Accurate coal mine rock burst monitoring, positioning and dynamic early warning method

Abstract

The invention discloses an accurate coal mine rock burst intelligent monitoring and early warning method. The method comprises the following steps that a plurality of micro-seismic monitoring sensors are arranged on a coal mine working face to form a monitoring network; microseismic signal data are collected in real time; the collected micro-seismic signals are preprocessed; a developed redundancy time difference set hyperboloid interpolation positioning method is used to accurately position the micro-seismic source; and based on the micro-seismic signal features and the positioning result, establishing a rock burst early warning model, determining a potential region where rock burst occurs, and realizing dynamic early warning and forecasting. The accuracy, the real-time performance and the reliability of rock burst monitoring and early warning are remarkably improved, and important technical support is provided for coal mine safety production.

Description

Technical field:

[0001] The invention relates to the technical field of coal mine rock burst monitoring, positioning and dynamic early warning, and in particular to an accurate coal mine rock burst monitoring, positioning and dynamic early warning method. Background technology:

[0002] Rock burst in coal mines is a dynamic disaster that seriously threatens the safe production of coal mines. It is sudden and destructive, posing a huge threat to the lives of coal miners and mining equipment. With the continuous increase in the depth of coal mining, the frequency and intensity of rock burst have also increased, becoming one of the main factors restricting the safe and efficient mining of coal mines. Therefore, accurate monitoring and early warning of rock burst are of great significance to ensure the safe production of coal mines.

[0003] Traditional rock burst monitoring methods mainly include stress monitoring, displacement monitoring and detection while drilling. Although these methods can reflect the stress state and deformation characteristics of coal rock mass to a certain extent, traditional monitoring methods can usually only monitor local areas and it is difficult to fully reflect the stress state of the entire working face. In addition, some monitoring methods require manual data collection and cannot achieve real-time monitoring and early warning. Finally, the early warning model is usually static and difficult to adapt to changes in different mining areas and different mining stages. In recent years, with the development of microseismic monitoring technology, rock burst monitoring and early warning methods based on microseismic signals have been widely used. Microseismic monitoring technology has the advantages of wide monitoring range, strong real-time performance, and non-destructive detection, which provides a new idea for rock burst early warning. However, the existing microseismic monitoring and early warning methods still have the following problems:

[0004] 1. The accuracy of microseismic source positioning needs to be improved: Traditional positioning algorithms are not accurate enough under complex geological conditions, making it difficult to accurately locate the location of microseismic sources, affecting the accuracy of early warning. There are mainly three important challenges: 1) Traditional methods require an initial iteration value that is close to the true source position, otherwise it is easy to cause the algorithm iteration to not converge or converge slowly; 2) The formation wave velocity often changes dynamically as the construction progresses, so it is difficult to obtain accurate prior wave velocity, which seriously affects the positioning accuracy; 3) Traditional methods rely too much on reference sensors, and their uncertainty has a significant impact on positioning accuracy.

[0005] 2. The early warning model lacks dynamism: it is difficult to adaptively adjust the early warning threshold based on real-time monitoring data.

[0006] In view of the above problems, the present invention proposes an accurate intelligent monitoring and early warning method and system for rock burst in coal mines. The method improves the positioning accuracy through an improved microseismic source positioning algorithm, establishes a risk assessment model that comprehensively considers multiple factors, and introduces a dynamic early warning mechanism, thereby achieving accurate monitoring and intelligent early warning of rock burst. At the same time, the method also establishes a historical database, effectively integrating historical data and real-time monitoring data, and providing a basis for the continuous optimization of the early warning model.

[0007] The method of the present invention not only improves the accuracy and real-time performance of rock burst monitoring and early warning, but also has good adaptability and scalability, and provides important technical support for safe production in coal mines. Summary of the invention:

[0008] In view of the above-mentioned defects of the prior art, the purpose of the present invention is to provide an accurate coal mine rock burst monitoring and positioning and dynamic early warning method, so as to effectively improve the accuracy of mine microseismic positioning and realize dynamic early warning of rock burst.

[0009] To achieve the above object, the present invention provides an accurate coal mine rock burst monitoring and positioning and dynamic early warning method, comprising the following steps:

[0010] An accurate coal mine rock burst monitoring and positioning and dynamic early warning method comprises the following steps:

[0011] S1 Arrange multiple microseismic monitoring sensors on the coal mine working face to form a monitoring network;

[0012] S2 collects microseismic signal data in real time;

[0013] S3 pre-processes the collected microseismic signals, including filtering and noise reduction;

[0014] S4 uses the improved full set redundant time difference set hyperbolic interpolation positioning method to accurately locate the microseismic source and obtain the microseismic event and spatial position;

[0015] S5 establishes a rock burst risk assessment model based on the energy and spatiotemporal characteristics of microseismic signals to achieve dynamic early warning of rock burst.

[0016] Furthermore, the layout of the monitoring network in step S1 is as follows:

[0017] Each sensor should cover the entire monitoring area as tightly as possible, and at the same time ensure that the distance between any two sensors is maximized, avoiding special layout methods such as co-linearity and co-planarity.

[0018] Further, the calculation process of the redundant time difference set hyperbolic interpolation positioning method in step S4 is as follows:

[0019]

[0020] Where ε is the residual of the formula Aθ-e, θ is the parameter to be solved, θ=[xyz V] T , (x, y, z) are the components of the earthquake source coordinates, V is the square of the medium propagation velocity, and e is the parameter e ijk The matrix A consists of four columns, each of which is composed of the parameters a i,j,k , b i,j,k , c i,j,k , d i,j,k The symbol T in the upper right corner indicates the matrix transpose, and the symbol -1 in the upper right corner indicates the inverse of the matrix. ijk =t ki x ij -t ji x ik , b ijk =t ki y ij -t ji y ik , c ijk =t ki z ij -t ji z ik , In the following table, i, j, k are the combination numbers of any three sensors among n sensors. jk ,t ki and t ij are the three time difference measurements made by sensors i, j and k, t jk =t j -t k , t ki =t k -t i , t ij =t i -t j .t i , t j and t k are the arrival times of the signals received by sensors i, j and k respectively; (x i ,y i ,z i ) are the sensor coordinates.

[0021] Furthermore, the calculation formula for the occurrence time of the microseismic event in step S4 is as follows:

[0022]

[0023] Among them, || || means to find the norm of the data, o is the coordinate of the source, s1 is the coordinate of sensor 1, and the parameters o and V are replaced by the corresponding elements of the estimated value θ.

[0024] Furthermore, the filtering and noise reduction methods in step S3 respectively adopt the following steps:

[0025] S1. Setting the frequency range: According to the frequency characteristics of the microseismic signal, the low-frequency cutoff frequency of the bandpass filter is set to 10 Hz, and the high-frequency cutoff frequency is set to 1000 Hz. This range will allow the frequency components of the main microseismic signal to pass through while suppressing the noise of other irrelevant frequencies;

[0026] S2. Design filter: Design or select a suitable bandpass filter according to the set frequency range. The digital filter design method FIR (finite impulse response) can be used to ensure the stability and effectiveness of signal processing;

[0027] S3. Apply filter: Input the original microseismic signal into a bandpass filter. The filter will filter out noise with a frequency lower than 10 Hz and higher than 1000 Hz, and only retain the valid signal within this range. This process can be achieved by digital signal processing tools (MATLAB signal processing library);

[0028] This bandpass filtering method can effectively filter out the noise in the microseismic signals of mines, especially the low-frequency mechanical noise and high-frequency electromagnetic interference, and can greatly improve the signal-to-noise ratio of the microseismic signals.

[0029] Further, the risk assessment model in step S5 is based on the following parameters:

[0030] ① Microseismic event energy information: The microseismic energy is obtained by calculating the area within the envelope of the microseismic event signal (expressed by level voltage).

[0031] Further, the dynamic early warning model in step S5 includes:

[0032] S1 data collection: continuously collect the energy values ​​of microseismic events within a certain period of time (24 hours), recorded as {E1, E2, ..., E n}, where n is the total number of events;

[0033] S2 statistical feature calculation:

[0034] a) Average value (μ):

[0035]

[0036] b) Standard deviation (σ):

[0037]

[0038] S3 warning threshold setting:

[0039] Normal range: E<μ+3σ

[0040] Warning range: E≥μ+3σ

[0041] S4 real-time monitoring and early warning:

[0042] For the energy E of the newly observed microseismic event:

[0043] If E<μ+3σ, it is considered to be within the normal range;

[0044] If E≥μ+3σ, an early warning is triggered, indicating that there may be a risk of rock burst;

[0045] In order to further explain the specific process of the microseismic positioning method, this part is now expanded in detail as follows:

[0046] In mathematics, the source localization problem involves solving an overdetermined system of equations, i.e., one with more equations than unknowns.

[0047]

[0048] Where s i is the propagation path of the wave from the source to the i-th trigger sensor; v is the velocity field of the propagation medium; t i and t0 represent the P wave arrival time measured by the ith sensor and the sensor trigger time, respectively. The classical single-source positioning system using a single velocity model is determined by the following formula

[0049] ||os i ||=v(t i -t0),i=1L n (2)

[0050] In the formula, the symbol || || indicates the norm of the data, and ||os i || represents the earthquake source o and the i-th trigger sensor s i The distance between them, v is the propagation velocity of the medium; the coordinates of the source o are (x, y, z), and the sensor s i The coordinates of (x i ,y i ,z i ).

[0051] In order to eliminate the triggering time t0 in the above equation, reduce the unknown quantity and improve the stability of the positioning process, the traditional method often selects the first sensor as the reference sensor, and then uses the equation with i>1 to subtract the equation with i=1 to obtain the following time difference equation:

[0052] ||os i||-||o-s1||=vt i1 =v(t i -t1),i=2L n (3)

[0053] The most common method to solve the above equations is to find the minimum value of the sum of squares of the residuals on both sides of the above equations. Thus, the positioning problem is transformed into an optimization problem of the objective function. We can get the solution by searching for a point in space and time by minimizing the total residual. However, the traditional method has three fatal weaknesses: 1) It is necessary to give an initial value of the iteration close to the true source position, otherwise it is easy to cause the algorithm iteration to not converge or converge slowly; 2) The formation wave velocity often changes dynamically with the progress of construction, so it is difficult to obtain an accurate prior wave velocity, and the positioning accuracy is seriously affected; 3) The reliance on the reference sensor is too high, and its uncertainty has a significant impact on the positioning accuracy. The above three factors are the important reasons why the seismic sources located by many automated microseismic monitoring systems cannot be clustered into larger clusters in space.

[0054] To solve the above three problems, this patent does not need to input the a priori measurement parameter of wave velocity, but uses it as the intermediate variable for solution, thereby eliminating the influence of wave velocity measurement error on the positioning result; secondly, this patent derives the analytical form of source location, which does not require iterative solution, avoiding the problem of non-convergence or slow convergence of the algorithm; finally, this patent further uses other easy time difference sets to construct control equations, rather than selecting a certain sensor as a reference, avoiding the serious influence of the uncertainty of the reference sensor on the positioning accuracy. The specific formula is deduced as follows:

[0055] First, the method of this patent abandons the determination of reference sensor 1, but simultaneously selects all sensors j different from sensor i to construct the control equation in order to utilize more redundant time difference data. The specific formula is as follows:

[0056] ||os i ||-||os j ||=vt ij ,i,j=1L ni≠j (4)

[0057] Among them l ij (x, y, z) is the distance difference between sensors i and j, t ij is the time difference between sensors i and j.

[0058] Through operations such as squaring and transposing, the above control equation is reorganized into the following new form:

[0059]

[0060] where x ij ,y ij , and zij is the difference between the three components of the i and j sensor coordinates, However, the above equations are still highly nonlinear. In order to linearize them, we further introduce the kth sensor and calculate the time difference equation between sensors i and k, as follows:

[0061]

[0062] Then, by further subtracting the above formulas (5) and (6) and introducing the intermediate variable V, the following linear equation system can be obtained:

[0063]

[0064] Where V = v 2 .

[0065] Multiply both sides of equation (7) by t ji t ki The simplified form can be obtained

[0066]

[0067] Finally, we further rearrange formula (8) to obtain

[0068] 0 = a ijk x+b ijk y+c ijk zd ijk V+e ijk (9) Among them, a ijk =t ki x ij -t ji x ik , b ijk =t ki y ij -t ji y ik , c ijk =t ki z ij -t ji z ik , In the following table, i, j, k are the combination numbers of any three sensors among the n sensors.

[0069] Further write formula (9) into matrix form:

[0070] 0=Aθ-e (10)

[0071] Among them, θ is the parameter to be solved, θ=[xyz V] T , (x, y, z) are the components of the earthquake source coordinates, v is the medium propagation velocity, and e is the parameter eijk The matrix A consists of four columns, each of which is composed of the parameters a i,j,k , b i,j,k , c i,j,k , d i,j,k All elements of .

[0072] In actual engineering practice, due to the existence of measurement errors, the above formula (10) is not exactly equal to 0, but has a certain residual ε, that is,

[0073] ε=Aθ-e (11)

[0074] Minimizing the residual sum of the above equations can obtain the solution of the source parameters and the square of the velocity, that is,

[0075]

[0076] According to the equation (2) with i=1, the occurrence time of the microseismic event can be calculated as

[0077]

[0078] Where s1 is the coordinate of sensor 1, and o and V are replaced by the calculation results of formula (12).

[0079] Beneficial effects of the present invention:

[0080] 1. Improve monitoring accuracy: By arranging multiple microseismic monitoring sensors to form a monitoring network and developing an improved redundant time difference set hyperbolic interpolation positioning algorithm, the positioning accuracy of microseismic sources has been significantly improved; specifically: ① The velocity-free control equation is constructed to eliminate the influence of wave velocity measurement error on the positioning result; ② The analytical form of source positioning is derived, and no iterative solution is required, avoiding the problem of non-convergence or slow convergence of the algorithm; ③ There is no need to select a certain reference sensor, avoiding the serious influence of the uncertainty of the reference sensor on the positioning accuracy. This provides reliable basic data for accurately assessing the risk of rock burst.

[0081] 2. By establishing a dynamic risk assessment model and combining the energy and spatial location characteristics of microseismic events, dynamic and real-time early warning of rock burst risk assessment is achieved, improving the flexibility and adaptability of the early warning system. Description of the drawings:

[0082] Figure 1 It is a schematic flow chart of the coal mine rock burst monitoring, positioning and dynamic early warning method provided by the present invention.

[0083] Figure 2 It is a schematic diagram comparing the positioning accuracy of the new method provided by the present invention and other methods. Specific implementation method:

[0084] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0085] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and / or processing steps closely related to the scheme of the present invention are shown in the drawings, while other details that are not closely related to the present invention are omitted.

[0086] In addition, it should be noted that the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or apparatus.

[0087] The present invention provides an accurate coal mine rock burst monitoring and positioning and dynamic early warning method, the flow diagram of which is as follows: Figure 1 As shown, the specific steps include:

[0088] S1 Arrange multiple microseismic monitoring sensors on the coal mine working face to form a monitoring network;

[0089] S2 collects microseismic signal data in real time;

[0090] S3 pre-processes the collected microseismic signals, including filtering and noise reduction;

[0091] S4 uses the improved full set redundant time difference set hyperbolic interpolation positioning method to accurately locate the microseismic source and obtain the microseismic event and spatial position;

[0092] S5 establishes a rock burst risk assessment model based on the energy and spatiotemporal characteristics of microseismic signals to achieve dynamic early warning of rock burst.

[0093] Furthermore, the layout of the monitoring network in step S1 is as follows:

[0094] Each sensor should cover the entire monitoring area as tightly as possible, and at the same time ensure that the distance between any two sensors is maximized, avoiding special layout methods such as co-linearity and co-planarity.

[0095] Further, the calculation process of the redundant time difference set hyperbolic interpolation positioning method in step S4 is as follows:

[0096]

[0097] Where ε is the residual of the formula Aθ-e, θ is the parameter to be solved, θ=[xyz V] T, (x, y, z) are the components of the earthquake source coordinates, V is the square of the medium propagation velocity, and e is the parameter e ijk The matrix A consists of four columns, each of which is composed of the parameters a i,j,k , b i,j,k , c i,j,k , d i,j,k The symbol T in the upper right corner indicates the matrix transpose, and the symbol -1 in the upper right corner indicates the inverse of the matrix. ijk =t ki x ij -t ji x ik , b ijk =t ki y ij -t ji y ik , c ijk =t ki z ij -t ji z ik , In the following table, i, j, k are the combination numbers of any three sensors among n sensors. jk ,t ki and t ij are the three time difference measurements made by sensors i, j and k, t jk =t j -t k , t ki =t k -t i , t j and t k are the arrival times of the signals received by sensors i, j and k respectively; (x i ,y i ,z i ) are the sensor coordinates.

[0098] Furthermore, the calculation formula for the occurrence time of the microseismic event in step S4 is as follows:

[0099]

[0100] Among them, || || means to find the norm of the data, o is the coordinate of the source, s1 is the coordinate of sensor 1, and the parameters o and V are replaced by the corresponding elements of the estimated value θ.

[0101] Furthermore, the filtering and noise reduction methods in step S3 respectively adopt the following steps:

[0102] S1. Setting the frequency range: According to the frequency characteristics of the microseismic signal, the low-frequency cutoff frequency of the bandpass filter is set to 10 Hz, and the high-frequency cutoff frequency is set to 1000 Hz.

[0103] S2. Design filter: According to the set frequency range, design the digital filter design method FIR (finite impulse response) to ensure the stability and effectiveness of signal processing.

[0104] S3. Apply filter: The original microseismic signal is input into a bandpass filter. The filter will filter out noise with frequencies below 10 Hz and above 1000 Hz, and only retain valid signals within this range. This process is implemented using digital signal processing tools (MATLAB signal processing library).

[0105] Further, the risk assessment model in step S5 is based on the following parameters:

[0106] ① Microseismic event energy information: The microseismic energy is obtained by calculating the area within the envelope of the microseismic event signal (expressed by level voltage).

[0107] Further, the dynamic early warning model in step S5 includes:

[0108] S1 data collection: continuously collect the energy values ​​of microseismic events within a certain period of time (24 hours), recorded as {E1, E2, ..., E n}, where n is the total number of events.

[0109] S2 statistical feature calculation:

[0110] a) Average value (μ):

[0111]

[0112] b) Standard deviation (σ):

[0113]

[0114] S3 warning threshold setting:

[0115] Normal range: E<μ+3σ

[0116] Warning range: E≥μ+3σ

[0117] S4 real-time monitoring and early warning:

[0118] For the energy E of the newly observed microseismic event:

[0119] If E<μ+3σ, it is considered to be within the normal range.

[0120] If E≥μ+3σ, an early warning is triggered, indicating that there may be a risk of rock burst.

[0121] Specific implementation case 1:

[0122] Given a mine microseismic monitoring system with a three-dimensional size of 300m×300m×300m, there are 16 sensors in the system, and their coordinates are (0,0,0),(300,0,0),(300,300,0),(0,300,0),(0,0,300),(300,0,300),(300,300,300),(0,300,300),(150,0,150),(300,150,150),(150,300,150),(0,150,150),(150,150,0),(150,150,300),(0,0,150),(300,300,150) (unit: meter). The real source coordinates are (92,222,236) (unit: meter), the real wave velocity value is set to v = 5000 meters per second, and the trigger time of the microseismic event is 100 microseconds, which is given here for the convenience of comparison with the positioning results. In addition, according to the real source coordinates, wave velocity and trigger time, the arrival time data of the signal received by each sensor can be simulated, that is, the real arrival time data. Then, in order to simulate the impact of mine environmental noise on the positioning results, we further added a random error with a standard deviation of 0.3 milliseconds to the real arrival time data.

[0123] Through the above steps, we can get a set of arrival time data containing noise, which are 167.77, 176.29, 164.64, 153.04, 150.17, 162.29, 146.28, 126.98, 149.08, 147.48, 126.49, 128.85, 150.52, 122.68, 150.84, 147.83 (in milliseconds).

[0124] Using the above arrival time data containing noise and sensor coordinates, the positioning method of this patent can obtain a positioning result of (92.73, 222.01, 235.18) in meters. The positioning error is (0.73, 0.014, -0.82) (meters), and the absolute distance error is 1.10 meters.

[0125] In addition, the patented method is also compared with other existing methods (including USBM, OLSUV, and ESX methods). The positioning results show that the positioning errors (absolute distance errors) of the existing OLSUV, ESX, and USBM methods are 2.72 meters, 2.81 meters, and 6.83 meters, respectively. Therefore, the positioning accuracy of the new method is improved by 59.5%, 60.8%, and 83.9% compared with the existing OLSUV, ESX, and USBM methods, respectively. For details, see Figure 2.

[0126] The patented method is further used to calculate the triggering time of the microseismic event, and the calculated result is 99.9954 microseconds, with an error of only 0.0046 microseconds, which can be ignored. The triggering time calculated by the patented formula is the time when the microseismic event occurs, and the source location is the location where the microseismic event occurs; the precise calculation of the spatiotemporal information of the above microseismic events can provide key support for the real-time monitoring of mine disasters, the determination of potential disaster areas, and disaster prevention and control.

[0127] Then, the energy information of the microseismic event is further calculated; the energy release information of the microseismic event can be obtained by calculating the envelope area of ​​the microseismic signal waveform (the microseismic waveform is usually represented by level voltage data). Then, the energy of the microseismic event is statistically analyzed. The specific steps include:

[0128] S1 data collection: continuously collect the energy values ​​of microseismic events within a certain period of time (24 hours), recorded as {E1, E2, ..., E n}, where n is the total number of events.

[0129] S2 statistical feature calculation:

[0130] a) Average value (μ):

[0131]

[0132] b) Standard deviation (σ):

[0133]

[0134] S3 warning threshold setting:

[0135] Normal range: E<μ+3σ

[0136] Warning range: E≥μ+3σ

[0137] S4 real-time monitoring and early warning:

[0138] For the energy E of the newly observed microseismic event:

[0139] If E<μ+3σ, it is considered to be within the normal range.

[0140] If E≥μ+3σ, an early warning is triggered, indicating that there may be a risk of rock burst.

Claims

1. An accurate coal mine rock burst monitoring, positioning and dynamic early warning method, characterized in that: The following steps are involved: S1 Arrange multiple microseismic monitoring sensors on the coal mine working face to form a monitoring network; S2 collects microseismic signal data in real time; S3 pre-processes the collected microseismic signals, including filtering and noise reduction; S4 uses the improved full set redundant time difference set hyperbolic interpolation positioning method to accurately locate the microseismic source and obtain the microseismic event and spatial position; S5 establishes a rock burst risk assessment model based on the energy and spatiotemporal characteristics of microseismic signals to achieve dynamic early warning of rock burst.

2. The method according to claim 1, characterized in that The layout of the monitoring network in step S1 is as follows: Each sensor should cover the entire monitoring area as tightly as possible, and at the same time ensure that the distance between any two sensors is maximized, avoiding special layout methods such as co-linearity and co-planarity.

3. The method according to claim 1, characterized in that The calculation process of the redundant time difference set hyperbolic interpolation positioning method in step S4 is as follows: Where ε is the residual of the formula Aθ-e, θ is the parameter to be solved, θ=[xyz V] T , (x, y, z) are the components of the earthquake source coordinates, V is the square of the medium propagation velocity, and e is the parameter e ijk The matrix A consists of four columns, each of which is composed of the parameters a i,j,k , b i,j,k , c i,j,k , d i,j,k The symbol T in the upper right corner indicates the matrix transpose, and the symbol -1 in the upper right corner indicates the inverse of the matrix. ijk =t ki x ij -t ji x ik , b ijk =t ki y ij -t ji y ik , c ijk =t ki z ij -t ji z ik , In the following table, i, j, k are the combination numbers of any three sensors among n sensors; t jk ,t ki and t ij are the three time difference measurements made by sensors i, j and k, t jk =t j -t k , t ki =t k -t i , t ij =t i -t j , t i , t j and t k are the arrival times of the signals received by sensors i, j and k respectively; (x i ,y i ,z i ) are the sensor coordinates.

4. The method according to claim 1, characterized in that: The calculation formula of the microseismic event occurrence time in step S4 is as follows: Among them, || || means to find the norm of the data, o is the coordinate of the source, s1 is the coordinate of sensor 1, and the parameters o and V are replaced by the corresponding elements of the estimated value θ.

5. The method according to claim 1, characterized in that The filtering and noise reduction methods in step S3 respectively adopt the following steps: S1. Setting frequency range: According to the frequency characteristics of microseismic signals, the low-frequency cutoff frequency of the bandpass filter is set to 10 Hz, and the high-frequency cutoff frequency is set to 1000 Hz; S2. Design filter: According to the set frequency range, design the digital filter design method finite impulse response FIR to ensure the stability and effectiveness of signal processing; S3. Apply filter: The original microseismic signal is input into a bandpass filter. The filter will filter out the noise with a frequency lower than 10 Hz and higher than 1000 Hz, and only retain the valid signal within this range. This process is implemented through the signal processing library of the digital signal processing tool MATLAB.

6. The method according to claim 1, characterized in that The risk assessment model in step S5 is based on the following parameters: ① Microseismic event energy information; the microseismic energy is obtained by calculating the area within the envelope of the microseismic event signal.

7. The method according to claim 1, characterized in that The dynamic early warning model in step S5 includes: The energy values ​​of microseismic events are statistically analyzed, and the following characteristic values ​​are mainly calculated: Average value μ: Calculate the average value of microseismic energy within a certain period of time, reflecting the average intensity of microseismic activity within this period of time; Standard deviation σ: Calculate the standard deviation of microseismic energy to measure the dispersion and fluctuation of microseismic energy; To set the warning threshold: Normal range: E<μ+3σ, E is the microseismic energy parameter; Warning range: When the microseismic energy value exceeds μ+3σ, it is considered abnormal and enters the warning state; Real-time monitoring: The microseismic energy in the current time is calculated in real time. If the microseismic energy value is found to exceed the set 3σ range, it is considered that there may be a risk of rock burst in the positioning area.

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