A method for evaluating the effectiveness of microseismic monitoring networks in coal mines

Abstract

The present invention discloses a method for evaluating the monitoring effect of a coal mine microseismic network, comprising: collecting microseismic data and establishing a database; iteratively clustering the earthquake sources in the database to obtain clustered microseismic data; calculating the average microseismic integrity level based on the clustered microseismic data; and evaluating the monitoring effects of different microseismic networks based on the average microseismic integrity level. The method for evaluating the monitoring effect of a microseismic network involved in the present invention can effectively evaluate the monitoring effect of the microseismic system during the continuous relocation of the coal mine microseismic network, provide reliable support for improving the accuracy of microseismic monitoring and improving the accuracy of dynamic disaster warning, and provide a reliable basis for relevant technical personnel to formulate plans for the relocation and optimization of microseismic networks.

Description

Technical Field

[0001] The invention relates to the field of coal mining and coal mine safety technology, and in particular to a method for evaluating the monitoring effect of a coal mine microseismic network. Background Art

[0002] Mine tremors are vibrations caused by the sudden release of elastic energy and deformation energy accumulated in coal and rock masses during mining. When the energy of mine tremors is large enough, it will cause tunnel damage such as coal ejection, roof subsidence, and bottom drumming, thus forming rock bursts. Rock bursts, coal and gas outbursts, and mine tremors are typical dynamic disasters in coal mines. They are sudden and violent when they occur, and are accompanied by continuous fractures of coal and rock masses. In order to effectively prevent and control dynamic disasters such as rock bursts, microseismic systems have been widely deployed in mines in recent years to capture coal and rock fracture signals.

[0003] Microseismic monitoring technology is a technical method based on a microseismic monitoring system, which uses the vibration waves emitted during the deformation and destruction of the rock mass to monitor the location of rock mass fractures and energy release in the mine. The main function of the microseismic monitoring system is to automatically record mine earthquake activities in real time, locate the source and calculate the energy of mine earthquakes, obtain the strength of mine earthquake activities in the mining area, and provide a basis for evaluating the risk of rock burst in the entire mine. This monitoring technology has the characteristics of long-distance, dynamic, three-dimensional, and real-time monitoring. Its principle is to use the time difference of the direct P wave starting point received by the seismometer to perform three-dimensional positioning and energy calculation under given wave velocity conditions, and according to the monitoring results of mine earthquakes in each period, give the strength of mine earthquake activities in each area of ​​the working face and the tunnel, and divide the rock burst danger area so that timely response measures can be taken.

[0004] The microseismic system consists of two parts: the underground system and the surface system. The underground system mainly includes microseismic stations, signal cables, etc.; the surface system includes signal acquisition stations, recorders, analyzers, etc. Through further post-processing of microseismic information, such as collecting vibration signal waveforms and accurately calculating the time of occurrence, spatial coordinates, and energy of the earthquake source, the possibility of dynamic disasters such as rock burst can be further analyzed and predicted, so as to achieve the prevention and control of dynamic disasters.

[0005] The key and prerequisite for using microseismic data for analysis is to ensure that the system has good monitoring effects and can capture a large amount of microseismic information. However, since the location of the underground mining area of ​​the coal mine is always moving, in order to achieve good monitoring of the mining area, the underground microseismic station layout needs to be transferred with the movement of the mining area. This is the dynamic adjustment process of the deployment of microseismic stations. During the dynamic adjustment of the microseismic network, affected by the complex underground geology and mining environment, the monitoring accuracy under the same network layout conditions varies greatly. Therefore, how to use existing data to evaluate the monitoring effects of different deployment schemes during the dynamic adjustment of the microseismic network is of great significance to improving the deployment plan of the microseismic network, further improving the microseismic monitoring effect, and improving the prediction and early warning accuracy of dynamic disasters such as rock burst. Summary of the invention

[0006] The purpose of the present invention is to provide a method for evaluating the monitoring effect of a coal mine microseismic network to solve the problems existing in the above-mentioned prior art, quantitatively evaluate the monitoring effect of the microseismic system during the continuous movement of the coal mine microseismic network, and provide reliable support for improving the accuracy of microseismic monitoring and improving the accuracy of dynamic disaster warning.

[0007] To achieve the above object, the present invention provides the following solution: a method for evaluating the monitoring effect of a coal mine microseismic network, comprising:

[0008] Collect microseismic data and establish a database;

[0009] Iteratively clustering the earthquake sources in the database to obtain clustered microseismic data;

[0010] Calculating an average microseismic integrity level based on the clustered microseismic data;

[0011] Based on the average microseismic integrity level, the monitoring effects of different microseismic networks are evaluated.

[0012] Preferably, the process of collecting microseismic data to establish a database includes collecting microseismic data under different microseismic network deployment schemes and establishing a database based on the microseismic data.

[0013] Preferably, the process of iteratively clustering the earthquake sources in the database includes calculating the earthquake occurrence time and three-dimensional coordinates of the earthquake sources of the samples as microseismic events.

[0014] Preferably, the process of iteratively clustering the earthquake sources in the database comprises:

[0015] Classifying the earthquake sources in the database to obtain the number of first target categories and the number of second target categories;

[0016] Based on the first target category number, obtaining a first microseismic average concentration degree through clustering calculation;

[0017] Based on the second target category number, obtaining a second microseismic average concentration degree through clustering calculation;

[0018] The first average microseismic concentration degree and the second average microseismic concentration degree are compared to obtain the clustered microseismic data.

[0019] Preferably, the process of comparing the first average microseismic concentration with the second average microseismic concentration includes:

[0020] If the second average microseismic concentration is greater than or equal to the first average microseismic concentration, adding a clustering category to continue clustering;

[0021] If the second average microseismic aggregation degree is less than the first average microseismic aggregation degree, clustering is stopped, and the clustered microseismic data are obtained based on the second target number of categories.

[0022] Preferably, the process of calculating the average microseismic concentration includes:

[0023] Collecting microseismic time-space coordinate values, and calculating the variance of the microseismic time-space coordinate values;

[0024] Based on the variance of all microseismic time-space coordinate values ​​under different clusters, the time-space coefficient of variation is obtained;

[0025] Based on the time-space variation coefficient and the time-space coordinate values ​​of microseismic, the geometric center coordinates of all earthquake sources in different clusters are obtained;

[0026] The average microseismic aggregation degree is obtained based on the geometric center coordinates, the microseismic time-space coordinate values, the time-space coefficient of variation, and the number of microseismic events of different clusters.

[0027] Preferably, the specific method for calculating the average microseismic integrity level is:

[0028] Draw the microseismic energy level and event cumulative frequency curves of all earthquake sources in different clusters;

[0029] Draw the energy level distribution probability curves of all earthquake sources in different clusters;

[0030] Obtaining microseismic integrity levels of different clusters according to the microseismic energy levels corresponding to the frequency peaks in the energy level distribution probability curve;

[0031] The average microseismic integrity level is obtained according to the microseismic integrity levels of the different clusters and the number of clusters under different microseismic arrangement schemes.

[0032] Preferably, the process of evaluating the monitoring effects of different microseismic networks includes ranking the average microseismic integrity levels to obtain a ranking of the monitoring effects of the different microseismic networks.

[0033] The technical effects of the present invention are as follows: the calculation model of the microseismic network monitoring effect evaluation method involved in the present invention is clear, has a solid theoretical support, and has strong universality and operability; it can effectively evaluate the monitoring effect of the microseismic system during the continuous relocation of the coal mine microseismic network, provide reliable support for improving the accuracy of microseismic monitoring and improving the accuracy of dynamic disaster warning, and provide a reliable basis for relevant technical personnel to formulate microseismic network relocation and optimization layout plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 This is a flow chart of a method for evaluating the monitoring effect of a microseismic network according to an embodiment of the present invention;

[0036] Figure 2 A schematic diagram of the deployment of a coal mine microseismic system according to an embodiment of the present invention;

[0037] Figure 3 A diagram showing the arrangement of a microseismic network before the microseismic stations are moved according to an embodiment of the present invention;

[0038] Figure 4 A diagram showing the arrangement of a microseismic network after the microseismic stations are moved according to an embodiment of the present invention;

[0039] Figure 5 This is a schematic diagram of clustering results when the number of clusters is 1 in the iterative clustering process of an embodiment of the present invention;

[0040] Figure 6 This is a schematic diagram of clustering results when the number of clusters is 2 in the iterative clustering process of an embodiment of the present invention;

[0041] Figure 7 This is a schematic diagram of clustering results when the number of clusters is 3 in the iterative clustering process of an embodiment of the present invention;

[0042] Figure 8 This is a schematic diagram of clustering results when the number of clusters is 4 in the iterative clustering process of an embodiment of the present invention;

[0043] Fig. 9 This is a schematic diagram of clustering results when the number of clusters is 5 in the iterative clustering process of an embodiment of the present invention;

[0044] Fig.10 The figure is a schematic diagram of calculating the microseismic integrity level of a cluster according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] Embodiment 1

[0048] like Figure 1-2 As shown in the figure, since the location of the underground mining area of ​​the coal mine is always moving, in order to achieve good monitoring of the mining area, the underground microseismic station layout needs to be moved with the movement of the mining area. This is the dynamic adjustment process of the deployment of microseismic stations. For example, in order to monitor the rock burst risk during the mining process of the LW250106-1 working face, a coal mine deployed a microseismic monitoring system in the mine, and focused on deploying microseismic stations in key monitoring areas and around the LW250106-1 working face. Figure 3 This is a schematic diagram of the layout of microseismic stations from 2016-7-15 to 2016-8-15. As the LW250106-1 working face continued to move forward, in order to prevent the microseismic stations from being affected or damaged by other machinery on the working face, the microseismic stations near the working face were moved, such as Figure 4 The schematic diagram of the arrangement of microseismic stations after the relocation from 2016-8-16 to 2016-9-15 is shown. In the future, as the working face continues to move forward, the microseismic stations will continue to undergo a similar relocation process. In this process, different microseismic station arrangement schemes capture different signals of mine earthquake signals, that is, the monitoring effects are different. Therefore, the present invention proposes a method for evaluating the monitoring effect of a coal mine microseismic network to quantitatively evaluate the monitoring effect of the microseismic system during the continuous relocation of the coal mine microseismic network, which can effectively improve the accuracy of microseismic monitoring and provide a reliable basis for relevant technical personnel to formulate a relocation and arrangement plan for the microseismic network.

[0049] To achieve the above-mentioned purpose, the present invention provides the following scheme: a method for evaluating the monitoring effect of a coal mine microseismic network: first, all microseismic network deployment schemes are determined and microseismic data under corresponding deployment schemes are collected to establish a database, and the seismic sources in the microseismic database under all microseismic network deployment schemes are clustered in turn using an iterative clustering method, and the clustered microseismic data under different microseismic network deployment schemes are screened to calculate the average microseismic integrity level to judge the monitoring effect of the microseismic network; specifically, the following includes: in the iterative clustering process, the samples are calculated as the occurrence time and three-dimensional coordinates of the seismic sources of the microseismic events, and when judging the monitoring effect of the microseismic network, the one with a smaller average microseismic integrity level is evaluated as having a good monitoring effect.

[0050] For example, the method proposed in the present invention is used to analyze the microseismic arrangement scheme 1 ( Figure 3 Microseismic station layout plan from July 15, 2016 to August 15, 2016) and plan 2 ( Figure 4 The monitoring effect of the microseismic station layout plan from August 16, 2016 to September 15, 2016 was evaluated.

[0051] First, the microseismic data monitored under different microseismic network deployment schemes are collected as a database, for example, Table 1 shows Figure 3 Part of the microseismic data collected under the microseismic station layout plan from July 15, 2016 to August 15, 2016 (due to the huge amount of microseismic data, only part of the data is listed here for illustration).

[0052] Table 1

[0053]

[0054]

[0055] Secondly, all the microseismic data collected under Scheme 1 and Scheme 2 were classified using a hierarchical clustering algorithm. In the clustering process, the target category was first set as category i (i = 1). After clustering, the average microseismic aggregation degree q under different clusters was calculated. i ; Then the target category is set as i+1, and then clustering is performed and the average microseismic aggregation degree q is calculated i+1 ; for q i With q i+1 If q i+1 ≥q i Then continue to add clustering categories for clustering; q i+1 <q i Then clustering stops and the current number of categories is used as the final number of categories.

[0056] The formula for calculating the average concentration of microseismic events in the clustering method is:

[0057]

[0058] In the formula, is the average concentration of microseismic events in a cluster; n is the number of microseismic events in a cluster; a i =(x,y,z,c 2 t) is the time-space coordinate of the ith earthquake source, x, y, z and t are the X, Y, Z spatial coordinate values ​​of the earthquake source and the time of earthquake occurrence respectively; is the geometric center coordinates of all earthquake sources in a cluster; c 2 is the time-space coefficient of variation; Var(X), Var(Y), Var(Z) and Var(T) are the variances of the time-space coordinates x, y, z, t of all microseisms under a certain cluster.

[0059] Since the calculation process of iterative clustering for Scheme 1 and Scheme 2 is similar, only the calculation process of Scheme 1 is used as an example. Formula (1) is used to perform hierarchical clustering analysis on all microseismic data monitored by Scheme 1. First, the target category is set to 1, and the clustering results are as follows: Figure 5 As shown, the average aggregation degree q is calculated at the same time i , and so on, continue to add target categories to cluster and calculate the average aggregation degree; when the target categories are 2, 3, 4, and 5, the corresponding clustering results are as follows Figure 6 to Figure 9 As shown in the figure, the average aggregation degrees corresponding to the five target categories are calculated to be 6511.32, 4567.47, 3136.17, 1979.78, and 6255.45, respectively. Therefore, the number of clustering categories for scheme one can be set to 4.

[0060] Then, the average microseismic integrity level is calculated for Scheme 1. According to the integrity level m of the microseismic data under the four clusters in Scheme 1, i , calculate the average microseismic integrity level under a certain microseismic layout scheme, and the specific method is as follows:

[0061] (1) Plot the microseismic energy level and event cumulative frequency curves of all earthquake sources in different clusters;

[0062] (2) draw the energy level distribution probability curves of all earthquake sources in different clusters;

[0063] (3) Take the microseismic energy level corresponding to the frequency peak in the energy level distribution probability curve as the microseismic integrity level m of the i-th cluster i .

[0064] Using the above method, the microseismic integrity level m can be calculated for each of the four categories. i For example, one of the microseismic integrity calculation results is as follows Fig.10 As shown, where m i=-1.28; the same method was used to obtain the other three microseismic integrity levels, which were -1.35, -1.46, and -1.24, respectively.

[0065] Furthermore, the average microseismic integrity level of all clusters in solution 1 is calculated using the following formula:

[0066]

[0067] Where k is the number of clusters under a certain microseismic layout scheme.

[0068] It can be obtained that the average microseismic integrity level of all clusters in scheme 1 is It is (-1.28-1.35-1.46-1.24) / 4=-1.33.

[0069] The above method is also used to calculate the average microseismic integrity level of Scheme 2, and the result is -1.41. It can be concluded that the microseismic monitoring effect of Scheme 2 is worse than that of Scheme 1. Subsequent monitoring personnel can further adjust the microseismic arrangement plan. After the adjustment is completed, the present invention can be used to further judge the microseismic monitoring effect. By cyclically adjusting in this way, a better microseismic arrangement plan can be finally obtained.

[0070] It should be noted that the scheme proposed in the present invention is also applicable to the situation where there are multiple microseismic monitoring schemes for monitoring effect analysis. In this case, when judging the monitoring effect of the microseismic network, the average microseismic integrity levels of different network layout schemes are compared in turn and ranked from small to large. The corresponding ranking result is the ranking of the monitoring effects of different microseismic network deployment schemes.

[0071] The embodiments described above are only descriptions of the preferred modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for evaluating the monitoring effect of a coal mine microseismic network, characterized in that: include: Collect microseismic data and establish a database; Iteratively clustering the earthquake sources in the database to obtain clustered microseismic data; Calculating an average microseismic integrity level based on the clustered microseismic data; Based on the average microseismic integrity level, the monitoring effects of different microseismic networks are evaluated; The specific method for calculating the average microseismic integrity level is: Draw the microseismic energy level and event cumulative frequency curves of all earthquake sources in different clusters; Draw the energy level distribution probability curves of all earthquake sources in different clusters; Obtaining microseismic integrity levels of different clusters according to the microseismic energy levels corresponding to the frequency peaks in the energy level distribution probability curve; According to the microseismic integrity levels of the different clusters and the number of clusters under different microseismic arrangement schemes, the average microseismic integrity level is obtained; The process of iteratively clustering the earthquake sources in the database includes: Classifying the earthquake sources in the database to obtain a first target category number and a second target category number, wherein the first target category number is smaller than the second target category number; Based on the first target category number, obtaining a first microseismic average concentration degree through clustering calculation; Based on the second target category number, obtaining a second microseismic average concentration degree through clustering calculation; Comparing the first average microseismic concentration degree with the second average microseismic concentration degree to obtain the clustered microseismic data; The process of comparing the first average microseismic concentration and the second average microseismic concentration includes: If the second average microseismic aggregation degree is less than the first average microseismic aggregation degree, adding a clustering category to continue clustering; If the second average microseismic aggregation degree is greater than or equal to the first average microseismic aggregation degree, stopping clustering, and obtaining the clustered microseismic data based on the first target number of categories; The formula for calculating the average concentration of microseismic events in the clustering method is: ， In the formula, is the average concentration of microseismic events in a certain cluster; n is the number of microseismic events in a certain cluster; is the time-space coordinate of the ith earthquake source, x, y, z and t are the X, Y, Z spatial coordinate values ​​of the earthquake source and the time coordinate value of the earthquake occurrence time T respectively; are the geometric center coordinates of all earthquake sources in a cluster; is the spatiotemporal coefficient of variation; , , and are the variances of the time-space coordinates x, y, z, t of all microseisms in a certain cluster, and i and j are the source numbers.

2. A method for evaluating the monitoring effect of a coal mine microseismic network according to claim 1, characterized in that: include: The process of collecting microseismic data and establishing a database includes collecting microseismic data under different microseismic network deployment schemes and establishing a database based on the microseismic data.

3. A method for evaluating the monitoring effect of a coal mine microseismic network according to claim 1, characterized in that: include: The process of iteratively clustering the earthquake sources in the database includes calculating the earthquake occurrence time and the three-dimensional coordinates of the earthquake sources of the samples as microseismic events.

4. A method for evaluating monitoring effect of a coal mine microseismic network according to claim 1, characterized in that: include: The process of calculating the average concentration of microseismic events includes: Collecting microseismic time-space coordinate values, and calculating the variance of the microseismic time-space coordinate values; Based on the variance of all microseismic time-space coordinate values ​​under different clusters, the time-space coefficient of variation is obtained; Based on the time-space variation coefficient and the time-space coordinate values ​​of microseismic, the geometric center coordinates of all earthquake sources in different clusters are obtained; The average microseismic aggregation degree is obtained based on the geometric center coordinates, the microseismic time-space coordinate values, the time-space coefficient of variation, and the number of microseismic events of different clusters.

5. A method for evaluating monitoring effect of a coal mine microseismic network according to claim 1, characterized in that: include: The process of evaluating the monitoring effects of different microseismic networks includes ranking the average microseismic integrity levels to obtain a ranking of the monitoring effects of the different microseismic networks.

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