing the calculated position of the test vibration source with the actual position, and correcting positioning errors according to the comparison result, so that the errors in the X-axis and Y-axis directions are less than +/-15 meters, and the errors in the Z-axis directions are less than +/-20 meters;
step four, the monitoring data of the microseism is called from a monitoring instrument, and the three-dimensional coordinates of the microseism are calculated according to a preset calculation method on the basis of corrected positioning errors;
And fifthly, importing the calculated three-dimensional coordinates into a three-dimensional seismic model, and displaying the simulation position of the micro-seismic source.
In many methods for determining the working path of the coal mining machine by utilizing microseism monitoring at present, a plurality of microseism detectors are generally utilized to collect data of microseism events, and then the damage depth of the base plate is determined according to the average elevation of the base plate of the coal seam of the working face, the number of the microseism events of the base plate and the distribution curve of energy along the depth direction, but actual errors are not considered in the positioning of the microseism finally, so that the accuracy of the final positioning data is not high. In the scheme, test data can be utilized to correct positioning errors firstly, the positioning errors are controlled within a specified range, underground geological structures are layered according to geological data such as coal mine ground three-dimensional seismic exploration and well logging, average propagation speeds of vibration waves in different layers are calculated respectively, and the propagation speeds of the waves in a current coal seam are calculated according to first arrival time of P waves and S waves, so that the positioning accuracy of a micro-seismic source is improved greatly finally, the reliability and the accuracy of the method are ensured, the calculated coordinates of the micro-seismic source can be led into a three-dimensional seismic model, the positions of the micro-seismic source are simulated and displayed, and the positions of the micro-seismic source are displayed more intuitively and vividly.
Example 2: unlike example 1, the difference is that: setting 3 test holes in the pit for explosion test, receiving signals by using 5 detectors, determining the average speed of the coal bed by using the ground three-dimensional earthquake, and performing positioning analysis to determine the position of the explosion hole by using the first arrival time detected by the 5 detectors.
The number of detectors is set to be 5 by changing the determination mode of the current number and the average speed, so that the interference among the detectors is avoided, the inaccuracy of detection data is avoided, meanwhile, in the calculation mode of the average speed, the relative relation with the detectors is not adopted, the average speed of earthquake waves in the coal seam is determined by utilizing the ground three-dimensional earthquake, and the calculation result is more accurate, so that the error is reduced.
The specific implementation procedure of this example is the same as that of example 1, except that:
Firstly, performing a manual test, namely installing 3 blasting test microseismic sources at a specific underground position by a worker, igniting and blasting, receiving detection signals by using 5 detectors, detecting the propagation distance between the center coordinates of the test microseismic sources and a plurality of detectors, and simultaneously detecting the first arrival time of vibration waves to the detectors;
Step two, the geometric position relation of 5 detectors and blastholes is obtained by fitting the absolute value of the propagation distance and the first arrival time difference, utilizing ground three-dimensional earthquake, utilizing geological data such as coal mine ground three-dimensional earthquake exploration, well logging and the like, and utilizing an average speed formula to obtain the average speed of the whole underground operation area;
And thirdly, carrying out positioning analysis according to the calculated average speed of the whole underground operation area and the first arrival time of 5 detectors, and reversely pushing out the specific location of the microseismic source according to the time difference of arrival of the microseismic to the detectors according to a preset calculation method, so as to determine the spatial position of the microseismic source.
And (3) calculating to obtain the calculated position of the test vibration source, comparing the calculated position of the test vibration source with the actual position, and correcting the positioning error according to the comparison result, so that the errors in the X-axis and Y-axis directions are less than +/-10 meters, and the errors in the Z-axis directions are less than +/-20 meters.
After the number of detectors and the determination mode of the average speed are changed, the accuracy of monitoring data can be effectively improved, and meanwhile, the average speed of seismic waves in the coal seam is determined by utilizing the ground three-dimensional earthquake, so that the positioning accuracy of the microseism is ensured to be in a specified range, and the accurate positioning of the microseism is realized.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (9)
1. A microseismic positioning method based on an improved ant lion optimization algorithm is characterized by comprising the following steps:
filtering the seismic data collected by the microseism monitoring system;
The time of arrival of the seismic data after the filtering treatment is picked up by an energy ratio method to obtain first arrival time;
Optimizing an objective function by adopting an improved ant-lion optimization algorithm according to the first arrival time to obtain a micro-focus positioning position, wherein the objective function is as follows:
，
wherein N is the number of all first arrival times obtained by the microseismic monitoring system, and the three-dimensional coordinate of one detector for receiving the seismic data is ，/>，/>First arrival time is/>The three-dimensional coordinates of another random detector are/>，/>，/>First arrival time is/>The position of the microseismic source is O (/ >)，/>，/>) P-wave velocity is/>The P wave velocity is replaced by the average velocity of the whole underground operation area, and the earthquake time is/>Let/>，/>，
，/>I, j=1, 2, …, N and i+.j, k is 1,2;
，
Wherein, Is the velocity in the subsurface uniform media model.
2. The microseismic localization method based on the improved ant lion optimization algorithm according to claim 1, wherein the underground uniform medium model is established by the following steps:
Arranging M detectors, simultaneously establishing a three-dimensional rectangular coordinate system, recording the three-dimensional coordinates of the detectors, simulating the work of a coal mining machine, and recording the real-time coordinates of blasting points;
Detecting the propagation distance between the coordinates of the coal mining machine and Y detectors in the coal mining machine, wherein Y is smaller than M, detecting the first arrival time of the vibration wave reaching the detectors at the same time, fitting the absolute value of the propagation distance and the first arrival time difference to obtain the average speed of the vibration wave on the current monitoring working surface, and popularizing the average speed of the vibration wave to the whole experimental area to obtain the average speed of the whole underground operation area; dividing the underground operation area according to grids, and taking weighted average of wave speeds when the grids span multiple strata to form an underground uniform medium model:
，
Wherein m is the number of grids; n is the number of layers occupied in the grid; l is the length of the grid; Is the thickness of the ith layer in the grid; /(I) Is the wave velocity of the i-th layer.
3. The microseismic localization method based on the improved ant lion optimization algorithm of claim 2, wherein the average velocity of the entire underground operation area is: Wherein/> For the average velocity of the entire subsurface region,/>For the speed of the i-th layer, t i is the vertical travel time of the i-th layer, and n is the number of layers.
4. The microseismic positioning method based on the improved ant lion optimization algorithm according to claim 2, wherein positioning analysis is performed according to the average speed of the whole underground operation area and the first arrival time of the remaining detectors, the specific location of the microseismic source is reversely deduced to serve as the calculated position of the test vibroseis source according to the time difference of arrival of the microseismic at the detectors, and the calculated position is compared with the actual position of the microseismic source to determine an error range.
5. The microseismic locating method based on the improved ant lion optimization algorithm according to claim 4, wherein the microseismic source locating position is corrected according to the error range.
6. The microseismic localization method based on the improved ant lion optimization algorithm according to claim 4, wherein,
Optimizing an objective function by adopting an improved ant lion optimization algorithm according to the first arrival time, and specifically comprises the following steps:
(1) Data initialization, determining the number of ants and ant lions and the dimension of variables, randomly initializing the positions of the ants and the ant lions in a feasible domain, and calculating the adaptability of the ants and the ant lions;
(2) Determining elite ant lion, and selecting the ant lion with the minimum adaptability in the ant lion population after initialization as the elite ant lion;
(3) Selecting one ant-lion for each ant through roulette, and updating according to ant-lion positions ，/>，/>，/>Of (1), wherein/>Is the minimum value of all variables at the t-th iteration,/>Vector representing the maximum of all variables at the t-th iteration,/>Is the minimum value of all variables at the ith iteration,/>The method comprises the steps that the method is the maximum value of all variables in the ith iteration, the ants randomly walk around the ant lion and elite ant lion, and finally the positions of the ants are updated by taking the average value of the maximum value and the minimum value of the same iteration;
(4) Re-calculating the adaptation value of ants and ant lions after each iteration, and updating the ant lion position according to the position and adaptation of the ants, wherein the position of the minimum adaptation value is the position of a new elite ant lion;
(5) Judging whether the maximum iteration times are reached, if so, outputting a result and ending the iteration, otherwise, repeating the step (3).
7. The microseismic localization method based on the improved ant lion optimization algorithm as claimed in claim 6, wherein the minimum value of all variables at the t-th iterationAnd vector/>, which contains the maximum of all variables at the t-th iterationUpdating according to the following formula:
，
Wherein: is a proportionality coefficient; /(I) For maximum iteration number,/>And/>Is two scaling factors for adjusting the accuracy level of the trap.
8. The microseismic localization method based on the improved ant lion optimization algorithm of claim 7, wherein updating the location of the ants comprises: introducing a dynamic weight coefficient based on the iteration number into an ant position updating formula:
，
Wherein: Random walk value of ants selected for wheel at t-th iteration; - For/>Random walk of elite on iteration,/>Represents the/>The ants at the/>Location at iteration,/>And/>Is a weight coefficient, and T is the maximum number of iterations.
9. The microseismic localization method based on the improved ant lion optimization algorithm according to claim 2, wherein M is 5 or more.
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