CN101770038A - Intelligent positioning method of mine microquake sources - Google Patents
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Abstract
The invention discloses an intelligent positioning method of mine microquake sources, which is a microquake resource intelligent positioning system which comprehensively considers various influencing factors and targets, and integrates with sensors distribution-noise wave filtration-microquake source positioning analysis-three-dimensional display in to a whole. The method realizes the real-time analysis and the forecast of the incubation, the development and the occurrence of mine underground geological disaster through a self-developed procedure, solves the defects that the traditional sensors distribution method is not systematic, the noise wave filtration is incomplete, the microquake wave speed model is not exact and the positioning method is easily diffused, and has the characteristics of friendly operation interface, good noise wave filtration performance, exact, convenient and fast microquake source positioning analysis, visual and pictorial result display, and wide application range. The method has wide application value in the fields of mineral engineering, water conservancy and electric power engineering, petroleum engineering, soil engineering and underground engineering and the like.
Description
Technical field
The invention belongs to the mine microquake monitoring technical field, more specifically relate to a kind of intelligent positioning method of mine microquake sources, this method can be widely used in Mineral Engineering, Hydraulic and Hydro-Power Engineering, petroleum engineering, Geotechnical Engineering and underground works.
Background technology
Along with the rapid growth of Chinese national economy, a main developing direction of deep mining having become China mining.The increasing of the mining degree of depth, the corresponding increase of terrestrial stress, the deterioration of mining conditions, phenomenon showed increased such as roof fall, wall caving appear when causing exploiting, even power ground such as rock burst occurs and press disaster, cause huge lives and properties and economic loss for country, bargh and the people, seriously restricted the sustainable development of national economy and bargh.Statistics show that along with the increase of mining depth, the dynamic disaster that is popular in mines such as mountain copper mine, the plumbous zinc ore of all mouths, Tongling Shizishan Copper Mine, the exquisite gold mine in Shandong, Xiangxi Gold Mine, ore deposit, wax gourd mountain obviously is increase trend, and supporting is overhauled engineering and also greatly increased; Compare China Mine Geological complex structure, mine accident pilosity with the main coal producer in the world, average 1,000,000 tons of mortality ratio are about 2-6, being about 100 times and about 30 times of South Africa of the U.S., about year death toll 6000 people, is 3 times of other producing coal country death toll summations of the whole world.Therefore, the monitoring of rock pressure and down-hole geologic hazard has become the difficult problem that China's mine safety needs to be resolved hurrily with forecast.Studies show that, no matter dynamic disasters such as mountain, right and wrong colliery rock burst, ore deposit shake, still the coal in colliery is given prominence to disasters such as (or gushing out) and mine floor gushing water with gas, all is the result of rock rupture process unstabilitys such as the micro rupture germinating brought out of the stress field disturbance in the mining process, development, perforation.Monitoring shows no matter be which kind of mine power disaster, in most cases, before dynamic disaster occurs, micro rupture (microseismic activity) omen is arranged all.Therefore, the monitoring microseismic activity, hazard forecasting is extremely important for mining geology to obtain the precursor information that disaster arrives.
For monitoring tool and equipment, carried out going deep into systematic research both at home and abroad, and for the research of microseismic monitoring sensor method for arranging, many indexs of noise technology for eliminating, microquake sources high-precision intelligent location technology, still need and will further improve, existing both at home and abroad method mainly has the following disadvantages:
1. sensor arrangement method
Transducer arrangements not only influences the monitoring of microseismic signals, and the uniqueness of locating speed, precision and the positioning result of different microseism location algorithms is also had in various degree influence.Reasonably the transducer arrangements scheme not only can monitor more effective microseismic signals more broadly, and can make location algorithm determine source location and origin time fast and accurately.It mainly is rule of thumb that moment sensor is arranged, the sensor monitors that the different staff of experience arranges to microseismic signals often differ greatly, often can not make sensor farthest monitor effective microseismic signals, be difficult to also guarantee that sensor forms an optimum array, cause microseism locating speed and precision to be subjected in various degree influence, the ore deposit shakes accurate prediction and also is restricted to a great extent.Therefore, be necessary very much transducer arrangements is optimized.
2. noise filtering technique
On-the-spot micro seismic monitoring, can noise eliminating be very important technical measures, be one of the determinative that successfully predict of rock burst.The present method of canceling noise single indexs (such as threshold value, average frequency or ring number etc.) that adopt more, but for many noises be interweaved (Construction traffic, roofbolter, air compressor machine, TBM operating noise etc.), and under the comparatively approaching condition of the actual fracturing features of noise signal feature and country rock, single index is the influence of elimination environmental noise preferably often.Therefore, be necessary to study many indexs filtering technique, further improve the recognition capability of useful signal.
3. microquake sources method for positioning analyzing
Microquake sources localization method forefathers have done big quantity research, in the practical application, and according to unusual two classes that are divided into of the parameter that participates in finding the solution, the one, the known speed model is found the solution the classical localization method of origin time and microquake sources position; The one, the combination method localization method that microquake sources position, origin time and rate pattern are found the solution together.The former, in seismic field, mining engineering, be most widely used, rate pattern is the maximum not enough of this method to forbidding, though forefathers have done many researchs to rate pattern, but because rock material is complicated, heterogeneous, contain a large amount of cracks, joint and little discontinuity surface, and position, size and the trend of prior very difficult definite these bad bodies, also be difficult to draw a clear the boundary between these bad bodies, reasonable given velocity of wave model remains difficulty in advance, and this has influenced the stability and the bearing accuracy of location algorithm to a great extent; The latter has solved rate pattern preferably and has given inaccurate problem, has improved the microquake sources bearing accuracy on largely, but microquake sources position, origin time and these parameters of medium velocity interrelated, has brought the positioning result problem of unstable again.These location algorithms mainly adopt least square method to find the solution in addition, and this class methods disadvantage is easily to disperse in the solution procedure.Although proposed various improving one's methods in order to improve the stability of solution scholars, as decomposition of singular matrix method, damped least square method etc., various improved methods also all belong to the category of linear location, have always solved this problem and have brought that problem.Therefore, be necessary to look for another way and explore new location algorithm and method for solving.
For this reason, gathering microseismic signals, when carrying out the microseism location, must carry out reasonably optimizing, making it monitor more effective information as far as possible, forming an optimum sensor array sensing station; Rate pattern is carried out correct identification, make its energy correct calculation travel-time of microseismic signals in medium; Many noises interleaved signal is carried out filtering, make it at utmost obtain useful signal; The focus localization method is improved, avoid the wild effect of separating in the microseism location as far as possible, locate position and time that microseism takes place fast and accurately, the result of conveniently omnibearing display analysis and forecast is for the prediction of mining geology disaster provides information more accurately and reliably.Microseism intelligence method for positioning analyzing proposes in order to address these problems, this method not only can monitor more microseismic signals, effectively eliminate the influence of environmental noise, accurately locate the position of mine microquake sources fast, but also can pass through rock interior Cracks Evolution real-time positioning research Cracks Evolution rule and mechanism, be the important means of indoor (or on-the-spot) experimental study rock failure mechanism of rock rule and pattern.
Summary of the invention
The objective of the invention is to be to provide a kind of microquake sources intelligent locating method, solve the problems referred to above and deficiency that existing method exists, further improve microquake sources space-time bearing accuracy, improved the mine disaster prediction precision.This method has realized that transducer arrangements Automatic Optimal, down-hole disaster preparation process real-time analysis, disaster occurrence positions and time high accuracy prediction and transducer arrangements scheme, monitoring analysis and forecast result's dynamic 3 D intuitively shows.The real-time monitoring that this method can be used for not only that the mine is underground, overall process is bred, develops, takes place in geologic hazard in the strip mining transformation process, also can be used for indoor (or on-the-spot) experimental study of hard rock failure mechanism in the loading procedure, failure mode and damage evolution rule, also be with a wide range of applications for fields such as Tunnel Engineering, Hydraulic and Hydro-Power Engineering, exploitation of oil-gas field, nuclear waste disposals simultaneously.
In order to realize above-mentioned purpose, the present invention adopts following technical measures:
1) based on numerical analysis and particle group optimizing method (Particle Swarm Optimization, PSO) microquake sources monitoring sensor layout optimization technology; 2) many indexs of microseismic signals intelligent filter technology; 3) microquake sources layering intelligence location technology; 4) microseismic analysis and the dynamic three-dimensional display technology that predicts the outcome.
A kind of microquake sources intelligent locating method the steps include:
A, based on numerical analysis and PSO optimization method microquake sources monitoring sensor layout optimization technology:
1, at first, according to the actual mining technology in mine (the foundation block chambering method is for example arranged), (for example horizontal tectonics stress is main to geologic condition, rock mass fracture growth), set up numerical simulator, the movable advanced line number value of mining is analyzed, held the zone that the mining geology disaster might occur in the recovery process on the whole, as the key area of microseismic monitoring sensor layout optimization.
2, in the recovery activity scope that Mine Safety in Production is concerned about,, determine N (for example N=100) microquake sources (three-dimensional coordinate and origin time) in conjunction with the numerical analysis result.
3, according to the distribution of heterogeneity ore deposit, mine rock wave velocity test, goaf wave velocity test and heterogeneity rock stratum, determine microseism velocity of wave propagation model and span.
4, then,, determine the scope of transducer arrangements, and in scope, utilize mixed congruence method to produce X group (for example X=16) sensing station (three-dimensional coordinate) at random, every group of M (for example M=30) sensing station according to the actual mining technology in mine.
5, to every group of sensor, judge that the focus microseismic signals propagates into tomography and the goaf that sensor experiences, and calculate the distance that microseismic signals is propagated therein, and select the proper speed model automatically, sensor monitors is then to calculate N * M (100 * 30=3000) according to formula (a).
Wherein, t
IjBe the time that j wave detector receives the signal that i microquake sources send, T
iBe i the microquake sources origin time of earthquake, V is the equivalent velocity of wave that ripple is propagated in medium, L
IjBe the distance of i microquake sources to j wave detector, computing formula is as follows:
In the formula, (x
i, y
i, z
i) an i microquake sources position coordinates, (x
j, y
j, z
j) be k wave detector position coordinates.
6, when calculating by formula (b) carry out random perturbation, Virtual Monitoring is then to obtain N * M (100 * 30=3000).
t
V＝(1+3c(-1)
xa
0)t
c (b)
Wherein, t
cFor Virtual Monitoring then, t
cWhen monitoring for calculating, a
0Be the number between the 0-0.05, c is the random number between 0-1, and x=(int) 3c rounds 3 times random numbers.
7, considering that transducer arrangements cost and optical cable cabling are easily under the prerequisite, so that when monitoring and the accumulation residual sum of squares (RSS) when calculating is minimum is objective function, utilize population (Particle SwarmOptimization, PSO) colony intelligence method, according to M (M=30) sensor monitors signal, N (N=100) microquake sources positioned.
8, if bearing accuracy meets the demands, and sensor can monitor microseismic signals preferably, and the position is reasonable, finishes transducer arrangements optimization; Otherwise, carry out next step.
9, if do not find the right sensors position, then utilize the PSO operation, in the scope that sensor can be arranged, produce X group (X=16) new sensing station, return (4) step of this section, new one group sensing station is carried out quality judge.
Whole optimizing process as shown in Figure 1.
B, many indexs of microseismic signals intelligent filter technology:
At first set up various types of noise databases, noise signal feature (as rising time, tale, peak counting, energy, amplitude duration etc.) is carried out analytic induction, make up neural network learning and test sample book by test; Then, utilize genetic algorithm (Genetic algorithm, GA) structure, weights and the threshold value of optimization neural network, and utilize superpower non-linear mapping capability of artificial neural network and self-learning capability neural network training, set up the mapping relations between different noise signal features and the noise types; At last, Input Monitor Connector to microseismic signals it is carried out filtering, obtain effective microseismic signals.Neural network filter construction synoptic diagram as shown in Figure 2.
C, microquake sources intelligence layering method for positioning analyzing:
1, at first utilize the intelligent filter technology that sensor array is monitored the processing such as dry, filtering that disappear of many noise signals, farthest the accurate recording microseismic signals is then chosen and is used for the microseism ripple type of focus location.
2, initialization PSO parameter, microquake sources scope and velocity of wave scope.
3, utilize mixing congruence random algorithm initialization population position (source location and velocity of wave) and particle flying speed (iteration step length).
4, according to time difference positioning principle, calculate the adaptive value of particle by formula (c), and judge whether to satisfy flight number of times and the bearing accuracy of setting in advance, satisfy, carry out (6) step of this section; Otherwise, carry out (5) step of this section;
In the formula, T
k MAnd T
k CBe respectively k sensor monitors and when calculating.
5, according to formula (d) and (e) the more position of new particle (microquake sources) and flying speed (iteration step length), returned for (3) step;
V
id＝wV
id+c
1r
1(P
id-X
id)+c
2r
2(P
gd-X
id) (d)
X
id＝X
id+V
id (e)
Wherein, w is an inertia weight; c
1And c
2Be the study factor of non-negative constant; r
1And r
2Be the random number between [0,1]; D=1,2 ..., D; With
Be respectively the optimum focal shock parameter that i particle position, the optimum focal shock parameter that searches up to now and whole population search up to now.
6, microquake sources coordinate and the rate pattern substitution formula (f) that recognizes obtained microquake sources origin time t;
7, judge that microseismic signals propagates into sensor and whether passes big tomography, dead zone, reject this signal, return (2) step of this section and reorientate if pass; Otherwise it is that correct locating with microquake sources is accurately that microseismic signals is chosen, and finishes the microquake sources location.
Whole microquake sources positioning analysis process as shown in Figure 3.
D, dimension display technologies:
The integrated AutoCAD core dynamic link library exploitation under the VC++ environment of this part forms, mainly be that monitoring result, analysis result and engineering three-dimensional stereo model image are shown, by the monitoring information database, geometric model information database and analysis result stored data base are formed, three databases are used for storing characteristics such as concrete physical dimension, position and the measuring point placement information at the various shape informations of microseism monitoring of equipment and feature, each position of Geotechnical Engineering respectively and utilize the result of above-mentioned intellectual technology Treatment Analysis, the three connects each other, information mutual communication.
The present invention compared with prior art has the following advantages and good effect:
1) sensor intelligent optimizing method for disposing, considered of the constraint of actual mining technology to sensing station, microseismic signals energy attenuation and tomography, goaf are to the influence of microseism wave velocity, under the certain situation of transducer sensitivity and monitoring range, take all factors into consideration transducer arrangements cost and microquake sources bearing accuracy, with the population intelligent optimization method for finding the solution means, may search for optimization in the position global space to sensor, reached and obtain manyly, more comprehensively and the purpose of more complete effective microseism information, Fig. 4 is the example that domestic certain metal mine is used; 2) method of utilizing genetic algorithm and neural network to combine, adopt many indexs to carry out filter analyses to the microseismic signals under many noise jamming, solved the difficult problem that single index filtering technique is difficult to handle the microseismic signals that many noises are interweaved, Fig. 5 and Fig. 6 are filter effect figure.3) microquake sources intelligent locating method, on the basis of handling (disappear dry, filtering etc.) and transducer arrangements optimization to ripple, consider of the influence of the heterogeneity of tomography, goaf and rock material to velocity of wave, based on time difference positioning principle, adopt population colony intelligence method to microseism wave velocity model and focal shock parameter (focus three-dimensional coordinate and origin time) joint inversion, the shortcoming of easily having dispersed when both having solved the classic method location, improved bearing accuracy again, Fig. 7 is the design sketch that this method is used in certain Tunnel Engineering; 4) optimize 3-D display as a result, integrated AutoCAD core dynamic link library exploitation forms under the VC++ environment, can be visual in image, aspect 3-D display transducer arrangements and microquake sources intelligence positioning result quickly.
Description of drawings
Fig. 1 is a kind of transducer arrangements optimisation technique process flow diagram;
Fig. 2 filters the synoptic diagram of making an uproar for the many indexs of artificial neural network;
Fig. 3 is a microquake sources intelligence layering positioning flow synoptic diagram;
Fig. 4 is certain stage casing, mine transducer arrangements floor map;
Fig. 5 is acoustie emission event rate before the filtering;
Fig. 6 is acoustie emission event rate after the filtering;
The design sketch of Fig. 7 for using in the microquake sources intelligence method for positioning analyzing engineering;
Fig. 8 is a numerical analysis integral grid illustraton of model;
Fig. 9 is the mining area cut-away view;
Figure 10 is a 3-D display software operation function menu.
Embodiment
A kind of microquake sources intelligent locating method (being applied as example explanation the specific embodiment of the present invention in certain mine with the present invention) describes in further detail the present invention below in conjunction with accompanying drawing, and a kind of microquake sources intelligent locating method the steps include:
1. based on numerical analysis and PSO optimization method microquake sources monitoring sensor layout optimization technology
Based on the zone that Primary Evaluation rock pressure and geologic hazard might occur, be target so that sensor monitors is wider, monitoring accuracy is higher, in conjunction with the microquake sources location algorithm, consider the influence of mining technology, utilize the PSO technology, sensing station is optimized, detailed process is as follows:
(1) at first, determine mechanics analysis model and parameter thereof according to shop experiment and field monitoring, according to geologic condition (big tomography, structural plane), consider to have foundation block chambering method mining technology characteristics, set up numerical analysis model, as Fig. 8 and as shown in Figure 9, with the energy release rate is evaluation index, numerical analysis and evaluation are carried out in the mining activity, hold the evolution rule of stress field in the recovery process on the whole, the zone that Primary Evaluation rock pressure and geologic hazard might occur is as the key area of microseismic monitoring sensor layout optimization.
(2) determine the monitoring range of 600m * 600m * 550m according to the actual recovery activity in mine,, determine 800 microquake sources in conjunction with the numerical analysis result.
(3), determine that microseism velocity of wave propagation model is V (v according to the distribution of heterogeneity ore deposit, mine rock wave velocity test, goaf wave velocity test and heterogeneity rock stratum
1, v
2, v
3, v
4) and velocity of wave span 4500m.s
-1--6500m.s
-1
(4) the scope 600m of transducer arrangements * 600m * 550m utilizes mixed congruence method to produce 20 groups of sensing stations at random, judges whether tomography and goaf are arranged between sensor and the microquake sources, selects the proper speed model, according to formula (a)
Calculate 24000 sensor monitors then;
(b) t by formula when (5) calculating
V=(1+3c (1)
xa
0) t
cCarry out random perturbation, obtain 24000 Virtual Monitoring then.
According to then virtual, microquake sources is positioned analysis, considering site operation easily under the prerequisite, make when monitoring and the accumulation residual sum of squares (RSS) when calculating less than 10
-4, finish to optimize, optimize as a result design sketch as shown in Figure 4.
2. many indexs of microseismic signals intelligent filter technology
1), determines that Construction traffic, electrical equipment noise, roofbolter, air compressor machine, fine bar such as knock at the feature of noise, set up noise database by site test; 2) be the neural network input with 12 characteristic quantities such as rise time, tale, peak counting, energy, amplitude duration, 5 kinds of noise types are that neural network output makes up 50 of neural network learnings and test sample book 10; 3) utilize structure, weights and the threshold value of genetic algorithm optimization neural network, determine that the neural network optimum structure is 12-34-8-1; 4) set up mapping relations between different noise signal features and the noise types by training; 5) Input Monitor Connector to microseismic signals it is carried out de-noising, filtering, obtain effective microseismic signals.Filter effect as shown in Figure 5 and Figure 6.
3. microquake sources intelligence layering method for positioning analyzing
(1) at first utilize the intelligent filter technology that sensor array is monitored the processing such as dry 1, filtering 2 that disappear of many noise signals, farthest the accurate recording microseismic signals 3, choose effective microseismic signals 5 and carry out the microquake sources location.
(2) then,, obtain useful signal more accurately to then revising again and picking up 6;
(3) initialization PS0 parameter 7: study factor c
1=c
2=2, population size N
Pop=100, w
0=1, termination condition ε
0=1.0 * 10
-10Least square method termination condition ε
0=1.0 * 10
-10The microquake sources scope is that 600m * 600m * 550m and velocity of wave scope are 4500m.s
-1-6500m.s
-1
(4) utilize mixing congruence random algorithm initialization population position (source location and velocity of wave 8) and particle flying speed (iteration step length).
(5) according to formula (c)
Calculate the adaptive value 9 of particle, and judge whether to satisfy flight number of times and the bearing accuracy of setting in advance 10, satisfy, carry out (6) step of this section; Otherwise, carry out (5) step of this section;
(6) according to formula (d) V
Id=wV
Id+ c
1r
1(P
Id-X
Id)+c
2r
2(P
Gd-X
Id) and (e) X
Id=X
Id+ V
IdMore the position of new particle (microquake sources) and flying speed (iteration step length) 11 returned for (3) step;
(7) with the microquake sources coordinate and the rate pattern substitution formula (f) that recognize
Obtain microquake sources origin time t12;
(8) judge that microseismic signals propagates into sensor and whether passes big tomography, dead zone 14, reject this signal, return (2) step of this section and reorientate if pass; Otherwise it is that correct and microquake sources location are accurately 15 that microseismic signals is chosen, and finishes microquake sources location 16.
Locating effect as shown in Figure 7.
4. dimension display technologies
According to independently developed real software (master menu as shown in figure 11), can directly show the characteristic such as concrete physical dimension, position and measuring point placement information at the various shape informations of micro seismic monitoring and feature, each each position of Geotechnical Engineering, mine efficiently comprehensively and utilize the result of above-mentioned intellectual technology Treatment Analysis.
Claims (1)
1. a microquake sources intelligent locating method the steps include:
A, arrange based on numerical analysis and PSO method microquake sources monitoring sensor:
(1), according to the actual mining technology in mine, geologic condition is set up numerical simulator, and the movable advanced line number value of mining is analyzed, and holds the zone that the mining geology disaster occurs in the recovery process on the whole, as the microseismic monitoring sensor layout area;
(2) in the mining scope of activities,, determine N microquake sources in conjunction with the numerical analysis result;
(3) according to the distribution of heterogeneity ore deposit, mine rock wave velocity test, goaf wave velocity test and heterogeneity rock stratum, determine microseism velocity of wave propagation model and span;
(4) then,, determine the scope of transducer arrangements, and in scope, utilize mixed congruence method to produce X group sensing station, every group of M sensing station at random according to the actual mining technology in mine;
(5) to every group of sensor, judge that the focus microseismic signals propagates into tomography and the goaf that sensor experiences, and calculate the distance that microseismic signals is propagated therein, select rate pattern automatically, calculate according to formula meter (a) and obtain N * M sensor monitors then:
Wherein, t
IjBe the time that j wave detector receives the signal that i microquake sources send, T
iBe i the microquake sources origin time of earthquake, V is the equivalent velocity of wave that ripple is propagated in medium, L
IjBe the distance of i microquake sources, be calculated as follows to j wave detector:
In the formula, (x
i, y
i, z
i) an i microquake sources position coordinates, (x
j, y
j, z
j) be k wave detector position coordinates;
(6) when calculating by formula (b) carry out random perturbation, obtain N * M Virtual Monitoring then:
t
V＝(1+3c(-1)
xa
0)t
c (b)
Wherein, t
cFor Virtual Monitoring then, t
cWhen monitoring for calculating, a
0Be the number between the 0-0.05, c is the random number between 0-1, and x=(int) 3c rounds 3 times random numbers;
(7) under the prerequisite of transducer arrangements, the accumulation residual sum of squares (RSS) when making when monitoring and calculating is minimum to be to utilize population colony intelligence method by objective function, according to M sensor monitors signal, N microquake sources is positioned;
(8) bearing accuracy meets the demands, and sensor monitors is to microseismic signals, and the position is reasonable, finishes transducer arrangements;
(9) do not find sensing station, utilize the PSO operation, in the scope of transducer arrangements, produce the new sensing station of X group, return (4) step of this section, new one group sensing station is judged;
B, many indexs of microseismic signals intelligent filter technology, at first set up various types of noise databases by test, to the noise signal feature: rise time, tale, peak counting, energy, amplitude duration are carried out analytic induction, make up neural network learning and test sample book; Then, utilize structure, weights and the threshold value of genetic algorithm optimization neural network, utilize superpower non-linear mapping capability of artificial neural network and self-learning capability neural network training, set up the mapping relations between different noise signal features and the noise types; At last, Input Monitor Connector to microseismic signals it is carried out filtering, obtain effective microseismic signals;
C, microquake sources intelligence layering location:
(1) utilize intelligent filter that sensor array is monitored many noise signals dry, Filtering Processing that disappears, farthest the accurate recording microseismic signals is then chosen and is used for the microseism ripple type of focus location;
(2) initialization PSO parameter, microquake sources scope and velocity of wave scope;
(3) utilize mixing congruence random algorithm initialization population position and particle flying speed;
(4) according to time difference positioning principle, by the value of formula (c) calculating particle, judge flight number of times and the bearing accuracy set in advance, satisfy, carry out this section (6) and go on foot;
In the formula, T
k MAnd T
k CBe respectively k sensor monitors and when calculating;
(5) microquake sources coordinate and the rate pattern substitution formula (f) that recognizes obtained microquake sources origin time t;
(6) judge that microseismic signals propagates into sensor and passes tomography, dead zone, pass and reject this signal, return (2) step of this section and reorientate;
D, dimension display technologies, this part integrated AutoCAD dynamic link library under the VC++ environment is developed to, be that monitoring result, analysis result and engineering three-dimensional stereo model image are shown, by the monitoring information database, geometric model information database and analysis result stored data base are formed, three databases are used for storing various shape informations and feature, concrete physical dimension, position and the measuring point placement information characteristic at each position of Geotechnical Engineering and the result who utilizes above-mentioned Intelligent treatment to analyze of microseism monitoring of equipment respectively, the three connects each other, information mutual communication.
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