CN110109176A - Rockburst risk appraisal procedure under tunnel microseismic sensors surveillance network ill-condition - Google Patents
Rockburst risk appraisal procedure under tunnel microseismic sensors surveillance network ill-condition Download PDFInfo
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- 239000011435 rock Substances 0.000 claims abstract description 92
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- 239000011159 matrix material Substances 0.000 claims abstract description 8
- 238000012502 risk assessment Methods 0.000 abstract description 6
- 238000005516 engineering process Methods 0.000 abstract description 2
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/288—Event detection in seismic signals, e.g. microseismics
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
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Abstract
The present invention provides the rockburst risk appraisal procedure under a kind of tunnel microseismic sensors surveillance network ill-condition, is related to deep tunnel rockburst risk assessment technology field.This method is established different rock burst databases by rock burst grade first；And it regards different brackets rock burst database as different group and calculates covariance matrix and mean vector；Then the microseismic event number and amplitude for extracting and establishing in Microseismic monitoring system software in the durations such as rock burst database are greater than 10‑4m/s2Microseismic event number is as sample；Determine that sample arrives the distance between different groups using mahalanobis distance；It is then the rockburst risk of potential generation apart from the smallest group with sample.Rockburst risk appraisal procedure under microseismic sensors ill-condition in tunnel provided by the invention, it can be under microseismic sensors ill-condition, the rockburst risk of face near zone is assessed according to existing microseism information, solving lower than 4 working sensors not can be carried out the predicament of rockburst risk assessment.
Description
Technical field
The present invention relates to deep tunnel rockburst risk assessment technology fields more particularly to a kind of tunnel microseismic sensors to monitor
Rockburst risk appraisal procedure under platform net ill-condition.
Background technique
Rock burst is a kind of Power geological disaster of complexity, it often in a manner of " surprise attack ", makes underground engineering
Catastrophic failure not only seriously threatens the safety of construction personnel and equipment, influences construction speed, but also will cause underground engineering
Backbreak, preliminary bracing failure, when serious even Tectonic earthquake, be one of the major casualty of deep tunnel engineering.Studies have shown that rock
Quick-fried is the product of the asymptotic destructive process of rock mass, is released energy in the form of elastic wave in the process, and this elastic wave is referred to as
Microseism.If being analyzed in the process microseism information, handling and can carry out Pre-Evaluation to potential rockburst risk.
On Microseismic Monitoring Technique has successfully referred to tunnel rock burst hazard prevention area, a plurality of such as Jinping hydropower station
Diversion tunnel and access tunnel are medium, the good rockburst risk Evaluated effect of acquirement.It is well known that there are four unknown for microseism source data
Basic parameter, i.e. microseism origin time t and its 4 parameter of spatial location coordinate (X, Y, Z).Therefore, for same microseism
Source must have 4 working sensors just to can determine that its basic parameter, and determine microseism release energy, view body on basis herein
Other seismological parameters such as product, apparent stress, could apply microseism information to assess potential rockburst risk.Due to work in tunnel
Make that environment is poor, every construction is intricate, microseismic sensors, microseism data Acquisition Instrument and its route are often subject to destroy,
Often there is the situation that real sensor working quantity is less than 4, not can determine that microquake sources parameter, causing can not be to potential rock burst
Risk is assessed, and is brought and is seriously threatened to the construction of tunnel safety.Therefore, it is necessary to which it is few to establish a kind of real work sensor
Rockburst risk appraisal procedure when 4.
Summary of the invention
The technical problem to be solved by the present invention is in view of the above shortcomings of the prior art, provide a kind of tunnel microseismic sensors
Rockburst risk appraisal procedure under surveillance network ill-condition, to the rockburst risk under various sensor surveillance network pathologic conditions
It is assessed.
In order to solve the above technical problems, the technical solution used in the present invention is: tunnel microseismic sensors surveillance network is sick
Rockburst risk appraisal procedure under the conditions of state, comprising the following steps:
Step 1: actually occurring grade by rock burst and establish different grades of rock burst database: the rock burst sample in rock burst database
This includes two parameters, i.e., the microseismic event number before rock burst generation in certain time and amplitude are greater than 10-4m/s2Microseismic event number；
It includes five no rock burst, slight rock burst, medium rock burst, strong rock burst and strong rock burst on active grades that the rock burst, which actually occurs grade,；
The microseismic event number includes microseismic event of the trigger sensor less than 4；
Step 2: using different grades of rock burst database as different groups, calculating the covariance matrix ∑ of different groupsi、
Mean vector ui；
Using different grades of rock burst database as different groups, i.e., by group G0、G1、G2、G3、G4, respectively represent no rock
Quick-fried, slight rock burst, medium rock burst, strong rock burst, five grades of strong rock burst on active rock burst database；
The covariance matrix ∑ of Ze Ge groupi, mean vector uiShown in following formula:
Wherein, Xi、YiRespectively group GiParameter microseismic event number and amplitude are greater than 10-4m/s2Microseismic event number；niFor
Group GiSample number；xijFor parameter XiJ-th of sample values；yijFor parameter YiJ-th of sample values, i=0,
1 ..., 4, j=1,2 ..., ni；
Step 3: under sensor surveillance network ill-condition, using the included analysis software extraction of Microseismic monitoring system and step
Microseismic event number and amplitude in the durations such as 1 are greater than 10-4m/s2Microseismic event number, and it is denoted as sample X=(x0, y0)T, wherein x0
Indicate microseismic event number, y0Indicate that amplitude is greater than 10-4m/s2Microseismic event number；
Step 4: using sample X to different groups G in mahalanobis distance determination step 30、G1、G2、G3、G4The distance between；
Sample X to group GiThe following formula of mahalanobis distance shown in:
Wherein, d (X, Gi) it is sample X to group GiMahalanobis distance；
Step 5: according to step 4 acquired results, sample X to group GiThe smallest group of mahalanobis distance corresponding to rock burst
Grade is potential rockburst risk grade, shown in following formula:
D (X, Gl)=min { d (X, Gi), i=0,1 ..., 4 } (4)
That is X ∈ Gl
Wherein, l=0,1 ..., 4.
The beneficial effects of adopting the technical scheme are that microseismic sensors monitor station in tunnel provided by the invention
Rockburst risk appraisal procedure under net ill-condition, (1) are destroyed in microseismic sensors, microseism data Acquisition Instrument and its route
Afterwards, when real sensor working quantity is lower than 4, potential rockburst risk is assessed using the microseism information of existing monitoring,
Influence of the external factor to microseismic system is reduced, the continuity of micro seismic monitoring rockburst risk assessment is improved, is the peace of tunnel
Full construction and rapid construction provide guarantee.(2) selected parameter only there are two but can directly react Rock Slide Stability state rock burst
It is mahalanobis distance diagnostic method that risk, which sentences appraisal procedure, simple direct, is convenient for operation and rockburst risk False Rate is lower.With tunnel
Driving, rock burst number are continuously increased, and rock burst database is also just more and more abundant.Sample is more, more can more really react true
Rule, rockburst risk assessment also can be more and more accurate.
Detailed description of the invention
Fig. 1 is the rockburst risk assessment under microseismic sensors surveillance network ill-condition in tunnel provided in an embodiment of the present invention
The flow chart of method；
Fig. 2 is the signal provided in an embodiment of the present invention that rockburst risk under sensor pathologic condition is determined using mahalanobis distance
Figure.
Specific embodiment
With reference to the accompanying drawings and examples, specific embodiments of the present invention will be described in further detail.Implement below
Example is not intended to limit the scope of the invention for illustrating the present invention.
The present embodiment is by taking deep-lying tunnel of certain real sensor working quantity less than 4 as an example, using tunnel of the invention
Rockburst risk appraisal procedure under microseismic sensors surveillance network ill-condition, comments the rockburst risk of the deep-lying tunnel
Estimate.
Rockburst risk appraisal procedure under tunnel microseismic sensors surveillance network ill-condition, as shown in Figure 1, including following
Step:
Step 1: actually occurring grade by rock burst and establish rock burst database；Rock burst sample in rock burst database includes two
The microseismic event number and amplitude of (one day or a few hours) are greater than 10 in certain time before parameter, i.e. rock burst occur-4m/s2Microseism thing
Number of packages；It includes no rock burst, slight rock burst, medium rock burst, strong rock burst and strong rock burst on active five that the rock burst, which actually occurs grade,
Grade；The microseismic event number includes microseismic event of the trigger sensor less than 4；
In the present embodiment, which is tunneled using TBM, using three-shift system, the middle class in a kindergarten, night shift production, mornig shift maintenance.?
Rock burst hazard, existing a set of South Africa ISS Microseismic monitoring system are used to monitor, assess face country rock frequent occurrence in tunneling process
Stability state.The tunnel has amounted to since micro seismic monitoring occurs 401 rock bursts, wherein slight rock burst 383 times, medium rock burst 16
Secondary, strong rock burst 3 times, strong rock burst on active does not occur, therefore the analysis without strong rockburst risk in the present embodiment.And randomly select 20
Secondary no rock burst sample counts each rock burst by rock burst plague grade (containing no rock burst) respectively and the previous day (08:00-08:00) occurs
Microseismic event number and amplitude be greater than 10-3m/s2Microseismic event number, data are stored in spare in EXCEL table.
Step 2: using different grades of rock burst database as different groups, calculating the covariance matrix ∑ of different groupsi、
Mean vector ui；
Using different grades of rock burst database as different groups, i.e., by group G0、G1、G2、G3Respectively represent no rock burst,
Slight rock burst, medium rock burst, four grades of strong rock burst rock burst database；
The covariance matrix ∑ of Ze Ge groupi, mean vector uiShown in following formula:
Wherein, Xi、YiRespectively group GiParameter microseismic event number and amplitude are greater than 10-4m/s2Microseismic event number；niFor
Group GiSample number；xijFor parameter XiJ-th of sample values；yijFor parameter YiJ-th of sample values, i=0,
1 ..., 4, j=1,2 ..., ni；
In the present embodiment, group G0、G1、G2、G3No rock burst, slight rock burst, medium rock burst, strong rock burst are respectively represented, respectively
Population sample number is respectively n0=20, n1=383, n2=16, n3=3.
Then it is computed G0、G1、G2、G3Covariance matrix, mean vector are respectively as follows:
G0:u0=(14.5,2)T,
G1:u1=(20.2,7)T,
G2:u2=(44,16.3)T,
G3:u3=(129.1,24.6)T,
Step 3: under sensor surveillance network ill-condition, using the included analysis software extraction of Microseismic monitoring system and step
Microseismic event number and amplitude in 1 equal times are greater than 10-4m/s2Microseismic event number, and it is denoted as sample X=(x0, y0)T, wherein x0
Indicate microseismic event number, y0Indicate that amplitude is greater than 10-4m/s2Microseismic event number；
The present embodiment selects rockburst risk that the data of proxima luce (prox. luc) 08:00- same day 08:00 occur to be adapted with production
Assessment same day rockburst risk.The deep-lying tunnel caused sensor line to be hung up on August 30th, 2016 because site operation is improper, led
It causes only there are two normal operation of sensor, triggers 1 by the included analysis software acquisition of Microseismic monitoring system in 08:00-08:00
The event number and amplitude that a and 2 sensors trigger simultaneously are greater than 10-4m/s2Microseismic event number parameter.Checked micro seismic monitoring system
Unite software, Microseismic monitoring system flutter altogether catch microseismic event number 30, wherein amplitude be greater than 10-4m/s2Microseismic event number 8, i.e. sample
This X=(30,8)T。
Step 4: using sample X to different groups G in mahalanobis distance determination step 30、G1、G2、G3The distance between；
Sample X to group GiThe following formula of mahalanobis distance shown in:
Wherein, d (X, Gi) it is sample X to group GiMahalanobis distance；
The present embodiment brings step 2 calculated results into mahalanobis distance formula, then sample X to no rock burst, slight rock burst,
Medium rock burst, strong rock burst mahalanobis distance be respectively as follows:
Step 5: according to step 4 acquired results, sample X to group GiThe smallest group of mahalanobis distance corresponding to rock burst
Grade is potential rockburst risk grade, shown in following formula:
D (X, Gl)=min { d (X, Gi), i=0,1,2,3 } (4)
That is X ∈ Gl
Wherein, l=0,1,2,3.
That is group G can occur for the time where sample XlThe probability of corresponding rockburst risk grade is higher.
In the present embodiment, sample X to group GiMahalanobis distance as shown in Fig. 2, d (X, G1) numerical value minimum, determine 2016
The risk that slight rock burst occurs on the 31st for August 30th second day, that is, Augusts is higher.
It in the present embodiment, is fed back: being occurred near face in certain tunnel on the 31st of August in 2016 primary slight according to scene
Rock burst, because early warning promptly and accurately not caused by personnel and equipment loss.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used
To modify to technical solution documented by previous embodiment, or some or all of the technical features are equal
Replacement；And these are modified or replaceed, model defined by the claims in the present invention that it does not separate the essence of the corresponding technical solution
It encloses.
Claims (4)
1. the rockburst risk appraisal procedure under a kind of tunnel microseismic sensors surveillance network ill-condition, it is characterised in that: including
Following steps:
Step 1: actually occurring grade by rock burst and establish different grades of rock burst database: the rock burst sample packet in rock burst database
Two parameters are included, i.e. microseismic event number before rock burst generation in certain time and amplitude is greater than 10-4m/s2Microseismic event number；It is described
It includes five no rock burst, slight rock burst, medium rock burst, strong rock burst and strong rock burst on active grades that rock burst, which actually occurs grade,；It is described
Microseismic event number includes microseismic event of the trigger sensor less than 4；
Step 2: using different grades of rock burst database as different groups, calculate different groups covariance matrix and mean value to
Amount；
Step 3: under sensor surveillance network ill-condition, using the included analysis software extraction of Microseismic monitoring system and step 1 etc.
Microseismic event number and amplitude in duration are greater than 10-4m/s2Microseismic event number, and it is denoted as sample X=(x0, y0)T, wherein x0Table
Show microseismic event number, y0Indicate that amplitude is greater than 10-4m/s2Microseismic event number；
Step 4: using sample X to different groups G in mahalanobis distance determination step 30、G1、G2、G3、G4The distance between；
Step 5: according to step 4 acquired results, sample X to group GiThe smallest group of mahalanobis distance corresponding to rock burst grade
As potential rockburst risk grade.
2. the rockburst risk appraisal procedure under microseismic sensors surveillance network ill-condition in tunnel according to claim 1,
It is characterized by: the step 2 method particularly includes:
Using different grades of rock burst database as different groups, i.e., by group G0、G1、G2、G3、G4, respectively represent no rock burst, light
Micro- rock burst, medium rock burst, strong rock burst, five grades of strong rock burst on active rock burst database；
The covariance matrix ∑ of Ze Ge groupi, mean vector uiShown in following formula:
Wherein, Xi、YiRespectively group GiParameter microseismic event number and amplitude are greater than 10-4m/s2Microseismic event number；niFor group
GiSample number；xijFor parameter XiJ-th of sample values；yijFor parameter YiJ-th of sample values, i=0,1 ...,
4, j=1,2 ..., ni。
3. the rockburst risk appraisal procedure under microseismic sensors surveillance network ill-condition in tunnel according to claim 2,
It is characterized by: sample X described in step 4 to group GiThe following formula of mahalanobis distance shown in:
Wherein, d (X, Gi) it is sample X to group GiMahalanobis distance.
4. the rockburst risk appraisal procedure under microseismic sensors surveillance network ill-condition in tunnel according to claim 3,
It is characterized by: sample X described in step 5 to group GiThe following formula of the smallest group of mahalanobis distance shown in:
D (X, Gl)=min { d (X, Gi), i=0,1 ..., 4 } (4)
That is X ∈ Gl
Wherein, l=0,1 ..., 4.
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