CN114087021A - Rock burst multi-parameter dynamic trend early warning method - Google Patents

Rock burst multi-parameter dynamic trend early warning method Download PDF

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CN114087021A
CN114087021A CN202111258391.3A CN202111258391A CN114087021A CN 114087021 A CN114087021 A CN 114087021A CN 202111258391 A CN202111258391 A CN 202111258391A CN 114087021 A CN114087021 A CN 114087021A
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宋大钊
薛雅荣
李振雷
何学秋
王洪磊
周超
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University of Science and Technology Beijing USTB
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Abstract

The invention provides a rock burst multi-parameter dynamic trend early warning method, and belongs to the technical field of underground excavation engineering and coal rock dynamic disaster early warning. The method comprises the following steps: describing the variation trend of a 'time-space-strong' multi-element precursor early warning index in the process of the inoculation evolution of the rock burst by utilizing a Mann-Kendall trend inspection method according to real-time monitoring data of a field microseismic monitoring system, and early warning by taking whether each index variation trend accords with the representation rule of the rock burst precursor; evaluating the early warning efficiency of each index; performing index optimization based on an early warning efficiency maximization principle; performing multi-index fusion by taking the early warning efficiency corresponding to the optimized index as weight to obtain an impact risk comprehensive abnormal index; comparing the impact risk comprehensive abnormal index with a corresponding quantitative grading standard to determine the impact risk grade; and (4) periodically carrying out index optimization and weight updating under the drive of field actual monitoring data. By adopting the method and the device, an efficient and accurate decision basis can be provided for the prevention and treatment of the underground rock burst.

Description

Rock burst multi-parameter dynamic trend early warning method
Technical Field
The invention relates to the technical field of underground excavation engineering and coal rock dynamic disaster early warning, in particular to a rock burst multi-parameter dynamic trend early warning method.
Background
Rock burst is one of the main dynamic disasters affecting underground coal mine production, and serious consequences such as casualties, major property loss and the like are often caused. In recent years, with the gradual depletion of shallow mineral resources, coal mining is continuously expanded to the deep part of the earth, the occurrence conditions of coal seam structures of stopes and surrounding rocks around roadways are gradually complicated, the dynamic response characteristics in the coal rock bodies are stronger, the occurrence frequency of rock burst accidents is rapidly increased, and accurate and efficient monitoring and early warning means for the disasters are the key for guaranteeing underground safe production.
At present, the on-line monitoring means commonly used in the underground comprises microseismic, earthquake sound, electromagnetic radiation, ground stress and the like, wherein a microseismic monitoring system can monitor the breakage of coal rock mass in a large-range area of a mine, and is widely applied to monitoring and early warning of rock burst, and related personnel can achieve the purpose of early warning by analyzing the change trend of indexes such as energy, frequency and the like of microseismic events. However, the judgment of the change trend of the early warning indexes of the impact risk precursor depends on manual experience, the efficiency is low, the reliability is low, and meanwhile, due to the fact that dimensions of the early warning indexes in the process of the inoculation evolution of the impact rock pressure are different, the phenomenon that results conflict with each other may occur, and the misjudgment of relevant personnel on the actual dangerous state is caused. Therefore, it is necessary to provide an early warning method capable of automatically and efficiently judging the time-series variation trend of the early warning index of the impact risk precursor and providing comprehensive, single and quantitative impact risk levels.
Disclosure of Invention
The embodiment of the invention provides a rock burst multi-parameter dynamic trend early warning method, which can carry out efficient and accurate monitoring and early warning on rock burst. The technical scheme is as follows:
the embodiment of the invention provides a rock burst multi-parameter dynamic trend early warning method, which comprises the following steps:
s101, according to real-time monitoring data of a field microseismic monitoring system, describing the variation trend of a 'time-space-strong' multi-element precursor early warning index in the process of the initiation and evolution of the rock burst by using a Mann-Kendall trend inspection method, and carrying out early warning by taking whether the variation trend of each index meets the representation rule of the rock burst precursor or not as an early warning criterion;
s102, evaluating the early warning efficiency of each index by using a confusion matrix;
s103, performing index optimization based on an early warning effectiveness maximization principle;
s104, performing multi-index fusion by taking the early warning efficiency corresponding to the optimized index as weight to obtain an impact risk comprehensive abnormal index;
s105, comparing the comprehensive impact risk abnormal index with a corresponding quantitative grading standard to determine the impact risk grade;
and S106, periodically carrying out index optimization and weight updating under the drive of field actual monitoring data.
Further, the step of describing a time-space-strong multi-element precursor early warning index change trend in the process of the development of the rock burst by using a Mann-Kendall trend inspection method according to real-time monitoring data of the on-site microseismic monitoring system, and the early warning by taking whether each index change trend meets the rock burst precursor characterization rule as an early warning criterion comprises the following steps:
acquiring real-time monitoring data of a field microseismic monitoring system, and preprocessing original monitoring data by using a specific time window and a slip step length to obtain a time sequence of a time-space-strong multi-precursor early warning index of rock burst danger; wherein, the time-space-strength multi-element precursor early warning indexes of the rock burst danger comprise:
reflecting the daily total frequency, frequency deviation value, average total frequency, lack of earthquake, A (b) value, microseismic activity scale, algorithm complexity, P (b) value and time information entropy of a time dimension;
reflecting the microseismic activity degree and the seismic focus concentration degree of the space dimension;
reflecting daily maximum energy, daily total energy, daily average energy, energy deviation value, average total energy, total fault area and b value of the intensity dimension;
the daily maximum energy, daily total frequency, daily average energy, energy deviation value, frequency deviation value, average total energy, microseismic activity degree, lack of shock, A (b) value, total fault area, average total frequency, microseismic activity scale and algorithm complexity belong to forward early warning indexes, namely the higher the value is, the greater the impact risk is; the seismic source concentration degree, the b value, the P (b) value and the time information entropy belong to negative early warning indexes, namely the lower the value is, the greater the impact risk is;
and judging the real-time change trend of each early warning index by using a Mann-Kendall trend inspection method, and early warning by taking whether the change trend of each index meets the rock burst precursor representation rule as an early warning criterion.
Further, the preprocessing the original monitoring data with a specific time window and a specific sliding step length means converting the original monitoring data from an irregular time sequence to a regular time sequence, and includes:
defining a sliding time window with the length delta T, dividing the acquired monitoring data time sequence into n data sets with the length delta T and respectively corresponding to the time moments at the end of the time window, namely TiThe data set at time is denoted Xi[x1,x2,x3,...,xk]，k≤t,0<i is less than or equal to n, where Δ T is the length of the sliding time window, TiThe time corresponding to the end of the ith time window;
calculating XiEarly warning index value y corresponding to all samples iniThe early warning index value yiArranging in sequence to obtain a converted regular time sequence marked as Y [ Y ]1,y2,y3,...,yn]。
Further, the step of judging the real-time change trend of each early warning index by using a Mann-Kendall trend test method comprises the following steps:
the early warning index is regulated to be in a time sequence Y [ Y ]1,y2,y3,...,yn]Dividing the data into m data sets with time window length delta a and respectively corresponding to time A at the end of the time windowiI.e. AiThe dataset of the time of day is Yi[y1,y2,y3,...,yq]，10≤q≤a,0<i is less than or equal to m, and Y is calculatediTest statistic S:
Figure BDA0003324671110000031
wherein sgn (·) represents a sign function,
Figure BDA0003324671110000032
q represents Yi[y1,y2,y3,...,yq]Length of data set, ypDenotes the pth data, p 1,2,3.. q-1, yjRepresents the jth data, j ═ p +1,2,3.. q;
according to the obtained YiOf the test statistic S, determining YiTest standard amount of (3) Z:
Figure BDA0003324671110000033
Figure BDA0003324671110000034
when Z is>At 0, TiThe early warning indexes at all times have a growing trend; when Z is<At 0, TiThe early warning index at the moment has a decreasing trend; when Z is 0, TiThe early warning indexes at the moment have no obvious variation trend.
Further, in this embodiment, the performing an early warning with the criterion of whether the change trend of each index meets the representation rule of the rock burst precursor includes:
and comparing the real-time change trend of each index with the rock burst precursor representation rule, performing early warning if the positive early warning index has an increasing trend, performing early warning if the negative early warning index has a decreasing trend, and not performing early warning if other change trends.
Further, the early warning effectiveness is expressed as:
Figure BDA0003324671110000035
wherein, EFF represents the early warning efficiency; recall represents the Recall rate of the call,
Figure BDA0003324671110000036
indicating that the pre-warning is a shock hazard and that a rock burst event has actually occurred,
Figure BDA0003324671110000041
indicating that the early warning is no impact danger but a rock burst event actually occurs; precision represents the rate of Precision at which,
Figure BDA0003324671110000042
wherein
Figure BDA0003324671110000043
Indicating that the pre-warning is a shock hazard and that a rock burst event has actually occurred,
Figure BDA0003324671110000044
indicating that the early warning is a shock hazard but that no rock burst event actually occurs.
Further, the preferably performing the index based on the early warning effectiveness maximization principle includes:
and arranging the early warning effect values of all the early warning indexes from large to small, and screening indexes n% of which are ranked before for the next data fusion, wherein n is a positive integer, and the number of the indexes is rounded upwards.
Further, the obtaining of the impact risk comprehensive anomaly index by performing multi-index fusion by using the early warning effectiveness corresponding to the preferred index as a weight includes:
and inputting the early warning efficiency corresponding to the optimized indexes into the comprehensive early warning model of rock burst as weight, and performing multi-index fusion on the comprehensive early warning model of rock burst by using a comprehensive abnormal index method to obtain a comprehensive abnormal index of the impact risk:
Figure BDA0003324671110000045
wherein Q represents a shock hazard comprehensive abnormality index; e represents a natural base number; n represents the total number of preferred indicators; EFFkThe early warning efficiency corresponding to the kth index; wk(+/-)Representing the abnormal membership of the kth positive/negative early warning index,
for the forward early warning index, the value of the abnormal membership degree is as follows:
Figure BDA0003324671110000046
for negative early warning indexes, the value of the abnormal membership degree is as follows:
Figure BDA0003324671110000047
further, the impact risk classes include: a no-impact-risk state, a weak-impact-risk state, a medium-impact-risk state, and a strong-impact-risk state.
Further, the performing of index optimization and weight update under the drive of the field actual monitoring data in the scheduled period includes:
and (4) performing index optimization periodically under the drive of field actual monitoring data, and inputting the early warning efficiency corresponding to the optimized index into the comprehensive early warning model of rock burst as weight so as to realize self-feedback updating of the comprehensive early warning model of rock burst.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
1) the real-time change trend of the early warning index can be automatically judged, and rock burst early warning is carried out by utilizing the change trend of the early warning index;
2) the comprehensive abnormal indexes of the impact dangers are used for quantitatively describing the impact ground pressure dangers, so that the phenomena of high false alarm/missing report rate caused by a single early warning index and early warning grade conflicts of different early warning indexes are avoided;
3) the method is driven by on-site actual monitoring data to perform periodic self-feedback updating, has strong expandability and adaptability, adapts to complex and variable working conditions in the well, is favorable for performing efficient and accurate monitoring and early warning on the rock burst, and provides efficient and accurate decision basis for prevention and treatment of the rock burst in the well.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a rock burst multi-parameter dynamic trend early warning method provided in an embodiment of the present invention;
fig. 2 is a detailed flowchart schematic diagram of a rock burst multi-parameter dynamic trend early warning method provided by an embodiment of the invention;
FIG. 3 is a schematic diagram illustrating a preprocessing process performed on raw monitoring data according to the present embodiment;
FIG. 4 shows the total daily frequency F in this embodimentsumA time sequence change curve of the early warning index;
FIG. 5 shows the minor deviation D of the frequency in this embodimentFA time sequence change curve of the early warning index;
FIG. 6 is a graph showing the average total frequency in the present embodiment
Figure BDA0003324671110000051
A time sequence change curve of the early warning index;
FIG. 7 shows the lack of vibration M in this embodimentmA time sequence change curve of the early warning index;
FIG. 8 is a time-series variation curve of the value of A (b) in the present embodiment;
FIG. 9 is a time series curve of the microseismic activity scale F pre-alarm indicator of the present embodiment;
FIG. 10 is a time sequence variation curve of the algorithm complexity AC early warning indicator in the present embodiment;
FIG. 11 is a time-series variation curve of the pre-warning indicator of the value P (b) in this embodiment;
FIG. 12 is the time information entropy Q in the present embodimenttA time sequence change curve of the early warning index;
FIG. 13 shows the microseismic activity S of the present embodimentDA time sequence change curve of the early warning index;
FIG. 14 is a time series curve of the seismic focus level λ warning indicator in the present embodiment;
FIG. 15 shows the daily maximum energy E in the present embodimentmaxA time sequence change curve of the early warning index;
FIG. 16 shows the total daily energy E in this examplesumA time sequence change curve of the early warning index;
FIG. 17 shows the daily average energy E in this exampleavgA time sequence change curve of the early warning index;
FIG. 18 shows the energy deviation D in this embodimentEA time sequence change curve of the early warning index;
FIG. 19 is the average total energy in this example
Figure BDA0003324671110000052
A time sequence change curve of the early warning index;
FIG. 20 is a time-series curve of the warning indicator for the total area A (t) of the interruption layer in this embodiment;
FIG. 21 is a time-series variation curve of the b-value warning indicator in the present embodiment;
fig. 22 is a time-series change curve of the impact risk comprehensive abnormality index Q in the present embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
As shown in fig. 1, an embodiment of the present invention provides a rock burst multi-parameter dynamic trend early warning method, including:
s101, according to real-time monitoring data of an on-site microseismic monitoring system, describing a time-space-strong multi-element precursor early warning index change trend in an impact ground pressure inoculation evolution process by using a Mann-Kendall (Mann-Kendall) trend inspection method, and performing early warning by taking whether each index change trend meets an impact ground pressure precursor characterization rule as an early warning criterion; the method specifically comprises the following steps:
a1, acquiring real-time monitoring data of a field microseismic monitoring system, preprocessing original monitoring data by a specific time window and a slip step length, and obtaining a time sequence of a time-space-strong multi-precursor early warning index of rock burst danger; wherein, the time-space-strong multivariate precursor early warning indexes of the rock burst danger comprise but are not limited to:
total daily frequency F reflecting the time dimensionsumFrequency deviation value DFAverage total frequency
Figure BDA0003324671110000061
Lack of vibration MmA (b) value, microseismic activity scale F, algorithm complexity AC, P (b) value, and time information entropy Qt(ii) a Wherein the A (b) and P (b) values are derived from the empirical constant b in a "G-R" relationship (the Gutenberg-Rickett equation) which is one of the basic laws of seismology;
wherein, the total daily frequency FsumThe value of (d) is calculated by:
Figure BDA0003324671110000062
wherein n represents the total frequency of microseismic events within the first 24h (hours);
deviation value of frequency DFThe value of (d) is calculated by:
Figure BDA0003324671110000063
wherein, FtIndicating the frequency of microseismic events within the first 24h,
Figure BDA0003324671110000064
to representAverage daily frequency of microseismic events within the first 30d (days);
average total frequency
Figure BDA0003324671110000065
The value of (d) is calculated by:
Figure BDA0003324671110000066
wherein T represents the time window length, F(t)Representing the microseismic frequency at the time t;
lack of vibration MmThe value of (d) is calculated by:
Figure BDA0003324671110000071
Figure BDA0003324671110000072
wherein m is the total number of the energy level grading; lgEiIs the ith gear energy level; n is a radical ofiThe actual microseismic number of the ith gear level; the value of A (b) is calculated by the following formula:
Figure BDA0003324671110000073
wherein N is the total number of microseisms, MiIs the microseismic event energy level;
the value of the microseismic activity scale F is calculated by:
Figure BDA0003324671110000074
F0＝106.11+1.09M
wherein T represents a time window length, MiIs the microseismic event energy level;
the value of the algorithm complexity AC is calculated by the following formula:
Figure BDA0003324671110000075
wherein n represents the number of changes in microseismic energy level within a time window, MmaxRepresenting the maximum energy level of the microseisms, MminRepresenting the microseismic minimum energy level;
the value of P (b) is calculated by the following formula:
Figure BDA0003324671110000076
wherein N is the total number of microseisms, MiIs the microseismic event energy level;
time information entropy QtThe value of (d) is calculated by:
Figure BDA0003324671110000077
Figure BDA0003324671110000078
wherein n is the frequency of microseismic events within the time window, tiThe ith mine earthquake occurrence time;
microseismic activity S reflecting spatial dimensionsDAnd a seismic focus concentration degree λ; wherein the content of the first and second substances,
microseismic activity SDThe value of (d) is calculated by:
Figure BDA0003324671110000079
wherein N is the total number of microseisms, EiIs the microseismic event energy, M is the microseismic maximum energy level;
the value of the seismic focus concentration λ is calculated by the following formula:
Figure BDA00033246711100000710
wherein λ is1、λ2、λ3Forming a characteristic root of a covariance matrix for microseismic event coordinates (x, y, z) within a time window;
daily maximum energy E reflecting the intensity dimensionmaxTotal daily energy EsumDaily average energy EavgEnergy deviation value DEAverage total energy
Figure BDA00033246711100000711
Total fault area A (t) and b values; wherein the content of the first and second substances,
maximum daily energy EmaxThe value of (d) is calculated by:
Emax＝max(E1,E2,...,En)
wherein E isiThe ith microseismic event within the first 24 h;
total daily energy EsumThe value of (d) is calculated by:
Figure BDA0003324671110000081
wherein n represents the total frequency of microseismic events within the first 24h, EiRepresenting the individual microseismic event energies;
average daily energy EavgThe value of (d) is calculated by:
Figure BDA0003324671110000082
wherein n represents the total frequency of microseismic events within the first 24h, EiRepresenting the individual microseismic event energies;
deviation of energy value DEThe value of (d) is calculated by:
Figure BDA0003324671110000083
wherein E istRepresenting the total energy of the microseismic events within the first 24h,
Figure BDA0003324671110000084
represents the mean daily energy of microseismic events within the first 30 d;
mean total energy
Figure BDA0003324671110000085
The value of (d) is calculated by:
Figure BDA0003324671110000086
wherein T represents the time window length, E(t)Representing the microseismic energy at time t;
the value of the total fault area A (t) is calculated by the following formula:
Figure BDA0003324671110000087
wherein the content of the first and second substances,k0the lower limit of the energy level of the microseisms in the time window, k is the energy level of each microseismic, and N (k) is the number of microseismic events with the energy level of k in the time window;
the value of b is calculated by the following formula:
Figure BDA0003324671110000088
wherein m is the total number of the energy level grading; lgEiIs the ith gear energy level; n is a radical ofiIs the actual microseismic number of the ith gear level.
The early warning indexes can construct a precursor early warning index library, wherein the daily maximum energy, daily total frequency, daily average energy, energy deviation value, frequency deviation value, average total energy, microseismic activity degree, lack of shock, A (b) value, total fault area, average total frequency, microseismic activity scale and algorithm complexity in the early warning indexes belong to forward early warning indexes, namely the higher the value is, the greater the impact risk is; the seismic source concentration degree, the b value, the P (b) value and the time information entropy belong to negative early warning indexes, namely, the lower the value is, the larger the impact risk is.
In this embodiment, the preprocessing the original monitoring data with a specific time window and a specific sliding step length means converting the original monitoring data from an irregular time sequence to a regular time sequence, and includes:
defining a sliding time window with the length delta T, dividing the acquired monitoring data time sequence into n data sets with the length delta T and respectively corresponding to the time moments at the end of the time window, namely TiThe data set at time is denoted Xi[x1,x2,x3,...,xk]，k≤t,0<i is less than or equal to n, where Δ T is the length of the sliding time window, TiThe time corresponding to the end of the ith time window;
calculating XiEarly warning index value y corresponding to all samples iniThe early warning index value yiArranging in sequence to obtain a converted regular time sequence marked as Y [ Y ]1,y2,y3,...,yn]As shown in fig. 3.
A2, judging the real-time change trend of each early warning index by using a Mann-Kendall trend test method, and carrying out early warning by taking whether the change trend of each index meets the rock burst precursor representation rule as an early warning criterion, wherein the method specifically comprises the following steps:
the early warning index is regulated to be in a time sequence Y [ Y ]1,y2,y3,...,yn]Dividing the data into m data sets with time window length delta a and respectively corresponding to time A at the end of the time windowiI.e. AiThe dataset of the time of day is Yi[y1,y2,y3,...,yq]，10≤q≤a,0<i is less than or equal to m, and Y is calculatediTest statistic S:
Figure BDA0003324671110000091
wherein sgn (·) represents a sign function,
Figure BDA0003324671110000092
q represents Yi[y1,y2,y3,...,yq]Length of data set, ypDenotes the pth data, p 1,2,3.. q-1, yjRepresents the jth data, j ═ p +1,2,3.. q;
according to the obtained YiOf the test statistic S, determining YiTest standard amount of (3) Z:
Figure BDA0003324671110000093
Figure BDA0003324671110000094
when Z is>At 0, TiThe early warning indexes at all times have a growing trend; when Z is<At 0, TiThe early warning index at the moment has a decreasing trend; when Z is 0, TiThe early warning indexes at the moment have no obvious variation trend. Repeating the calculation until the early warning index change trends corresponding to all the moments are obtained;
and comparing the real-time change trend of each index with the rock burst precursor representation rule, performing early warning if the positive early warning index has an increasing trend, performing early warning if the negative early warning index has a decreasing trend, and not performing early warning if other change trends.
S102, evaluating the early warning efficiency of each index by using a confusion matrix;
in this embodiment, the confusion matrix is specifically the following 2 × 2 matrix:
Figure BDA0003324671110000101
in this embodiment, the early warning performance of the early warning index obtained according to the confusion matrix is:
Figure BDA0003324671110000102
wherein, EFF represents the early warning efficiency; recall represents the Recall rate of the call,
Figure BDA0003324671110000103
wherein
Figure BDA0003324671110000104
Indicating that the pre-warning is a shock hazard and that a rock burst event has actually occurred,
Figure BDA0003324671110000105
indicating that the early warning is no impact danger but a rock burst event actually occurs; precision represents the rate of Precision at which,
Figure BDA0003324671110000106
wherein
Figure BDA0003324671110000107
Indicating that the pre-warning is a shock hazard and that a rock burst event has actually occurred,
Figure BDA0003324671110000108
indicating that the early warning is a shock hazard but that no rock burst event actually occurs. .
In the embodiment, the trend range of the EFF is 0-1, and the closer the value is to 1, the better the early warning effect is.
S103, performing index optimization based on an early warning effectiveness maximization principle;
in this embodiment, the early warning effect values of all the early warning indicators are arranged from large to small, and the indicators n% before ranking are screened for the next data fusion, where n is a positive integer and the number of the indicators is rounded up.
S104, performing multi-index fusion by taking the early warning efficiency corresponding to the optimized index as weight to obtain an impact risk comprehensive abnormal index;
in this embodiment, the early warning performance corresponding to the preferred index is input into the comprehensive early warning model of rock burst as a weight, and the comprehensive early warning model of rock burst performs multi-index fusion by using a comprehensive anomaly index method to obtain a comprehensive anomaly index of the impact risk:
Figure BDA0003324671110000109
wherein Q represents a shock hazard comprehensive abnormality index; e represents a natural base number, equal to about 2.718; n represents the total number of preferred indicators; EFFkThe early warning efficiency corresponding to the kth index; wk(+/-)The abnormal membership degree of the kth positive/negative early warning index is represented and is 0-1, and the specific calculation mode is as follows:
for the forward early warning index, the value of the abnormal membership degree is as follows:
Figure BDA0003324671110000111
for negative early warning indexes, the value of the abnormal membership degree is as follows:
Figure BDA0003324671110000112
and S105, comparing the impact risk comprehensive abnormal index with the corresponding quantitative grading standard to determine the impact risk level.
In this embodiment, the impact risk quantization classification standard specifically includes:
when Q is more than or equal to 0 and less than or equal to 0.25, the state is in a non-impact dangerous state;
when Q is more than 0.25 and less than or equal to 0.5, the state is a weak impact dangerous state;
when Q is more than 0.5 and less than or equal to 0.75, the state is a medium impact dangerous state;
when Q is more than 0.75 and less than or equal to 1, the state is a strong impact dangerous state.
And S106, periodically carrying out index optimization and weight updating under the drive of field actual monitoring data.
In this embodiment, the indexes are optimized periodically under the drive of the actual field monitoring data, and the early warning performance corresponding to the optimized indexes is input into the comprehensive early warning model of rock burst as a weight, so as to realize the self-feedback update of the comprehensive early warning model of rock burst.
In this embodiment, for example, the steps S101 to S104 may be repeated at intervals of 1 month or more (or under the conditions of replacement of working face, great change of geological conditions, occurrence of high-energy mine earthquake or rock burst event, and the like) on site by using all existing historical monitoring data, and the adaptive indexes are preferably selected from the precursor early warning index library again while the corresponding early warning performance is used as weight to be input into the rock burst comprehensive early warning model, so as to achieve the purpose of improving the adaptability by self-feedback updating of the rock burst comprehensive early warning model.
In summary, the early warning method for the multi-parameter dynamic trend of rock burst according to the embodiment of the invention has at least the following beneficial effects:
1) the real-time change trend of the early warning index can be automatically judged, and rock burst early warning is carried out by utilizing the change trend of the early warning index;
2) the comprehensive abnormal indexes of the impact dangers are used for quantitatively describing the impact ground pressure dangers, so that the phenomena of high false alarm/missing report rate caused by a single early warning index and early warning grade conflicts of different early warning indexes are avoided;
3) the method is driven by on-site actual monitoring data to perform periodic self-feedback updating, has strong expandability and adaptability, adapts to complex and variable working conditions in the well, is favorable for performing efficient and accurate monitoring and early warning on the rock burst, and provides efficient and accurate decision basis for prevention and treatment of the rock burst in the well.
For better understanding of the present invention, the rock burst multi-parameter dynamic trend early warning method provided by the present embodiment is further described with reference to specific application scenarios:
in this embodiment, taking a certain working face of a certain coal mine as an example, the microseismic raw monitoring data and related information during the mining period of the working face (2 month 1 in 2018 to 2 month 1 in 2019, wherein the mining period is stopped due to the face-on inspection in 2018 to 10 month 19 in 2018, and no monitoring data is collected: the method comprises the following steps of generating 15 rock burst events in the working face mining process, and early warning the 15 rock burst events, wherein the specific steps are as follows:
acquiring real-time monitoring data of a field microseismic monitoring system, preprocessing original monitoring data by taking 15 days as a time window and 1 day as a sliding step length, and calculating to obtain a time sequence of time-space-strong multi-element precursor early warning indexes of rock burst danger, wherein each early warning index comprises: the total daily frequency, the frequency deviation value, the average total frequency, the lack of earthquake, the A (b) value, the microseismic activity scale, the algorithm complexity, the P (b) value, the time information entropy, the microseismic activity degree, the seismic source concentration degree, the daily maximum energy, the daily total energy, the daily average energy, the energy deviation value, the average total energy, the total fault area and the b value are 18 indexes, and the time sequence change curve of the calculation result is shown in fig. 4-21. Wherein the forward early warning indicators include: daily maximum energy, daily total frequency, daily average energy, energy deviation value, frequency deviation value, average total energy, microseismic activity, lack of seism, A (b) value, total fault area, average total frequency, microseismic activity scale and algorithm complexity; negative early warning indexes include: the degree of focus of the seismic source, the value of b, the value of P (b), and the entropy of time information.
The real-time change trend of each early warning index is judged by using a Mann-Kendall trend test method, and the trend judgment result of each early warning index at the moment of occurrence of the rock burst event for 15 times is shown in Table 1:
TABLE 1 Trend determination results of various early warning indicators before rock burst
Figure BDA0003324671110000131
Note: "/" indicates no significant trend in the index.
And combining the change trend of each index with the rock burst precursor representation rule to perform early warning, wherein if the index is early warned within 5d before the rock burst event, the early warning on the rock burst danger is correct.
The early warning performance of each index is evaluated by using a confusion matrix, and the calculation result is shown in table 2:
table 2 evaluation of rock burst warning index warning effectiveness
Figure BDA0003324671110000141
Index optimization is carried out based on the principle of maximization of early warning efficiency, and as can be seen from table 2, the early warning efficiencies of the early warning indexes are arranged in the following sequence:
Figure BDA0003324671110000142
Figure BDA0003324671110000143
according to the early warning efficiency maximization principle, preferably, early warning efficiencies corresponding to indexes within 40% of the early warning efficiency ranking in the precursor early warning index library are input into the rock burst comprehensive early warning model, and E is selected according to the calculation result in the table 2max,DF,Eavg,Esum,DE,FsumAnd 8 indexes such as AC, P (b), etc.
The time sequence variation curve of the impact risk comprehensive abnormal index Q obtained by carrying out multi-index fusion by using the comprehensive abnormal index method is shown in FIG. 22. Different values of Q correspond to different impact hazard levels, as shown in table 3.
TABLE 3 impact ground pressure hazard classification
Comprehensive abnormal index Q of coal rock dynamic disaster danger Hazard class State of impact hazard
0≤Q＜0.25 Class I Without risk of impact
0.25≤Q＜0.5 Class II Danger of weak impact
0.5≤Q＜0.75 Class III Danger of medium impact
0.75≤Q≤1 Grade IV Danger of high impact
E is selected as the early warning efficiency of the impact danger comprehensive abnormity index Qmax,DF,Eavg,Esum,DE,FsumAnd 8 indexes such as AC, P (b), and the like reach 0.563, which is higher than the early warning efficiency of any single early warning index. And then, when the site is changed every 1 month or more (or the working surface is changed, the geological conditions are greatly changed, and a high-energy mine earthquake or rock burst event occurs, and the like), the optimization and weight determination of the indexes are carried out again so as to adapt to the complicated change of the working conditions on the site.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (10)

1. A rock burst multi-parameter dynamic trend early warning method is characterized by comprising the following steps:
s101, according to real-time monitoring data of a field microseismic monitoring system, describing the variation trend of a 'time-space-strong' multi-element precursor early warning index in the process of the initiation and evolution of the rock burst by using a Mann-Kendall trend inspection method, and carrying out early warning by taking whether the variation trend of each index meets the representation rule of the rock burst precursor or not as an early warning criterion;
s102, evaluating the early warning efficiency of each index by using a confusion matrix;
s103, performing index optimization based on an early warning effectiveness maximization principle;
s104, performing multi-index fusion by taking the early warning efficiency corresponding to the optimized index as weight to obtain an impact risk comprehensive abnormal index;
s105, comparing the comprehensive impact risk abnormal index with a corresponding quantitative grading standard to determine the impact risk grade;
and S106, periodically carrying out index optimization and weight updating under the drive of field actual monitoring data.
2. The method for dynamically early warning the trend of the multiple parameters of the rock burst according to the claim 1, wherein the step of describing the variation trend of the early warning indexes of the multiple elements of time-space-strength in the process of the inoculation and evolution of the rock burst by using a Mann-Kendall trend inspection method according to the real-time monitoring data of the on-site microseismic monitoring system, and the early warning by taking whether the variation trend of each index accords with the characterization rule of the rock burst precursor comprises the following steps:
acquiring real-time monitoring data of a field microseismic monitoring system, and preprocessing original monitoring data by using a specific time window and a slip step length to obtain a time sequence of a time-space-strong multi-precursor early warning index of rock burst danger; wherein, the time-space-strength multi-element precursor early warning indexes of the rock burst danger comprise:
reflecting the daily total frequency, frequency deviation value, average total frequency, lack of earthquake, A (b) value, microseismic activity scale, algorithm complexity, P (b) value and time information entropy of a time dimension;
reflecting the microseismic activity degree and the seismic focus concentration degree of the space dimension;
reflecting daily maximum energy, daily total energy, daily average energy, energy deviation value, average total energy, total fault area and b value of the intensity dimension;
the daily maximum energy, daily total frequency, daily average energy, energy deviation value, frequency deviation value, average total energy, microseismic activity degree, lack of shock, A (b) value, total fault area, average total frequency, microseismic activity scale and algorithm complexity belong to forward early warning indexes, namely the higher the value is, the greater the impact risk is; the seismic source concentration degree, the b value, the P (b) value and the time information entropy belong to negative early warning indexes, namely the lower the value is, the greater the impact risk is;
and judging the real-time change trend of each early warning index by using a Mann-Kendall trend inspection method, and early warning by taking whether the change trend of each index meets the rock burst precursor representation rule as an early warning criterion.
3. The rock burst multi-parameter dynamic trend early warning method as claimed in claim 2, wherein the preprocessing of the original monitoring data with a specific time window and a specific slip step length means that the original monitoring data are converted from an irregular time sequence to a regular time sequence, and the method comprises the following steps:
defining a sliding time window with the length delta T, dividing the acquired monitoring data time sequence into n data sets with the length delta T and respectively corresponding to the time moments at the end of the time window, namely TiThe data set at time is denoted Xi[x1,x2,x3,...,xk]，k≤t,0<i is less than or equal to n, where Δ T is the length of the sliding time window, TiThe time corresponding to the end of the ith time window;
calculating XiEarly warning index value y corresponding to all samples iniThe early warning index value yiArranging in sequence to obtain a converted regular time sequence marked as Y [ Y ]1,y2,y3,...,yn]。
4. The rock burst multi-parameter dynamic trend early warning method according to claim 1, wherein the judging the real-time change trend of each early warning index by using a Mann-Kendall trend test method comprises:
the early warning index is regulated to be in a time sequence Y [ Y ]1,y2,y3,...,yn]Dividing the data into m data sets with time window length delta a and respectively corresponding to time A at the end of the time windowiI.e. AiThe dataset of the time of day is Yi[y1,y2,y3,...,yq]，10≤q≤a,0<i is less than or equal to m, and Y is calculatediTest statistic S:
Figure FDA0003324671100000021
wherein sgn (·) represents a sign function,
Figure FDA0003324671100000022
q represents Yi[y1,y2,y3,...,yq]Length of data set, ypDenotes the pth data, p 1,2,3.. q-1, yjRepresents the jth data, j ═ p +1,2,3.. q;
according to the obtained YiOf the test statistic S, determining YiTest standard amount of (3) Z:
Figure FDA0003324671100000031
Figure FDA0003324671100000032
when Z is>At 0, TiThe early warning indexes at all times have a growing trend; when Z is<At 0, TiThe early warning index at the moment has a decreasing trend; when Z is 0, TiThe early warning indexes at the moment have no obvious variation trend.
5. The method for early warning of multi-parameter dynamic trend of rock burst according to claim 1, wherein in this embodiment, the early warning using whether the change trend of each index meets the pre-warning sign law of rock burst as an early warning criterion includes:
and comparing the real-time change trend of each index with the rock burst precursor representation rule, performing early warning if the positive early warning index has an increasing trend, performing early warning if the negative early warning index has a decreasing trend, and not performing early warning if other change trends.
6. The rock burst multi-parameter dynamic trend warning method according to claim 1, wherein the warning effectiveness is expressed as:
Figure FDA0003324671100000033
wherein, EFF represents the early warning efficiency; recall represents the Recall rate of the call,
Figure FDA0003324671100000034
Figure FDA0003324671100000035
indicating that the pre-warning is a shock hazard and that a rock burst event has actually occurred,
Figure FDA0003324671100000036
indicating that the early warning is no impact danger but a rock burst event actually occurs; precision represents the rate of Precision at which,
Figure FDA0003324671100000037
wherein
Figure FDA0003324671100000038
Indicating that the pre-warning is a shock hazard and that a rock burst event has actually occurred,
Figure FDA0003324671100000039
indicating that the early warning is a shock hazard but that no rock burst event actually occurs.
7. The rock burst multi-parameter dynamic trend early warning method according to claim 1, wherein the performing indexes based on an early warning effectiveness maximization principle preferably comprises:
and arranging the early warning effect values of all the early warning indexes from large to small, and screening indexes n% of which are ranked before for the next data fusion, wherein n is a positive integer, and the number of the indexes is rounded upwards.
8. The rock burst multi-parameter dynamic trend early warning method according to claim 1, wherein the obtaining of the comprehensive impact risk abnormality index by performing multi-index fusion with early warning effectiveness corresponding to the preferred index as a weight comprises:
and inputting the early warning efficiency corresponding to the optimized indexes into the comprehensive early warning model of rock burst as weight, and performing multi-index fusion on the comprehensive early warning model of rock burst by using a comprehensive abnormal index method to obtain a comprehensive abnormal index of the impact risk:
Figure FDA0003324671100000041
wherein Q represents a shock hazard comprehensive abnormality index; e represents a natural base number; n represents the total number of preferred indicators; EFFkThe early warning efficiency corresponding to the kth index; wk(+/-)Representing the abnormal membership of the kth positive/negative early warning index,
for the forward early warning index, the value of the abnormal membership degree is as follows:
Figure FDA0003324671100000042
for negative early warning indexes, the value of the abnormal membership degree is as follows:
Figure FDA0003324671100000043
9. the rock burst multi-parameter dynamic trend warning method according to claim 8, wherein the impact risk level comprises: a no-impact-risk state, a weak-impact-risk state, a medium-impact-risk state, and a strong-impact-risk state.
10. The early warning method for the multiparameter dynamic trend of rock burst as claimed in claim 1, wherein said periodically performing index optimization and weight update under the drive of on-site actual monitoring data comprises:
and (4) performing index optimization periodically under the drive of field actual monitoring data, and inputting the early warning efficiency corresponding to the optimized index into the comprehensive early warning model of rock burst as weight so as to realize self-feedback updating of the comprehensive early warning model of rock burst.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114889546A (en) * 2022-03-28 2022-08-12 郑州炜盛电子科技有限公司 Living body detection method and device based on carbon dioxide sensor
CN115239840A (en) * 2022-07-21 2022-10-25 临沂矿业集团菏泽煤电有限公司郭屯煤矿 Impact early warning method based on microseismic activity process time sequence analysis
CN116048235A (en) * 2023-03-29 2023-05-02 南京群顶科技股份有限公司 Temperature-sensing future trend detection method based on bidirectional GRU and Mankendel method

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU1788289C (en) * 1990-10-22 1993-01-15 Институт Физики И Механики Горных Пород Ан Киргсср Method for prediction of rock bursts in mine workings
CN101582191A (en) * 2009-06-24 2009-11-18 上海添成电子科技有限公司 Method for realizing micro-seismic monitoring and real-time early warning to mine power disaster
CN102644482A (en) * 2012-05-18 2012-08-22 河南大有能源股份有限公司 Rock burst predicting and warning method
CN109447837A (en) * 2018-11-15 2019-03-08 国家能源投资集团有限责任公司 A kind of risk assessment method of mining area bump
CN112324506A (en) * 2020-11-20 2021-02-05 上海大屯能源股份有限公司江苏分公司 Dynamic early warning method for preventing and controlling rock burst of coal mine based on micro-seismic
US20210134135A1 (en) * 2019-10-30 2021-05-06 University Of Science And Technology Beijing Multi-system, multi-parameter, integrated, comprehensive early warning method and system for coal and rock dynamic disaster

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU1788289C (en) * 1990-10-22 1993-01-15 Институт Физики И Механики Горных Пород Ан Киргсср Method for prediction of rock bursts in mine workings
CN101582191A (en) * 2009-06-24 2009-11-18 上海添成电子科技有限公司 Method for realizing micro-seismic monitoring and real-time early warning to mine power disaster
CN102644482A (en) * 2012-05-18 2012-08-22 河南大有能源股份有限公司 Rock burst predicting and warning method
CN109447837A (en) * 2018-11-15 2019-03-08 国家能源投资集团有限责任公司 A kind of risk assessment method of mining area bump
US20210134135A1 (en) * 2019-10-30 2021-05-06 University Of Science And Technology Beijing Multi-system, multi-parameter, integrated, comprehensive early warning method and system for coal and rock dynamic disaster
CN112324506A (en) * 2020-11-20 2021-02-05 上海大屯能源股份有限公司江苏分公司 Dynamic early warning method for preventing and controlling rock burst of coal mine based on micro-seismic

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
贾宝新等: "多参量综合指标冲击地压预测技术研究", 《防灾减灾工程学报》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114889546A (en) * 2022-03-28 2022-08-12 郑州炜盛电子科技有限公司 Living body detection method and device based on carbon dioxide sensor
CN115239840A (en) * 2022-07-21 2022-10-25 临沂矿业集团菏泽煤电有限公司郭屯煤矿 Impact early warning method based on microseismic activity process time sequence analysis
CN116048235A (en) * 2023-03-29 2023-05-02 南京群顶科技股份有限公司 Temperature-sensing future trend detection method based on bidirectional GRU and Mankendel method

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