CN108319915A - A kind of dynamic adjustment of rock burst signal threshold value it is more when window reduced form recognizer - Google Patents
A kind of dynamic adjustment of rock burst signal threshold value it is more when window reduced form recognizer Download PDFInfo
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Abstract
The invention discloses a kind of rock burst signal threshold value dynamic adjustment it is more when window reduced form recognizer, include the following steps：All kinds of typical rock burst signals and typical noise signal are obtained, rock burst signal threshold value dynamic adjusting data library is established；Determine call parameter and judgment threshold；Automatic identification processing；Judgment threshold dynamic is periodically carried out to adjust.The present invention is handled in real time suitable for microseism data, meets actual demands of engineering, is improved waveform and is picked up efficiency, and manual identified workload is reduced, and then improves the real-time of the geo-hazard early-warnings such as rock burst, mine shake, landslide.
Description
Technical field
The present invention relates to On Microseismic Monitoring Technique fields.More particularly to a kind of rock burst signal threshold value dynamic adjustment it is more when window letter
Change form recognizer, this method can be widely used for mineral engineering, hydraulic and hydroelectric engineering, petroleum works, geotechnical engineering and ground
Lower engineering.
Background technology
Rock burst signal automatic identification technology is the key that microquake sources positioning.Existing rock burst signal recognition method mainly has：
According to the STA/LTA algorithms in time-domain energy and energy variation construction feature versus time domain；According to rock burst signal and noise
The difference of signal waveform feature, such as Fisher diagnostic methods, Fast Fourier Transform (FFT), Maximum likelihood classification, logistic regression and god
Through network, form fractal dimension, statistical method, energy extremum method etc..However these methods mostly only to blast signal have compared with
Good filtration result, is with or without jumbolter signal common in Practical Project, electric signal, secondary blasting signal etc. less
Effect, which results in rock burst signals to be submerged in a large amount of data, it is difficult to pass through software Automatic analysis.
By practical engineering application, it is found that algorithm needs to consider following three points in automatic identification rock burst signal：1) in real time
Whether calculation amount disclosure satisfy that software and hardware condition in processing；2) how threshold value fast and effeciently determines the complex wave met in engineering
Shape；3) whether can adapt to the variation of rock burst signal in engineering.
Therefore, in view of the above-mentioned problems, taking into account algorithm Practical Condition, invent a kind of dynamic adjustment of rock burst signal threshold value it is more when
Window reduced form recognizer is handled in real time suitable for microseism data, meets actual demands of engineering, is improved waveform and is picked up efficiency,
Manual identified workload is reduced, and then enhances the real-time of the geo-hazard early-warnings such as rock burst, mine shake, landslide.
Invention content
The object of the present invention is to overcome the problems of the prior art, takes into account algorithm Practical Condition, provides a kind of rock
The dynamic adjustment of quick-fried signal threshold value it is more when window reduced form recognizer, handled in real time suitable for microseism data, meet engineering reality
Border needs, and improves waveform and picks up efficiency, reduces manual identified workload, and then it is pre- to enhance the geological disasters such as rock burst, mine shake, landslide
Alert real-time.
A kind of dynamic adjustment of rock burst signal threshold value it is more when window reduced form recognizer, include the following steps：
Step 1 selects typical rock burst signal and typical noise signal.
Rock burst signal refers generally to the elastic wave signal that Progressive failure generates inside rock mass before rock burst occurs, and noise signal is general
Include blast signal, jumbolter signal, electric signal, field operation signal etc..All types of rock burst signal/noise signals
Amplitude of wave form and Frequency scaling algorithm it is more similar, the signal that waveform can represent certain a kind of rock burst signal/noise signal is typical
Signal.
The RK functions of step 2, the RK functions for seeking typical rock burst signal and typical noise signal.
Step 3, using the time as abscissa, RK functional values be ordinate,
It determines the number a of reference point, chooses reference point rj, reference point rjAbscissa be delay position t0+dj, ordinate
For judgment threshold Rj, judgment threshold RjPositioned at delay position t0+djThe RK functional values and delay position t of the typical rock burst signal at place0+
djBetween the RK functional values of the typical noise signal at place, delay position t0+djThe RK functional values of the typical rock burst signal at place and delay
Position t0+djThe RK functional values of the typical noise signal at place are unequal；Wherein, { 1,2 } a ∈, j are the serial number of reference point, t0It is tactile
Send out moment, djTo postpone length；
In the present invention, reference point rjCan be 1 or 2, i.e. reference point rjCan be reference point r1, or
Reference point r1With reference point r2；Judgment threshold RjCorrespond to 1 or 2, you can with for judgment threshold R1, can be to judge threshold
Value R1With judgment threshold R2。
The RK functional values of microseism data to be identified are identified in step 4, include the following steps：
Step 4.1 reads the microseism data to be identified generated in real time；
Step 4.2, the RK functional values for calculating microseism data to be identified in real time, the RK functional values first of microseism data to be identified
It is secondary to be more than activation threshold value RqIt is triggering moment t at the time of corresponding0, activation threshold value RqFor preset value；
Step 4.3, computing relay position t0+djThe RK functional values of microseism data to be identified,
In delay position t0+djThe RK functional values of the typical rock burst signal at place are more than the RK functional values of typical noise signal
In the case of：
If delay position t0+djThe RK functional values of the microseism data to be identified at place are more than judgment threshold Rj, then this reading is identified
The microseism data to be identified taken is rock burst signal undetermined；Otherwise, this microseism data to be identified read is noise signal；
In delay position t0+djThe RK functional values of the typical rock burst signal at place are less than the RK functional values of typical noise signal
In the case of：
If delay position t0+djThe RK functional values of the microseism data to be identified at place are less than judgment threshold Rj, then this reading is identified
The microseism data to be identified taken is rock burst signal undetermined；Otherwise, this microseism data to be identified read is noise signal；
If all delay position t0+djThe recognition result at place is rock burst signal undetermined, then this microseism to be identified read
Data are rock burst signal.
In the present invention, reference point rjCan be 1 or 2, corresponding delay position t0+djIt can be delay position
t0+d1, or delay position t0+d1With delay position t0+d2.In the case of there are two reference point, it is necessary to two ginsengs
Identification at the corresponding delay position of examination point is rock burst signal undetermined, just can determine that the microseism data to be identified that this reads is
Rock burst signal.
Judgment threshold R as described abovejDynamic adjustment realized by following steps：
Step 5.1 establishes rock burst signal threshold value dynamic adjusting data library, and rock burst signal threshold value dynamic adjusting data is pressed in library
According to the morning and evening sequential storage rock burst signal of time of origin, each rock burst signal is denoted as in rock burst signal threshold value dynamic adjusting data library
X1, X2..., Xp, wherein p is the maximum number of rock burst signal in rock burst signal threshold value dynamic adjusting data library, and time of origin is the latest
Rock burst signal be Xp；
Under normal circumstances, the maximum number p of rock burst signal takes 400 in rock burst signal threshold value dynamic adjusting data library；
If field geology conditions are changeable, such as deep-lying tunnel driving or large-sized workshop excavation engineering can reduce rock burst signal threshold
It is worth the maximum number p of rock burst signal in dynamic adjusting data library, threshold value dynamic adjustment is made to accelerate；
If field geology conditions variation is smaller, such as fixed point long term monitoring engineering can increase rock burst signal threshold value dynamic and adjust
The maximum number p of rock burst signal in entire data library makes threshold value dynamic adjustment slow down.
Step 5.2, by each rock burst signal in rock burst signal threshold value dynamic adjusting data library in delay position t0+djRK
The array that functional value is arranged to make up is denoted as RKj(k), wherein k is the serial number of rock burst signal, and j is the serial number of reference point；
In delay position t0+djThe RK functional values of the typical rock burst signal at place are more than the RK functional values of typical noise signal
In the case of：Each rock burst signal is in delay position t in rock burst signal threshold value dynamic adjusting data library0+djRK functional values from greatly to
It is small to be arranged；
In delay position t0+djThe RK functional values of the typical rock burst signal at place are less than the RK functional values of typical noise signal
In the case of：Each rock burst signal is in delay position t in rock burst signal threshold value dynamic adjusting data library0+djRK functional values from it is small to
It is arranged greatly；
Step 5.3 determines selection rate Bj, choose and meet the minimum value of following formula for selection rate Bj
Wherein, BzFor always selection rate, BzValue range be 50%~100%；
Step 5.4 obtains kjValue, kj=int (p × Bj), int is rounding operation；
If kj﹤ p, then judgment threshold RjFor RKj-(kj) and RKj-(kj+ 1) average value；
If kj=p, then judgment threshold RjFor RKj-(kj)；
Step 5.5 obtains new microseism data, the rock burst event number a in new microseism data1With noise event number b1It is
Know parameter,
According to judgment threshold RjUsing step 1~4, to new microseism data carry out automatic identification processing obtain rock burst signal and
Noise signal obtains corresponding rock burst event number a2With noise event number b2；By the rock burst signal newly obtained according to time of origin into
Row arrangement, and it is denoted as Y1, Y2..., Yq, q is the maximum number of the rock burst signal newly obtained, q≤p, the rock burst of time of origin the latest
Signal is Yq；
It is generally believed that the number of rock burst signal/noise signal of generation in setting time can be identified as more than or equal to 4
One rock burst event/noise signal, can't be all signals of event will all filter out.Wherein, above-mentioned setting time is generally advised
It is set to 0.5s.
Step 5.6 calculates new microseism data rock burst event automatic identification accuracy E according to formula (5)
If new microseism data rock burst event automatic identification accuracy E is more than preset rock burst event automatic identification accuracy threshold
Value Rb, then step 5.7 is carried out, step 5.8 is otherwise carried out；Wherein, rock burst event automatic identification accuracy threshold value RbValue range
It is 50%~100%；
Step 5.7 calculates new microseism data noise event automatic identification accuracy G according to formula (6)
If new microseism data noise event automatic identification accuracy G is more than noise event recognition correct rate threshold value Rg, then really
Recognize judgment threshold Rj, otherwise carry out step 5.8；Wherein, noise event recognition correct rate threshold value RgValue range be 0~50%；
Step 5.8, the rock burst signal Y newly to obtainsReplace the rock burst letter in rock burst signal threshold value dynamic adjusting data library
Number Xs, s values add 1, wherein s initial values are 1, s ∈ { 1,2 ..., (q+1) }, judge whether s is more than q, if s is more than q, then it represents that threshold value
Dynamic adjustment failure, is set as initial value 1, and return to step 5.1 after setting time by s；Otherwise return to step 5.2.
The RK functions of typical case's rock burst signal as described above and the RK functions of typical noise signal are based on formula (1),
RK (t)=R (t) formula (1)
R function in formula (1) is based on formula (2),
Wherein, STA (t) is short time-window STA functions, and window LTA values when LTA (t) is long, t is the moment, and n is that STA short time-windows are long
Degree, m are LTA long time window lengths,
CF functions in formula (2) are based on formula (3),
CF (t)=Y (t)2- Y (t-1) Y (t+1) formula (3)
Wherein, Y (t) is magnitude function.
The present invention compared with the existing technology, has the advantages that：For rock burst signal characteristic, algorithm practicality item is taken into account
Part, provide a kind of dynamic adjustment of rock burst signal threshold value it is more when window reduced form recognizer, it is real-time to be suitable for microseism data
Processing, meets actual demands of engineering, improves waveform and picks up efficiency, reduces manual identified workload, so improve rock burst, mine shake,
The real-time of the geo-hazard early-warnings such as landslide.
Description of the drawings
Fig. 1 is using RK functional values as ordinate, and the time (being expressed with sampling number) is abscissa, the curve graph of RK functions；
Fig. 2 is reference point r in embodiment1With reference point r2Schematic diagram；Wherein, (a) is reference point r1Selection；(b) it is
Reference point r2Selection；
Fig. 3 is recognizer proof diagram；Wherein, (a) is RK (2040) value density function regularity of distribution；(b) it is RK
(2130) the value density function regularity of distribution.
Specific implementation mode
Technical scheme of the present invention is further described below in conjunction with attached drawing：
In order to make technological means, creation characteristic, workflow, application method reached purpose and effect of the present invention be easy to bright
It is white to understand with reference to specific embodiments the present invention is further explained.Protection scope of the present invention is not limited by following instance
System.
It is domestic that Hongtoushan Copper Mine is located at Manchu Autonomous County of Qingyuan of Fushun City of Liaoning Province, is the typical deep metal mine in China
One of.The power types such as the polytopic rock burst of copper mine deep-seated setting, stress type landslide destroy, and have become and restrict mine peace
The matter of utmost importance that standard-sized sheet is adopted.It is found during live micro seismic monitoring, since stope and tunnel mostly use electrical equipment, microseism data
In there are a large amount of noise signal, including electric signal, secondary blasting signal, locomotive hit rail signal, jumbolter signal
Etc..This example is illustrated by taking Hongtoushan Copper Mine deep-seated setting microseism data as an example.
A kind of dynamic adjustment of rock burst signal threshold value it is more when window reduced form recognizer, include the following steps：
Step 1 selects typical rock burst signal and typical noise signal.
Rock burst signal refers generally to the elastic wave signal that Progressive failure generates inside rock mass before rock burst occurs, and noise signal is general
Include blast signal, jumbolter signal, electric signal, field operation signal etc..All types of rock burst signal/noise signals
Amplitude of wave form and Frequency scaling algorithm it is more similar, the signal that waveform can represent certain a kind of rock burst signal/noise signal is typical
Signal.
In this example, the data decimation period monitored in real time from Hongtoushan Copper Mine deep-seated setting is that on June 1st, 2016 is micro-
Shake data.Typical rock burst signal, typical short arc electric signal, typical high amplitude electric signal, allusion quotation are picked out from the data
Short electric signal, typical long lasting electric signal, typical secondary blasting signal, the typical locomotive of continuing of type hits rail signal, allusion quotation
Type jumbolter signal.
The RK functions of step 2, the RK functions for seeking typical rock burst signal and typical noise signal.
The RK functions of typical rock burst signal and the RK functions of typical noise signal are based on formula (1)
RK (t)=R (t) formula (1)
Wherein, R function is the STA/LTA functions in the included triggering algorithm of Microseismic monitoring system, common calculation base
In formula (2), but not limited to this formula
Wherein, STA (t) is short time-window STA functions, and window LTA values when LTA (t) is long, t is the moment, and n is that STA short time-windows are long
Degree, m are LTA long time window lengths,
CF functions in formula (2) are the characteristic function in the included triggering algorithm of Microseismic monitoring system, common calculating side
Formula is based on formula (3), but not limited to this formula
CF (t)=Y (t)2- Y (t-1) Y (t+1) formula (3)
Wherein, Y (t) is magnitude function.
Step 3, using the time as abscissa, RK functional values be ordinate,
It determines the number a of reference point, chooses reference point rj, reference point rjAbscissa be delay position t0+dj, ordinate
For judgment threshold Rj, judgment threshold RjPositioned at delay position t0+djThe RK functional values and delay position t of the typical rock burst signal at place0+
djBetween the RK functional values of the typical noise signal at place, delay position t0+djThe RK functional values of the typical rock burst signal at place and delay
Position t0+djThe RK functional values of the typical noise signal at place are unequal；Wherein, { 1,2 } a ∈, j are the serial number of reference point, t0It is tactile
Send out moment, djTo postpone length.
In the present invention, reference point rjCan be 1 or 2, i.e. reference point rjCan be reference point r1, can also be
Reference point r1With reference point r2；Judgment threshold RjCorrespond to 1 or 2, you can with for judgment threshold R1, can be to judge threshold
Value R1With judgment threshold R2。
In this example, triggering moment t in the included triggering algorithm of Microseismic monitoring system0It is for 2000, STA short time-window length
20, LTA long time window length are that 200, CF functions are based on formula (3), and R function is based on formula (2).As shown in Fig. 2, according to typical rock
Quick-fried signal and typical noise signal difference situation determine that the number a of reference point is 2, reference point r1Coordinate be (2040,2.5),
Reference point r2Coordinate be (2130,1), that is, determine delay position t0+d1It is 2040, judgment threshold R1It is 2.5, delay position t0+
d2It is 2130, judgment threshold R2It is 1.
The RK functional values of microseism data to be identified are identified in step 4, include the following steps：
Step 4.1 reads the microseism data to be identified generated in real time.
Step 4.2, the RK functional values for calculating microseism data to be identified in real time, the RK functional values first of microseism data to be identified
It is secondary to be more than activation threshold value RqIt is triggering moment t at the time of corresponding0, activation threshold value RqFor established values, Microseismic monitoring system may be used
The parameter of included triggering algorithm.
Step 4.3, computing relay position t0+djThe RK functional values of microseism data to be identified,
In delay position t0+djThe RK functional values of the typical rock burst signal at place are more than the RK functional values of typical noise signal
In the case of：
If delay position t0+djThe RK functional values of the microseism data to be identified at place are more than judgment threshold Rj, then this reading is identified
The microseism data to be identified taken is rock burst signal undetermined；Otherwise, this microseism data to be identified read is noise signal；
In delay position t0+djThe RK functional values of the typical rock burst signal at place are less than the RK functional values of typical noise signal
In the case of：
If delay position t0+djThe RK functional values of the microseism data to be identified at place are less than judgment threshold Rj, then this reading is identified
The microseism data to be identified taken is rock burst signal undetermined；Otherwise, this microseism data to be identified read is noise signal；
If all delay position t0+djThe recognition result at place is rock burst signal undetermined, then this microseism to be identified read
Data are rock burst signal.
In the present invention, reference point rjCan be 1 or 2, corresponding delay position t0+djIt can be delay position
t0+d1, or delay position t0+d1With delay position t0+d2.In the case of there are two reference point, it is necessary to two ginsengs
Identification at the corresponding delay position of examination point is rock burst signal undetermined, just can determine that the microseism data to be identified that this reads is
Rock burst signal.
In this example, Rule of judgment is RK (2040)>2.5 and RK (2130)<1, activation threshold value RqIt is 6.
Judgment threshold R as described abovejRealize that dynamic adjusts by following steps：
Step 5.1 establishes rock burst signal threshold value dynamic adjusting data library, and rock burst signal threshold value dynamic adjusting data is pressed in library
According to the morning and evening sequential storage rock burst signal of time of origin, each rock burst signal is denoted as in rock burst signal threshold value dynamic adjusting data library
X1, X2..., Xp, wherein p is the maximum number of rock burst signal in rock burst signal threshold value dynamic adjusting data library, and time of origin is the latest
Rock burst signal be Xp。
Under normal circumstances, the maximum number p of rock burst signal takes 400 in rock burst signal threshold value dynamic adjusting data library；
If field geology conditions are changeable, such as deep-lying tunnel driving or large-sized workshop excavation engineering can reduce rock burst signal threshold
It is worth the maximum number p of rock burst signal in dynamic adjusting data library, threshold value dynamic adjustment is made to accelerate；
If field geology conditions variation is smaller, such as fixed point long term monitoring engineering can increase rock burst signal threshold value dynamic and adjust
The maximum number p of rock burst signal in entire data library makes threshold value dynamic adjustment slow down.
In this example, the maximum number p of rock burst signal takes 400 in rock burst signal threshold value dynamic adjusting data library.
Step 5.2, by each rock burst signal in rock burst signal threshold value dynamic adjusting data library in delay position t0+djRK
The array that functional value is arranged to make up is denoted as RKj(k), wherein k is the serial number of rock burst signal, and j is the serial number of reference point；
In delay position t0+djThe RK functional values of the typical rock burst signal at place are more than the RK functional values of typical noise signal
In the case of：Each rock burst signal is in delay position t in rock burst signal threshold value dynamic adjusting data library0+djRK functional values from greatly to
It is small to be arranged；
In delay position t0+djThe RK functional values of the typical rock burst signal at place are less than the RK functional values of typical noise signal
In the case of：Each rock burst signal is in delay position t in rock burst signal threshold value dynamic adjusting data library0+djRK functional values from it is small to
It is arranged greatly.
In this example, each rock burst signal is in delay position t in rock burst signal threshold value dynamic adjusting data library0+d1RK letters
Numerical value RK (2040) arranges (i.e. array RK from big to small1(k) arrange from big to small), rock burst signal threshold value dynamic adjusting data
Each rock burst signal is in delay position t in library0+d2RK functional values RK (2130) arrange (i.e. array RK from small to large2(k) from
It is small to arrive longer spread).
Step 5.3 determines selection rate Bj, choose and meet the minimum value of following formula for selection rate Bj
Wherein, BzFor always selection rate, BzValue range be 50%~100%.
In this example, total selection rate Bz95% is taken, selection rate B can be obtained1And B2It is 97.5%.
Step 5.4 obtains kjValue, kj=int (p × Bj), int is rounding operation；
If kj﹤ p, then judgment threshold RjFor RKj-(kj) and RKj-(kj+ 1) average value；
If kj=p, then judgment threshold RjFor RKj-(kj)。
In this example, k1Value and k2Value is 390.
Step 5.5 obtains new microseism data, the rock burst event number a in new microseism data1With noise event number b1It is
Know parameter,
According to judgment threshold RjUsing step 1~4, to new microseism data carry out automatic identification processing obtain rock burst signal and
Noise signal obtains corresponding rock burst event number a2With noise event number b2；By the rock burst signal newly obtained according to time of origin into
Row arrangement, and it is denoted as Y1, Y2..., Yq, q is the maximum number of the rock burst signal newly obtained, q≤p, the rock burst of time of origin the latest
Signal is Yq。
It is generally believed that the number of rock burst signal/noise signal of generation in setting time can be identified as more than or equal to 4
One rock burst event/noise signal, can't be all signals of event will all filter out.Wherein, above-mentioned setting time is generally advised
It is set to 0.5s.
Step 5.6 calculates new microseism data rock burst event automatic identification accuracy E according to formula (5)
If new microseism data rock burst event automatic identification accuracy E is more than preset rock burst event automatic identification accuracy threshold
Value Rb, then step 5.7 is carried out, step 5.8 is otherwise carried out；Wherein, rock burst event automatic identification accuracy threshold value RbValue range
It is 50%~100%.
In this example, rock burst event automatic identification accuracy threshold value RbTake 80%.
Step 5.7 calculates new microseism data noise event automatic identification accuracy G according to formula (6)
If new microseism data noise event automatic identification accuracy G is more than noise event recognition correct rate threshold value Rg, then really
Recognize judgment threshold Rj, otherwise carry out step 5.8；Wherein, noise event recognition correct rate threshold value RgValue range be 0~50%.
In this example, noise event recognition correct rate threshold value RgTake 30%.
Step 5.8, the rock burst signal Y newly to obtainsReplace the rock burst letter in rock burst signal threshold value dynamic adjusting data library
Number Xs, s values add 1, wherein s initial values are 1, s ∈ { 1,2 ..., (q+1) }, judge whether s is more than q, if s is more than q, then it represents that threshold value
Dynamic adjustment failure, is set as initial value 1, and return to step 5.1 after setting time by s；Otherwise return to step 5.2.
In this example, after threshold value dynamic adjusts, judgment threshold R1It is 1.306, judgment threshold R2It is 1.367.
In this example, access time section is 5109 microseism datas on June 12,2 days to 2016 June in 2016, wherein
Including 1602 rock burst signals, other are noise signal, including 440 short arc electric signals, 419 high amplitudes are electrically believed
Number, it is 560 short continue electric signal, it is 440 long continue electric signal, 822 locomotives hit rail signals, 487 it is secondary quick-fried
Broken signal, 339 jumbolter signals, make respectively it is all types of in all signals RK (2040) values and RK (2130) value, such as
Shown in Fig. 3.As seen from Figure 3, all rock burst signals and all short arc electric signals, all high amplitude electric signals, institute
RK (2040) the value Density Distribution notable difference for continuing electric signal with the presence of short lasting electric signal, all length, is concentrated respectively
2~5,1~2,1~2,1~3,1~3；All rock burst signals continue electric signal with all length, all locomotives hit rail
Signal, all secondary blasting signals, all jumbolter signals RK (2130) value Density Distribution there are notable differences, collect respectively
In 0~1,0~3,1~3,0~2,1~3.RK (2040) values and RK (2130) values and Rule of judgment RK (2040)>1.306、
RK(2130)<1.367 are judged, most rock burst signals can be automatically identified from microseism data, and filter out big portion
Divide noise signal.
It can be analyzed by Fig. 3, due to initial judgment threshold R1With initial judgment threshold R2Very rely on the typical case of signal
Situation, and recognition effect cannot be assessed, therefore the dynamic adjustment of judgment threshold has in terms of the determination of judgment threshold
There is greater advantage.Dynamic judgment threshold has omission factor that is lower, can assessing and control compared with the judgment threshold of definite value,
Therefore it can ensure higher recognition efficiency.
Judgement relationship RK (2040) is corresponded to by RK (2040) value>1.306 and RK (2040) value corresponds to judgement relationship RK
(2130)<1.367, it is measured data progress automatic identification processing 13~19 June in 2016 to the period.In measured data
Have determined that include 212 rock burst events and 5191 noise events, recognizer automatic identification result be 202 rock burst events and
4222 noise events.Rock burst event automatic identification accuracy E reaches 95.28%, and noise event automatic identification accuracy G reaches
81.33%, reduce the workload of 78.32% manual identified.
Specific embodiment described herein is only an example for the spirit of the invention.Technology belonging to the present invention is led
The technical staff in domain can make various modifications or additions to the described embodiments or replace by a similar method
In generation, however, it does not deviate from the spirit of the invention or beyond the scope of the appended claims.
Claims (3)
1. a kind of dynamic adjustment of rock burst signal threshold value it is more when window reduced form recognizer, which is characterized in that including following step
Suddenly：
Step 1 selects typical rock burst signal and typical noise signal；
The RK functions of step 2, the RK functions for seeking typical rock burst signal and typical noise signal；
Step 3, using the time as abscissa, RK functional values be ordinate,
It determines the number a of reference point, chooses reference point rj, reference point rjAbscissa be delay position t0+dj, ordinate is to judge
Threshold value Rj, judgment threshold RjPositioned at delay position t0+djThe RK functional values and delay position t of the typical rock burst signal at place0+djPlace
Between the RK functional values of typical noise signal, delay position t0+djThe RK functional values and delay position t of the typical rock burst signal at place0
+djThe RK functional values of the typical noise signal at place are unequal；Wherein, { 1,2 } a ∈, j are the serial number of reference point, t0When to trigger
It carves, djTo postpone length；
The RK functional values of microseism data to be identified are identified in step 4, include the following steps：
Step 4.1 reads the microseism data to be identified generated in real time；
The RK functional values of step 4.2, the RK functional values for calculating microseism data to be identified in real time, microseism data to be identified are big for the first time
In activation threshold value RqIt is triggering moment t at the time of corresponding0, activation threshold value RqFor preset value；
Step 4.3, computing relay position t0+djThe RK functional values of microseism data to be identified,
In delay position t0+djThe RK functional values of the typical rock burst signal at place are more than the case where RK functional values of typical noise signal
Under：
If delay position t0+djThe RK functional values of the microseism data to be identified at place are more than judgment threshold Rj, then identify what this read
Microseism data to be identified is rock burst signal undetermined；Otherwise, this microseism data to be identified read is noise signal；
In delay position t0+djThe RK functional values of the typical rock burst signal at place are less than the case where RK functional values of typical noise signal
Under：
If delay position t0+djThe RK functional values of the microseism data to be identified at place are less than judgment threshold Rj, then identify what this read
Microseism data to be identified is rock burst signal undetermined；Otherwise, this microseism data to be identified read is noise signal；
If all delay position t0+djThe recognition result at place is rock burst signal undetermined, then this microseism data to be identified read
For rock burst signal.
2. a kind of rock burst signal threshold value dynamic adjustment according to claim 1 it is more when window reduced form recognizer,
It is characterized in that, the judgment threshold RjRealize that dynamic adjusts by following steps：
Step 5.1 establishes rock burst signal threshold value dynamic adjusting data library, according to hair in rock burst signal threshold value dynamic adjusting data library
The morning and evening sequential storage rock burst signal of time is given birth to, each rock burst signal is denoted as X in rock burst signal threshold value dynamic adjusting data library1,
X2..., Xp, wherein p is the maximum number of rock burst signal in rock burst signal threshold value dynamic adjusting data library, and time of origin is the latest
Rock burst signal is Xp；
Step 5.2, by each rock burst signal in rock burst signal threshold value dynamic adjusting data library in delay position t0+djRK functions
The array that value is arranged to make up is denoted as RKj(k), wherein k is the serial number of rock burst signal, and j is the serial number of reference point；
In delay position t0+djThe RK functional values of the typical rock burst signal at place are more than the case where RK functional values of typical noise signal
Under：Each rock burst signal is in delay position t in rock burst signal threshold value dynamic adjusting data library0+djRK functional values from big to small into
Row arrangement；
In delay position t0+djThe RK functional values of the typical rock burst signal at place are less than the case where RK functional values of typical noise signal
Under：Each rock burst signal is in delay position t in rock burst signal threshold value dynamic adjusting data library0+djRK functional values from small to large into
Row arrangement；
Step 5.3 determines selection rate Bj, choose and meet the minimum value of following formula for selection rate Bj
Wherein, BzFor always selection rate, BzValue range be 50%~100%；
Step 5.4 obtains kjValue, kj=int (p × Bj), int is rounding operation；
If kj﹤ p, then judgment threshold RjFor RKj-(kj) and RKj-(kj+ 1) average value；
If kj=p, then judgment threshold RjFor RKj-(kj)；
Step 5.5 obtains new microseism data, the rock burst event number a in new microseism data1With noise event number b1It is known ginseng
Number,
According to judgment threshold RjUsing step 1~4, automatic identification processing is carried out to new microseism data and obtains rock burst signal and noise
Signal obtains corresponding rock burst event number a2With noise event number b2；The rock burst signal newly obtained is arranged according to time of origin
Row, and it is denoted as Y1, Y2..., Yq, q is the maximum number of the rock burst signal newly obtained, q≤p, the rock burst signal of time of origin the latest
It is Yq；
Step 5.6 calculates new microseism data rock burst event automatic identification accuracy E according to formula (5)
If new microseism data rock burst event automatic identification accuracy E is more than preset rock burst event automatic identification accuracy threshold value Rb,
Step 5.7 is then carried out, step 5.8 is otherwise carried out；Wherein, rock burst event automatic identification accuracy threshold value RbValue range be
50%~100%；
Step 5.7 calculates new microseism data noise event automatic identification accuracy G according to formula (6)
If new microseism data noise event automatic identification accuracy G is more than noise event recognition correct rate threshold value Rg, then confirm judgement
Threshold value Rj, otherwise carry out step 5.8；Wherein, noise event recognition correct rate threshold value RgValue range be 0~50%；
Step 5.8, the rock burst signal Y newly to obtainsReplace the rock burst signal X in rock burst signal threshold value dynamic adjusting data librarys, s
Value plus 1, wherein s initial values are 1, s ∈ { 1,2 ..., (q+1) }, judge whether s is more than q, if s is more than q, then it represents that threshold value dynamic is adjusted
S is set as initial value 1, and return to step 5.1 after setting time by whole failure；Otherwise return to step 5.2.
3. a kind of rock burst signal threshold value dynamic adjustment according to claim 1 it is more when window reduced form recognizer,
It being characterized in that, the RK functions of the typical rock burst signal and the RK functions of typical noise signal are based on formula (1),
RK (t)=R (t) formula (1)
R function in formula (1) is based on formula (2),
Wherein, STA (t) is short time-window STA functions, and window LTA values when LTA (t) is long, t is the moment, and n is STA short time-window length, m
For LTA long time window lengths,
CF functions in formula (2) are based on formula (3),
CF (t)=Y (t)2- Y (t-1) Y (t+1) formula (3)
Wherein, Y (t) is magnitude function.
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