Coal seam rock burst risk assessment method based on avalanche dynamics theory

Abstract

The invention discloses a coal seam rock burst risk assessment method based on an avalanche dynamics theory, and the method comprises the steps: monitoring discrete and sudden acoustic emission signals of a coal mine working face in real time, and building an acoustic emission monitoring database; screening the acoustic emission data, and only counting the acoustic emission data in the range of the working surface; real-time positioning is conducted on acoustic emission signals in the coal rock mass according to a working face acoustic emission monitoring system; performing cross-scale statistical analysis on the acoustic emission absolute energy signals acquired in real time by using a basic statistical rule in an avalanche dynamics theory; uniformly processing the acoustic emission signals monitored every day to generate a hotspot map of the deformation and fracture positions of the coal and rock mass; according to the hot spot map, judging an important attention area; and based on an avalanche dynamics theory, carrying out real-time statistical analysis on the acoustic emission energy signal of the important attention area, observing an avalanche event power law distribution rule satisfied by the acoustic emission absolute energy signal, and evaluating the impact risk caused by evolution from micro-fracture to macro-fracture of the coal rock mass.

Description

Technical Field

[0001] The invention relates to the field of safe mining of coal mines, and in particular to a method for assessing the risk of coal seam rock burst. Background Art

[0002] The rapid development of modernization construction has led to the continuous expansion of energy demand in countries around the world. Therefore, as shallow energy is decreasing year by year, it is inevitable to explore resources in depth. The current coal mining depth has reached 1,500 meters. The mining depth of mines in my country is expected to reach 1,000-2,000 meters in the near future. The current mining speed is also expanding to the depth at 10-25 meters per year. With the increase in mining depth, the coal mining environment is becoming increasingly harsh, and coal mine dynamic disasters caused by rock burst are becoming increasingly prominent. The multi-field and multi-phase coupling in the complex underground environment leads to strong randomness in the interaction of materials, and the rock burst control parameters are numerous, fast-changing, and strongly coupled, lacking an indicator system that matches the disaster mechanism. The control parameters are highly nonlinear with the "time, space, and intensity" of the disaster, and the algorithm is difficult to construct. Therefore, under the new form of rock burst disasters and new prevention and control needs, new methods need to be proposed to effectively assess the risk of coal seam rock burst.

[0003] In the past, the risk assessment of rock burst was mostly done by using mechanical devices to measure rock burst. Although the results were highly accurate, they consumed a lot of manpower and material resources, and could not effectively assess the risk of rock burst in real time. In addition, most of the current rock burst assessment methods still analyze and identify risks by releasing large coal-rock rupture signals generated in a short period of time before the rock burst occurs, and are unable to analyze and evaluate small coal-rock rupture signals, making it difficult to predict and assess rock burst disasters that suddenly occur due to the expansion of small coal-rock ruptures to large ruptures. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a new theoretical method to evaluate the risk of coal seam rock burst. The proposed coal seam rock burst risk assessment method based on avalanche dynamics theory can ignore the differences in geometric scales, boundary conditions, interacting media, etc., and can perform cross-scale analysis and evaluation of the fracture signals of coal and rock masses. The research object of avalanche dynamics is the effect that spans several orders of magnitude when the material is subjected to external stimulation. Because the absolute energy of acoustic emissions collected during deep coal mining spans several orders of magnitude, the statistical method of avalanche dynamics can be used to study it, and then evaluate the rock burst disaster caused by the transformation of coal and rock microscopic fractures to macroscopic destruction during coal mining.

[0005] The present invention provides a coal seam rock burst risk assessment method based on avalanche dynamics theory, comprising the following steps:

[0006] S1. Real-time monitoring of discrete and sudden changes in acoustic emission (AE) signals at coal mine working faces, and establishment of an AE monitoring database;

[0007] S2. Screen the acoustic emission data, remove the acoustic emission events caused by faults and coal pillars, and only count the acoustic emission data within the working face;

[0008] S3, real-time positioning of acoustic emission signals in the coal and rock bodies according to the working face acoustic emission monitoring system;

[0009] S4. Use the basic statistical laws in avalanche dynamics theory to perform cross-scale statistical analysis on the absolute energy signals of acoustic emission collected in real time;

[0010] S5. The acoustic emission signals monitored every day are processed uniformly on a daily basis to generate a hotspot map of the coal-rock deformation and fracture locations, as well as a trend map of the acoustic emission energy signals after statistical analysis;

[0011] S6. Determine the key areas of concern based on the hotspot map of coal and rock mass deformation and fracture locations;

[0012] S7. Based on the avalanche dynamics theory, real-time statistical analysis focuses on the acoustic emission energy signal of the area, observes the power-law distribution of avalanche events satisfied by the absolute energy signal of the acoustic emission, and evaluates the impact risk caused by the evolution of micro-fractures of coal and rock mass to macro-fractures.

[0013] Furthermore, the acoustic emission signals for real-time monitoring of discrete mutations of coal mine working faces include: three-dimensional spatial positions of coal and rock mass deformation and fracture, and acoustic emission absolute energy signals.

[0014] Furthermore, the avalanche dynamics theory can be expressed by a power law:

[0015] N(S)=S t

[0016] Among them, S is the energy released by the earthquake, which is manifested as the energy generated by the rupture of coal rock mass in coal seam rock burst disasters; N(S) represents the frequency of earthquakes with energy S, which is manifested as the frequency of coal rock rupture with energy S in coal seam rock burst disasters.

[0017] Furthermore, the absolute energy of the acoustic emission can be expressed by the following formula:

[0018]

[0019] Where U(t) and R are the voltage and internal resistance of the workstation respectively; ti and tj are the start time and end time of the AE signal respectively.

[0020] Furthermore, the basic statistical laws include: three major statistical laws of seismology, maximum likelihood estimation method and Bath's law.

[0021] Furthermore, the three major statistical laws of seismology include:

[0022] (1) Gutenberg-Richter law. The Gutenberg-Richter law expresses the relationship between the number of earthquakes and the energy they generate, which can be expressed as follows:

[0023]

[0024] Among them, Emin is the lower limit used for normalization, E>Emin; ε is the distribution index of the absolute AE energy generated by coal rock fracture (also called critical index).

[0025] When processing the absolute energy data of the monitored acoustic emission, the Histogram in the Igor Pro software is used for histogram statistics, and finally expressed as a probability density distribution function P(E), which satisfies the power law of distribution. The probability of micro-fractures and large fractures is determined by the obtained critical index ε (the slope of the distribution function). The larger the critical index, the greater the possibility of acoustic emission events with smaller energy, which means that the coal and rock mass will fracture more slowly and safely. Conversely, the smaller the critical index, the greater the possibility of acoustic emission events with larger energy, which means that the coal and rock mass will fracture faster and more dangerous. The probability of occurrence of acoustic emission events with larger energy can be used to evaluate the risk of rock burst in coal seams.

[0026] (2) Omori's law (aftershock distribution) describes the distribution of multiple aftershocks caused by the initial vibration, and in the coal-rock mass fracture, it describes the secondary fractures caused by large fractures, and this phenomenon is demonstrated by the acoustic emission energy signal. Omori's law is used to statistically analyze the acoustic emission data. First, the main shock energy (EMS) generated by the large fractures in the coal-rock mass is divided and selected, and then the frequency of aftershocks (rAS) occurring in the time interval (t-tMS) from the main shock is statistically analyzed. Therefore, it can be used to analyze and prevent the impact of secondary shocks caused by mining disturbances.

[0027] (3) Waiting time distribution. Waiting time distribution is closely related to time and describes the time intervals between different earthquakes (acoustic emission events). When processing the data of waiting time distribution, we first compare the waiting time distributions of different energy events, and then use (refers to the average number of events per unit time that are greater than a given energy E*min) and Draw a graph described by the new scale coordinates. D(δ, E*min) is the waiting time distribution.

[0028] The waiting time distribution is divided into small time intervals (corresponding to small-scale fractures) and large time intervals (corresponding to large-scale fractures) by two power functions. The power critical exponent size is obtained to analyze the fractures of different scales and the energy release characteristics of coal and rock mass under the influence of mining disturbance, so as to judge the risk of rock burst caused by mining disturbance.

[0029] Furthermore, the maximum likelihood estimation method can be expressed by the following formula:

[0030]

[0031] Where xi,i=1…n are all observed values ​​that satisfy the inequality xi≥xmin, and ε is the distribution index (also called critical index) of the absolute energy of AE generated by coal rock mass rupture.

[0032] The error is:

[0033]

[0034] The maximum likelihood estimation method is another method to obtain the distribution index of AE absolute energy. When processing data, the traditional histogram drawing method has the problem of artificially dividing the energy interval, and the artificial division of the energy interval range will inevitably bring errors to the data statistical results. Therefore, it is necessary to use the maximum likelihood estimation method to reprocess the monitored AE absolute energy data to obtain a new distribution index, and then compare and verify it with the distribution index obtained by the Gutenberg-Richter law to increase the credibility of the results.

[0035] Furthermore, Bath's law can be expressed as follows:

[0036]

[0037] Where E MS The main shock energy generated by the large fault in the coal rock mass. is the maximum aftershock energy caused by the main shock.

[0038] Bath's law shows the relationship between the main shock energy generated by large faults in coal and rock mass and the maximum aftershock energy caused by the main shock, and believes that the ratio of the two is close to 1.2 in the logarithmic solution relationship. Therefore, the main shock energy caused by the monitored mining disturbance can be used to assess the maximum aftershock impact in the future, so as to prevent the secondary shock risk caused by mining disturbance in a targeted manner.

[0039] Beneficial effects of the present invention:

[0040] 1. The coal seam rock burst risk assessment method based on avalanche dynamics theory can ignore the differences in geometric scale, boundary conditions, interaction media, etc., and can perform cross-scale analysis and evaluation of coal rock fracture signals. By analyzing and evaluating small fracture signals of coal rock mass, it is possible to pre-evaluate the rock burst disaster that suddenly occurs due to the expansion of small fractures of coal rock mass to large fractures.

[0041] 2. The coal seam rock burst risk assessment method based on avalanche dynamics theory adopts avalanche dynamics theory, based on the three major statistical laws of seismology, maximum likelihood estimation method and Bath's law, and combines mathematical statistical methods to perform cross-scale real-time and efficient calculations, greatly reducing the impact of human factors on the assessment results. Therefore, the present invention can better ensure the real-time nature of the assessment and ensure long-term assessment results.

[0042] 3. The coal seam rock burst risk assessment method based on avalanche dynamics theory of the present invention not only divides small time intervals (corresponding to small-scale fractures) and large time intervals (corresponding to large-scale fractures), but also analyzes the impact of mining disturbances on large-scale fractures of coal and rock mass and the release of their energy, and determines the rock burst risk caused by mining disturbances. In addition, the energy of the main shock caused by mining disturbances is monitored to evaluate the maximum aftershock response in the future, and the frequency of aftershocks in the time interval from the main shock is statistically analyzed, so as to prevent the secondary shock risk caused by mining disturbances in a targeted manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flow chart of the coal seam rock burst risk assessment method based on avalanche dynamics theory. DETAILED DESCRIPTION

[0044] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below with reference to specific diagrams.

[0045] Please refer to Figure 1 As shown, the coal seam rock burst risk assessment method based on avalanche dynamics theory provided by the present invention comprises the following steps:

[0046] S1. Real-time monitoring of discrete and sudden changes in acoustic emission (AE) signals at coal mine working faces, and establishment of an AE monitoring database;

[0047] The real-time monitoring of discrete mutation acoustic emission (AE) signals of coal mine working faces includes: three-dimensional spatial positions of coal and rock mass deformation and fracture, and AE absolute energy signals.

[0048] S2. Screen the acoustic emission data, remove the acoustic emission events caused by faults and coal pillars, and only count the acoustic emission data within the working face;

[0049] S3, real-time positioning of acoustic emission signals in the coal and rock bodies according to the working face acoustic emission monitoring system;

[0050] S4. Use the basic statistical laws in avalanche dynamics theory to perform cross-scale statistical analysis on the absolute energy signals of acoustic emission collected in real time;

[0051] The avalanche dynamics theory can be expressed by the power law:

[0052] N(S)=S t

[0053] Among them, S is the energy released by the earthquake, which is manifested as the energy generated by the rupture of coal rock mass in coal seam rock burst disasters; N(S) represents the frequency of earthquakes with energy S, which is manifested as the frequency of coal rock rupture with energy S in coal seam rock burst disasters.

[0054] The absolute energy of the acoustic emission can be expressed by the following formula:

[0055]

[0056] Where U(t) and R are the voltage and internal resistance of the workstation respectively; t i and t j They are the start time and end time of the AE signal.

[0057] The basic statistical laws include: three major statistical laws of seismology, maximum likelihood estimation method and Bath's law.

[0058] The three major statistical laws of seismology include:

[0059] (1) Gutenberg-Richter law. The Gutenberg-Richter law expresses the relationship between the number of earthquakes and the energy they generate, which can be expressed as follows:

[0060]

[0061] Among them, E min is the lower limit used for normalization, E>E min ; ε is the distribution index of the absolute AE energy generated by coal rock fracture (also called critical index).

[0062] When processing the absolute energy data of the monitored acoustic emission, the Histogram in the Igor Pro software is used for histogram statistics, and finally expressed as a probability density distribution function P(E), which satisfies the power law of distribution. The probability of micro-fractures and large fractures is determined by the obtained critical index ε (the slope of the distribution function). The larger the critical index, the greater the possibility of acoustic emission events with smaller energy, which means that the coal and rock mass will fracture more slowly and safely. Conversely, the smaller the critical index, the greater the possibility of acoustic emission events with larger energy, which means that the coal and rock mass will fracture faster and more dangerous. The probability of occurrence of acoustic emission events with larger energy can be used to evaluate the risk of rock burst in coal seams.

[0063] (2) Omori's law (aftershock distribution) describes the distribution of multiple aftershocks caused by the initial shock, and in the case of coal-rock mass fracture, it describes the secondary fractures caused by large fractures, and this phenomenon is demonstrated by the acoustic emission energy signal. Omori's law is used to perform statistical analysis on the acoustic emission data. First, the main shock energy (E MS ) to divide and select the intervals, and then statistically analyze the time interval to the main shock (tt MS ) aftershock frequency (r AS ). Therefore, it can be used to analyze and prevent secondary impacts caused by mining disturbances.

[0064] (3) Waiting time distribution. Waiting time distribution is closely related to time and describes the time intervals between different earthquakes (acoustic emission events). When processing the data of waiting time distribution, we first compare the waiting time distributions of different energy events, and then use (refers to energy greater than a given energy E * min The average number of events per unit time) and Draw the graph described by the new scale coordinates. D(δ, E * min ) is the Waiting time distribution.

[0065] The waiting time distribution is divided into small time intervals (corresponding to small-scale fractures) and large time intervals (corresponding to large-scale fractures) by two power functions. The power critical exponent size is obtained to analyze the fractures of different scales and the energy release characteristics of coal and rock mass under the influence of mining disturbance, so as to judge the risk of rock burst caused by mining disturbance.

[0066] The maximum likelihood estimation method can be expressed by the following formula:

[0067]

[0068] where x i , i=1…n satisfies the inequality x i ≥x min All observed values ​​of , ε is the distribution index of the absolute energy of AE generated by coal rock fracture (also called critical index).

[0069] The error is:

[0070]

[0071] The maximum likelihood estimation method is another method to obtain the distribution index of AE absolute energy. When processing data, the traditional histogram drawing method has the problem of artificially dividing the energy interval, and the artificial division of the energy interval range will inevitably bring errors to the data statistical results. Therefore, it is necessary to use the maximum likelihood estimation method to reprocess the monitored AE absolute energy data to obtain a new distribution index, and then compare and verify it with the distribution index obtained by the Gutenberg-Richter law to increase the credibility of the results.

[0072] The Bath's law can be expressed as follows

[0073]

[0074] Where E MS The main shock energy generated by the large fault in the coal rock mass. is the maximum aftershock energy caused by the main shock.

[0075] Bath's law shows the relationship between the main shock energy generated by large faults in coal and rock mass and the maximum aftershock energy caused by the main shock, and believes that the ratio of the two is close to 1.2 in the logarithmic solution relationship. Therefore, the main shock energy caused by the monitored mining disturbance can be used to assess the maximum aftershock impact in the future, so as to prevent the secondary shock risk caused by mining disturbance in a targeted manner.

[0076] S5. The acoustic emission signals monitored every day are processed uniformly on a daily basis to generate a hotspot map of the coal-rock mass deformation and fracture locations, as well as a trend map of the acoustic emission energy signals after statistical analysis;

[0077] S6. Determine the key areas of concern based on the hotspot map of coal and rock mass deformation and fracture locations;

[0078] S7. Based on the avalanche dynamics theory, real-time statistical analysis is performed on the acoustic emission energy signals in the focus area, and the power law distribution of the avalanche events satisfied by the acoustic emission absolute energy signals is observed, and the impact risk caused by the evolution of coal and rock micro-fractures to macro-fractures is evaluated. The distribution index ε of the absolute acoustic emission energy generated by the complete rupture of the coal and rock mass on site is calculated. A For reference, the real-time statistical distribution index of the absolute energy of acoustic emission is defined as ε B , the shock risk can be divided into the following categories:

[0079] ①ε B Much larger than ε A , indicating that the probability of acoustic emission events with smaller energy is higher, which means that the slower the coal rock ruptures, the safer it is.

[0080] ②ε B Greater than ε A and slowly approaches ε A , indicating that the possibility of acoustic emission events with larger energy is increasing, which means that the fracture of coal rock mass is beginning to increase, but it is still relatively stable and needs to be monitored.

[0081] ③ε B Greater than ε A and quickly approaches ε A , indicating that the possibility of acoustic emission events with larger energy is increasing rapidly, which means that the rupture of coal rock mass is accelerating and prevention and control are needed.

[0082] ④ε B Reach ε A Even greater than ε A , indicating that the possibility of a relatively large-energy acoustic emission event has reached the limit of coal-rock rupture, which means that the coal-rock rupture is extremely serious and underground personnel need to evacuate quickly until the risk subsides.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A coal seam rock burst risk assessment method based on avalanche dynamics theory, characterized in that: The following steps are involved: S1. Real-time monitoring of discrete and sudden changes in acoustic emission (AE) signals at coal mine working faces, and establishment of an acoustic emission monitoring database; S2. Screen the acoustic emission data, remove the acoustic emission events caused by faults and coal pillars, and only count the acoustic emission data within the working face; S3, real-time positioning of acoustic emission signals in the coal and rock bodies according to the working face acoustic emission monitoring system; S4. Use the basic statistical laws in avalanche dynamics theory to perform cross-scale statistical analysis on the absolute energy signals of acoustic emission collected in real time; S5. The acoustic emission signals monitored every day are processed uniformly on a daily basis to generate a hotspot map of the coal-rock mass deformation and fracture locations, as well as a trend map of the acoustic emission energy signals after statistical analysis; S6. Determine the key areas of concern based on the hotspot map of coal and rock mass deformation and fracture locations; S7. Based on the avalanche dynamics theory, real-time statistical analysis focuses on the acoustic emission energy signal of the area, observes the power law distribution of avalanche events satisfied by the absolute energy signal of acoustic emission, and evaluates the impact risk caused by the evolution of micro-fractures of coal and rock mass to macro-fractures.

2. The coal seam rock burst risk assessment method based on avalanche dynamics theory according to claim 1 is characterized in that: The acoustic emission signals for real-time monitoring of discrete mutations of coal mine working faces include: three-dimensional spatial positions of coal and rock mass deformation and fracture, and acoustic emission absolute energy signals.

3. The coal seam rock burst risk assessment method based on avalanche dynamics theory according to claim 1 is characterized in that: The avalanche dynamics theory can be expressed by the power law: N(S)=S t Among them, S is the energy released by the earthquake, which is manifested as the energy generated by the rupture of coal rock mass in coal seam rock burst disasters; N(S) represents the frequency of earthquakes with energy S, which is manifested as the frequency of coal rock rupture with energy S in coal seam rock burst disasters.

4. The method for assessing coal seam rock burst risk based on avalanche dynamics theory according to claim 1, characterized in that: The absolute energy of the acoustic emission can be expressed by the following formula: Where U(t) and R are the voltage and internal resistance of the workstation respectively; ti and tj are the start time and end time of the AE signal respectively.

5. The method for assessing coal seam rock burst risk based on avalanche dynamics theory according to claim 1, characterized in that: The basic statistical laws include: three major statistical laws of seismology, maximum likelihood estimation method and Bath's law.

6. The method for assessing coal seam rock burst risk based on avalanche dynamics theory according to claim 5, characterized in that: The three major statistical laws of seismology include: (1) Gutenberg-Richter law: The Gutenberg-Richter law expresses the relationship between the number of earthquakes and the energy they generate, which can be expressed as follows: Among them, Emin is the lower limit used for normalization, E>Emin; ε is the distribution index of the absolute energy of AE generated by coal rock fracture, also known as the critical index; When processing the absolute energy data of acoustic emission monitored, the Histogram in the Igor Pro software is used for histogram statistics, and finally expressed as a probability density distribution function P(E), which satisfies the power law of distribution. The probability of micro-fracture and large fracture is determined by the obtained critical index ε; the larger the critical index, the greater the possibility of acoustic emission events with smaller energy, which means that the coal rock mass fractures more slowly and is safer; conversely, the smaller the critical index, the greater the possibility of acoustic emission events with larger energy, which means that the coal rock mass fractures faster and is more dangerous. The probability of acoustic emission events with larger energy can be used to evaluate the risk of rock burst in coal seams; (2) Omori's law describes the distribution of multiple aftershocks caused by the initial vibration, and in the coal-rock mass fracture, it describes the secondary fractures caused by large fractures. This phenomenon is displayed by the acoustic emission energy signal. Omori's law is used to perform statistical analysis on the acoustic emission data. First, the main shock energy generated by the large fractures in the coal-rock mass is divided and selected into intervals. Then, the frequency of aftershocks rAS occurring in the time interval t-tMS from the main shock is statistically analyzed. Therefore, it can be used to analyze and prevent the secondary shock effects caused by mining disturbances. (3) Waiting time distribution: Waiting time distribution is closely related to time. It describes the time intervals between different seismic acoustic emission events. When processing the waiting time distribution data, we first compare the waiting time distributions of different energy events, and then use Refers to the average number of events per unit time greater than a given energy E*min and Draw a graph described by the new scale coordinates, D(δ, E*min) is the Waitingtime distribution; The waiting time distribution is divided into small time intervals and large time intervals by two power functions. The size of the power critical exponent is obtained to analyze the fractures of different scales and the energy release characteristics of the coal and rock mass under the influence of mining disturbance, so as to determine the risk of rock burst caused by mining disturbance.

7. The method for assessing coal seam rock burst risk based on avalanche dynamics theory according to claim 5, characterized in that: The maximum likelihood estimation method can be expressed by the following formula: Where xi,i=1…n are all observed values ​​that satisfy the inequality xi≥xmin, and ε is the distribution index of the absolute energy of AE generated by coal-rock mass rupture, also known as the critical index; The error is: The maximum likelihood estimation method is another method to obtain the distribution index of AE absolute energy. When processing data, the traditional histogram drawing method artificially divides the energy interval, and the artificial division of the energy interval range will inevitably bring errors to the data statistical results; therefore, it is necessary to use the maximum likelihood estimation method to reprocess the monitored AE absolute energy data to obtain a new distribution index, and then compare and verify it with the distribution index obtained by the Gutenberg-Richter law to increase the credibility of the results.

8. The method for assessing coal seam rock burst risk based on avalanche dynamics theory according to claim 4, characterized in that: The Bath's law can be expressed as follows: Where E MS The main shock energy generated by the large fault in the coal rock mass. is the maximum aftershock energy caused by the main shock; Bath's law shows the relationship between the main shock energy generated by the large fault in the coal rock mass and the maximum aftershock energy caused by the main shock, and it is believed that the ratio of the two is close to 1.2 in the logarithmic solution relationship; Therefore, the main shock energy caused by monitored mining disturbances can be used to assess the impact of the maximum aftershocks that may occur in the future, thereby taking targeted measures to prevent the risks of secondary shocks caused by mining disturbances.

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