Mine dynamic disaster fine monitoring and dynamic early warning method without scale effect

Abstract

The invention discloses a scale-effect-free mine dynamic disaster fine monitoring and dynamic early warning method, which comprises the following steps of: arranging a multi-point micro-seismic monitoring network on a coal mine working face, and continuously acquiring and preprocessing micro-seismic signals; an innovative analysis positioning algorithm is adopted to accurately analyze the accurate position and time information of the micro-seismic source; in combination with signal features and positioning data, a four-dimensional joint kernel density estimation and confidence interval theory is innovatively applied, a rock burst risk assessment model is constructed, and a danger level classification standard is determined; and the system calculates risk indexes in real time and dynamically issues early warning. According to the invention, the precision, timeliness and reliability of monitoring and early warning of the dynamic disasters in the mining area are obviously improved, and key technical support is provided for safe production of a coal mine.

Description

Technical Field

[0001] The invention relates to the technical field of mine safety, and in particular to a fine monitoring and early warning method for mine dynamic disasters without scale effect. Background Art

[0002] As global mineral resource mining develops deeper, mining depth continues to increase, and the geostress environment becomes increasingly complex, the frequency and intensity of mine dynamic disasters are on the rise. Especially in deep mines, the "three highs" characteristics of high geostress, high geotemperature, and high osmotic pressure make the prediction and prevention of dynamic disasters more difficult. At the same time, the expansion of mining scale and the increase in mining intensity have further aggravated the severity of this problem.

[0003] In recent years, the rapid development of microseismic monitoring technology and methods has opened up new ways for monitoring and early warning of mine dynamic disasters. This technological innovation enables us to capture the microscopic fracture information inside the rock mass in real time, providing massive data support for the mine dynamic disaster early warning system. The application of the microseismic monitoring system not only greatly improves the monitoring accuracy and coverage, but also provides valuable real-time data for a deep understanding of the mechanical behavior of the rock mass and the process of disaster incubation. However, although microseismic monitoring technology has made significant progress in the field of mine dynamic disaster monitoring and early warning, it still faces many challenges in practical applications:

[0004] 1. The accuracy of microseismic source positioning needs to be improved: ① The traditional positioning algorithm has low computational efficiency, and often there are situations where iterations do not converge or converge locally; ② The traditional positioning method cannot comprehensively utilize all arrival data at one time, and the positioning accuracy cannot achieve maximum likelihood estimation; ③ The traditional positioning method requires matrix solution, which may face matrix singularity and pathological situations; ④ The arrival measurement data is seriously affected by abnormal signals such as environmental noise; ⑤ The sensor layout of the traditional mining microseismic monitoring network is not reasonable, which may cause the sensor to be unable to receive the signal, or the unreasonable sensor geometry structure may cause serious system errors.

[0005] 2. Insufficient adaptability of the model: Traditional early warning models often have scale effects and are not practical. Statistical models based on data from specific mining areas are often difficult to directly apply to other mining areas with different geological conditions.

[0006] 3. The risk assessment model is not comprehensive enough: it does not fully consider the multidimensional characteristics of microseismic events, relies only on a single parameter or simple threshold, fails to fully utilize multi-source information, and makes it difficult to adaptively adjust the warning threshold based on real-time monitoring data.

[0007] 4. Lack of real-time performance: Real-time processing and analysis of large-scale data remains a challenge, affecting the timeliness of early warning.

[0008] 5. Scale effect of early warning models: Traditional models are usually built based on local observation data and are difficult to accurately reflect the overall behavior of large-scale geological bodies. When generalized from small scale to large scale, the predictive ability of the model tends to be significantly reduced. Summary of the invention

[0009] In view of the deficiencies in the above-mentioned prior art, the present invention provides a method for fine monitoring and dynamic early warning of mine dynamic disasters without scale effect, so as to solve the technical problems of traditional rock burst monitoring methods in data utilization, model evaluation and dynamic early warning.

[0010] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0011] A method for fine monitoring and dynamic early warning of mine dynamic disasters without scale effect, comprising the following steps:

[0012] S1: Arrange multiple high-sensitivity microseismic monitoring sensors in the mining area to form a full-dimensional three-dimensional monitoring network;

[0013] S2: Real-time collection of microseismic signal data through a high-speed data acquisition system to ensure data continuity and integrity;

[0014] S3: Adaptively perform noise reduction processing on the collected microseismic signals;

[0015] S4: Using the improved analytical positioning model, high-precision three-dimensional positioning of microseismic sources is performed;

[0016] S5: Segment the microseismic energy data into multi-scale time windows (e.g., hours, days, weeks, and months) to facilitate multi-level statistical analysis and trend identification;

[0017] S6: Based on the time-frequency characteristics, spatial distribution and energy release law of microseismic signals, combined with the kernel probability density estimation and probability confidence interval principle, a dynamic assessment model for rock burst risk is established;

[0018] S7: Calculate the risk index of rock burst in real time and dynamically classify disaster warnings;

[0019] S8: Establish a feedback mechanism for early warning results, continuously optimize and update risk assessment models and early warning systems through historical data analysis, post-disaster investigations, numerical simulation technology and expert knowledge base, and build a comprehensive database of mine dynamic disasters.

[0020] As a preferred solution of the present invention, the layout of the sensor network in step S1 is as follows:

[0021] (1) A regular grid layout is adopted within the mining area to ensure that sensors cover the entire monitoring area;

[0022] (2) According to the geological structure of the mining area, sensors are arranged at different depths to form a three-dimensional monitoring network;

[0023] (3) Increase sensor density in key areas (such as mining faces, fault zones, and rock layer interfaces);

[0024] (4) The sensors are dispersedly arranged, and the distance between each two sensors is maximized to facilitate the subsequent microseismic source positioning.

[0025] As a preferred solution of the present invention, the process of performing adaptive noise reduction processing on the collected microseismic signals in step S3 is as follows:

[0026] (1) Signal preprocessing:

[0027] Sample and digitize the original microseismic signals to ensure signal integrity and continuity;

[0028] (2) Empirical Mode Decomposition:

[0029] a) Decompose the preprocessed microseismic signal x(t) to obtain a series of eigenmode functions and a residual term:

[0030]

[0031] Among them, IMF i (t) is the i-th eigenmode function, r n (t) is the residual term;

[0032] b) Steps of signal decomposition:

[0033] Identify all local extreme points of the signal x(t);

[0034] The upper and lower envelopes are generated by cubic spline interpolation;

[0035] Calculate the average value m1(t) of the upper and lower envelopes;

[0036] Subtract m1(t) from the original signal to obtain h1(t)=x(t)-m1(t);

[0037] Repeat the above steps until h1(t) meets the IMF definition conditions;

[0038] Define h1(t) as the first IMF, i.e. c1(t) = h1(t);

[0039] Subtract c1(t) from the original signal and repeat the above process to obtain subsequent IMFs;

[0040] (3) Noise IMF identification:

[0041] a) Calculate the energy density and center frequency of each IMF;

[0042] b) Analyze the spectrum characteristics of IMF and identify high-frequency noise IMF;

[0043] c) Use correlation analysis or signal-to-noise ratio threshold method to determine the noise IMF that needs to be removed;

[0044] (4) Adaptive threshold denoising:

[0045] The specific operation of identifying the IMF components representing noise can be carried out from the following aspects:

[0046] a) Calculate the energy of each IMF component: Where N is the signal length;

[0047] b) Calculate the average energy:

[0048]

[0049] Where N is the total number of IMF components;

[0050] c) Calculate the standard deviation σ of all IMF energies

[0051]

[0052] d) Setting the adaptive threshold: T = μ - ασ, where α = 2;

[0053] e) IMFs with indicators lower than the threshold T are judged as noise components;

[0054] (5) Signal reconstruction:

[0055] a) Reconstruct the denoised IMF, the retained low-frequency IMF and the residual term to obtain the denoised microseismic signal:

[0056]

[0057] Among them, IMF i (t) is the denoised IMF, k is the number of noisy IMFs;

[0058] Through the above steps, the empirical mode decomposition technology can be effectively used to reduce the noise of microseismic signals in mining areas, improve signal quality, and provide a more reliable data basis for subsequent microseismic event analysis and early warning.

[0059] As a preferred solution of the present invention, the calculation formula of the improved source analysis positioning model in step S4 is as follows:

[0060]

[0061] Among them, α1=l1l6l 11 -l1l7l 10 -l2l5l 11 +l2l7l9+l3l5l 10 -l3l6l9

[0062]

[0063] Among them, χ (j) The jth (j=1, 2, 3, 4) index of takes parameters a, b, c, d respectively; a i =2(x i -x1), b i =2(y i -y1), c i =2(z i -z1), d i =-2v 2 (t i -t1), e i =v 2 (t i 2 -t1 2 )-[(x i 2 -x1 2 )+(y i 2 -y1 2 )+(z i 2 -z1 2 )], the sensor coordinates are (x i ,y i ,z i ), i = 1, 2, ..., n, n is the number of sensors, t i represents the arrival time data of sensor i, t1 represents the arrival time of the first triggered sensor, v represents the propagation velocity of the medium, (x, y, z) represents the coordinates of the microseismic source with solution, and t0 represents the time when the microseismic event occurs.

[0064] As a preferred solution of the present invention, the dynamic early warning model in step S6 includes:

[0065] S1. Obtain the four-dimensional joint probability density function of the microseismic event location

[0066] The density estimation method is used to fit the spatial position and energy of microseismic events and calculate the joint probability density function of the position and energy of microseismic events. It can be expressed as follows:

[0067]

[0068] Wherein: θ is the source coordinate and energy of the microseismic source, θ = [x, y, z, e] T , the superscript T represents the transpose of a matrix or vector; is the event of the kth (k = 1, 2,..., m) time series, and m is the number of all events within a certain time period; H is the bandwidth matrix, d is the data dimension, d = 4; σ i is the standard deviation of the ith vector of θ, med(·) represents taking the median; H ii = 0, i ≠ j;

[0069] S2. Define the confidence interval

[0070] ① Select an appropriate confidence level, such as P = 85%, 95% or 99%, which depends on the mine's acceptance of different levels of risk;

[0071] ② For the above probability distribution, the confidence interval [θ1, θ2] depends on the corresponding confidence level P, and [θ1, θ2] can be calculated by the following formula:

[0072]

[0073] where P(θ1 ≤ X < E) = P(E < X < θ2), and E is the position where the probability density reaches its extreme value;

[0074] S3. Determine the threshold for classifying the hazard level

[0075] ① According to the confidence interval, the microseismic disaster hazard can be divided into multiple levels:

[0076] Low hazard: X < θ1;

[0077] Medium hazard: θ1 ≤ X ≤ θ2;

[0078] High hazard: X > θ2;

[0079] ② For the specific threshold selection, the following three factors can be considered: the energy level that caused disasters in historical data, expert opinions and empirical judgments, and the specific geological conditions and mining methods of the mine.

[0080] To further illustrate the specific process of the microseismic positioning method, the microseismic positioning process is detailed as follows:

[0081] Assume that the sound source position coordinates are (x, y, z), and the sensor coordinates are (x i , y i , z i), the wave speed of the propagation medium is v. According to the positive proportional relationship between the propagation distance and the travel time, the control equation of the sound source can be obtained as follows:

[0082]

[0083] Among them, t i It refers to the time when sensor i receives the sound source signal, and t0 refers to the time when the sound source sends the signal. By squaring formula (1), we can get:

[0084] v 2 (t i -t0) 2 =(xx i ) 2 +(yy i ) 2 +(zz i ) 2 (2)

[0085] Subtract the formula for i>1 from the formula for i=1, and we get:

[0086] 2(x i -x1)x+2(y i -y1)y+2(z i -z1)z-2v 2 t0(t i -t1)+v 2 (t i 2 +t1 2 )-[(x i 2 -x1 2 )+(y i 2 -y1 2 )+(z i 2 -z1 2 )]=0 (3)

[0087] Let a i =2(x i -x1), b i =2(y i -y1), c i =2(z i -z1), d i =-2v 2 (t i -t1), e i =v 2 (t i 2 -t1 2 )-[(x i2 -x1 2 )+(y i 2 -y1 2 )+(z i 2 -z1 2 )], then formula (3) can be expressed as:

[0088] a i x+b i y+c i z+d i t0+e i =0 (4)

[0089] In fact, in a real monitoring environment, due to the influence of many factors such as noise interference and system errors, the above formula (4) cannot be exactly equal to 0, but there is a certain deviation, which is called the equation residual, which is specifically expressed as follows:

[0090] a i x+b i y+c i z+d i t0+e i =ε (5)

[0091] The residual sum of squares of the above control equation is expressed as follows:

[0092]

[0093] Solving the partial derivatives of the above residual sum of squares with respect to x, y, z, and t0, we can obtain a positive definite system of equations about the focal coordinates:

[0094]

[0095] Let the above partial derivative equal to 0 and organize it into the following expression:

[0096]

[0097] in, The j-th (j=1, 2, 3, 4) index takes vectors a, b, c, d respectively.

[0098] By using the elimination method, the unknown quantity t0 in formula (8) is eliminated, and the control equation only about the source coordinates x, y, z is obtained as follows:

[0099]

[0100] in:

[0101]

[0102]

[0103] Solving formula (9) can obtain the specific values ​​of the earthquake source coordinates x, y, and z, which are expressed as follows:

[0104]

[0105] Among them, σ1=l1l6l 11 -l1l7l 10 -l2l5l 11 +l2l7l9+l3l5l 10 -l3l6l9.

[0106] This method does not require the combination of various probes, and all sensors can be used. In addition, the least square solution method is used to eliminate the influence of maximum and minimum values ​​to a certain extent (especially for the case of a large number of sensors), thereby improving the calculation accuracy.

[0107] Compared with the prior art, the present invention has the following technical advantages:

[0108] 1. Improve the positioning accuracy of microseismic events: By rationally arranging a multi-sensor network in the monitoring area, the system error of positioning can be effectively reduced; environmental noise can be effectively reduced and the interference of abnormal signals can be eliminated; an improved analytical positioning algorithm can be developed to effectively solve the problem of non-convergence or local convergence of iterations; by solving the residual gradient, the approximate maximum likelihood estimation of the microseismic source can be achieved; the new method of the present invention significantly improves the positioning accuracy and calculation efficiency of the microseismic source, and provides reliable basic data for accurately assessing the risk of rock burst.

[0109] 2. Dynamic division of disaster thresholds: Using probability confidence interval theory, a dynamic multi-level threshold for disasters in mining areas is established. The warning threshold is adaptively adjusted according to real-time monitoring data to accurately divide the disaster level.

[0110] 3. Strong model generalization ability: The introduction of a dynamic early warning model based on the four-dimensional joint kernel probability density function does not rely on the specific probability distribution assumptions of the data, nor does it rely on a fixed data parameter structure. It ignores the scale effect of the data and has a strong model generalization ability. It is suitable for mine disaster early warning at different monitoring scales.

[0111] 4. Full utilization of microseismic data: The risk assessment model comprehensively considers the multi-dimensional characteristics of microseismic events, including the energy release rate, spatial distribution characteristics, and event time series (i.e., event frequency), which greatly improves the comprehensiveness and accuracy of rock burst risk assessment.

[0112] 5. Good real-time warning: The real-time processing and analysis of large-scale data have been effectively solved, and the real-time collection and processing of microseismic signals have been realized. The calculation is efficient and the use is simple. It can capture the dynamic changes of coal and rock masses in time, providing time guarantee for rapid response to potential dangers.

[0113] 6. The early warning model has no scale effect: The present invention adopts an effective non-parametric kernel density estimation method, which does not rely on the preset data distribution assumptions. This enables the model to better adapt to the complex distribution characteristics of geological data at different scales, avoiding the deviations that may be caused by traditional parameterization methods when applied across scales. In addition, the confidence interval theory used is calculated based on the statistical characteristics of data distribution, rather than relying on absolute values, so it can also maintain statistical consistency and stability at different scales, reducing the impact of scale changes.

[0114] 7. The method of the present invention improves positioning accuracy and real-time performance through an improved linear positioning algorithm, and establishes a risk assessment model that comprehensively considers "space-time energy" information through kernel density estimation and probability confidence interval theory, providing strong support for accurate early warning of mine disasters.

[0115] 8. The technical solution proposed in the present invention significantly enhances the monitoring accuracy and early warning timeliness of mine power disasters. The method has excellent environmental adaptability and functional expansion potential, and provides key technical support for ensuring safe production in mines. BRIEF DESCRIPTION OF THE DRAWINGS

[0116] Figure 1 The figure is a flow chart of a method for fine monitoring and dynamic early warning of mine dynamic disasters without scale effect;

[0117] Figure 2 This is a rendering of a method for fine monitoring and dynamic early warning of mine dynamic disasters without scale effect. DETAILED DESCRIPTION

[0118] The present invention is further described in detail below in conjunction with specific implementation modes and accompanying drawings.

[0119] A method for fine monitoring and dynamic early warning of mine dynamic disasters without scale effect, the process is as follows Figure 1 As shown, the specific steps include:

[0120] S1: Arrange multiple high-sensitivity microseismic monitoring sensors in the mining area to form a full-dimensional three-dimensional monitoring network;

[0121] S2: Real-time collection of microseismic signal data through a high-speed data acquisition system to ensure data continuity and integrity;

[0122] S3: Adaptively perform noise reduction processing on the collected microseismic signals;

[0123] S4: Using the improved analytical positioning model, high-precision three-dimensional positioning of microseismic sources is performed;

[0124] S5: Segment the microseismic energy data into multi-scale time windows (e.g., hours, days, weeks, and months) to facilitate multi-level statistical analysis and trend identification;

[0125] S6: Based on the time-frequency characteristics, spatial distribution and energy release law of microseismic signals, combined with the kernel probability density estimation and probability confidence interval principle, a dynamic assessment model for rock burst risk is established;

[0126] S7: Calculate the risk index of rock burst in real time and dynamically classify disaster warnings;

[0127] S8: Establish a feedback mechanism for early warning results, continuously optimize and update risk assessment models and early warning systems through historical data analysis, post-disaster investigations, numerical simulation technology and expert knowledge base, and build a comprehensive database of mine dynamic disasters.

[0128] The layout of the sensor network in step S1 is as follows:

[0129] (1) A regular grid layout is adopted within the mining area to ensure that sensors cover the entire monitoring area;

[0130] (2) According to the geological structure of the mining area, sensors are arranged at different depths to form a three-dimensional monitoring network;

[0131] (3) Increase sensor density in key areas (such as mining faces, fault zones, and rock layer interfaces);

[0132] (4) The sensors are dispersedly arranged, and the distance between each two sensors is maximized to facilitate the subsequent microseismic source positioning.

[0133] The process of performing adaptive noise reduction processing on the collected microseismic signals in step S3 is as follows:

[0134] (1) Signal preprocessing:

[0135] The original microseismic signal is sampled and digitized to ensure the integrity and continuity of the signal.

[0136] (2) Empirical Mode Decomposition:

[0137] a) Decompose the preprocessed microseismic signal x(t) to obtain a series of eigenmode functions and a residual term:

[0138]

[0139] Among them, IMF i(t) is the i-th eigenmode function, r n (t) is the residual term.

[0140] b) Steps of signal decomposition:

[0141] Identify all local extreme points of the signal x(t);

[0142] The upper and lower envelopes are generated by cubic spline interpolation;

[0143] Calculate the average value m1(t) of the upper and lower envelopes;

[0144] Subtract m1(t) from the original signal to obtain h1(t)=x(t)-m1(t);

[0145] Repeat the above steps until h1(t) meets the IMF definition conditions;

[0146] Define h1(t) as the first IMF, i.e. c1(t) = h1(t);

[0147] Subtract c1(t) from the original signal and repeat the above process to obtain subsequent IMFs.

[0148] (3) Noise IMF identification:

[0149] a) Calculate the energy density and center frequency of each IMF;

[0150] b) Analyze the spectrum characteristics of IMF and identify high-frequency noise IMF;

[0151] c) Use correlation analysis or signal-to-noise ratio threshold method to determine the noise IMF that needs to be removed.

[0152] (4) Adaptive threshold denoising:

[0153] The specific operation of identifying the IMF components representing noise can be carried out from the following aspects:

[0154] a) Calculate the energy of each IMF component: Where N is the signal length;

[0155] b) Calculate the average energy:

[0156]

[0157] Where N is the total number of IMF components;

[0158] c) Calculate the standard deviation σ of all IMF energies

[0159]

[0160] d) Setting the adaptive threshold: T = μ - ασ, where α = 2;

[0161] e) IMFs with indicators lower than the threshold T are determined to be noise components.

[0162] (5) Signal reconstruction:

[0163] a) Reconstruct the denoised IMF, the retained low-frequency IMF and the residual term to obtain the denoised microseismic signal:

[0164]

[0165] Among them, IMF i (t) is the denoised IMF, and k is the number of noisy IMFs.

[0166] Through the above steps, the empirical mode decomposition technology can be effectively used to reduce the noise of microseismic signals in mining areas, improve signal quality, and provide a more reliable data basis for subsequent microseismic event analysis and early warning.

[0167] The calculation process of the improved source inversion model in step S4 is as follows:

[0168]

[0169] Among them, α1=l1l6l 11 -l1l7l 10 -l2l5l 11 +l2l7l9+l3l5l 10 -l3l6l9

[0170]

[0171] Among them, χ (j) The jth (j=1, 2, 3, 4) index of takes parameters a, b, c, d respectively. i =2(x i -x1), b i =2(y i -y1), c i =2(z i -z1), d i =-2v 2 (t i -t1), e i =v 2 (t i 2 -t1 2 )-[(x i 2 -x1 2 )+(y i2 -y1 2 )+(z i 2 -z1 2 )], the sensor coordinates are (x i ,y2,z i ), i = 1, 2, ..., n, n is the number of sensors, t i represents the arrival time data of sensor i, t1 represents the arrival time of the first triggered sensor, v represents the propagation velocity of the medium, (x, y, z) represents the coordinates of the microseismic source with solution, and t0 represents the time when the microseismic event occurs.

[0172] The dynamic early warning model in step S6 includes:

[0173] S1. Obtain the four-dimensional joint probability density function of the microseismic event location

[0174] The density estimation method is used to fit the spatial position and energy of microseismic events and calculate the joint probability density function of the position and energy of microseismic events. It can be expressed as follows:

[0175]

[0176] in: θ is the source coordinate and energy of the microseismic source, θ=[x,y,z,e] T , the superscript T indicates the matrix or vector transpose; is the event of the kth (k=1,2,...,m) time series, m is the number of all events in a certain time period; H is the bandwidth matrix, d is the data dimension, d = 4; σ i is the standard deviation of the ith vector of θ, med(·) means taking the median value; H ii =0, i≠j.

[0177] S2. Define confidence intervals

[0178] ① Select an appropriate confidence level, such as P = 85%, 95% or 99%, depending on the mine's acceptance of different levels of risk;

[0179] ②For the above probability distribution, the confidence interval [θ1,θ2] depends on the corresponding confidence level P, and [θ1,θ2] can be calculated by the following formula:

[0180]

[0181] Among them, P(θ1≤X<E) = P(E<X<θ2), where E is the position where the probability density reaches its extreme value.

[0182] S3. Determine the threshold for risk level classification

[0183] ① According to the confidence interval, the microseismic disaster risk can be divided into multiple levels:

[0184] Low risk: X < θ1;

[0185] Medium risk: θ1≤X≤θ2;

[0186] High risk: X > θ2.

[0187] ② For the specific threshold selection, the following three factors can be considered: the energy level that caused disasters in historical data, expert opinions and empirical judgments, and the specific geological conditions and mining methods of the mine.

[0188] Specific implementation case 1:

[0189] In a microseismic monitoring system of a 1000-meter × 1000-meter × 1000-meter mine, 16 sensors are deployed at key positions in the system. The coordinates of these sensors cover various central areas of the monitoring space, ensuring comprehensive coverage. The coordinates of these sensors are (0,0,0), (1000,0,0), (1000,1000,0), (0,1000,0), (0,0,1000), (1000,0,1000), (1000,1000,1000), (0,1000,1000), (500,0,500), (1000,500,500), (500,1000,500), (0,500,500), (500,500,0), (500,500,1000), (0,0,500), (1000,1000,500) (meters).

[0190] To verify the effectiveness of the method of the present invention, a microseismic event with a true hypocenter coordinate of (248.36, 833.78, 16.37) (meters) and a wave velocity of 5000 m / s is set. Through simulation calculations, the arrival time data of the signals received by each sensor are obtained. To simulate the noise interference in the actual environment, random errors with a standard deviation of 0.3 milliseconds are added to these data.

[0191] Using this set of noisy arrival time data, the source coordinates calculated by the method of the present invention are (248.54, 833.87, 15.27) meters, the positioning error is (0.18, 0.09, -1.1) meters, and the absolute distance error is only 1.11 meters. In comparison, the positioning errors of existing methods such as LLSUV (linear least square method without wave velocity), NIUV (non-iterative method without wave velocity), USBM (method proposed by the U.S. Bureau of Mines) and CAS (comprehensive analytical method) are 1.86 meters, 1.67 meters, 3.13 meters and 16.69 meters respectively.

[0192] The positioning accuracy of this method is improved by 40.2%, 33.4%, 64.5% and 93.3% respectively compared with these existing methods. In addition, this method can also accurately calculate the triggering time of microseismic events with a calculation error of 1.83×10 -7 , the error can be ignored.

[0193] The effect of the method for fine monitoring and dynamic early warning of mine dynamic disasters without scale effect of the present invention is as follows: Figure 2 As shown, these results indicate that the method of the present invention has significant advantages in microseismic positioning accuracy and time determination, and provides more reliable technical support for real-time monitoring and prevention of mine disasters.

[0194] The energy release of the microseismic event can be further obtained through the envelope area of ​​the microseismic signal waveform (represented by the level voltage data). Then, the acquired spatiotemporal energy information of the microseismic event can be statistically analyzed to achieve accurate graded early warning of mine dynamic disasters.

[0195] 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 method for fine monitoring and early warning of mine dynamic disasters without scale effect, characterized in that: The steps include: S1: Arrange multiple high-sensitivity microseismic monitoring sensors in the mining area to form a full-dimensional three-dimensional monitoring network; S2: Real-time microseismic signal data is collected through a high-speed data acquisition system to ensure data continuity and integrity; S3: Adaptively perform noise reduction processing on the collected microseismic signals; S4: Using the improved analytical positioning model, high-precision three-dimensional positioning of microseismic sources is performed; S5: Segment the microseismic energy data into multi-scale time windows to facilitate multi-level statistical analysis and trend identification; S6: Based on the time-frequency characteristics, spatial distribution and energy release law of microseismic signals, combined with the kernel probability density estimation and probability confidence interval principle, a dynamic assessment model for rock burst risk is established; S7: Calculate the risk index of rock burst in real time and dynamically classify disaster warnings; S8: Establish a feedback mechanism for early warning results, continuously optimize and update risk assessment models and early warning systems through historical data analysis, post-disaster investigations, numerical simulation technology and expert knowledge base, and build a comprehensive database of mine dynamic disasters.

2. The method for fine monitoring and early warning of mine dynamic disasters without scale effect according to claim 1 is characterized in that: The layout of the sensor network in step S1 is as follows: (1) A regular grid layout is adopted within the mining area to ensure that sensors cover the entire monitoring area; (2) According to the geological structure of the mining area, sensors are arranged at different depths to form a three-dimensional monitoring network; (3) Increase sensor density in key areas; (4) The sensors are dispersedly arranged, and the distance between each two sensors is maximized to facilitate the subsequent microseismic source positioning.

3. The method for fine monitoring and early warning of mine dynamic disasters without scale effect according to claim 1 is characterized in that: The process of performing adaptive noise reduction processing on the collected microseismic signals in step S3 is as follows: (1) Signal preprocessing: Sample and digitize the original microseismic signals to ensure signal integrity and continuity; (2) Empirical Mode Decomposition: a) Decompose the preprocessed microseismic signal x(t) to obtain a series of eigenmode functions and a residual term: Among them, IMF i (t) is the i-th eigenmode function, r n (t) is the residual term; b) Steps of signal decomposition: Identify all local extreme points of the signal x(t); The upper and lower envelopes are generated by cubic spline interpolation; Calculate the average value m1(t) of the upper and lower envelopes; Subtract m1(t) from the original signal to obtain h1(t)=x(t)-m1(t); Repeat the above steps until h1(t) meets the IMF definition conditions; Define h1(t) as the first IMF, i.e. c1(t) = h1(t); Subtract c1(t) from the original signal and repeat the above process to obtain subsequent IMFs; (3) Noise IMF identification: a) Calculate the energy density and center frequency of each IMF; b) Analyze the spectrum characteristics of IMF and identify high-frequency noise IMF; c) Use correlation analysis or signal-to-noise ratio threshold method to determine the noise IMF that needs to be removed; (4) Adaptive threshold denoising: The specific operation of identifying the IMF components representing noise can be carried out from the following aspects: a) Calculate the energy of each IMF component: Where N is the signal length; b) Calculate the average energy: Where N is the total number of IMF components; c) Calculate the standard deviation σ of all IMF energies d) Setting the adaptive threshold: T = v - ασ, where α = 2; e) IMFs with indicators lower than the threshold T are determined as noise components; (5) Signal reconstruction: a) Reconstruct the denoised IMF, the retained low-frequency IMF and the residual term to obtain the denoised microseismic signal: Among them, IMF i (t) is the denoised IMF, k is the number of noisy IMFs; Through the above steps, the empirical mode decomposition technology can be effectively used to reduce the noise of microseismic signals in mining areas, improve signal quality, and provide a more reliable data basis for subsequent microseismic event analysis and early warning.

4. The method for fine monitoring and early warning of mine dynamic disasters without scale effect according to claim 1 is characterized in that: The calculation formula of the improved source analysis positioning model in step S4 is as follows: Among them, α1=l1l6l 11 -l1l7l 10 -l2l5l 11 +l2l7l9+l3l5l 10 -l3l6l9 Among them, χ (j) The jth (j=1, 2, 3, 4) index of takes parameters a, b, c, d respectively; a i =2(x i -x1), b i =2(y i -y1), c i =2(z i -z1), d i =-2v 2 (t i -t1), e i =v 2 (t i 2 -t1 2 )-[(x i 2 -x1 2 )+(y i 2 -y1 2 )+(z i 2 -z1 2 )], the sensor coordinates are (x i ,y i ,z i ), i = 1, 2, ..., n, n is the number of sensors, t i represents the arrival time data of sensor i, t1 represents the arrival time of the first triggered sensor, v represents the propagation velocity of the medium, (x, y, z) represents the coordinates of the microseismic source with solution, t i Indicates the time when the microseismic event occurs.

5. The method for fine monitoring and early warning of mine dynamic disasters without scale effect according to claim 1 is characterized in that: The dynamic early warning model in step S6 includes: S1. Obtain the four-dimensional joint probability density function of the microseismic event location The density estimation method is used to fit the spatial position and energy of microseismic events and calculate the joint probability density function of the position and energy of microseismic events. It can be expressed as follows: in: θ is the source coordinate and energy of the microseismic source, θ=[x,y,z,e] T , the superscript T indicates the matrix or vector transpose; is the event of the kth (k=1,2,...,m) time series, m is the number of all events in a certain time period; H is the bandwidth matrix, d is the data dimension, d = 4; σ i is the standard deviation of the ith vector of θ, med(·) means taking the median value; H ii =0, i≠j; S2. Define confidence intervals ① Select an appropriate confidence level, which depends on the mine's acceptance of different levels of risk; ②For the above probability distribution, the confidence interval [θ1,θ2] depends on the corresponding confidence level P, and [θ1,θ2] can be calculated by the following formula: Among them, P(θ1≤X <E)=P(E<X<θ i ), E is the location of the extreme value of probability density; S3. Determine the threshold for hazard classification ① Based on the confidence interval, the risk of microseismic disasters can be divided into multiple levels: Low risk: X<θ1; Medium risk: θ1≤X≤θ2; High risk: X>θ2; ② The specific threshold selection can consider the following three factors: the energy level that caused the disaster in historical data, expert opinions and experience judgment, and the specific geological conditions and mining methods of the mine.

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