CN108254781B - A kind of dynamic adjustment of rock burst signal threshold value it is more when window complete form recognition methods - Google Patents
A kind of dynamic adjustment of rock burst signal threshold value it is more when window complete form recognition methods Download PDFInfo
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Abstract
The invention discloses a kind of dynamic adjustment of rock burst signal threshold value it is more when window complete form recognizer, comprising the following steps: obtain all kinds of typical rock burst signals and typical noise signal, establish rock burst signal threshold value dynamic adjusting data library；Determine call parameter and judgment threshold；Automatic identification processing；Judgment threshold dynamic is periodically carried out to adjust.The present invention is post-processed 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 timeliness 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 dynamic adjustment of rock burst signal threshold value it is more when window it is complete
Shaping type recognition methods, this method can be widely used for mineral engineering, hydraulic and hydroelectric engineering, petroleum works, geotechnical engineering and ground
Lower engineering.
Background technique
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 algorithm 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 method, Fast Fourier Transform (FFT), Maximum likelihood classification, logistic regression and mind
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, is difficult through software Automatic analysis.
By practical engineering application, discovery algorithm in automatic identification rock burst signal needs to consider following three points: 1) in real time
Whether calculation amount can satisfy 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 complete form recognizer is suitable for microseism data and post-processes, meets actual demands of engineering, improves waveform and picks up efficiency, subtracts
Few manual identified workload, and then enhance the timeliness of the geo-hazard early-warnings such as rock burst, mine shake, landslide.
Summary of the invention
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 complete form recognition methods, be suitable for microseism data and post-process, meet engineering reality
It needs, improves waveform and pick up efficiency, reduce manual identified workload, and then enhance the geo-hazard early-warnings such as rock burst, mine shake, landslide
Timeliness.
A kind of dynamic adjustment of rock burst signal threshold value it is more when window complete form recognition methods, comprising 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
It 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, signal that waveform can represent certain a kind of rock burst signal/noise signal is typical
Signal.
The RK function of step 2, the RK function for seeking typical rock burst signal and typical noise signal；
The RK function of typical rock burst signal and the RK function of typical noise signal are based on formula (1)
Wherein, window YTA function when YTA (t) is delay, LTA0(t0) window LTA when being background0Value, t are the moment, and n is that STA is short
Time window length, m are LTA long time window length, qjTo postpone time window length, delay time window length is qjGenerally take STA short time-window length
N, CF function are characterized function；
Step 3, using the time as abscissa, RK functional value 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 value and delay position t of the typical rock burst signal at place0+
djBetween the RK functional value of the typical noise signal at place, delay position t0+djThe RK functional value of the typical rock burst signal at place and delay
Position t0+djThe RK functional value of the typical noise signal at place is unequal；Wherein, j=1,2 ..., a, t0For triggering moment, djTo prolong
Slow length, window number when the number a of reference point is delay.
Step 4 identifies the RK functional value of microseism data to be identified, comprising the following steps:
The microseism data to be identified and corresponding triggering moment t that step 4.1, reading have generated0, triggering moment t0By microseism
The included triggering algorithm of monitoring system determines；
Step 4.2, the triggering moment t that the waveform to be identified in step 4.1 is calculated based on formula (3)0Each delay when
The RK of window YTAj(t0) value；
Wherein, YTAj(t0) be j-th of delay when window YTA function；
Step 4.3, in delay position t0+djThe RK functional value of the typical rock burst signal at place is greater than the RK of typical noise signal
In the case where functional value:
If RKj(t0) value be greater than judgment threshold Rj, then identify the microseism data to be identified that this reads for rock burst undetermined letter
Number；Otherwise, this microseism data to be identified read is noise signal；
In delay position t0+djThe RK functional value of the typical rock burst signal at place is less than the RK functional value of typical noise signal
In the case of:
If RKj(t0) value be less than judgment threshold Rj, then identify the microseism data to be identified that this reads for rock burst undetermined letter
Number；Otherwise, this microseism data to be identified read is noise signal；
If the RK of window YTA when each delayj(t0) value identify waveform to be identified be rock burst signal undetermined, then this read
Microseism data to be identified be rock burst signal.
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, accelerates threshold value dynamic adjustment；
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 slows down threshold value dynamic adjustment.
Step 5.2, by the triggering moment t of rock burst signal in rock burst signal threshold value dynamic adjusting data library0Each delay
When window YTA RKj(t0) array that is arranged to make up of value is denoted as RKj(k), wherein k be rock burst signal serial number, j=1,
2 ..., a；Window number when the number a of reference point is delay；
In delay position t0+djThe RK functional value of the typical rock burst signal at place is greater than the RK functional value of typical noise signal
In the case of: array RKj(k) it is arranged from big to small；
In delay position t0+djThe RK functional value of the typical rock burst signal at place is less than the RK functional value of typical noise signal
In the case of: array RKj(k) it is arranged from small to large；
Step 5.3 determines selection rate Bj, the minimum value that selection meets formula (4) is 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, rock burst event number a in new microseism data1With noise event number b1It is known
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 can be identified as it is generally believed that rock burst signal/noise signal number of the generation in setting time is greater than or equal to 4
One rock burst event/noise event, all signals that can't be 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 greater 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 greater 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 value adds 1, wherein s initial value is 1, s ∈ { 1,2 ..., (q+1) }, judges whether s is greater than q, if s is greater than q, then it represents that threshold value
Dynamic adjustment failure, is set as initial value 1, and return step 5.1 after setting time for s；Otherwise return step 5.2.
CF function in the formula (1) and formula (3) is based on formula (2)
CF (t)=Y (t)2- Y (t-1) Y (t+1) formula (2)
Wherein, Y (t) is magnitude function.
The present invention compared with the existing technology, has the advantages that for rock burst signal characteristic, takes into account the practical item of algorithm
Part, provide a kind of dynamic adjustment of rock burst signal threshold value it is more when window complete form recognition methods, after microseism data
Reason meets actual demands of engineering, improves waveform and picks up efficiency, reduces manual identified workload, and then enhances rock burst, mine shake, collapses
The timeliness of the geo-hazard early-warnings such as side.
Detailed description of the invention
Fig. 1 is using RK functional value as ordinate, and the time (being expressed with sampling number) is abscissa, the curve graph of RK function；
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 RK1(2000) the value density function regularity of distribution；It (b) is RK2
(2000) the value density function regularity of distribution.
Specific embodiment
Technical solution of the present invention is further described below in conjunction with attached drawing:
In order to make, technological means of the invention, creation characteristic, workflow, application method reach purpose and effect is easy to bright
White understanding with reference to specific embodiments the present invention is further explained.Protection scope of the present invention is not by the limit of following instance
System.
It is domestic that Hongtoushan Copper Mine is located at Manchu Autonomous County of Qingyuan, Fushun City, 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 complete form recognition methods, comprising 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
It 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, signal that waveform can represent certain a kind of rock burst signal/noise signal is typical
Signal.
It is that on June 1st, 2016 is micro- from the data decimation period of Hongtoushan Copper Mine deep-seated setting real-time monitoring in this example
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 function of step 2, the RK function for seeking typical rock burst signal and typical noise signal.
The RK function of typical rock burst signal and the RK function of typical noise signal are based on formula (1)
Wherein, window YTA function when YTA (t) is delay, LTA0(t) window LTA when being background0Value, t are the moment, and n is that STA is short
Time window length, m are LTA long time window length, qjTo postpone time window length, delay time window length is qjGenerally take STA short time-window length
N,
CF function in formula (1) can be the characteristic function in the included triggering algorithm of Microseismic monitoring system, and CF function is normal
Calculation method is based on formula (2), but is not limited to formula (2):
CF (t)=Y (t)2- Y (t-1) Y (t+1) formula (2)
Wherein, Y (t) is magnitude function.
Step 3, using the time as abscissa, RK functional value 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 RjThe delay position t being located at0+djThe RK functional value of the typical rock burst signal at place and delay position
t0+djBetween the RK functional value of the typical noise signal at place, delay position t0+djThe RK functional value of the typical rock burst signal at place and
Delay position t0+djThe RK functional value of the typical noise signal at place is unequal；Wherein, j=1,2 ..., a, t0For triggering moment, dj
To postpone length；Window number when the number a of reference point is delay.
In this example, triggering moment t in the included triggering algorithm of Microseismic monitoring system0(sampling number table is used for 2000
Up to), STA short time-window length is 20, and LTA long time window length is that 200, CF function is based on formula (3), and R function is based on formula (2).
As shown in Fig. 2, determining that the number a of reference point is 2, reference point according to typical rock burst signal and typical noise signal difference situation
r1Coordinate be (2035,2), reference point r2Coordinate be (2250,10), that is, determine delay length d1It is 35, judgment threshold R1For
2, postpone length d2It is 250, judgment threshold R2It is 10.
Step 4 identifies the RK functional value of microseism data to be identified, comprising the following steps:
The microseism data to be identified and corresponding triggering moment t that step 4.1, reading have generated0, triggering moment t0By microseism
The included triggering algorithm of monitoring system determines.
Step 4.2, the triggering moment t that the waveform to be identified in step 4.1 is calculated based on formula (3)0Each delay when
The RK of window YTAj(t0) value.
Wherein, YTAj(t0) window YTA function when being j-th of delay, the CF function in formula (3) be Microseismic monitoring system oneself
With the characteristic function in triggering algorithm, common calculation method is based on formula (2), but is not limited to formula (2).
Step 4.3, in delay position t0+djThe RK functional value of the typical rock burst signal at place is greater than the RK of typical noise signal
In the case where functional value:
If RKj(t0) value be greater than judgment threshold Rj, then identify the microseism data to be identified that this reads for rock burst undetermined letter
Number；Otherwise, this microseism data to be identified read is noise signal；
In delay position t0+djThe RK functional value of the typical rock burst signal at place is less than the RK functional value of typical noise signal
In the case of:
If RKj(t0) value be less than judgment threshold Rj, then identify the microseism data to be identified that this reads for rock burst undetermined letter
Number；Otherwise, this microseism data to be identified read is noise signal；
If the RK of window YTA when each delayj(t0) value identify waveform to be identified be rock burst signal undetermined, then this read
Microseism data to be identified be rock burst signal.
In this example, judgment threshold R1It is 2, judgment threshold R2It is 10.
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, accelerates threshold value dynamic adjustment；
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 slows down threshold value dynamic adjustment.
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 the triggering moment t of rock burst signal in rock burst signal threshold value dynamic adjusting data library0Each delay
When window YTA RKj(t0) array that is arranged to make up of value is denoted as RKj(k), wherein k be rock burst signal serial number, j=1,
2 ..., a；Window number when the number a of reference point is delay；
In delay position t0+djThe RK functional value of the typical rock burst signal at place is greater than the RK functional value of typical noise signal
In the case of: array RKj(k) it is arranged from big to small；
In delay position t0+djThe RK functional value of the typical rock burst signal at place is less than the RK functional value of typical noise signal
In the case of: array RKj(k) it is arranged from small to large.
In this example, the triggering moment t of rock burst signal in rock burst signal threshold value dynamic adjusting data library0Each delay when
The RK of window YTA1(2000) (i.e. RK is arranged from big to small1(k) arrange from big to small), rock burst signal threshold value dynamic adjusting data library
The triggering moment t of interior rock burst signal0Each delay when window YTA RK2(2000) (i.e. RK is arranged from small to large2(k) from it is small to
Play arrangement).
Step 5.3 determines selection rate Bj, the minimum value that selection meets following formula is 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, rock burst event number a in new microseism data1With noise event number b1It is known
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 can be identified as it is generally believed that rock burst signal/noise signal number of the generation in setting time is greater than or equal to 4
One rock burst event/noise event, all signals that can't be 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 greater than preset rock burst event automatic identification accuracy threshold
Value Rb, then step 5.7 is carried out, otherwise enters step 5.8；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 greater 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 value adds 1, wherein s initial value is 1, s ∈ { 1,2 ..., (q+1) }, judges whether s is greater than q, if s is greater than q, then it represents that threshold value
Dynamic adjustment failure, is set as initial value 1, and return step 5.1 after setting time for s；Otherwise return step 5.2.
In this example, after threshold value dynamic adjusts, judgment threshold R1It is 1.362, judgment threshold R2It is 14.205.
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 the RK of all signals in all types of respectively1(2000) value (i.e. array RK1-(k))
And RK2(2000) value (i.e. array RK2(k)), as shown in Figure 3.As seen from Figure 3, all high amplitude electric signals are not concentrated,
All rock burst signals and all short arc electric signals, all short lasting electric signals, all length continue the RK of electric signal1
(2000) there are notable differences for value Density Distribution, concentrate on 1~3 magnitude, -1~1 magnitude, -1~2 magnitudes, -1~2 amounts respectively
Grade；All rock burst signals and all length continue electric signal, all locomotives hit rail signal, all secondary blasting signals, institute
There is the RK of jumbolter signal2(2000) there are notable differences for value Density Distribution, concentrate on respectively -0.5~1 magnitude, -0.5~
1.5 magnitudes, 1~4 magnitude, 1~5 magnitude, 0~3 magnitude.RK1(2000) value and RK2(2000) value and Rule of judgment RK1(2000)
>1.362、RK2(2000) < 14.205 judged, most rock burst signals can be automatically identified from microseism data, and
Filter out most of noise signal.
It can analyze 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 guarantee higher recognition efficiency.
Pass through RK1(2000) value corresponds to judgement relationship RK1And RK (2000) > 1.3622(2000) value corresponds to judgement relationship RK2
It (2000) < 14.205 is, that measured data in 13~19 June in 2016 carries out automatic identification processing to the period.Measured data
In have determined that comprising 212 rock burst events and 5191 noise events, recognizer automatic identification result is 201 rock burst events
With 4270 noise events.Rock burst event automatic identification accuracy E reaches 94.81%, and noise event automatic identification accuracy G reaches
To 82.26%, reduce the workload of 79.23% manual identified.
Specific embodiment described herein is only an example for the spirit of the invention.The neck of technology belonging to the present invention
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 (2)
1. a kind of dynamic adjustment of rock burst signal threshold value it is more when window complete form recognition methods, which is characterized in that including following step
It is rapid:
Step 1 selects typical rock burst signal and typical noise signal；
The RK function of step 2, the RK function for seeking typical rock burst signal and typical noise signal；
The RK function of typical rock burst signal and the RK function of typical noise signal are based on formula (1)；
Wherein, window YTA function when YTA (t) is delay, LTA0(t0) window LTA when being background0Value, t are the moment, and n is STA short time-window
Length, m are LTA long time window length, qjTo postpone time window length, delay time window length is qjTake STA short time-window length n, CF function
It is characterized function；
Step 3, using the time as abscissa, RK functional value be ordinate,
It determines the number a of reference point, chooses reference point rj, reference point rjAbscissa be delay position t0+dj, ordinate is judgement
Threshold value Rj, judgment threshold RjPositioned at delay position t0+djThe RK functional value and delay position t of the typical rock burst signal at place0+djPlace
Between the RK functional value of typical noise signal, delay position t0+djThe RK functional value and delay position t of the typical rock burst signal at place0
+djThe RK functional value of the typical noise signal at place is unequal；Wherein, j=1,2 ..., a, t0For triggering moment, djFor delay length
Degree, window number when the number a of reference point is delay；
Step 4 identifies the RK functional value of microseism data to be identified, comprising the following steps:
The microseism data to be identified and corresponding triggering moment t that step 4.1, reading have generated0, triggering moment t0By micro seismic monitoring
The included triggering algorithm of system determines；
Step 4.2, the triggering moment t that the waveform to be identified in step 4.1 is calculated based on formula (3)0Each delay when window YTA
RKj(t0) value；
Wherein, YTAj(t0) be j-th of delay when window YTA function；
Step 4.3, in delay position t0+djThe RK functional value of the typical rock burst signal at place is greater than the RK function of typical noise signal
In the case where value:
If RKj(t0) value be greater than judgment threshold Rj, then identify that the microseism data to be identified that this reads is rock burst signal undetermined；It is no
Then, this microseism data to be identified read is noise signal；
In delay position t0+djThe RK functional value of the typical rock burst signal at place is less than the case where RK functional value of typical noise signal
Under:
If RKj(t0) value be less than judgment threshold Rj, then identify that the microseism data to be identified that this reads is rock burst signal undetermined；It is no
Then, this microseism data to be identified read is noise signal；
If the RK of window YTA when each delayj(t0) value identify waveform to be identified be rock burst signal undetermined, then this read to
Identification microseism data is rock burst signal；
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 the triggering moment t of rock burst signal in rock burst signal threshold value dynamic adjusting data library0Each delay when window
The RK of YTAj(t0) array that is arranged to make up of value is denoted as RKj(k), wherein k be rock burst signal serial number, j=1,2 ..., a；
Window number when the number a of reference point is delay；
In delay position t0+djThe RK functional value of the typical rock burst signal at place is greater than the case where RK functional value of typical noise signal
Under: array RKj(k) it is arranged from big to small；
In delay position t0+djThe RK functional value of the typical rock burst signal at place is less than the case where RK functional value of typical noise signal
Under: array RKj(k) it is arranged from small to large；
Step 5.3 determines selection rate Bj, the minimum value that selection meets formula (4) is 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, rock burst event number a in new microseism data1With noise event number b1It is known parameters,
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
Column, 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 greater 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 greater 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 value is 1, s ∈ { 1,2 ..., (q+1) }, judges whether s is greater than q, if s is greater than q, then it represents that threshold value dynamic is adjusted
S is set as initial value 1, and return step 5.1 after setting time by whole failure；Otherwise return step 5.2.
2. a kind of rock burst signal threshold value dynamic adjustment according to claim 1 it is more when window complete form recognition methods,
It is characterized in that, the CF function in the formula (1) and formula (3) is based on formula (2)；
CF (t)=Y (t)2- Y (t-1) Y (t+1) formula (2)
Wherein, Y (t) is magnitude function.
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