A rock burst early warning method for near-vertical seam

Abstract

The application discloses a rock burst early warning method for near-vertical seam, comprising the following steps: determining dominant energy levels of rock burst occurrence according to energy data of a plurality of large-energy microseismic events in recent times; dividing dominant energy level frequency fluctuation intervals, and determining fluctuation critical values of each dominant energy level frequency fluctuation interval according to frequency data of the dominant energy levels in the energy data of the plurality of large-energy microseismic events in recent times; obtaining dominant energy level frequency data of a statistical day and a plurality of days before and after the statistical day; and early warning rock burst events according to the dominant energy level frequency data of the statistical day and the plurality of days before and after the statistical day and the fluctuation critical values of the dominant energy level frequency fluctuation intervals. The application can perform early warning analysis on different coal mines based on regular analysis on energy data of previous large-energy microseismic events, and has higher early warning accuracy.

Description

Technical Field

[0001] This application relates to the field of coal mining technology, and in particular to a method for early warning of rockburst in near-vertical coal seams. Background Technology

[0002] Rockbursts are one of the most serious disasters facing coal mines in my country, posing a significant threat to the safety of underground operations due to their suddenness, severity, and rapid occurrence. Therefore, accurate early warning of rockbursts is a crucial link in achieving safe coal mine production. Microseismic monitoring, with its wide monitoring range, is widely used in coal mines, covering everything from the compression process of surrounding rock to the generation and propagation of internal micro-cracks and even the failure of the coal and rock mass.

[0003] In recent years, various early warning methods for predicting rockbursts have been proposed both domestically and internationally based on microseismic monitoring, such as simultaneous increase in energy and frequency of microseismic events; increase in frequency and decrease in energy; simultaneous decrease in frequency and energy; and decrease in frequency and increase in energy. However, the number of missed and false alarms is relatively high, and the early warning effect is poor. Summary of the Invention

[0004] The purpose of this application is to overcome the shortcomings of poor early warning effect in the existing technology and to provide a more accurate early warning method for rockburst in near vertical coal seams based on the frequency of the dominant energy level of microseismic events.

[0005] The technical solution of this application provides a method for early warning of rockburst in near-vertical coal seams, including...

[0006] Based on the energy data of several recent high-energy microseismic events, the dominant energy level for rockbursts was determined.

[0007] The frequency fluctuation range of the dominant energy level is divided, and the fluctuation critical value of each dominant energy level frequency fluctuation range is determined based on the frequency data of the dominant energy level in the energy data of several recent high-energy microseismic events.

[0008] Obtain the frequency data of the dominant energy level on the statistical day and the number of days before and after it;

[0009] Based on the frequency data of the dominant energy level on the statistical day and the set number of days before and after it, and the fluctuation threshold value of the frequency fluctuation range of the dominant energy level, an early warning of rockburst events is issued.

[0010] Furthermore, the energy data of the recent high-energy microseismic events includes energy data of at least the recent five high-energy microseismic events;

[0011] The determination of the dominant energy level for rockburst occurrence based on energy data from several recent high-energy microseismic events specifically includes:

[0012] The average frequency percentage of each energy level in each high-energy microseismic event is determined based on the energy data of each high-energy microseismic event.

[0013] The energy level with the highest average frequency proportion in the energy data of each large-energy microseismic event is taken as the main energy level of that large-energy microseismic event.

[0014] The energy level with the highest proportion among the main energy levels of several recent high-energy microseismic events is taken as the dominant energy level for rockburst occurrence.

[0015] Furthermore, the energy data for each high-energy microseismic event includes daily microseismic energy data for at least five days prior to the occurrence of the high-energy microseismic event;

[0016] The determination of the average frequency proportion of each energy level in a high-energy microseismic event based on the energy data of that event specifically includes:

[0017] Calculate the frequency percentage of level i on day j.

[0018] K i,j =P i,j / P j

[0019] Among them, P i,j Let P be the frequency value of the i-th energy level on day j. j This represents the total frequency value on day j.

[0020] Calculate the average frequency percentage of level i within m days.

[0021]

[0022] Where m is greater than or equal to 5.

[0023] Furthermore, the step of dividing the frequency fluctuation range of the dominant energy level and determining the fluctuation critical value of each dominant energy level frequency fluctuation range based on the frequency data of the dominant energy level in the energy data of several recent high-energy microseismic events specifically includes:

[0024] The frequency fluctuation range of the dominant energy level is divided according to the span of ten for each range.

[0025] The maximum fluctuation value of the dominant energy level frequency for each large-energy microseismic event is determined based on the dominant energy level frequency data of several recent large-energy microseismic events.

[0026] The critical value of the fluctuation range of each dominant energy level frequency is determined based on the maximum fluctuation value of the dominant energy level frequency of several high-energy microseismic events.

[0027] Furthermore, determining the maximum fluctuation value of the dominant energy level frequency for each large-energy microseismic event based on the dominant energy level frequency data of several recent large-energy microseismic events specifically includes:

[0028] Calculate the daily frequency fluctuation of the dominant energy level during each high-energy microseismic event.

[0029] L F,j =|P F,j -P F,j+1 |

[0030] Among them, L F,j P represents the frequency fluctuation value of the dominant energy level on day j. F,j P represents the frequency of the dominant energy level on day j. F,j+1 This represents the frequency of the dominant energy level on day j+1.

[0031] The maximum daily fluctuation value of the dominant energy level frequency is taken as the maximum fluctuation value of the dominant energy level frequency for this large-energy microseismic event.

[0032] L w =Max{L F,j}

[0033] Among them, L w This represents the maximum fluctuation value of the dominant energy level frequency of the w-th energy microseismic event.

[0034] Furthermore, determining the critical value of the fluctuation range of each dominant energy level frequency based on the maximum fluctuation value of the dominant energy level frequency of several high-energy microseismic events specifically includes:

[0035] Determine the fluctuation critical value of each dominant energy level frequency fluctuation range.

[0036]

[0037] Among them, Y n L is the critical value for fluctuation within the frequency fluctuation range of the nth dominant energy level. W This represents the maximum fluctuation value of the dominant energy level frequency of the w-th energy microseismic event.

[0038] Furthermore, the dominant energy level frequency data for the statistical day and the number of days before and after it includes the dominant energy level frequency data for the statistical day and the following day.

[0039] The method of issuing early warnings for rockburst events based on the dominant energy level frequency data of the statistical day and the number of days before and after it, and the fluctuation threshold value of the dominant energy level frequency fluctuation range, specifically includes:

[0040] The frequency fluctuation value for the statistical day is determined based on the dominant energy level frequency data for the statistical day and the following day.

[0041] If the frequency fluctuation value of the statistical day is greater than the fluctuation threshold value corresponding to the frequency fluctuation range of the dominant energy level, the statistical day is determined to be a frequency anomaly day, and an early warning is issued for the danger of rockburst events.

[0042] Furthermore, the dominant energy level frequency of the statistical day and the set number of days before and after it includes the dominant energy level frequency data of the statistical day and the five days prior.

[0043] The aforementioned early warning of the danger of rockburst events specifically includes:

[0044] The frequency abnormal volatility of the statistical day is obtained based on the frequency data of the dominant energy level of the statistical day and the five days prior.

[0045] The risk warning level of rockburst events is determined based on the abnormal fluctuation rate of the frequency of the statistical days.

[0046] Furthermore, the process of obtaining the frequency anomaly volatility of the statistical day based on the dominant energy level frequency data of the statistical day and the previous five days specifically includes:

[0047] Calculate the frequency fluctuation values ​​for each consecutive two days, and obtain the maximum and minimum frequency fluctuation values.

[0048] Calculate the abnormal volatility f = P a / P b , where P a P is the maximum value of the frequency fluctuation. b This is the minimum value of the frequency fluctuation.

[0049] Furthermore, the determination of the risk warning level of rockburst events based on the abnormal fluctuation rate of the frequency of the statistical days specifically includes:

[0050] When the frequency abnormal volatility of the statistical day is less than or equal to the first volatility threshold, the risk warning level of the rockburst event is determined to be low risk.

[0051] When the frequency abnormal volatility of the statistical day is greater than the first volatility threshold and less than or equal to the second volatility threshold, the risk warning level of the rockburst event is determined to be medium risk, and the second volatility threshold is greater than the first volatility threshold.

[0052] When the frequency of abnormal fluctuations on the statistical day exceeds the second volatility threshold, the risk warning level for rockburst time is determined to be high risk.

[0053] The above technical solution has the following beneficial effects:

[0054] This application analyzes the energy data of several recent high-energy microseismic events to determine the dominant energy level and the critical values ​​of the frequency fluctuation range of each dominant energy level as reference data. It then combines the frequency data of the dominant energy level on the statistical day and the number of days before and after it with the reference data for analysis and processing, thereby enabling early warning of rockburst events. This early warning method, based on the regularity analysis of energy data from past high-energy microseismic events, can perform early warning analysis for different coal mines and has higher early warning accuracy. Attached Figure Description

[0055] The disclosure of this application will become more readily understood with reference to the accompanying drawings. It should be understood that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application. In the drawings:

[0056] Figure 1 This is a flowchart of a rockburst early warning method for near-vertical coal seams in one embodiment of this application;

[0057] Figure 2 This is a flowchart of a rockburst early warning method for near-vertical coal seams in another embodiment of this application. Detailed Implementation

[0058] The specific embodiments of this application will be further described below with reference to the accompanying drawings.

[0059] It is readily understood that, based on the technical solution of this application, various structural and implementation methods can be interchanged by those skilled in the art without altering the essential spirit of this application. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this application and should not be considered as the entirety of this application or as limitations or restrictions on the technical solution of the application.

[0060] The directional terms such as up, down, left, right, front, back, front, back, top, and bottom mentioned or possibly used in this specification are defined relative to the structures shown in the accompanying drawings. These are relative concepts and may therefore vary depending on their location and usage. Therefore, these or other directional terms should not be interpreted as restrictive. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0061] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meanings of the above in this application according to the specific circumstances.

[0062] The rockburst early warning method for near-vertical coal seams in this application embodiment, such as Figure 1 As shown, it includes:

[0063] Step S101: Based on the energy data of several recent high-energy microseismic events, determine the dominant energy level for rockburst occurrence;

[0064] Step S102: Divide the frequency fluctuation range of the dominant energy level, and determine the fluctuation critical value of each dominant energy level frequency fluctuation range based on the frequency data of the dominant energy level in the energy data of several recent large-energy microseismic events.

[0065] Step S103: Obtain the frequency data of the dominant energy level for the statistical day and the set number of days before and after it;

[0066] Step S104: Based on the dominant energy level frequency data of the statistical day and the set number of days before and after it, and the fluctuation threshold of the dominant energy level frequency fluctuation range, provide an early warning for rockburst events.

[0067] Specifically, the energy data of the recent high-energy microseismic events includes the total number of microseismic events and their corresponding energy levels within a certain period prior to each high-energy event. Step S101 involves statistically analyzing the energy data of the recent high-energy microseismic events to determine the dominant energy level for rockburst occurrence. The energy of the microseismic events is divided into different energy levels, and the frequency of microseismic events at each energy level is statistically analyzed to determine the dominant energy level.

[0068] It should be noted that the energy data of the recent high-energy microseismic events should be data detected in coal seams with the same or similar geographical environment as the current coal seam. Generally speaking, data collected within a set distance from the current coal mine can be used to determine the dominant energy level of the current coal mine. Priority should be given to energy data of high-energy microseismic events that are closer to the current coal mine and occurred later.

[0069] Step S102: First, divide the frequency fluctuation range of the dominant energy level as needed, and extract the frequency data of the dominant energy level from the energy data of the most recent high-energy microseismic events to determine the fluctuation critical value of each dominant energy level frequency fluctuation range.

[0070] In subsequent steps S103-S104, the frequency data of the dominant energy level of the detected statistical day and the set number of days before and after are analyzed and calculated, and compared with the fluctuation critical value of the frequency fluctuation range of the dominant energy level. Based on the comparison results, an early warning of rockburst events is issued.

[0071] This application statistically analyzes the energy data of several recent high-energy microseismic events to analyze the frequency and fluctuation of dominant energy level microseismic events. Based on the patterns of dominant energy level microseismic events, it provides early warning of rockburst events, enabling early warning analysis for different coal mines with higher accuracy.

[0072] In one embodiment, the energy data of the most recent high-energy microseismic events includes energy data of at least five recent high-energy microseismic events;

[0073] The determination of the dominant energy level for rockburst occurrence based on energy data from several recent high-energy microseismic events specifically includes:

[0074] The average frequency percentage of each energy level in each high-energy microseismic event is determined based on the energy data of each high-energy microseismic event.

[0075] The energy level with the highest average frequency proportion in the energy data of each large-energy microseismic event is taken as the main energy level of that large-energy microseismic event.

[0076] The energy level with the highest proportion among the main energy levels of several recent high-energy microseismic events is taken as the dominant energy level for rockburst occurrence.

[0077] Furthermore, the energy data for each high-energy microseismic event includes daily microseismic energy data for at least five days prior to the occurrence of the high-energy microseismic event;

[0078] The determination of the average frequency proportion of each energy level in a high-energy microseismic event based on the energy data of that event specifically includes:

[0079] Calculate the frequency percentage of level i on day j.

[0080] K i,j =P i,j / P j (1)

[0081] Among them, P i,j Let P be the frequency value of the i-th energy level on day j. j This represents the total frequency value on day j.

[0082] Calculate the average frequency percentage of level i within m days.

[0083]

[0084] Where m is greater than or equal to 5.

[0085] As an example, the energy of microseismic events can be divided into three levels, with the first energy level being 10. 0 -10 1 J. Second energy level: 10 2 -10 3 J. Third Energy Level: 10 4 J and above.

[0086] Taking a high-energy event of 4.5 × 10⁶ J that occurred on January 10, 2014 as an example, statistical analysis of microseismic events over the nine days from January 1 to January 9, 2014, yields the distribution curves of the daily energy levels and total frequency of microseismic events over time, as shown below. Figure 2 As shown, K is calculated according to formulas (1) and (2). max =K2=66.4%, therefore the main energy level of the high-energy event on January 10, 2014 was the second energy level.

[0087] Similarly, the main energy levels of other high-energy events can be calculated, as shown in Table 1 below.

[0088] Table 1. Main energy levels of several recent high-energy microseismic events.

[0089]

[0090] Based on the data in Table 1, it can be concluded that the dominant energy level is the second energy level.

[0091] In this embodiment, the average frequency proportion of each energy level in a high-energy microseismic event is determined by calculating the energy data of each high-energy microseismic event, thereby obtaining the dominant energy level of the microseismic event with a higher occurrence frequency.

[0092] In one embodiment, the step of dividing the frequency fluctuation range of the dominant energy level and determining the fluctuation threshold value of each dominant energy level frequency fluctuation range based on the frequency data of the dominant energy level in the energy data of several recent high-energy microseismic events specifically includes:

[0093] The frequency fluctuation range of the dominant energy level is divided according to the span of ten for each range.

[0094] The maximum fluctuation value of the dominant energy level frequency for each large-energy microseismic event is determined based on the dominant energy level frequency data of several recent large-energy microseismic events.

[0095] The critical value of the fluctuation range of each dominant energy level frequency is determined based on the maximum fluctuation value of the dominant energy level frequency of several high-energy microseismic events.

[0096] Specifically, the frequency fluctuation value of the dominant energy level starts from 0, and every 10 increments are divided into a frequency fluctuation interval of the dominant energy level. The interval division table of the frequency fluctuation value of the dominant energy level is shown in Table 2.

[0097] Table 2. Division of Frequency Fluctuation Values ​​of Dominant Energy Levels

[0098] Frequency fluctuation value Interval partitioning 0～10 Interval 1 10～20 Interval 2 20～30 Interval 3 … … 10n~10(n+1) interval n+1

[0099] Then, based on the dominant energy level frequency data of several recent high-energy microseismic events, the maximum fluctuation value of the dominant energy level frequency for each high-energy microseismic event was determined, specifically including:

[0100] Calculate the daily frequency fluctuation of the dominant energy level during each high-energy microseismic event.

[0101] L F,j =|P F,j -P F,j+1 |

[0102] Among them, L F,j P represents the frequency fluctuation value of the dominant energy level on day j. F,j P represents the frequency of the dominant energy level on day j. F,j+1 This represents the frequency of the dominant energy level on day j+1.

[0103] The maximum daily fluctuation value of the dominant energy level frequency is taken as the maximum fluctuation value of the dominant energy level frequency for this large-energy microseismic event.

[0104] L w =Max{L F,j}

[0105] Among them, L w This represents the maximum fluctuation value of the dominant energy level frequency of the w-th energy microseismic event.

[0106] Finally, based on the maximum fluctuation values ​​of the dominant energy level frequencies of several high-energy microseismic events, the critical fluctuation values ​​for each dominant energy level frequency fluctuation range are determined, specifically including:

[0107] Determine the fluctuation critical value of each dominant energy level frequency fluctuation range.

[0108]

[0109] Among them, Y n L is the critical value for fluctuation within the frequency fluctuation range of the nth dominant energy level. W This represents the maximum fluctuation value of the dominant energy level frequency of the w-th energy microseismic event.

[0110] Finally, a table of fluctuation critical values ​​was obtained, in which each dominant energy level frequency fluctuation range corresponds to a fluctuation critical value.

[0111] Preferably, the energy microseismic event most recent to the present time is designated as the first energy microseismic event, and as the time of occurrence progresses backward, it is successively defined as the second, third, and so on.

[0112] In this embodiment, the maximum value of the frequency fluctuation of the dominant energy level over two consecutive days is selected as the maximum fluctuation value of the dominant energy level frequency. The obtained maximum fluctuation value of the dominant energy level frequency can reflect the fluctuation of the dominant energy level frequency in a short period of time, thereby obtaining a more accurate fluctuation threshold value.

[0113] In one embodiment, the dominant energy level frequency data of the statistical day and the number of days before and after it includes the dominant energy level frequency data of the statistical day and the following day.

[0114] The method of issuing early warnings for rockburst events based on the dominant energy level frequency data of the statistical day and the number of days before and after it, and the fluctuation threshold value of the dominant energy level frequency fluctuation range, specifically includes:

[0115] The frequency fluctuation value for the statistical day is determined based on the dominant energy level frequency data for the statistical day and the following day.

[0116] If the frequency fluctuation value of the statistical day is greater than the fluctuation threshold value corresponding to the frequency fluctuation range of the dominant energy level, the statistical day is determined to be a frequency anomaly day, and an early warning is issued for the danger of rockburst events.

[0117] Specifically, the absolute value of the difference between the dominant energy level frequency on the statistical day and the dominant energy level frequency on the following day is used as the frequency fluctuation value for the statistical day. The frequency fluctuation range of the dominant energy level within which the frequency fluctuation value of the statistical day falls is determined. The corresponding fluctuation threshold value is obtained by consulting the fluctuation threshold value table. If the frequency fluctuation value of the statistical day is greater than the fluctuation threshold value, the statistical day is considered an abnormal frequency day, and a risk warning for a rockburst event is issued. Otherwise, the statistical day is considered a normal frequency day, with a low probability of a high-energy event, and no risk warning for a rockburst event is issued. Table 3 provides an example of determining abnormal frequency days.

[0118] Table 3. Example Table for Judging Abnormal Frequency Days

[0119] date Dominant frequency fluctuation value L Fluctuation range Corresponding critical value y Is the frequency abnormal? 2014 / 1 / 10 40 4 26.2 yes 2014 / 9 / 25 36 4 26.2 yes 2015 / 3 / 13 7 1 5.25 yes 2015 / 12 / 12 18 2 11.9 yes 2016 / 1 / 10 8 1 5.25 yes 2016 / 6 / 17 16 2 11.9 yes 2016 / 12 / 10 17 2 11.9 yes 2017 / 4 / 26 70 7 49 yes

[0120] This application embodiment calculates the frequency fluctuation value of a statistical day and compares it with the fluctuation threshold value corresponding to the frequency fluctuation range of the corresponding dominant energy level. It then selects days with abnormal frequencies for risk warning of rockburst events, while not executing subsequent risk warning steps on days with normal frequencies, making the warning more targeted.

[0121] In one embodiment, the dominant energy level frequency of the statistical day and the set number of days before and after it includes the dominant energy level frequency data of the statistical day and the five days before it.

[0122] The aforementioned early warning of the danger of rockburst events specifically includes:

[0123] The frequency abnormal volatility of the statistical day is obtained based on the frequency data of the dominant energy level of the statistical day and the five days prior.

[0124] The risk warning level of rockburst events is determined based on the abnormal fluctuation rate of the frequency of the statistical days.

[0125] In this embodiment, for days with abnormal frequencies, the dominant energy level frequency data of the statistical day and the five days preceding it are obtained, and the frequency anomaly fluctuation rate of the statistical day is calculated to determine the hazard warning level of a rockburst event. Generally, the higher the frequency anomaly fluctuation rate, the higher the hazard warning level. For rockburst events, the greater the energy fluctuation of the detected microseismic event and the more unstable the ground pressure, the higher the probability of a rockburst event and the greater the hazard.

[0126] In one embodiment, obtaining the frequency anomaly volatility of the statistical day based on the dominant energy level frequency data of the statistical day and the previous five days specifically includes:

[0127] Calculate the frequency fluctuation values ​​for each consecutive two days, and obtain the maximum and minimum frequency fluctuation values.

[0128] Calculate the abnormal volatility f = P a / P b , where P a P is the maximum value of the frequency fluctuation. b This is the minimum value of the frequency fluctuation.

[0129] Specifically, the frequency anomaly volatility on the statistical day is calculated based on the dominant energy level frequency data of the statistical day and the five days preceding it. For example, if the dominant energy level frequency data of the statistical day and the five days preceding it are D5, D4, D3, D2, D1, and D0 (dominant energy level frequency on the statistical day), then the frequency volatility values ​​for each consecutive two days are calculated as follows: P1 = |D5 - D4|, P2 = |D4 - D3|, P3 = |D3 - D2|, P4 = |D2 - D1|, P5 = |D2 - D1|. a P is the maximum value among P1-P5. b It is the minimum value among P1-P5.

[0130] In this application embodiment, the abnormal volatility rate of the statistical day is calculated by taking the maximum and minimum values ​​of the frequency fluctuation values ​​of each consecutive two days, and the abnormal volatility rate is used for the risk warning of rockburst events.

[0131] In one embodiment, determining the risk warning level of a rockburst event based on the abnormal frequency fluctuation rate of the statistical day specifically includes:

[0132] When the frequency abnormal volatility of the statistical day is less than or equal to the first volatility threshold, the risk warning level of the rockburst event is determined to be low risk.

[0133] When the frequency abnormal volatility of the statistical day is greater than the first volatility threshold and less than or equal to the second volatility threshold, the risk warning level of the rockburst event is determined to be medium risk, and the second volatility threshold is greater than the first volatility threshold.

[0134] When the frequency of abnormal fluctuations on the statistical day exceeds the second volatility threshold, the risk warning level for rockburst time is determined to be high risk.

[0135] Specifically, for days with abnormal frequency, the frequency abnormality volatility of the statistical day is calculated and compared with a preset volatility threshold. The preset volatility threshold includes a first volatility threshold and a second volatility threshold, which are set according to the geological conditions of the coal mine.

[0136] As an example, the first volatility threshold is set to 1.25, and the second volatility threshold is set to 1.7. Therefore, when the frequency abnormal volatility on a statistical day is less than or equal to 1.25, the risk level for a rockburst event is determined to be low; when the frequency abnormal volatility on a statistical day is greater than 1.25 and less than or equal to 1.7, the risk level for a rockburst event is determined to be medium; and when the frequency abnormal volatility on a statistical day is greater than 1.7, the risk level for a rockburst event is determined to be high.

[0137] This application embodiment divides the risk warning level of rockburst events into three levels: low risk, medium risk, and high risk by setting a first volatility threshold and a second volatility threshold, and can make corresponding rockburst prevention preparations according to the risk warning level.

[0138] Figure 2 A flowchart of a preferred embodiment of the rockburst early warning method for near-vertical coal seams is shown, including:

[0139] Step S201: Determine the average frequency proportion of each energy level in each high-energy microseismic event based on the energy data of each event;

[0140] Step S202: The energy level with the highest average frequency proportion in the energy data of each large-energy microseismic event is taken as the main energy level of that large-energy microseismic event;

[0141] Step S203: Select the energy level with the highest proportion among the main energy levels of the recent high-energy microseismic events as the dominant energy level for the occurrence of rockburst;

[0142] Step S204: Divide the dominant energy level frequency fluctuation intervals according to the span of the dominant energy level frequency fluctuation value of each interval being 10;

[0143] Step S205: Calculate the daily dominant energy level frequency fluctuation value for each of the recent high-energy microseismic events;

[0144] Step S206: Take the maximum value of the daily dominant energy level frequency fluctuation as the maximum value of the dominant energy level frequency fluctuation for this high-energy microseismic event;

[0145] Step S207: Determine the critical value of the fluctuation range of each dominant energy level frequency based on the maximum fluctuation value of the dominant energy level frequency of several large-energy microseismic events.

[0146] Step S208: Obtain the frequency data of the dominant energy level for the statistical day and the set number of days before and after it;

[0147] Step S209: Determine the frequency fluctuation value of the statistical day based on the dominant energy level frequency data of the statistical day and the following day;

[0148] Step S210: If the frequency fluctuation value of the statistical day is greater than the fluctuation threshold value corresponding to the frequency fluctuation range of the dominant energy level, then the statistical day is determined to be a frequency abnormal day, and step S211 is executed; otherwise, the statistical day is determined to be a frequency normal day.

[0149] Step S211: Obtain the frequency anomaly volatility of the statistical day based on the dominant energy level frequency data of the statistical day and the previous five days;

[0150] Step S212: When the frequency abnormal fluctuation rate of the statistical day is less than or equal to the first volatility threshold, the risk warning level of the rockburst event is determined to be low risk;

[0151] When the frequency abnormal volatility of the statistical day is greater than the first volatility threshold and less than or equal to the second volatility threshold, the risk warning level of the rockburst event is determined to be medium risk, and the second volatility threshold is greater than the first volatility threshold.

[0152] When the frequency of abnormal fluctuations on the statistical day exceeds the second volatility threshold, the risk warning level for rockburst time is determined to be high risk.

[0153] The above description is merely the principle and preferred embodiment of this application. It should be noted that for those skilled in the art, implementation methods obtained by appropriately combining the technical solutions disclosed in different embodiments are also included within the technical scope of this invention. Based on the principle of this application, several other modifications can also be made, which should also be considered within the protection scope of this application.

Claims

1. A method for early warning of rockburst in near-vertical coal seams, characterized in that, include Based on the energy data of several recent high-energy microseismic events, the dominant energy level for rockbursts was determined. The frequency fluctuation range of the dominant energy level is divided, and the fluctuation critical value of each dominant energy level frequency fluctuation range is determined based on the frequency data of the dominant energy level in the energy data of several recent high-energy microseismic events. Obtain the frequency data of the dominant energy level on the statistical day and the number of days before and after it; Based on the frequency data of the dominant energy level on the statistical day and the set number of days before and after it, and the fluctuation threshold value of the frequency fluctuation range of the dominant energy level, early warning of rockburst events is provided. The energy data of the recent high-energy microseismic events includes the energy data of at least five recent high-energy microseismic events. The determination of the dominant energy level for rockburst occurrence based on energy data from several recent high-energy microseismic events specifically includes: The average frequency percentage of each energy level in each high-energy microseismic event is determined based on the energy data of each high-energy microseismic event. The energy level with the highest average frequency proportion in the energy data of each large-energy microseismic event is taken as the main energy level of that large-energy microseismic event. The energy level with the highest proportion among the main energy levels of several recent high-energy microseismic events is taken as the dominant energy level for rockburst occurrence.

2. The method for early warning of rockburst in near-vertical coal seams according to claim 1, characterized in that, The energy data for each high-energy microseismic event includes the daily microseismic energy data for at least five days prior to the occurrence of the high-energy microseismic event; The determination of the average frequency proportion of each energy level in a high-energy microseismic event based on the energy data of that event specifically includes: Calculate the frequency percentage of level i on day j. K i,j = P i,j / P j Among them, P i,j Let P be the frequency value of the i-th energy level on day j. j This represents the total frequency value on day j. Calculate the average frequency percentage of level i within m days. Where m is greater than or equal to 5.

3. The method for early warning of rockburst in near-vertical coal seams according to claim 1, characterized in that, The process of dividing the frequency fluctuation range of the dominant energy level and determining the fluctuation threshold value of each dominant energy level frequency fluctuation range based on the frequency data of the dominant energy level in the energy data of several recent high-energy microseismic events specifically includes: The frequency fluctuation range of the dominant energy level is divided according to the span of ten for each range. The maximum fluctuation value of the dominant energy level frequency for each large-energy microseismic event is determined based on the dominant energy level frequency data of several recent large-energy microseismic events. The critical value of the fluctuation range of each dominant energy level frequency is determined based on the maximum fluctuation value of the dominant energy level frequency of several high-energy microseismic events.

4. The method for early warning of rockburst in near-vertical coal seams according to claim 3, characterized in that, The determination of the maximum fluctuation value of the dominant energy level frequency for each large-energy microseismic event based on the dominant energy level frequency data of several recent large-energy microseismic events specifically includes: Calculate the daily frequency fluctuation of the dominant energy level during each high-energy microseismic event. L F,j =|P F,j -P F,j+1 | Among them, L F,j P represents the frequency fluctuation value of the dominant energy level on day j. F,j P represents the frequency of the dominant energy level on day j. F,j+1 This represents the frequency of the dominant energy level on day j+1. The maximum daily fluctuation value of the dominant energy level frequency is taken as the maximum fluctuation value of the dominant energy level frequency for this large-energy microseismic event. L w <Max{L F,j } Among them, L w This represents the maximum fluctuation value of the dominant energy level frequency of the w-th energy microseismic event.

5. The method for early warning of rockburst in near-vertical coal seams according to claim 4, characterized in that, The determination of the fluctuation threshold values ​​for each dominant energy level frequency fluctuation range based on the maximum fluctuation values ​​of the dominant energy level frequencies of several high-energy microseismic events specifically includes: Determine the fluctuation critical value of each dominant energy level frequency fluctuation range. Among them, Y n L is the critical value for fluctuation within the frequency fluctuation range of the nth dominant energy level. W This represents the maximum fluctuation value of the dominant energy level frequency of the w-th energy microseismic event.

6. The method for early warning of rockburst in near-vertical coal seams according to any one of claims 1-5, characterized in that, The dominant energy level frequency data for the statistical day and the number of days before and after it includes the dominant energy level frequency data for the statistical day and the day after it. The method of issuing early warnings for rockburst events based on the dominant energy level frequency data of the statistical day and the number of days before and after it, and the fluctuation threshold value of the dominant energy level frequency fluctuation range, specifically includes: The frequency fluctuation value for the statistical day is determined based on the dominant energy level frequency data for the statistical day and the following day. If the frequency fluctuation value of the statistical day is greater than the fluctuation threshold value corresponding to the frequency fluctuation range of the dominant energy level, the statistical day is determined to be a frequency anomaly day, and an early warning is issued for the danger of rockburst events.

7. The method for early warning of rockburst in near-vertical coal seams according to claim 6, characterized in that, The dominant energy level frequency of the statistical day and the set number of days before and after it includes the dominant energy level frequency data of the statistical day and the five days before it. The aforementioned early warning of the danger of rockburst events specifically includes: The frequency abnormal volatility of the statistical day is obtained based on the frequency data of the dominant energy level of the statistical day and the five days prior. The risk warning level of rockburst events is determined based on the abnormal fluctuation rate of the frequency of the statistical days.

8. The method for early warning of rockburst in near-vertical coal seams according to claim 7, characterized in that, The frequency anomaly volatility rate for the statistical day is obtained based on the dominant energy level frequency data of the statistical day and the previous five days, specifically including: Calculate the frequency fluctuation values ​​for each consecutive two days, and obtain the maximum and minimum frequency fluctuation values. Calculate the abnormal volatility f = P a / P b , where P a P is the maximum value of the frequency fluctuation. b This is the minimum value of the frequency fluctuation.

9. The method for early warning of rockburst in near-vertical coal seams according to claim 7, characterized in that, The determination of the risk warning level for rockburst events based on the abnormal fluctuation rate of the frequency of the statistical days specifically includes: When the frequency abnormal volatility of the statistical day is less than or equal to the first volatility threshold, the risk warning level of the rockburst event is determined to be low risk. When the frequency abnormal volatility of the statistical day is greater than the first volatility threshold and less than or equal to the second volatility threshold, the risk warning level of the rockburst event is determined to be medium risk, and the second volatility threshold is greater than the first volatility threshold. When the frequency of abnormal fluctuations on the statistical day exceeds the second volatility threshold, the risk warning level for rockburst time is determined to be high risk.

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