Rock burst warning method based on cumulative microseismic energy

Abstract

The rock burst early warning method based on the cumulative energy of microseismicity disclosed by the present invention collects the monitoring data of the coal body stress early warning measurement points in the working face with severe rock bursts in the mine, draws the curve of the monitoring data of each stress early warning measurement point changing with the distance between the stress early warning measurement point and the working face, and statistically calculates the cumulative energy of microseismic events near each stress early warning measurement point; combines the distance change curve with the cumulative energy, analyzes their correlation, and is used to judge whether early warning is required for the experimental mine. If early warning is required, the cumulative energy threshold of the sample working face and the energy values of the remaining working faces are collected. When the energy value of the remaining working face is not less than the cumulative energy threshold of the sample working face, danger removal measures are taken. The rock burst early warning method based on the cumulative energy of microseismicity of the present invention improves the accuracy of rock burst early warning.

Description

Technical Field

[0001] The invention belongs to the technical field of coal mine rock burst monitoring and early warning, and particularly relates to a rock burst early warning method based on microseismic cumulative energy. Background Technique

[0002] Rock burst is one of the main disasters faced in deep coal mining. In China, relevant research on the occurrence mechanism, monitoring and early warning, prevention and control measures of rock burst has been continuously deepened, but accurate early warning cannot be achieved in the aspect of rock burst monitoring and early warning.

[0003] At present, the commonly used rock burst risk early warning methods in domestic rock burst mines mainly include the drill cuttings method, coal body stress measurement method, microseismic monitoring method, electromagnetic radiation method, etc. Among them, the drill cuttings method, coal body stress measurement method, and electromagnetic radiation method are mostly used to monitor the static load concentration degree of the coal body; and the dynamic load monitoring mainly relies on microseismic monitoring. Microseismic events are a physical effect during the instantaneous release of the elastic energy accumulated during the process of fissure and fracture of the overlying strata. Using a microseismic monitoring system, various microseismic activity parameters including the occurrence time, location, and intensity of microseismic events can be monitored and calculated more accurately, providing the possibility for analyzing the spatio-temporal variation law of mine microseismic activities. Using microseismic activity parameters to evaluate and predict rock burst can improve the accuracy of rock burst prediction.

[0004] During the production process, the rock burst early warning technology based on the microseismic monitoring system is the microseismic energy analysis and prediction method, that is, based on the maximum microseismic energy actually monitored on the current day and the microseismic energy released within a certain advancing distance as the comprehensive rock burst risk early warning index. When the actual monitored value is greater than the threshold, it is determined that there is a possibility of rock burst occurrence. It is found during the actual monitoring process that any single early warning index of the current microseismic monitoring cannot achieve accurate early warning, and it is necessary to combine various indexes to achieve multi-parameter comprehensive early warning. Therefore, the early warning accuracy of this method is not high at present. Summary of the Invention

[0005] The purpose of the invention is to provide a rock burst early warning method based on microseismic cumulative energy, which improves the early warning accuracy of rock burst.

[0006] The technical solution adopted by the invention is a rock burst early warning method based on microseismic cumulative energy. Collect the monitoring data of the coal body stress early warning points in the severely impacted working face of the mine, draw the curve of the monitoring data of each stress early warning point changing with the distance between the stress early warning point and the working face, and count the cumulative energy of microseismic events near each stress early warning point; combine the distance change curve with the cumulative energy, analyze their correlation, and use it to judge whether the experimental mine needs early warning. If early warning is required, collect the cumulative energy threshold of the sample working face and the energy values of the other working faces. When the energy value of the other working faces is not less than the cumulative energy threshold of the sample working face, take danger-removing measures.

[0007] The features of the present invention also lie in that

[0008] Step 1: Take the working face with severe mine shock as the sample working face, and collect the information of the sample working face and the monitoring data of the coal body stress warning measuring points.

[0009] Step 2: Draw the curve of the monitoring data of each stress warning measuring point collected in Step 1 changing with the distance between the stress warning measuring point and the working face.

[0010] Step 3: Statistically count the cumulative energy of microseismic events near each coal body stress warning measuring point, and draw a curve in combination with the distance change curve in Step 2.

[0011] Step 4: Judge whether this method is suitable for the experimental mine according to the curve in Step 3. If it is suitable, proceed to the next step.

[0012] Step 5: Divide the statistical interval and statistical boundary for the sample working face, and statistically count the cumulative energy value of the sample working face based on the statistical interval and statistical boundary to obtain the energy warning threshold.

[0013] Step 6: Similarly divide the statistical interval and statistical boundary for the target working face to be predicted, and statistically count the cumulative energy value of its working face. When the cumulative energy value is not less than the energy warning threshold of the sample working face, take on-site danger relief measures.

[0014] The specific steps of Step 1 are as follows:

[0015] ① Select the working face with the most severe shock manifestation among the mined-out working faces of this mine as the sample working face.

[0016] ② Collect all microseismic monitoring data of the sample working face, the plane positioning error △m of the microseismic system after roof blasting and coal seam blasting; the unit of △m is meter.

[0017] Collect the date and location information of each shock manifestation event of the sample working face.

[0018] Collect the monitoring data of no less than 3 coal body stress warning measuring points during the mining of the sample working face.

[0019] The specific steps of Step 2 are as follows:

[0020] ① Statistically count the average stress σ of each day for every 10m between the sample of each coal body stress warning measuring point and the working face ij , where i represents the i-th warning measuring point and j is the j-th 10m interval.

[0021] ② Take the distance between each stress warning measuring point and the working face as the horizontal axis; take the average daily stress σ of each stress warning measuring point ij as the vertical axis, and draw the σ ij —distance curve.

[0022] Step 3 The specific steps are:

[0023] ① With the installation point of each stress warning measuring point as the center and △m as the radius, set the plane microseismic data picking frame, where △m is the plane positioning error of the microseismic system for the blasting event;

[0024] ② Count the number and energy of microseismic events that occur in the microseismic data picking frame every 10m of mining on the sample working face, and use the cumulative form to obtain the daily cumulative energy n of each stress warning measuring point ij , where i represents the i-th warning measuring point, and j represents the j-th 10m interval;

[0025] ③In σ ij —Add the daily accumulated energy n of each stress warning measuring point to the vertical axis of the distance curve ij , we get ij —n ij —Distance curve.

[0026] Step 4 is to calculate the σ obtained in step 3. ij —n ij —distance curve. If the stress monitoring data curve of each stress warning measuring point shows a positive correlation with the cumulative energy curve of the microseismic event, proceed to the next step. Otherwise, this method is not applicable to the experimental mine.

[0027] Step 5 The specific steps are:

[0028] ① Along the strike of the sample working face, the tunnel is divided into several statistical intervals in units of 20 meters. The number of divisions is the strike length of the working face tunnel divided by 20 meters. The above statistical interval is the strike statistical interval;

[0029] ② Collect daily microseismic events on the sample working face, set △m meters on both sides of the positive wall of the roadway in the working face inclination direction as the statistical boundary, and eliminate microseismic events whose plane positions exceed the statistical boundary;

[0030] ③ After the working face is mined, the cumulative energy E of microseismic events within the daily statistical boundary in each statistical interval is counted. ry , where r represents the rth trend statistical interval;

[0031] ④ The minimum value of the microseismic cumulative energy corresponding to all the impact manifestation events of the sample working face is selected as the microseismic cumulative energy warning threshold, which is recorded as the cumulative energy threshold |E ry |.

[0032] Step 6 The specific steps are:

[0033] ①During the mining of the target working face to be predicted in this mine, the roadway is divided into several statistical intervals with 20 meters as a unit. The number of divisions is the length of the working face roadway divided by 20 meters. The above statistical intervals are named strike statistical intervals;

[0034] ②Collect the daily microseismic events during the mining of the target working face to be predicted. Set △m meters on both sides of the positive side of the roadway in the dip direction of the working face as the statistical boundary, and eliminate the microseismic events whose planar positions exceed the statistical boundary;

[0035] ③Since the mining of the target working face to be predicted, count the cumulative energy E of the microseismic events within the statistical boundary every day in each strike statistical interval r , where r represents the r-th strike statistical interval;

[0036] ④If the cumulative energy Er of the strike statistical interval of the roadway of the target working face to be predicted on the current day is ≥ |E ry |, it is determined that there is an impact risk in this strike statistical interval, and on-site danger elimination measures are taken.

[0037] The beneficial effects of the present invention are as follows

[0038] (1) By combining microseismic monitoring data, stress warning measurement point monitoring data, and information such as planar positioning error, time, and position for determination, the present invention realizes the early detection of risk areas. The warning experiment results show that it can detect the risk areas of mine pressure and impact manifestation more than 10 days in advance, which is of great significance for guiding the on-site danger elimination in advance in the mine.

[0039] (2) For roof-type rock bursts, in the two roadways of the working face, especially the goaf-side roadway, the static and dynamic load sources of the roadway surrounding rock come from the hanging roof in the goaf. During the period affected by secondary mining, on the one hand, the rupture of the hanging roof in the goaf generates dynamic loads, and on the other hand, the continuous rupture of the overlying strata also intensifies the concentration degree of the static load of the roadway surrounding rock. By using microseismic monitoring data to predict in advance the concentration degree of the static and dynamic loads of the roadway surrounding rock, the impact risk is further predicted. Description of the Drawings

[0040] Figure 1 is a flow chart of the rock burst warning method based on microseismic cumulative energy of the present invention;

[0041] Figure 2 is the σ 1j —distance curve of the 37# stress warning measurement point in step 2 of the embodiment of the rock burst warning method based on microseismic cumulative energy of the present invention;

[0042] Figure 3 is the σ 1j —n 1j—Distance curve;

[0043] Figure 4 is the σ of the 34# stress warning measurement point in step 3 of the embodiment of the rock burst warning method based on the cumulative microseismic energy of the present invention 2j —n 2j —Distance curve;

[0044] Figure 5 is the σ of the 39# stress warning measurement point in step 3 of the embodiment of the rock burst warning method based on the cumulative microseismic energy of the present invention 3j —n 3j —Distance curve;

[0045] Figure 6 is the σ of the 42# stress warning measurement point in step 3 of the embodiment of the rock burst warning method based on the cumulative microseismic energy of the present invention 4j —n 4j —Distance curve;

[0046] Figure 7 is the schematic diagram of the statistical boundary of the sample working face in step 5 of the embodiment of the rock burst warning method based on the cumulative microseismic energy of the present invention;

[0047] Figure 8 is the statistical chart of the cumulative energy of the sample working face in step 5 of the embodiment of the rock burst warning method based on the cumulative microseismic energy of the present invention;

[0048] Figure 9 is the relationship between the cumulative microseismic energy and the impact manifestation of the sample working face in step 5 of the embodiment of the rock burst warning method based on the cumulative microseismic energy of the present invention;

[0049] Figure 10 is the statistical result of the cumulative microseismic energy of the 31104-2 working face in step 6 of the embodiment of the rock burst warning method based on the cumulative microseismic energy of the present invention. Detailed implementation mode

[0050] The present invention will be described in detail below in conjunction with the accompanying drawings and specific implementation modes.

[0051] The specific process of the rock burst warning method based on the cumulative microseismic energy of the present invention is as follows Figure 1 As shown, collect the monitoring data of the coal body stress warning measurement points of the sample working face with serious rock bursts in the mine, draw the curve of the monitoring data of each stress warning measurement point changing with the distance between the stress warning measurement point and the working face, and statistically calculate the cumulative energy of the microseismic events near each stress warning measurement point; combine the distance change curve with the cumulative energy to judge whether this method is suitable for the experimental mine. If it is suitable, proceed to the next step, collect the cumulative energy threshold of the sample working face and the energy values of the other working faces. When the energy value of the other working faces is not less than the cumulative energy threshold of the sample working face, take danger removal measures.

[0052] The specific steps are:

[0053] Step 1: Take the working face with severe impact in the mine as a sample working face, and collect various data of the sample working face and monitoring data of coal body stress early warning measuring points;

[0054] ① Select the working face with the most serious impact among the working faces that have been mined in this mine as the sample working face;

[0055] Collect all microseismic monitoring data of the sample working face, roof blasting, and coal seam blasting, and the plane positioning error △m of the microseismic system for the blasting event; the unit of △m is meter;

[0056] Collect the date and location information of all impact manifestation events on the sample working face;

[0057] Collect monitoring data from no less than 3 coal stress warning measurement points during the mining period of the sample working face.

[0058] Step 2, drawing a curve of the monitoring data of each stress warning measuring point collected in step 1 versus the distance between the stress warning measuring point and the working surface;

[0059] ① The stress average value σ of each coal body stress warning measurement point is calculated every 10m from the working face. ij , where i represents the i-th warning measuring point, and j represents the j-th 10m interval;

[0060] ② The distance between each stress warning measuring point and the working surface is taken as the horizontal axis; the daily average stress value σ of each stress warning measuring point is taken as the horizontal axis. ij As the vertical axis, plot σ ij —Distance curve.

[0061] Step 3, counting the accumulated energy of microseismic events near each coal body stress warning measuring point, and drawing a curve in combination with the distance change curve in step 2;

[0062] ① With the installation point of each stress warning measuring point as the center and △m as the radius, set the plane microseismic data picking frame, where △m is the plane positioning error of the microseismic system for the blasting event, in meters;

[0063] ② Count the number and energy of microseismic events that occur in the microseismic data picking frame every 10m of mining on the sample working face, and use the cumulative form to obtain the daily cumulative energy n of each stress warning measuring point ij , where i represents the i-th warning measuring point, and j represents the j-th 10m interval;

[0064] ③In σ ij —Add the daily accumulated energy n of each stress warning measuring point to the vertical axis of the distance curve ij， Get σ ij —n ij —Distance curve.

[0065] Step 4. According to the σ obtained in Step 3 ij —n ij —distance curve result. If the stress monitoring data curve of each stress warning measuring point shows a positive correlation with the cumulative energy curve of microseismic events, then proceed to the next step; otherwise, this method is not applicable to the experimental mine.

[0066] Step 5. Divide the statistical intervals and statistical boundaries for the sample working face, and based on the statistical intervals and statistical boundaries, count the cumulative energy value of the sample working face to obtain the energy warning threshold;

[0067] ① Along the strike of the sample working face, divide the roadway into several statistical intervals at a unit of 20 meters. The number of divisions is the length of the roadway along the strike of the working face divided by 20 meters. The above statistical intervals are the strike statistical intervals;

[0068] ② Collect the daily microseismic events of the sample working face, set △m meters on both sides of the positive rib of the roadway in the dip direction of the working face as the statistical boundary, and exclude the microseismic events whose planar positions exceed the statistical boundary;

[0069] ③ Since the working face is mined, count the cumulative energy E of the microseismic events within the statistical boundary every day in each strike statistical interval ry , where r represents the r-th strike statistical interval;

[0070] ④ Select the minimum value among the microseismic cumulative energies corresponding to the previous rock burst occurrence events of the sample working face as the microseismic cumulative energy warning threshold, denoted as the cumulative energy threshold |E ry |.

[0071] Step 6. Similarly divide the statistical intervals and statistical boundaries for the target working face to be predicted, and use them to count the cumulative energy value of its working face. When the cumulative energy value is not less than the energy warning threshold of the sample working face, take on-site danger relief measures;

[0072] ① During the mining of the target working face to be predicted in this mine, divide the roadway into several statistical intervals at a unit of 20 meters. The number of divisions is the length of the roadway of the working face divided by 20 meters; the above statistical intervals are named strike statistical intervals;

[0073] ② Collect the daily microseismic events during the mining of the working face, set △m meters on both sides of the positive rib of the roadway in the dip direction of the working face as the statistical boundary, and exclude the microseismic events whose planar positions exceed the statistical boundary;

[0074] ③ Since the working face is mined, count the cumulative energy E of the microseismic events within the statistical boundary every day in each strike statistical interval r , where r represents the r-th strike statistical interval;

[0075] ④ If the cumulative energy Er in the statistical interval of the roadway strike of the working face on the same day is ≥ |E ry |, it is determined that there is an impact risk in this strike statistical interval, and on-site danger relief measures need to be taken.

[0076] Embodiment

[0077] Step 1: Collect various data of the sample working face of the mine's severely impacted working face and the monitoring data of the coal body stress warning measurement points;

[0078] ① The 31103-1 working face of the target mine involved in this embodiment is the working face with the most severe impact manifestation among the mined-out working faces. The 31103-1 working face is used as the sample working face;

[0079] ② Collect the microseismic monitoring data of the sample working face. Through the microseismic system to locate the blasting event plane after the roof blasting of this working face, it is found that the positioning error is 0-50 meters;

[0080] Collect the date and location information of each impact manifestation event in the sample working face as shown in Table 1.

[0081] Table 1 Date and location information table of each impact manifestation event in the sample working face

[0082] Serial number Date Accumulative footage Advance distance 1 2019.7.29 566m 67m 2 2019.8.1 599m 40m 3 8.8 647m 5m 4 8.11 661m 60m 5 2019.9.3 740.05m 67m 6 2019.9.21 789m 40m 7 2019.10.18 816.5m 40m 8 2020.1.7 1018.7m 30m

[0083] Collect the monitoring data of the 37#, 34#, 39#, and 42# coal body stress warning measurement points during the mining of the sample working face.

[0084] Step 2: Draw the curve of the monitoring data of each stress warning measurement point collected in Step 1 changing with the distance between the stress warning measurement point and the working face;

[0085] ① For the 37# coal body stress warning measurement point sample, the average stress σ on the same day is statistically calculated every 10m from the working face 1j , as shown in Table 2 specifically.

[0086] Table 2 σ of the 37# stress warning measurement point in the sample working face 1j Statistical table

[0087]

[0088]

[0089] ② Taking the distance between the 37# stress warning measurement point and the working face as the horizontal axis; taking the average daily stress σ of each stress warning measurement point 1j as the vertical axis, draw the σ 1j —distance curve as Figure 2 shown.

[0090] Step 3: Statistically calculate the cumulative energy of microseismic events near each stress warning measurement point of the coal body, and draw a curve in combination with the distance change curve in Step 2.

[0091] ① Set a planar microseismic data acquisition frame with the installation point of the 37# stress warning measurement point as the center and a radius of 50 m, which is the positioning error of the blasting event distance.

[0092] ② Statistically calculate the number and energy of microseismic events occurring within the microseismic data acquisition frame every 10 m of coal mining in the sample working face, and calculate the daily cumulative energy n of the 37# stress warning measurement point in an accumulative form. 1j , and the statistical results are shown in Table 3.

[0093] Table 3 Daily cumulative energy n of the 37# in the sample working face 1j Statistical table

[0094]

[0095] ③ Add the daily cumulative energy n of the 37# stress warning measurement point to the vertical axis in the σ 1j —distance curve in Step 2, and obtain the σ 1j —n Figure 3 —distance curve of the 37# stress warning measurement point as shown in 1j —n 1j .

[0096] Obtain the σ Figures 4 - 6 —n 2j —distance curve of the 34# stress warning measurement point, the σ 2j —n 3j —distance curve of the 39# stress warning measurement point, the σ 3j —n 4j —distance curve of the 42# stress warning measurement point, and the σ 4j —n

[0097] Step 4: Judge whether this method is suitable for the experimental mine according to the curve in Step 3. If it is suitable, proceed to the next step;

[0098] Check the σ ij —n ij —distance curves of each measurement point obtained in Step 3. If the stress monitoring data curves of each stress warning measurement point and the cumulative energy curve of microseismic events show a positive correlation, proceed to the next step.

[0099] Step 5: Divide the statistical interval and statistical boundary for the sample working face, and statistically calculate the cumulative energy value of the sample working face based on the statistical interval and statistical boundary to obtain the energy warning threshold;

[0100] ① Along the strike of the sample working face, the roadway is divided into several statistical intervals at a unit of 20 meters, and the number of divisions is the length of the roadway along the strike of the working face divided by 20 meters; the above statistical intervals are named strike statistical intervals.

[0101] ② Collect the daily microseismic events of the sample working face, and set △m meters on both sides of the positive side of the roadway in the dip direction of the working face as the statistical boundary. As Figure 7 shown, eliminate the microseismic events whose planar positions exceed the statistical boundary.

[0102] ③ After the working face is mined and recovered, count the cumulative energy E ry of the microseismic events within the statistical boundary every day in each strike statistical interval. As Figure 8 and Table 4 show, where r represents the r-th strike statistical interval.

[0103] Table 4 Statistical table of cumulative energy of the sample working face

[0104]

[0105]

[0106] ④ Mark the previous rock burst occurrence events of the sample working face according to the daily cumulative mining footage. As Figure 9 shown, it can be found that the minimum value of the cumulative microseismic energy corresponding to the previous rock burst occurrence events is about 1.6E+06J. Therefore, |E ry | = 1.0E+06J.

[0107] Step 6: Similarly divide the statistical intervals and statistical boundaries for the target working face to be predicted to count the cumulative energy value of its working face. When the cumulative energy value is not less than the energy warning threshold of the sample working face, take on-site danger relief measures.

[0108] ① During the mining of the 31104-2 working face in this mine, the roadway is divided into several statistical intervals at a unit of 20 meters, and the number of divisions is the length of the roadway of the working face divided by 20 meters.

[0109] ② Collect the daily microseismic events during the mining of the 31104-2 working face, and set 50 meters on both sides of the positive side of the roadway in the dip direction of the working face as the statistical boundary, and eliminate the microseismic events whose planar positions exceed the statistical boundary.

[0110] ③ After the working face is mined and recovered, count the cumulative energy E r of the microseismic events within the statistical boundary every day in each strike statistical interval. As Figure 10 shown, where r represents the r-th strike statistical interval.

[0111] ④As of June 23, 2022, the cumulative mining length of the 31104-2 working face was 344 m. The cumulative microseismic energy within the range of 96 - 116 m ahead of the working face reached the warning threshold, and a warning was issued.

[0112] During the underground exploration, it was found that the shoulder sockets on the positive sides of the roadways in this area were deformed, a net pocket was formed on the roof, and the two sides of the roadway bulged. This was the area with the most intense mine pressure manifestation during the mining of this work. Subsequently, measures to reduce the mining speed, reinforce the support, and take danger relief measures such as large diameter were formulated.

[0113] This embodiment illustrates the implementation steps and prediction effects of the method of the present invention, can better achieve risk investigation in the advanced area of the working face for the dynamic manifestation and mine pressure manifestation of the two roadways of the working face, especially the roadway adjacent to the goaf, and has certain guiding significance for the on-site prevention and control work of rock bursts in the mine.

Claims

1. A rock burst warning method based on the cumulative energy of microseismicity, characterized in that, Collect monitoring data of coal stress warning measurement points of working faces with severe impact in mines, draw curves of monitoring data of each stress warning measurement point as the distance between the stress warning measurement point and the working face changes, and count the accumulated energy of microseismic events near each stress warning measurement point; combine the distance change curve with the accumulated energy, analyze their correlation, and use it to determine whether the experimental mine needs an early warning. If an early warning is needed, collect the accumulated energy threshold of the sample working face and the energy values of the remaining working faces. When the energy values of the remaining working faces are not less than the accumulated energy threshold of the sample working face, take emergency measures; The following steps are involved: Step 1: Take the working face with severe impact in the mine as a sample working face, and collect the information of the sample working face and the monitoring data of the coal body stress early warning measuring point; Step 2, drawing a curve of the monitoring data of each stress warning measuring point collected in step 1 versus the distance between the stress warning measuring point and the working surface; Step 3, counting the accumulated energy of microseismic events near each coal body stress warning measuring point, and drawing a curve in combination with the distance change curve in step 2; Step 4, judging whether the method is suitable for the experimental mine according to the curve in step 3, and proceeding to the next step if it is suitable; Step 5, dividing the sample working surface into statistical intervals and statistical boundaries, and calculating the cumulative energy value of the sample working surface based on the statistical intervals and statistical boundaries to obtain an energy warning threshold; Step 6: Divide the target working face to be predicted into statistical intervals and statistical boundaries, and count the cumulative energy value of the working face. When the cumulative energy value is not less than the energy warning threshold of the sample working face, take on-the-spot emergency measures.

2. The rock burst early warning method based on the cumulative energy of microseismicity according to claim 1, wherein The specific steps of step 1 are: ① Select the working face with the most serious impact among the working faces that have been mined in this mine as the sample working face; ② Collect all microseismic monitoring data of the sample working face, roof blasting, and the plane positioning error △m of the microseismic system for the blasting event after the coal seam blasting; The unit of △m is meter; Collect the date and location information of all impact manifestation events on the sample working face; Collect monitoring data from no less than 3 coal stress warning measurement points during the mining period of the sample working face.

3. The rock burst early warning method based on the cumulative energy of microseismicity according to claim 2, wherein, The specific steps of step 2 are: ① The average stress value σ of each stress warning measurement point sample in the coal body is statistically calculated every 10m from the working face for the stress value of the day. Here, i represents the i-th warning measurement point, and j represents the j-th 10m interval. ij ​ ②With the distance between each stress warning measurement point and the working face as the horizontal axis; and with the average daily stress σ of each stress warning measurement point ij as the vertical axis, plot the σ ij -distance curve.

4. The rock burst early warning method based on the cumulative energy of microseismicity according to claim 3, characterized in that, The specific steps of step 3 are: ① With the installation point of each stress warning measuring point as the center and △m as the radius, set the plane microseismic data picking frame, where △m is the plane positioning error of the microseismic system for the blasting event; ② Statistically count the number and energy of microseismic events occurring within the microseismic data acquisition frame every 10 m of mining in the working face of the statistical sample, and obtain the daily cumulative energy n of each stress warning measurement point in an accumulative form ij , where i represents the i-th warning measurement point and j is the j-th 10 m interval; ③ In σ ij — Add the daily cumulative energy n of each stress warning measurement point to the vertical axis in the distance curve ij， to obtain σ ij — n ij — distance curve.

5. The rock burst early warning method based on the cumulative energy of microseismicity according to claim 4, wherein Step 4 specifically involves obtaining σ according to Step 3 ij —n ij —distance curve. If the stress monitoring data curves of each stress warning measurement point show a positive correlation with the cumulative energy curve of microseismic events, then proceed to the next step; otherwise, this method is not applicable to the experimental mine.

6. The rock burst early warning method based on the cumulative energy of microseismicity according to claim 5, wherein The specific steps of step 5 are: ① Along the strike of the sample working face, the tunnel is divided into several statistical intervals in units of 20 meters. The number of divisions is the strike length of the working face tunnel divided by 20 meters. The above statistical interval is the strike statistical interval; ② Collect daily microseismic events on the sample working face, set △m meters on both sides of the positive wall of the roadway in the working face inclination direction as the statistical boundary, and eliminate microseismic events whose plane positions exceed the statistical boundary; ③ After the mining of the working face, the cumulative energy E of microseismic events within the statistical boundary is counted daily in each strike statistical interval ry , where r represents the r-th strike statistical interval; ④ Select the minimum value of the cumulative microseismic energy corresponding to each rock burst occurrence event on the sample working face as the early warning threshold of the cumulative microseismic energy, denoted as the cumulative energy threshold .

7. The rock burst early warning method based on the cumulative energy of microseismicity according to claim 6, characterized in that, The specific steps of step 6 are: ① During the mining period of the target working face to be predicted in this mine, the tunnel is divided into several statistical intervals with a unit of 20 meters. The number of divisions is the length of the working face tunnel divided by 20 meters. The above statistical intervals are named as strike statistical intervals; ② Collect daily microseismic events during the mining period of the target working face to be predicted, set △m meters on both sides of the positive wall of the tunnel in the working face inclination direction as the statistical boundary, and eliminate microseismic events whose plane positions exceed the statistical boundary; ③After the target working face to be predicted is mined and extracted, the cumulative energy E of microseismic events within the statistical boundary is counted daily in each strike statistical interval r , where r represents the r-th strike statistical interval; ④ If the cumulative energy Er in the statistical interval of the roadway strike of the target working face to be predicted on the same day is ≥ , it is determined that there is an impact risk in this strike statistical interval, and on-site danger relief measures are taken.

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