CN113673119B - Coal mine rock burst danger dynamic and static coupling evaluation method based on Bayesian method - Google Patents
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Abstract
The application provides a coal mine rock burst risk dynamic and static coupling evaluation method based on a Bayesian method, which comprises the following steps: s1, obtaining dynamic index parameter data; s2, static index parameter data are obtained; s3, carrying out normalization processing on the dynamic and static index parameter data; s4, carrying out abnormal index conversion on each microseismic index obtained by monitoring the microseismic sensor; s5, carrying out comprehensive dangerous calculation on the drilling stress index value obtained by the drilling stress sensor; s6, carrying out fusion calculation on each dynamic and static index through a Bayesian probability combination model; and S7, performing rock burst grading on the calculation result of the combined model to realize intelligent grading early warning. According to the application, the data indexes of all monitoring systems are effectively fused, dynamic and static indexes are comprehensively considered, dynamic weight calculation of all indexes based on time sequences is realized, the rock burst safety early warning capability is improved, and the problems that the existing multi-index safety early warning threshold is difficult to determine and the data fusion degree of all monitoring systems is low are solved.
Description
Technical Field
The application relates to the technical field of coal mine underground rock burst dynamic disaster safety monitoring and early warning, in particular to a coal mine rock burst danger dynamic and static coupling evaluation method based on a Bayesian method.
Background
Along with the increase of the mining depth of the coal mine, the stress of the deep coal seam is increased, and the possibility of disasters is also higher, so that the underground monitoring and early warning of underground common dynamic disasters are particularly important. In deep mining areas, rock burst is taken as a typical common coal-rock dynamic disaster, and has important engineering value for underground monitoring and early warning.
Good results are obtained in the work of rock burst monitoring, early warning and research. The monitoring system for microseismic, ground sound, electromagnetic radiation, drilling stress and the like is widely popularized and applied, and good application effects are achieved in many mine enterprises. At present, rock burst monitoring and early warning mainly adopts dynamic and static comprehensive analysis, wherein the static comprehensive analysis is carried out according to two major indexes of geological conditions, mining condition comprehensive indexes and possibility indexes, and the dynamic indexes are data obtained through microseismic events, energy, stress and other monitoring systems to study the rock burst. The inventor of the application finds that the present rock burst safety monitoring and early warning system has the following problems:
(1) Monitoring data mining inadequacy
In the process of preventing rock burst, a large amount of geological data and monitoring data are accumulated in China at present, the data fusion degree of each monitoring system is low, and a Bayesian method is applied to discover beneficial data capable of pre-warning the safety of the impact disaster through the large data, so that a reliable and effective calculation method is lacking at present.
(2) Determination of safety precaution threshold
When the constant analysis of each index of the rock burst is carried out, most of safety early warning thresholds influencing the rock burst risk are obtained according to the actual conditions of the site, the safety early warning thresholds are biased to engineering experience, have certain uniqueness, are difficult to determine and have randomness, and cannot be suitable for all projects.
(3) Linkage lacking dynamic and static indexes
For a large number of monitoring indexes, one or two independent indexes are often adopted as early warning thresholds at present, and a plurality of constant and multi-monitoring data indexes cannot be uniformly characterized, which is obviously insufficient.
Disclosure of Invention
Aiming at the technical problems that the existing rock burst safety monitoring and early warning system has low data fusion degree of each monitoring system, each index safety early warning threshold has difficulty in determining and randomness, and dynamic indexes and static indexes lack linkage, the application provides a coal mine rock burst risk dynamic and static coupling evaluation method based on a Bayesian method.
In order to solve the technical problems, the application adopts the following technical scheme:
a coal mine rock burst risk dynamic and static coupling evaluation method based on a Bayesian method comprises the following steps:
s1, monitoring received data by a microseismic sensor and a borehole stress sensor which are installed underground, transmitting and storing the data to a monitoring system through a data transmission device, and obtaining dynamic index parameter data with time sequence characteristics;
s2, performing comprehensive index evaluation calculation from two aspects of geological factors and mining technologies, performing possibility index calculation from stress and coal seam impact tendency, and obtaining regional static index parameter data based on the comprehensive index evaluation calculation and the possibility index calculation;
s3, carrying out normalization processing on the dynamic index parameter data and the static index parameter data preliminarily by introducing an abnormal conversion function to obtain multi-parameter abnormal indexes uniformly constrained in non-dimensional quantities of 0-1 closed interval;
s4, carrying out abnormal index conversion on index values of each microseismic index obtained through monitoring by the microseismic sensor at different moments according to the following formula:
wherein lambda is ij The value range is 0-1 for the membership degree of the corresponding index in the statistical time window t;
s5, carrying out comprehensive risk calculation on drilling stress index values obtained by monitoring the drilling stress sensor at different moments according to the following formula to obtain risk levels at any moment:
P I ＝k 1 I 1 +k 2 I 2 +k 3 I 3
wherein I is 1 The stress value is a dangerous index of the stress value of the measuring point; i 2 The stress increment value is a dangerous index, namely a value obtained by calculating the difference between the stress value of the current measuring point and the stress value of the initial setting point; i 3 Is a dangerous index of stress acceleration value; k (k) 1 、k 2 、k 3 Respectively is I 1 、I 2 、I 3 Weight ratio of (2);
s6, analyzing all dynamic and static indexes after comprehensive risk calculation by using a Bayesian probability model, and comprehensively evaluating the risk by using a combined model; let each dynamic and static index be the independent evaluation model, assume that the kth evaluation model weight is P k And coexisting n mutually independent evaluation models, wherein the posterior probability of the occurrence of the combined model is as follows:
wherein O (C) represents the posterior probability of event C; p (C) represents the prior probability of event C, and is generally set to 1/n;is that the event does not occur; the weights of the evaluation models are obtained in accordance with steps S2, S3, S5The result is taken as probability;
and S7, calculating the combined model to obtain a dimensionless quantity P belonging to a closed zone of 0-1, grading the dimensionless quantity P according to a preset rock burst risk grading standard to obtain rock burst grades at all times, and realizing intelligent grading early warning according to the rock burst grades.
Compared with the prior art, the coal mine rock burst risk dynamic and static coupling evaluation method based on the Bayesian method provided by the application has the advantages that firstly, regional dynamic index parameter data and regional static index parameter data are collected, then, abnormal conversion functions are introduced to convert all dynamic and static indexes to obtain multi-constant abnormal indexes, so that all dynamic and static indexes are constrained in dimensionless quantities of a 0-1 closed zone, then, all dynamic and static indexes are subjected to fusion calculation through a mathematical probability statistical method, the comprehensive risk degree of all index weights and rock burst is obtained, and safety early warning is carried out according to a preset rock burst risk grade division standard. According to the application, based on a Bayesian method, the multi-index integrated rock burst disaster safety monitoring and early warning is established, the data indexes of all monitoring systems are effectively integrated, meanwhile, the dynamic indexes and the static indexes are comprehensively considered, the dynamic weight calculation of all indexes based on a time sequence is realized, the uncertainty, the randomness and the ambiguity in the calculation process are reduced, and the rock burst safety early warning capability is improved, so that the problem that the integration unification effect of all indexes and modules of the existing online monitoring systems is poor is effectively solved, and the technical problem that the existing multi-index safety early warning threshold is difficult to determine is solved.
Further, the microseismic index obtained by the microseismic sensor monitoring in step S4 includes a microseismic intensity factor, a microseismic equivalent energy level parameter, a microseismic time sequence factor, a microseismic b value and a microseismic a (b) value.
Further, the parameter lambda in step S4 ij The calculation method of (2) is as follows:
for the forward anomaly index:
λ ij ＝(S ij -S min )/(S max -S min )
for negative anomaly index:
λ ij ＝(S max -S ij )/(S max -S min )
wherein S is ij The value of the microseismic index at a certain moment in a time window is S max For the maximum value of the values of each microseismic index at each moment in the time window, S min And taking the minimum value of the values of each microseismic index at each moment in the time window.
Further, the preset rock burst risk grade classification standard in the step S7 specifically classifies the possibility of the dimensionless quantity P into four grades, namely, no grade, weak grade, medium grade and strong grade, wherein the classification standard values of the no grade, the weak grade, the medium grade and the strong grade are respectively 0-P <0.25, 0.25-P <0.5, 0.5-P <0.75 and 0.75-P <1, and the four grades are respectively early-warned by adopting green, yellow, orange and red.
Drawings
FIG. 1 is a schematic flow chart of a coal mine rock burst risk dynamic and static coupling evaluation method based on a Bayesian method.
Detailed Description
The application is further described with reference to the following detailed drawings in order to make the technical means, the creation characteristics, the achievement of the purpose and the effect of the implementation of the application easy to understand.
Referring to fig. 1, the method for evaluating dynamic and static coupling of coal mine rock burst dangers based on a bayesian method provided by the application comprises the following steps:
s1, monitoring received data by a microseismic sensor and a borehole stress sensor which are installed underground, transmitting and storing the data to a monitoring system through a data transmission device, and obtaining dynamic index parameter data with time sequence characteristics; and the data of the multiple monitoring systems are fused and uniformly analyzed by performing interpretation coding on the database types of the monitoring systems and then writing an interface.
S2, performing comprehensive index evaluation calculation from two aspects of geological factors and mining technology, performing probability index calculation from stress and coal seam impact tendency, and obtaining regional static index parameter data based on the comprehensive index evaluation calculation and the probability index calculation, namely, discussing dangerous static indexes from the comprehensive index evaluation calculation and the probability index calculation, wherein specific calculation processing of the comprehensive index evaluation calculation and the probability index calculation is a prior art well known to a person skilled in the art, and is not repeated herein.
S3, carrying out normalization processing on the dynamic index parameter data and the static index parameter data preliminarily by introducing an abnormal conversion function to obtain multi-parameter abnormal indexes uniformly constrained in the dimensionless range of 0-1 closed zone.
S4, monitoring each obtained microseism index by the microseism sensor: the abnormal index conversion is carried out on the index values at different moments according to the following formula:
wherein lambda is ij The value range is 0-1 for the membership degree of the corresponding index in the statistical time window t; parameter lambda ij The calculation method of (2) is as follows:
for the forward anomaly index:
λ ij ＝(S ij -S min )/(S max -S min )
for negative anomaly index:
λ ij ＝(S max -S ij )/(S max -S min )
wherein S is ij The value of the microseismic index at a certain moment in a time window is S max For the maximum value of the values of each microseismic index at each moment in the time window, S min And taking the minimum value of the values of each microseismic index at each moment in the time window.
S5, carrying out comprehensive risk calculation on drilling stress index values obtained by monitoring the drilling stress sensor at different moments according to the following formula to obtain risk levels at any moment:
P I ＝k 1 I 1 +k 2 I 2 +k 3 I 3
wherein I is 1 The stress value is a dangerous index of the stress value of the measuring point; i 2 The stress increment value is a dangerous index, namely a value obtained by calculating the difference between the stress value of the current measuring point and the stress value of the initial setting point; i 3 Is a dangerous index of stress acceleration value; k (k) 1 、k 2 、k 3 Respectively is I 1 、I 2 、I 3 Weight ratio of (c).
S6, after dimensionless quantities of dynamic and static indexes of rock burst are calculated, analyzing the dynamic and static indexes after comprehensive risk calculation by using a Bayesian probability model, and comprehensively evaluating the risk by using a combined model; let each dynamic and static index be the independent evaluation model, assume that the kth evaluation model weight is P k And coexisting n mutually independent evaluation models, wherein the posterior probability of the occurrence of the combined model is as follows:
wherein O (C) represents the posterior probability of event C; p (C) represents the prior probability of event C, and is generally set to 1/n;is that the event does not occur; the weight of each evaluation model is taken as probability according to the results obtained in the steps S2, S3 and S5;
and S7, calculating the combined model to obtain a dimensionless quantity P belonging to a closed zone of 0-1, grading the dimensionless quantity P according to a preset rock burst risk grading standard to obtain rock burst grades at all times, and realizing intelligent grading early warning according to the rock burst grades.
Compared with the prior art, the coal mine rock burst risk dynamic and static coupling evaluation method based on the Bayesian method provided by the application has the advantages that firstly, regional dynamic index parameter data and regional static index parameter data are collected, then, abnormal conversion functions are introduced to convert all dynamic and static indexes to obtain multi-constant abnormal indexes, so that all dynamic and static indexes are constrained in dimensionless quantities of a 0-1 closed zone, then, all dynamic and static indexes are subjected to fusion calculation through a mathematical probability statistical method, the comprehensive risk degree of all index weights and rock burst is obtained, and safety early warning is carried out according to a preset rock burst risk grade division standard. According to the application, based on a Bayesian method, the multi-index integrated rock burst disaster safety monitoring and early warning is established, the data indexes of all monitoring systems are effectively integrated, meanwhile, the dynamic indexes and the static indexes are comprehensively considered, the dynamic weight calculation of all indexes based on a time sequence is realized, the uncertainty, the randomness and the ambiguity in the calculation process are reduced, and the rock burst safety early warning capability is improved, so that the problem that the integration unification effect of all indexes and modules of the existing online monitoring systems is poor is effectively solved, and the technical problem that the existing multi-index safety early warning threshold is difficult to determine is solved.
As a specific example, the preset rock burst risk classification criteria in step S7 specifically classifies the possibility of the dimensionless number P into four classes of none, weak, medium and strong, and the classification criteria of each class of none, weak, medium and strong are respectively 0.ltoreq.p <0.25, 0.25.ltoreq.p <0.5, 0.5.ltoreq.p <0.75, 0.75.ltoreq.p <1, as shown in table 1 below:
TABLE 1 rock burst hazard classification
The none, weak, medium and strong grades respectively adopt green, yellow, orange and red to perform early warning, so that intelligent grading safety early warning is realized.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the technical solution of the present application, which is intended to be covered by the scope of the claims of the present application.
Claims (4)
1. The coal mine rock burst risk dynamic and static coupling evaluation method based on the Bayesian method is characterized by comprising the following steps of:
s1, monitoring received data by a microseismic sensor and a borehole stress sensor which are installed underground, transmitting and storing the data to a monitoring system through a data transmission device, and obtaining dynamic index parameter data with time sequence characteristics;
s2, performing comprehensive index evaluation calculation from two aspects of geological factors and mining technologies, performing possibility index calculation from stress and coal seam impact tendency, and obtaining regional static index parameter data based on the comprehensive index evaluation calculation and the possibility index calculation;
s3, carrying out normalization processing on the dynamic index parameter data and the static index parameter data preliminarily by introducing an abnormal conversion function to obtain multi-parameter abnormal indexes uniformly constrained in non-dimensional quantities of 0-1 closed interval;
s4, carrying out abnormal index conversion on index values of each microseismic index obtained through monitoring by the microseismic sensor at different moments according to the following formula:
wherein lambda is ij The value range is 0-1 for the membership degree of the corresponding index in the statistical time window t;
s5, carrying out comprehensive risk calculation on drilling stress index values obtained by monitoring the drilling stress sensor at different moments according to the following formula to obtain risk levels at any moment:
P I ＝k 1 I 1 +k 2 I 2 +k 3 I 3
wherein I is 1 The stress value is a dangerous index of the stress value of the measuring point; i 2 The stress increment value is a dangerous index, namely a value obtained by calculating the difference between the stress value of the current measuring point and the stress value of the initial setting point; i 3 Is a dangerous index of stress acceleration value; k (k) 1 、k 2 、k 3 Respectively is I 1 、I 2 、I 3 Weight ratio of (2);
s6, analyzing all dynamic and static indexes after comprehensive risk calculation by using a Bayesian probability model, and comprehensively evaluating the risk by using a combined model; let each dynamic and static index be the independent evaluation model, assume that the kth evaluation model weight is P k And coexisting n mutually independent evaluation models, wherein the posterior probability of the occurrence of the combined model is as follows:
wherein O (C) represents the posterior probability of event C; p (C) represents the prior probability of the event C and is set to be 1/n;is that the event does not occur; the weight of each evaluation model is taken as probability according to the results obtained in the steps S2, S3 and S5;
and S7, calculating the combined model to obtain a dimensionless quantity P belonging to a closed zone of 0-1, grading the dimensionless quantity P according to a preset rock burst risk grading standard to obtain rock burst grades at all times, and realizing intelligent grading early warning according to the rock burst grades.
2. The method for evaluating the risk dynamic and static coupling of the rock burst of the coal mine based on the Bayesian method as claimed in claim 1, wherein the microseismic indexes obtained by monitoring the microseismic sensor in the step S4 comprise microseismic intensity factors, microseismic class parameters, microseismic time sequence factors, microseismic b values and microseismic A (b) values.
3. The bayesian-based coal mine rock burst risk dynamic-static coupling evaluation method according to claim 1, wherein the parameter λ in the step S4 is ij The calculation method of (2) is as follows:
for the forward anomaly index:
λ ij ＝(S ij -S min )/(S max -S min )
for negative anomaly index:
λ ij ＝(S max -S ij )/(S max -S min )
wherein S is ij The value of the microseismic index at a certain moment in a time window is S max For the maximum value of the values of each microseismic index at each moment in the time window, S min And taking the minimum value of the values of each microseismic index at each moment in the time window.
4. The bayesian method-based coal mine rock burst risk dynamic and static coupling evaluation method according to claim 1, wherein the preset rock burst risk classification standard in the step S7 specifically classifies the possibility of the dimensionless quantity P into four classes of none, weak, medium and strong, and the classification standard values of the respective classes of none, weak, medium and strong are respectively 0.ltoreq P <0.25, 0.25.ltoreq P <0.5, 0.5.ltoreq P <0.75, 0.75.ltoreq P <1, and the four classes of none, weak, medium and strong are respectively pre-warned by green, yellow, orange and red.
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