CN114047546A - Crowd-sourcing spiral mine earthquake positioning method based on three-dimensional spatial joint arrangement of sensors - Google Patents

Crowd-sourcing spiral mine earthquake positioning method based on three-dimensional spatial joint arrangement of sensors Download PDF

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CN114047546A
CN114047546A CN202111372896.2A CN202111372896A CN114047546A CN 114047546 A CN114047546 A CN 114047546A CN 202111372896 A CN202111372896 A CN 202111372896A CN 114047546 A CN114047546 A CN 114047546A
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CN114047546B (en
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罗浩
潘一山
史金朋
张寅�
丁琳琳
刘阳军
刘乐
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Liaoning University
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Abstract

The invention relates to a crowd-sourcing spiral mine earthquake positioning method based on three-dimensional spatial joint arrangement of sensors, and belongs to the field of mine earthquake monitoring and positioning in coal mining. The sensors are jointly arranged on the ground, the deep hole and the underground to carry out full-surrounding detection on the mine to form a three-dimensional space monitoring network, a crowd-sourcing spiral mine earthquake positioning algorithm based on the three-dimensional space joint arrangement of the sensors is designed, and the mine earthquake monitoring capability and the positioning precision of the mine earthquake in the direction vertical to the stratum are greatly improved. The real-time monitoring sensor receives the vibration wave signal, when the mine earthquake is judged to occur, the triggered sensor is intercepted and receives the vibration wave signal, the station number and the arrival time information are led into a crowd-sourcing spiral mine earthquake positioning algorithm, and the earthquake source position is further determined. The method carries out positioning through the spiral search of the interaction among individuals in the crowd-sourcing algorithm, increases the search space, avoids the traditional algorithm from falling into a local optimum point, and improves the positioning precision of the mineral earthquake and the stability of the positioning result.

Description

Crowd-sourcing spiral mine earthquake positioning method based on three-dimensional spatial joint arrangement of sensors
Technical Field
The invention belongs to the field of coal mining and mine earthquake monitoring and positioning, and particularly relates to a mine earthquake crowd-sourcing spiral positioning method based on three-dimensional spatial joint arrangement of sensors.
Background
The mine earthquake is called mine earthquake for short, and is mainly caused by that mining activities destroy the stability of an underground rock body structure, so that energy is released, ground shaking or underground destruction is caused, and disasters are caused. Mines in many countries in the world have suffered from mine earthquake hazards in different periods, and large-energy mine earthquake events occur frequently along with the increase of mining depth and the improvement of mechanization degree in China.
When mine earthquake happens, the rock body is broken, energy is released, vibration waves are generated, the sensor can monitor the vibration waves in real time, and mine earthquake event positioning is carried out through different positioning algorithms. With the development of geophysics, scholars at home and abroad put forward a plurality of large-scale earthquake positioning algorithms on the basis of seismology research, and can calculate earthquake occurrence time, space and intensity. Mine earthquakes are of a small scale compared to earthquakes, and typically occur in the well field over a depth range of several square kilometers to tens of square kilometers, 1500 meters. Although the learners provide a plurality of mineral earthquake positioning and optimizing methods, the positioning accuracy and the stability are still insufficient, particularly, most of the existing systems are arranged in underground roadways, most of sensors are positioned at the same level, and the mineral earthquake positioning method in a three-dimensional space is lacked, so that the positioning accuracy of the existing systems in the vertical stratum direction is low, and meanwhile, the traditional positioning method is limited in search space, easy to fall into local optimization, causes the positioning accuracy to be reduced, and is difficult to meet the actual requirements in field application.
Disclosure of Invention
The invention aims to provide a crowd-sourcing spiral mine earthquake positioning method based on three-dimensional spatial joint arrangement of sensors. Meanwhile, a crowd-sourcing spiral mine earthquake positioning method is adopted, the search space is enlarged in a spiral search mode, the positioning accuracy of the algorithm is improved, the algorithm is prevented from falling into a local optimum point, positioning solution is carried out through interaction among individuals in the crowd-sourcing algorithm, and the mine earthquake positioning accuracy and the stability of positioning results are improved.
In order to achieve the purpose, the invention adopts the technical scheme that: a crowd-sourcing spiral mine earthquake positioning method based on sensor three-dimensional space combined arrangement is characterized by comprising the following steps:
the method comprises the following steps: through ground, deep hole and the joint arrangement microseismic sensor in pit, carry out real-time supervision, specifically do:
respectively arranging a plurality of micro-seismic sensors at appropriate positions on the ground, a deep hole and underground to form a three-dimensional space three-dimensional monitoring network for real-time combined monitoring of mine seismic waves in a mine area range;
step two: designing a crowd-sourcing spiral mine earthquake positioning algorithm based on three-dimensional spatial joint arrangement of sensors, and introducing station numbers, position coordinate information, vibration wave speed, the number of virtual seismic sources, search dimensions and maximum iteration times of a mine earthquake crowd-sourcing spiral positioning method into a system for algorithm calculation;
step three: the real-time monitoring sensor three-dimensional space receives the vibration wave signal and judges the mine earthquake triggering condition: judging results of a long-short time window method of mine earthquake waves (judging conditions of a trigger long-short time window method), a threshold value (the maximum amplitude of energy waves is more than 100mv), and the number of trigger stations in unit time (the number of triggered stations is more than or equal to 4), and judging that mine earthquake occurs when the triggering conditions are met; continuing monitoring when the trigger condition is not met;
step four: when mine earthquake is judged to occur, the triggered sensor three-dimensional space is intercepted to receive the vibration wave signal, the station number and the arrival time information are imported into the crowd-sourcing spiral mine earthquake positioning method, and the earthquake source position is further determined.
The distribution of the microseismic sensors is set to be 6-50 within the range of every 5km multiplied by 1 km.
The crowd-sourcing spiral mine earthquake positioning method based on the sensor three-dimensional space combined arrangement comprises the following steps:
(21) initializing algorithm parameters; including the number of virtual sources I, the maximum number of iterations T, the search dimension d, and the initial position (x) of all virtual sourcesi，yi，zi)，i≤I；
(2.2) calculating the objective function values of all virtual sources and the optimal virtual source position Qbs(t)；
(2.2.1) calculating a sensor difference matrix D;
calculating arrival time difference according to the positions triggering the N sensors and arrival time information, calculating the distance difference from a seismic source point to each sensor and other N-1 sensors according to the arrival time difference and the propagation velocity of the shock wave, and obtaining a two-dimensional distance difference matrix D of Nx (N-1), wherein the two-dimensional distance difference matrix D is as follows:
D[n，k]＝(H[n]-H[m])×V，(n≤N，m≤N，n≠m，k≤N-1)
wherein [ N, k ] is a matrix index, since m is not equal to N, m (m is not more than N) has N-1 cases in total, and is in one-to-one correspondence with k (k is not more than N-1), Hn is the arrival time recorded by the nth sensor, N also represents the nth row of the D matrix, Hm is the arrival time recorded by the mth sensor, m is other sensors except the current nth sensor, and V is the propagation speed of the shock wave;
(2.2.2) calculating the difference matrix P of the current virtual seismic source positioni；
Calculating a difference matrix P from the current ith virtual source to N sensorsiThe matrix dimension is N × (N-1), as follows:
Figure BDA0003361613580000031
Pi[n，k]＝Li，n-Li，m(i≤T，n≤N，m≤N，n≠m，k≤N-1)
wherein (x)i，yi，zi) Is the ith virtual source location, (X)n，Yn，Zn) Is the position of the nth sensor, (X)m，Ym，Zm) Is the position of the m-th sensor, Li，nIs the distance from the ith virtual source to the nth sensor, n also represents the nth row of the P matrix, Li，mIs the distance from the ith virtual source to the mth sensor, where n ≠ m, k is the matrix index;
(2.2.3) calculating an objective function value;
computing matrices D and PiThe final objective function is established as follows:
Mi＝|D-Pi|
Figure BDA0003361613580000032
wherein the matrix MiIs matrix D and matrix PiOf the difference matrix, MimIs a matrix MiThe median of (1), the fitness is an objective function and is the sum of minimized absolute errors, and the smaller the fitness is, the smaller the computed seismic source error is, the closer the seismic source error is to the real seismic source;
(2.2.4) selecting the virtual seismic source position with the minimum objective function value as the optimal virtual seismic source position Qbs(t)；
(2.3) updating the inertial weights and all virtual source positions;
(2.3.1) updating the nonlinear inertial weight;
Figure BDA0003361613580000033
wherein T is the current iteration number, T is the maximum iteration number, A (T) is the inertia weight of the iterative T-th mineral earthquake crowd-sourcing spiral positioning method, and the value of A (T) is reduced from 2 to 0 in a nonlinear way along with the iteration;
(2.3.2) acquiring a virtual seismic source spiral search initial position;
in the process of acquiring the initial position of the spiral search of the virtual seismic source group, the method mainly comprises the following three steps:
firstly, avoiding the superposition of different virtual seismic source positions: in order to maximize the search capability of each virtual source, the positions of the virtual sources should be updated by the additional variables a (t) calculation, avoiding coincidence with other virtual source positions during the search.
Cs(t)＝A(t)×Qs(t)
Where t is the current iteration number, Qs(t) is a matrix of dimension I x d, representing the current location of all virtual sources, Cs(t) is a new position that does not coincide with the other virtual source positions.
Secondly, obtaining the optimal virtual seismic source position direction: the virtual sources in the group try to move towards the direction in which the best virtual source position is located, namely the direction in which the best virtual source position is located is obtained.
Ms(t)＝B(t)×(Qbs(t)-Qs(t))
Wherein Qbs(t) is obtained from (2.2.4) and is the best virtual source position in the population, Qbs(t)-Qs(t) represents the optimal virtual source location in the population minus the current locations of all virtual sources in the population, Ms(t) is a matrix with dimension I × d, which represents the direction of the virtual sources in the population moving to the optimal virtual source position, and B (t) is a random number for the t-th iteration to balance the global and local searches, and the updating method is as follows:
B(t)＝2×A(t)2×rd
wherein r isdIs [0, 1]]Random numbers within a range.
And finally, acquiring a virtual seismic source spiral search initial position: under the condition of avoiding the superposition of the positions of the virtual seismic sources, the virtual seismic sources in the group acquire the direction of the optimal virtual seismic source position, and the virtual seismic sources are moved to the initial position of the spiral search of the virtual seismic sources, and the implementation process is as follows:
Ds(t)＝|Cs(t)+Ms(t)|
wherein Ds(t) is a matrix of dimension I x d, representing a virtualThe source helix searches for an initial position.
(2.3.3) updating the spiral search position of the virtual seismic source group;
the search angle and speed of the virtual seismic source position are continuously changed in the updating process, when the optimal virtual seismic source position is found, spiral shape search is carried out on the initial position of spiral search of the virtual seismic source in a three-dimensional space, and the spiral search behavior of the virtual seismic source is described as follows:
r＝eθ
v＝r3×sin(θ)×cos(θ)×θ
where θ is the random angle in the range of [0,2 π ], r is the radius of the helix, e is the base of the natural logarithm, and v is the virtual source's own search velocity. The spiral search process for the virtual seismic source is as follows:
Qs(t+1)＝Ds(t)×v+Qbs(t)
Qs(t +1) is the position after the virtual source spiral search, and is also the new position after the t iteration of the algorithm.
(24) Continuously updating the optimal virtual seismic source position;
repeating the step (2.2) and the step (23), and continuously updating the virtual seismic source spiral search position Q in the mine earthquake crowd-sourcing spiral positioning methods(t) and optimal virtual Source location Qbs(t)；
Along with the increase of the iteration times, when the maximum iteration times are reached, the seismic source positioning calculation is finished, and the optimal virtual seismic source position Q is outputbsAnd (T), namely the final seismic source position of the mine seismic positioning.
The beneficial effects created by the invention are as follows: according to the invention, the sensors are jointly arranged on the ground, the deep hole and the underground to carry out full-surrounding detection on the mine, so that a three-dimensional space monitoring network is formed, and the mine earthquake monitoring capability and the positioning precision of the mine earthquake in the direction vertical to the stratum are greatly improved. The mine earthquake crowd-sourcing spiral positioning method performs positioning through spiral search of interaction among individuals in the crowd-sourcing algorithm, and improves the positioning precision and the positioning result stability of the algorithm.
Drawings
FIG. 1 is a diagram of the location of the sensor association in the event of a mineral earthquake.
FIG. 2 is a flow chart of the method for locating the crowd-sourcing spiral mine earthquake according to the present invention.
FIG. 3 is a diagram of an iterative update of the optimal virtual source location in the method.
Detailed Description
A crowd-sourcing spiral mine earthquake positioning method based on sensor three-dimensional space combined arrangement comprises the following steps:
1) real-time monitoring is carried out through jointly arranging microseismic sensors on the ground, the deep hole and the underground;
through a well-ground combined arrangement method, a plurality of micro-seismic sensors are respectively arranged on the ground, deep holes and underground positions, the sensors are prevented from being in the same straight line or the same plane in the arrangement process, a three-dimensional structure is ensured, the underground mining area is fully surrounded as much as possible, and a three-dimensional arrangement network is formed. And then detecting the energy waves in the detection range in real time, wherein areas with impact risks are evaluated for important detection.
The number of the microseismic sensors is more than 4, the more the number of the sensors in a certain range is, the higher the positioning precision is, and generally 6-50 microseismic sensors are selected for the range of 5km multiplied by 1 km.
2) Leading in initial information of a crowd-sourcing spiral mine earthquake positioning algorithm and a station into a system;
and importing station numbers, position coordinate information, vibration wave speed, the number of virtual earthquake sources of the crowd-sourcing spiral mine earthquake positioning algorithm, search dimensionality and maximum iteration times into the system for algorithm calculation.
3) Monitoring the vibration wave signal in real time;
the real-time monitoring sensor three-dimensional space receives the vibration wave signal and judges the mine earthquake triggering condition: judging results of a mineral earthquake wavelength short-time window method (judging conditions of a trigger long-time window method), a threshold value (the maximum amplitude of energy waves is greater than 100mv), and the number of trigger stations in unit time (the number of trigger stations is greater than or equal to 4), and judging that the mineral earthquake occurs when the triggering conditions are met; and continuing monitoring without reaching the trigger condition.
4) When mine earthquake is judged to occur, sensor numbers and arrival time information which meet triggering conditions are imported;
and when the mine earthquake is judged to occur, intercepting the triggered three-dimensional space of the sensor to receive the vibration wave signal, and importing station numbers and arrival time information into a crowd-sourcing spiral mine earthquake positioning algorithm to further determine the position of the earthquake source.
5) Initializing a virtual seismic source parameter of a mine earthquake crowd-sourcing spiral positioning method;
initializing parameters of a mineral earthquake crowd-sourcing spiral positioning method; including the number of virtual sources I, the maximum number of iterations T, the search dimension d, and the initial position (x) of all virtual sourcesi，yi，zi)，i≤I。
6) Calculating objective function values of all virtual sources and optimal virtual source position Qbs(t)；
61) Calculating a sensor difference matrix D;
calculating arrival time difference according to the positions triggering the N sensors and arrival time information, calculating the distance difference from a seismic source point to each sensor and other N-1 sensors according to the arrival time difference and the propagation velocity of the shock wave, and obtaining a two-dimensional distance difference matrix D of Nx (N-1), wherein the two-dimensional distance difference matrix D is as follows:
D[n，k]＝(H[n]-H[m])×V，(n≤N，m≤N，n≠m，k≤N-1)
wherein [ N, k ] is a matrix index, since m is not equal to N, m (m is not more than N) has N-1 cases in total, and is in one-to-one correspondence with k (k is not more than N-1), Hn is the arrival time recorded by the nth sensor, N also represents the nth row of the D matrix, Hm is the arrival time recorded by the mth sensor, m is other sensors except the current nth sensor, and V is the propagation speed of the shock wave;
6. 2) calculating a difference matrix P of the current virtual seismic source positioni；
Calculating a difference matrix P from the current ith virtual source to N sensorsiThe matrix dimension is N × (N-1), as follows:
Figure BDA0003361613580000061
Pi[n，k]＝Li，n-Li，m(i≤T，n≤N，m≤N，N≠m，k≤N-1)
wherein (x)i，yi，zi) Is the ith virtual source location, (X)n，Yn，Zn) Is the position of the nth sensor, (X)m，Ym，Zm) Is the position of the m-th sensor, Li，nIs the distance from the ith virtual source to the nth sensor, Li，nIs the distance from the ith virtual source to the mth sensor, n also represents the nth row of the P matrix, where n ≠ m, k is the matrix index;
63) calculating an objective function value;
computing matrices D and PiThe absolute value of the difference, establishes the final objective function, as follows:
Mi＝|D-Pi|
Figure BDA0003361613580000071
wherein the matrix MiIs matrix D and matrix PiOf the difference matrix, MimIs a matrix MiThe median of (1), fitness is an objective function, which is the sum of the minimized absolute errors, and the smaller the fitness, the smaller the computed seismic source error, the closer it is to the true seismic source. M calculated by N sensors without erroriThe matrices should all be 0, and the fitness is also 0 at this time.
6.4) selecting the virtual seismic source position with the minimum objective function value as the optimal virtual seismic source position Qbs(t)；
7) Updating the inertial weight and all virtual seismic source positions;
7.1) updating the nonlinear inertia weight;
Figure BDA0003361613580000072
wherein T is the current iteration number, T is the maximum iteration number, A (T) is the inertia weight of the iterative T-th mineral earthquake crowd-sourcing spiral positioning method, and the value of A (T) is reduced from 2 to 0 in a nonlinear way along with the iteration.
72) Acquiring a virtual seismic source spiral search initial position;
in the process of acquiring the initial position of the spiral search of the virtual seismic source group, the method mainly comprises the following three steps:
firstly, avoiding the superposition of different virtual seismic source positions: in order to maximize the search capability of each virtual source, the positions of the virtual sources should be updated by additional variable calculations while avoiding coincidence with the positions of other virtual sources during the search.
Cs(t)＝(t)×Qs(t)
Where t is the current iteration number, Qs(t) is a matrix of dimension I x d, representing the current location of all virtual sources, Cs(t) is a new position that does not coincide with the other virtual source positions.
Secondly, obtaining the optimal virtual seismic source position direction: the virtual sources in the group try to move towards the direction in which the best virtual source position is located, namely the direction in which the best virtual source position is located is obtained.
Ms(t)＝B(t)×(Qbs(t)-Qs(t))
Wherein Qbs(t) is obtained from 6.4) and is the best virtual source position in the population, Qbs(t)-Qs(t) represents the optimal virtual source location in the population minus the current locations of all virtual sources in the population, Ms(t) is a matrix with dimension I × d, representing the direction of movement of the virtual sources in the population to the optimal virtual source position, where B (t) is a random number for the t-th iteration to balance the global and local searches, and is updated as follows:
B(t)＝2×A(t)2×rd
wherein r isdIs [0, 1]]Random numbers within a range.
And finally, acquiring a virtual seismic source spiral search initial position: under the condition of avoiding the superposition of the positions of the virtual seismic sources, the virtual seismic sources in the group acquire the direction of the optimal virtual seismic source position, and the virtual seismic sources are moved to the initial position of the spiral search of the virtual seismic sources, and the implementation process is as follows:
Ds(t)＝|Cs(t)+Ms(t)|
wherein DsAnd (t) is a matrix with dimension I x d, and represents the initial position of the spiral search of the virtual seismic source.
7.3) updating the spiral search position of the virtual seismic source group;
the search angle and speed of the virtual seismic source position are continuously changed in the updating process, spiral shape search is carried out in a three-dimensional space every time the optimal virtual seismic source position is found, and the spiral search behavior of the virtual seismic source is described as follows:
r＝eθ
v＝r3×sin(θ)×cos(θ)×θ
where θ is the random angle in the range of [0,2 π ], r is the radius of the helix, e is the base of the natural logarithm, and v is the virtual source's own search velocity. The spiral search process for the virtual seismic source is as follows:
Qs(t+1)＝Ds(t)×v+Qbs(t)
Qs(t +1) is the position after the virtual source spiral search, and is also the new position after the t iteration of the algorithm.
8) Continuously updating the optimal virtual seismic source position;
repeating the step 6) and the step 7), and continuously updating the spiral search position Q of the virtual seismic source in the mine earthquake crowd-sourcing spiral positioning methods(t) and optimal virtual Source location Qbs(t)；
9) Determining a seismic source point;
along with the increase of the iteration times, when the maximum iteration times are reached, the seismic source positioning calculation is finished, and the optimal virtual seismic source position Q is outputbsAnd (T), namely the final seismic source position of the mine seismic positioning.
The specific application example is as follows:
the mining depth of a certain coal mine is close to 1000m, and the underground mining process has multiple mineral earthquake events, so that the surface earthquake sense is obvious. Therefore, a mine earthquake monitoring station is installed in the mine in a well-ground combined mode, as shown in figure 1, the underground sensor transmits a ground GPS signal through an optical fiber to carry out time service, and the ground and the deep hole sensor carry out time service uniformly through a GPS.
Step S1: arranging sensors by a well-ground combined method, and monitoring in real time;
the sensors are respectively arranged on the ground, the deep hole and the underground position by a well-ground combined arrangement method, so that a plurality of sensors are prevented from being in the same straight line or the same plane in the arrangement process, a three-dimensional structure is ensured, the underground mining area is fully surrounded as much as possible, and a three-dimensional arrangement network is formed. The positions are T1 underground stations [6738m, 7965m, -842m ], T2 underground stations [8126m, 7406m, -1034m ], T3 underground stations [7386m, 7795m, -863m ], T4 underground stations [7790m, 8277m, -962m ], T5 deep-hole stations [6690m, 8177m, -441m ], T6 deep-hole stations [7802m, 7336m, -510m ], T7 ground stations [7792m, 7825m, 41m ], as shown in FIG. 1, and then the energy wave in the detection range is detected in real time, wherein the areas with impact risk are evaluated for emphasis detection.
Step S2: leading initial information of a mine earthquake crowd-sourcing spiral positioning method and a station into a system;
station numbers T1-T7, position coordinate information, a seismic wave velocity V of 3850m/s, the number of virtual seismic sources of the mine earthquake crowd-sourcing spiral positioning method of 100, a search dimension of 3 and the maximum iteration number of 100 are introduced into the system.
Step S3: monitoring the vibration wave signal in real time;
the real-time monitoring sensor three-dimensional space receives the vibration wave signal and judges the mine earthquake triggering condition: judging results (judging conditions of a trigger long-short time window method) of mine earthquake wavelength short-time window method, threshold values (the maximum amplitude of energy waves is more than 100mv), and the number of trigger stations in unit time (the number of triggered stations is more than or equal to 4), and judging that mine earthquake occurs when the trigger conditions are met; and continuing monitoring without reaching the trigger condition.
Step S4: when mine earthquake is judged to occur, sensor numbers and arrival time information are imported;
the sensors of the station detect vibration wave signals in real time, at about 25 minutes and 37 seconds at 14 pm on a certain day, all the sensors receive large energy wave signals, the occurrence of the mine earthquake is judged, the position and arrival time information of the trigger sensor are transmitted into the mine earthquake crowd-sourcing spiral positioning method, and the arrival time information is shown in table 1.
TABLE 1 sensor placement coordinates and arrival times
Figure BDA0003361613580000101
Step S5: initializing a virtual seismic source parameter of a mine earthquake crowd-sourcing spiral positioning method;
initializing parameters of a mineral earthquake crowd-sourcing spiral positioning method; including a number of virtual sources I of 100, a maximum number of iterations T of 100, a search dimension d of 3, and initial positions (x) of all virtual sourcesi，yi，zi) I is less than or equal to 100, all the seismic source positions are randomly positioned in the space formed by the trigger sensors, the 1 st iteration and the 2 nd virtual seismic source point are taken as examples, namely t is 1, i is 2, and the positions (x) are2，y2，z2) Is [7717, 8049, -610 ]]。
Step S6: calculating objective function values of all virtual sources and optimal virtual source position Qbs(t)；
(1) Calculating a sensor difference matrix D;
calculating arrival time difference according to the triggered positions of 7 sensors and arrival time information, calculating the distance difference from a seismic source point to each sensor and other 6 sensors according to the arrival time difference and the propagation velocity of the shock wave, and obtaining a 7 multiplied by 6 two-dimensional distance difference matrix D, wherein the calculation process is as follows:
when n is 1 and m is 2, since when n is 1, the minimum value of m is 2 according to n ≠ m, and k is 1, and k increases with the increase of m, it is found that D [1, 1] ═ H [1] -H [2]) × V, and D [1, 1] ═ 273, and H is the arrival time of the sensor. (Note: D [1, 1] at this time represents the distance difference between the 1 st and 2 nd sensors to the seismic source, k 1 is the matrix index, i.e., the first difference when n 1)
Solving the D matrix value by the same method as:
Figure BDA0003361613580000111
(2) calculating a difference matrix P of the current virtual seismic source positioni；
When n is 1 and m is 2, L2，1That is, the linear distance from the 2 nd virtual source to the 1 st sensor is calculated to obtain 1010m, and similarly, L2，2Which is the linear distance from the 2 nd virtual source to the 2 nd sensor, 872m is calculated. In the same step (1), k is 1
According to formula P2[1，1]＝L2，1-L2，2To obtain P2[1，1]＝138。
Working out P in the same way2Matrix:
Figure BDA0003361613580000112
(3) calculating an objective function value;
computing matrices D and P2Absolute value M of the difference2Matrix:
Figure BDA0003361613580000113
at this time M2Median M of the matrix2mIs 284.
According to the formula
Figure BDA0003361613580000114
The target function fitness value of the 2 nd virtual source at this time is calculated to be 6248.
(4) Calculating objective function values of all 100 virtual sources, and selecting the virtual source position with the minimum objective function value as the best virtual source position Qbs(t) 1 st iteration of the optimal virtual source position Q through the calculation of 100 virtual sourcesbs(1) Is [7638, 7983, -677 ]]；
Step S7: updating the inertial weight and all virtual seismic source positions;
(1) updating the nonlinear inertial weight;
Figure BDA0003361613580000121
(2) acquiring a virtual seismic source spiral search initial position;
in the process of acquiring the initial position of the spiral search of the virtual seismic source group, the method mainly comprises the following three steps:
firstly, avoiding the superposition of different virtual seismic source positions: at this time Cs(1)＝A(1)×Qs(1) In iteration 1, Qs(1) Middle 2 virtual source as an example, Cs(1) The 2 nd virtual source in (1) has corresponding values of [13890.6, 14488.2, -1098 ]]
Secondly, obtaining the optimal virtual seismic source position direction: let r bedIs 0.5.
According to B (1) ═ 2 × A (1)2×rdResult in that B (1) ═ 324
According to Ms(1)＝B(1)×(Qbs(1)-Qs(1) M) for the 2 nd virtual source point in the 1 st iterations(1) Is [ -255.96, -21384, -21708]。
And finally, acquiring a virtual seismic source spiral search initial position:
according to Ds(1)＝|Cs(1)+Ms(1) I derive, in 1 st iteration, initial position D of spiral search of 2 nd virtual source points(1) Is [1363464, 1427436, 131508 ]]。
(3) Updating a virtual seismic source group spiral searching position;
let θ be pi, and obtain r-23.14 and v-4.77 × 10 according to the formula-12
Then according to formula Qs(t+1)＝Ds(t)×v+Qbs(t) obtaining the new position of the 2 nd virtual source after the 1 st iteration and the spiral attack according to the optimal source position as [7638, 7983, -677 ]]And is also the initial position of the 2 nd iteration of the 2 nd virtual source.
Step S8: continuously updating the optimal virtual seismic source position;
and repeating the step S6 and the step S7, and continuously updating the virtual seismic source position Q in the mine earthquake crowd-sourcing spiral positioning methods(t) and optimal virtual Source location Qbs(t) recording the optimal virtual source position Q each timebs(t), as shown in FIG. 3;
step S9: determining a seismic source point;
with the increase of the iteration times, when the maximum iteration times are 100, the seismic source positioning calculation is finished, and the optimal virtual seismic source position Q is outputbs(100) That is, the final seismic source position is obtained, the positioning result is positioned on the top plate above the goaf, and the coordinates are [7421m, 7964m, -706m]The analysis shows that the current mineral earthquake event is mainly caused by fracture of a horizontal hard thick roof layer of-706 above a goaf due to mining activities, the positioning result accords with the layer of a hard fine sand rock layer above a mining area, and the fracture of the layer influenced by mining provides energy of the current mineral earthquake event.

Claims (3)

1. A crowd-sourcing spiral mine earthquake positioning method based on sensor three-dimensional space combined arrangement is characterized by comprising the following steps:
the method comprises the following steps: through ground, deep hole and the joint arrangement microseismic sensor in pit, carry out real-time supervision, specifically do:
respectively arranging a plurality of micro-seismic sensors at appropriate positions on the ground, a deep hole and underground to form a three-dimensional space three-dimensional monitoring network for real-time combined monitoring of mine seismic waves in a mine area range;
step two: designing a crowd-sourcing spiral mine earthquake positioning algorithm based on three-dimensional spatial joint arrangement of sensors, and introducing station numbers, position coordinate information, vibration wave speed, the number of virtual seismic sources, search dimensions and maximum iteration times of a mine earthquake crowd-sourcing spiral positioning method into a system for algorithm calculation;
step three: the real-time monitoring sensor receives the vibration wave signal and judges the mine earthquake triggering condition: judging results including the mineral earthquake wavelength short-time window method, threshold values and the number of trigger stations in unit time, and judging that mineral earthquake occurs when a trigger condition is met; continuing monitoring when the trigger condition is not met;
step four: when mine earthquake is judged to occur, the triggered sensor is intercepted and receives the earthquake wave signal, and station numbers and arrival time information are led into a crowd-sourcing spiral mine earthquake positioning algorithm to further determine the position of an earthquake source.
2. The method for locating the crowd spiral mine earthquake according to claim 1, wherein the distribution of the microseismic sensors is the combined arrangement of the ground, deep holes and the underground, and the number of the sensors is set to 6-50 within the range of every 5km x 1 km.
3. The method for the crowd spiral positioning of the mineral earthquakes based on the three-dimensional spatial joint arrangement of the sensors according to claim 1 or 2, characterized in that the method for the crowd spiral positioning of the mineral earthquakes based on the three-dimensional spatial joint arrangement of the sensors comprises the following steps:
(2.1) initializing algorithm parameters; including the number of virtual sources I, the maximum number of iterations T, the search dimension d, and the initial position (x) of all virtual sourcesi,yi,zi)，i≤I；
(2.2) calculating the objective function values of all virtual sources and the optimal virtual source position Qbs(t)；
(2.2.1) calculating a sensor difference matrix D;
calculating arrival time difference according to the positions triggering the N sensors and arrival time information, calculating the distance difference from a seismic source point to each sensor and other N-1 sensors according to the arrival time difference and the propagation velocity of the shock wave, and obtaining a two-dimensional distance difference matrix D of Nx (N-1), wherein the two-dimensional distance difference matrix D is as follows:
D[n,k]＝(H[n]-H[m])×V,(n≤N,m≤N,n≠m,k≤N-1)
wherein [ N, k ] is a matrix index, since m is not equal to N, m (m is not more than N) has N-1 cases in total, and is in one-to-one correspondence with k (k is not more than N-1), Hn is the arrival time recorded by the nth sensor, N also represents the nth row of the D matrix, Hm is the arrival time recorded by the mth sensor, m is other sensors except the current nth sensor, and V is the propagation speed of the shock wave;
(2.2.2) calculating the difference matrix P of the current virtual seismic source positioni；
Calculating a difference matrix P from the current ith virtual source to N sensorsiThe matrix dimension is N × (N-1), as follows:
Figure FDA0003361613570000021
Pi[n,k]＝Li,n-Li,m(i≤T,n≤N,m≤N,n≠m,k≤N-1)
wherein (x)i,yi,zi) Is the ith virtual source location, (X)n,Yn,Zn) Is the position of the nth sensor, (X)m,Ym,Zm) Is the position of the m-th sensor, Li,nIs the distance from the ith virtual source to the nth sensor, n also represents the nth row of the P matrix, Li,mIs the distance from the ith virtual source to the mth sensor, where n ≠ m, k is the matrix index;
(2.2.3) calculating an objective function value;
computing matrices D and PiThe final objective function is established as follows:
Mi＝|D-Pi|
Figure FDA0003361613570000022
wherein the matrix MiIs matrix D and matrix PiOf the difference matrix, MimIs a matrix MiThe median of (1), the fitness is an objective function and is the sum of minimized absolute errors, and the smaller the fitness is, the smaller the computed seismic source error is, the closer the seismic source error is to the real seismic source;
(2.2.4) selecting the virtual seismic source position with the minimum objective function value as the optimal virtual seismic source position Qbs(t)；
(2.3) updating the inertial weights and all virtual source positions;
(2.3.1) updating the nonlinear inertial weight;
Figure FDA0003361613570000023
wherein T is the current iteration number, T is the maximum iteration number, A (T) is the inertia weight of the iterative T-th mineral earthquake crowd-sourcing spiral positioning method, and the value of A (T) is reduced from 2 to 0 in a nonlinear way along with the iteration;
(2.3.2) acquiring a virtual seismic source spiral search initial position;
in the process of acquiring the initial position of the spiral search of the virtual seismic source group, the method mainly comprises the following three steps:
firstly, avoiding the superposition of different virtual seismic source positions: in order to maximize the search capability of each virtual source, the positions of the virtual sources should be updated by the additional variables a (t) calculation, avoiding coincidence with other virtual source positions during the search.
Cs(t)＝A(t)×Qs(t)
Where t is the current iteration number, Qs(t) is a matrix of dimension I x d, representing the current location of all virtual sources, Cs(t) is a new position that does not coincide with the other virtual source positions.
Secondly, obtaining the optimal virtual seismic source position direction: the virtual sources in the group try to move towards the direction in which the best virtual source position is located, namely the direction in which the best virtual source position is located is obtained.
Ms(t)＝B(t)×(Qbs(t)-Qs(t))
Wherein Qbs(t) is obtained from (2.2.4) and is the best virtual source position in the population, Qbs(t)-Qs(t) represents the optimal virtual source location in the population minus the current locations of all virtual sources in the population, Ms(t) is a matrix with dimension I × d, which represents the direction of the virtual sources in the population moving to the optimal virtual source position, and B (t) is a random number for the t-th iteration to balance the global and local searches, and the updating method is as follows:
B(t)＝2×A(t)2×rd
wherein r isdIs [0, 1]]Random numbers within a range.
And finally, acquiring a virtual seismic source spiral search initial position: under the condition of avoiding the superposition of the positions of the virtual seismic sources, the virtual seismic sources in the group acquire the direction of the optimal virtual seismic source position, and the virtual seismic sources are moved to the initial position of the spiral search of the virtual seismic sources, and the implementation process is as follows:
Ds(t)＝|Cs(t)+Ms(t)|
wherein Ds(t) is a matrix with dimension I x d, representing the initial position of the virtual seismic source spiral search;
(2.3.3) updating the spiral search position of the virtual seismic source group;
the search angle and speed of the virtual seismic source position are continuously changed in the updating process, when the optimal virtual seismic source position is found, spiral shape search is carried out on the initial position of spiral search of the virtual seismic source in a three-dimensional space, and the spiral search behavior of the virtual seismic source is described as follows:
r＝eθ
v＝r3×sin(θ)×cos(θ)×θ
where θ is the random angle in the range of [0,2 π ], r is the radius of the helix, e is the base of the natural logarithm, and v is the virtual source's own search velocity. The spiral search process for the virtual seismic source is as follows:
Qs(t+1)＝Ds(t)×v+Qbs(t)
Qs(t +1) is the position after the virtual seismic source spiral search, and is also the new position after the t iteration of the algorithm;
(2.4) continuously updating the optimal virtual seismic source position;
repeating the step (2.2) and the step (2.3), and continuously updating the spiral search position Q of the virtual seismic source in the algorithms(t) and optimal virtual Source location Qbs(t)；
Along with the increase of the iteration times, when the maximum iteration times are reached, the seismic source positioning calculation is finished, and the optimal virtual seismic source position Q is outputbsAnd (T), namely the final seismic source position of the mine seismic positioning.
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