Rock burst global monitoring and dynamic and static far and near field cooperative early warning method and system

Abstract

The invention discloses a rock burst global monitoring and dynamic and static far and near field cooperative early warning method and system, and the early warning method comprises the steps: determining a stope space key monitoring region and a stress wave type based on a dynamic load stress wave far field generation and propagation mechanism, delineating a working face surrounding rock high-stress region, and analyzing the structural characteristics of a rock stratum. Arranging a micro-seismic sensor in a far-field key area, and obtaining parameters such as seismic source fracture size, energy and magnitude through waveform inversion; an acoustoelectric system is arranged in a near-field high-stress area, coal rock fracture characteristics are dynamically monitored, and the static load energy storage state of surrounding rock is quantitatively analyzed. And in combination with a stress wave propagation attenuation coefficient, a far and near field fusion early warning criterion under the action of dynamic and static energy superposition is established, and advanced accurate early warning of the rock burst is realized through collaborative analysis of multi-source monitoring data. The disaster-pregnant space global monitoring technology fusing earthquake, sound and electricity is provided, the chain evolution disaster-causing process of dynamic load generation, cross-space propagation and working face surrounding rock damage is considered, and the accuracy of rock burst early warning can be remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of rock burst, and in particular to a method and system for global monitoring of rock burst and collaborative early warning of static and dynamic near and far fields. Background Technique

[0002] With the continuous development of coal mining from shallow to deep, the prevention and control of rock burst under strong disturbance conditions caused thereby have become a key scientific problem restricting deep mining. Although a series of breakthroughs have been made in the fields of the disaster-causing mechanism of rock burst and monitoring and early warning technologies in the existing technologies, and a technical system of "prediction - monitoring - prevention and control" has been initially constructed, in the face of the "three highs and four complexities" characteristics (high ground stress, high osmotic pressure, high ground temperature, complex geological structure, complex engineering disturbance, complex stress path, complex disaster response) presented by deep mining, traditional monitoring and early warning technologies face major challenges.

[0003] At present, the dynamic coupling disaster-causing mechanism of multi-source stress fields in deep mining areas has not been fully clarified, specifically manifested as: there are still cognitive blind spots in the whole-process evolution law of the damage - fracture - instability of coal and rock masses under the combined action of static and dynamic loads; the spatio-temporal correlation characteristics of multi-field coupling disaster-causing precursor information lack systematic characterization; existing monitoring technologies have the dual defects of insufficient spatial coverage and low multi-source information fusion degree. Although, for example, Patent CN118859311A proposes a comprehensive monitoring and early warning method and system for mine disasters based on microseismic monitoring technology, and determines the source location and energy through surface microseismic wave data to complete disaster monitoring; Patent CN114017121A realizes the monitoring and early warning of disasters through the real-time monitoring of rock burst in the strain field; Patent CN106437854A synchronously and real-time monitors at multiple points and collects the waveform of acoustic signals and electromagnetic radiation signals generated by the loaded coal and rock masses, reflects the evolution process of coal and rock dynamic disasters according to the signal changes, and conducts early warning, real-time locates the acoustic source position and electromagnetic anomaly area, and determines the dangerous area according to its distribution; Patent CN114137600A inversely analyzes the fracture mechanism through microseismic monitoring data, and on this basis, establishes an instability prediction index for disaster monitoring and early warning. However, there are the following significant limitations: single indicators are insufficiently sensitive to complex disaster precursors and have a high false alarm rate; the single monitoring dimension leads to limited spatial resolution and it is difficult to achieve three-dimensional dynamic perception of kilometer-level stope; the spatio-temporal correlation analysis of multi-source information is lacking and the chain evolution law of disasters cannot be revealed. Summary of the Invention

[0004] In view of the problems and requirements mentioned above, the present solution proposes a method and system for global monitoring of rock burst and collaborative early warning of static and dynamic near and far fields, and can achieve the above technical objectives and bring many other technical effects due to the following technical features.

[0005] An object of the present invention is to propose a method for global monitoring of rock burst and collaborative early warning of static and dynamic near and far fields, including the following steps:

[0006] S10: According to the disaster-causing mechanism of dynamic load stress wave generation in the far field, cross-space propagation, and near-field surrounding rock coupling failure of rock bursts, analyze the key areas and stress wave types in the large stope structure where large-energy far-field stress waves are likely to be generated, delineate the high-stress areas within the near field of the mining and tunneling faces and the intake and return air roadways, and clarify the propagation rock formation structure and lithological characteristics between the far field and the near field;

[0007] S20: Arrange microseismic sensors around the key areas where far-field stress waves are generated and obtain the original waveform data of far-field stress wave generation; deploy an acoustic-electric system in the delineated near-field high-stress areas, and use acoustic emission and electromagnetic radiation technologies to dynamically and real-timely monitor the working face and the roadway, and obtain the characteristic parameters of near-field surrounding rock fracture and stress; form an integrated global exploration and monitoring system for the far field and the near field combining microseismic and acoustic-electric;

[0008] S30: Calculate the time-frequency data of microseismic daily frequency, energy rate, dominant frequency, and amplitude based on the far-field original waveform data, locate and invert the spatial position and source mechanism of far-field stress wave generation, obtain density cloud maps, spatial fractals, tensile-shear types, fracture sizes, occurrence orientations, and released energies, establish and fuse and optimize a multi-parameter index system of far-field microseismic spatio-temporal intensity, quantitatively evaluate the source characteristics of far-field stress wave generation and the activity degree of far-field rock formation fracture, and identify and predict whether large-magnitude far-field dynamic load stress waves and their energies will be generated subsequently;

[0009] S40: Calculate the real-time and cumulative intensities of electromagnetic radiation, the characteristic parameters of real-time and cumulative pulse time series based on the characteristic parameters of near-field surrounding rock fracture and stress, obtain acoustic emission ring counts, acoustic wave intensities, fractal b values, spatial positions, released energies, and mechanism solution parameters, quantitatively reflect the fracture frequency and intensity of coal and rock based on the acoustic-electric index set and monitoring data, and evaluate the static load bearing and energy storage states of near-field surrounding rock according to the changes in acoustic-electric monitoring data;

[0010] S50: Select the same fracture data monitored by multiple far-field microseismic and near-field acoustic emission, calculate the propagation attenuation coefficient of stress wave energy in the rock formation structure, based on the static and dynamic energy superposition principle of stress wave action, and consider the coupling action disaster-causing process of dynamic load stress wave generation in the far field, cross-space propagation, and near-field surrounding rock dynamic failure, establish a global monitoring and early warning criterion for the integration of far and near fields of rock bursts, and combine the far-field microseismic and near-field acoustic-electric monitoring big data to achieve accurate early warning of rock bursts in advance through the coordination of stress wave action mechanism and monitoring big data.

[0011] In addition, according to a global monitoring and static-dynamic far-near field collaborative early warning method of rock bursts of the present invention, the following technical features may also be possessed:

[0012] In an example of the present invention, in the step S10, the key areas and stress wave types where large-energy far-field stress waves are likely to be generated in the large stope structure of rock bursts include: the fracture of the overlying thick rock strata and the activation of faults in the geological structure zone:

[0013] For the fracture of the overlying thick rock strata, the expression of the distribution characteristics of the dynamic displacement field of the stress wave is:

[0014]

[0015] In the formula, u I (x, t) is the dynamic displacement field of the stress wave generated by the fracture of the overlying thick rock strata. The superscripts R, Ф, and V are the P-wave, SH-wave, and SV-wave components of the stress wave respectively; λ and μ are the Lame constants of the coal and rock; θ and are the spatial azimuth angles of the observation points; ρ is the density; c d and c s are the P-wave and S-wave velocities; ξ is the coordinate of the coal and rock break point; r is the distance from the break point ξ to the observation point x; is the crack propagation speed during the roof fracture process;

[0016] For the activation of faults in the geological structure zone, according to the theory of quantitative seismology and the theory of dynamic fracture mechanics, the spatial characteristics of the dynamic displacement field of the stress wave caused by fault slip are obtained as:

[0017]

[0018] In the formula, u II (x, t) is the dynamic displacement field of the stress wave generated by fault slip; is the fault slip velocity.

[0019] In an example of the present invention, in the step S30, locating and inversely analyzing the spatial position and source mechanism of the far-field stress wave specifically includes the following steps:

[0020] Use the non-linear least squares travel time difference earthquake source location method to determine the earthquake source location. Pick up the travel time data of the waveforms collected by each sensor in turn, and calculate the travel time difference Δt ij =(t i -t j )-(t i0 -t j0 ) between the measured travel time and the theoretical travel time of the i-th and j-th sensors. Take the sum of the squares of the difference between the theoretical travel time and the actual observed travel time of the ray propagation as the objective function, and the calculation result corresponding to the minimum value of the objective function is the earthquake source coordinate;

[0021] A displacement discontinuity source model for generating far-field stress waves is established, and the displacement discontinuity tensor constraint condition in the microseismic source model is transformed into the form of moment tensor invariants. The Lagrange multiplier is combined with the Levenberg-Marquardt optimization algorithm to iteratively solve the moment tensor components under the constraint condition that the middle eigenvalue of the displacement discontinuity tensor is zero, so as to minimize the least square error between the theoretical normal displacement and the measured normal displacement of the P wave at different sensor positions. According to the relationship between the moment tensor, the displacement discontinuity tensor and the fracture source parameters, the fracture tensile-shear property, the fracture scale and the strike orientation source parameters of the far-field stress wave are calculated from the solved moment tensor components.

[0022] In an example of the present invention, in the step S30, establishing a far-field microseismic spatio-temporal intensity multi-parameter index system includes the following steps:

[0023] Let the set of far-field microseismic parameters P for rockburst dynamic prediction;

[0024] Calculate the cumulative difference index. Obtain the parameter time series observation sequences Q1, Q2,..., Q n-1 , Q n (n>3) where Q n is the parameter at time sequence n; Calculate the difference between adjacent time sequence parameters: ΔQ j =Q j -Q j-1 (j = 2, 3,..., N), let G j =min{ΔQ j , 0}, H j =max{ΔQ j , 0}, then:

[0025]

[0026] Let the cumulative difference R w =F1 / (F1 + F2), R w ∈[0, 1], calculate the cumulative difference values of each parameter in the far-field microseismic parameter P for rockburst dynamic prediction, and obtain the cumulative difference set R of microseismic evaluation parameters;

[0027] Determine that the risk level evaluation index expression is:

[0028] F p＝ R m a1 + R K a2 + R ΔV a3 + R bVt a4 + … + R w a w

[0029] In the formula, a1, a2, a3, …, a wis the evaluation grade weight for the far-field fracture activity degree;

[0030] Divide the far-field fracture activity level. Through the far-field fracture activity level, construct a three-level evaluation mechanism: low activity (F p ≤θ1), medium activity (θ1 < F p ≤θ2), high activity (F p >θ2); where, when the far-field is in a low-activity fracture state, it is basically in a safe state, will not generate large-magnitude stress waves, and releases less energy; when it is in a medium-activity fracture state, large-magnitude stress waves may be generated subsequently, releasing more energy, and timely protection should be carried out; when the fracture is highly active, large-magnitude far-field dynamic load stress waves are generated, releasing a large amount of energy, and safety measures must be taken immediately for treatment.

[0031] In an example of the present invention, in the step S40, the coal-rock fracture frequency and intensity are quantitatively reflected based on the acoustic-electric index set and the monitoring data, and the static load force and energy storage state of the near-field surrounding rock are evaluated according to the changes in the acoustic-electric monitoring data, including:

[0032] Quantitatively reflect the coal-rock fracture frequency and intensity based on 10 parameters including the real-time and cumulative intensity of electromagnetic radiation, the real-time and cumulative pulse time-series characteristic parameters, the acoustic emission ring count, the acoustic wave intensity, the fractal b value, the spatial position, the released energy, and the fracture degree. Evaluate the static load force and energy storage state of the near-field surrounding rock according to its growth value, growth rate, and critical threshold.

[0033] In an example of the present invention, evaluating the static load force and energy storage state of the near-field surrounding rock according to its growth value, growth rate, and critical threshold specifically includes the following:

[0034] Calculate the maximum difference of each parameter within the statistical period, denoted as a; calculate the average value and standard deviation of the growth rate of the acoustic-electric index within the statistical period, and move the standard deviation upward based on the average value of the growth rate as b; calculate the average value and standard deviation of the acoustic-electric index within the statistical period, and move the standard deviation upward based on the average value as the critical threshold C;

[0035] When the number of parameters j whose growth value exceeds a is ≤ 3, the near-field surrounding rock hardly fractures; when 3 < j ≤ 7, further explore the growth rate;

[0036] When the number of parameters k whose growth rate exceeds b is ≤ 3, it is grade I, the surrounding rock fractures in a small range, accompanied by a small amount of energy release; when 3 < k ≤ 7, then explore whether its value exceeds the critical threshold;

[0037] When the number of parameters l exceeding the critical threshold C is ≤ 3, it is grade II, the surrounding rock fractures in a larger range, and more energy is released; when l > 3, it is grade III, the near-field surrounding rock fractures in a large range, releasing huge energy; in addition, when j > 7 or k > 7, it is directly judged as grade III.

[0038] In an example of the present invention, in the step S50, calculating the propagation attenuation coefficient of stress wave energy in the rock formation structure specifically includes the following steps:

[0039] Select the vibration velocity, energy, fracture scale, magnitude and b value of the same fracture monitored by far-field microseismic and near-field acoustic emission, calculate the propagation attenuation coefficient respectively, and take the average value;

[0040] Select a large-scale far-field stress wave fracture data that can be monitored by far-field microseismic and near-field acoustic emission at the same time. According to the fracture energy E1 calculated by far-field microseismic monitoring, the energy E2 of the waveform signal collected by near-field acoustic emission, the distance r1 from the fracture source to the far-field microseismic sensor, and the distance r2 from the near-field acoustic emission sensor, calculate the propagation attenuation coefficient of stress wave energy in the rock formation structure, and its expression is as follows:

[0041]

[0042] Calculate the energy propagation attenuation coefficient respectively according to multiple large-scale far-field stress wave fracture data, and take the average value of multiple energy propagation coefficients to obtain β as the final energy propagation attenuation coefficient.

[0043] In an example of the present invention, in the step S50, establishing a global monitoring and early warning criterion for the integration of far and near fields of rockburst includes the following steps:

[0044] S51: Combine the far-field fracture activity degree, the static load stress and the energy storage state of the near-field surrounding rock, and assign scores to them respectively, denoted as K s ; When the far-field fracture is in low, medium and high activity degrees, assign 2, 4, and 6 points respectively; when the near-field surrounding rock fracture and energy release are in grades I, II, and III, assign 2, 4, and 6 points respectively;

[0045] S52: According to the far-field energy E 远 , the near-field energy E 近 and the energy attenuation coefficient, obtain the near-field total energy E 总 , and its expression is as follows:

[0046] E 总 = E 近 + βE 远

[0047] Wherein, when the near-field total energy exceeds the critical energy, assign a dynamic early warning score K d = 8 points, otherwise record it as 0 points;

[0048] S53: Establish a composite early warning index K = K s + K d, whose theoretical threshold is [0, 20]; a three - level early warning system is set up: Level - I early warning (4 ≤ K < 8), weak rock burst danger, regularly check the stability of surrounding rock, maintain normal operation and pay attention to abnormal signals; Level - II early warning (8 ≤ K < 12): medium rock burst danger, strengthen the monitoring frequency, restrict non - essential operations and formulate prevention and control plans; Level - III early warning (K ≥ 12): strong rock burst danger, immediately evacuate personnel.

[0049] Another object of the present invention is to propose a comprehensive monitoring and dynamic - static far - near - field collaborative early warning system for rock bursts, including:

[0050] A structural analysis and regional delineation unit, configured to analyze the key areas and stress wave types in the large stope structure where large - energy far - field stress waves are likely to be generated according to the disaster - causing mechanism of the generation of far - field dynamic load stress waves of rock bursts, cross - space propagation and near - field surrounding - rock coupling failure, delineate the high - stress areas within the near - field range of the mining and tunneling working faces, intake and return air roadways, and clarify the propagation rock - layer structure and lithological characteristics between the far - field and the near - field;

[0051] A far - near - field data monitoring and acquisition unit, configured to arrange micro - seismic sensors around the key areas where far - field stress waves are generated and obtain the original waveform data of far - field stress wave generation; arrange an acoustic - electric system in the delineated near - field high - stress area, and use acoustic emission and electromagnetic radiation technologies to dynamically and real - time monitor the working face and roadway, and obtain the characteristic parameters of near - field surrounding - rock fracture and stress; form an integrated far - field and near - field comprehensive exploration and monitoring system combining micro - seismic and acoustic - electric;

[0052] A micro - seismic data processing and evaluation unit, configured to calculate the time - frequency data of micro - seismic daily frequency, energy rate, main frequency and amplitude based on the far - field original waveform data, locate and invert the spatial position and source mechanism of far - field stress wave generation, obtain density cloud maps, spatial fractals, tension - shear types, fracture sizes, occurrence orientations and released energies, establish a multi - parameter index system of far - field micro - seismic space - time intensity and fuse and optimize it, quantitatively evaluate the source characteristics of far - field stress wave generation and the activity degree of far - field rock - layer fracture, and identify and predict whether large - magnitude far - field dynamic load stress waves and their energies will be generated subsequently;

[0053] An acoustic - electric data processing and evaluation unit, configured to calculate the real - time and cumulative intensities of electromagnetic radiation, real - time and cumulative pulse time - series characteristic parameters based on the characteristic parameters of near - field surrounding - rock fracture and stress, obtain acoustic emission ring counts, acoustic wave intensities, fractal b - values, spatial positions, released energies and mechanism solution parameters, quantitatively reflect the fracture frequency and intensity of coal and rock based on the acoustic - electric index set and monitoring data, and evaluate the static load stress and energy storage state of near - field surrounding rock according to the changes in acoustic - electric monitoring data;

[0054] The global monitoring and early warning unit is configured to select the same fracture data for multiple far-field microseismic and near-field acoustic emission monitoring, calculate the propagation attenuation coefficient of stress wave energy in the rock stratum structure, and based on the principle of superposition of static and dynamic energies under the action of stress waves, and considering the disaster-causing process of the coupling action of the generation of far-field dynamic stress waves, cross-space propagation, and dynamic failure of near-field surrounding rocks, establish a global monitoring and early warning criterion for the integration of far and near fields of rock bursts. Combining big data of far-field microseismic and near-field acoustic and electrical monitoring, it realizes the advanced and accurate early warning of rock bursts by coordinating the stress wave action mechanism and big monitoring data.

[0055] In an example of the present invention, the key areas and stress wave types where large-energy far-field stress waves are likely to be generated in the large stope structure of rock bursts include: the fracture of the overlying thick rock stratum and the activation of faults in the geological structure zone:

[0056] For the fracture of the overlying thick rock stratum, the expression of the distribution characteristics of the stress wave dynamic displacement field is:

[0057]

[0058] In the formula, u I (x, t) is the stress wave dynamic displacement field generated by the fracture of the overlying thick rock stratum. The superscripts R, Ф, and V are the P-wave, SH-wave, and SV-wave components of the stress wave respectively; λ and μ are the Lame constants of the coal and rock; θ and are the spatial azimuth angles of the observation points; ρ is the density; c d and c s are the P-wave and S-wave velocities; ξ is the coordinate of the coal and rock break point; r is the distance from the break point ξ to the observation point x; is the crack propagation speed during the roof fracture process;

[0059] For the activation of faults in the geological structure zone, according to the theory of quantitative seismology and the theory of dynamic fracture mechanics, the spatial characteristics of the stress wave dynamic displacement field of fault slip are obtained as:

[0060]

[0061] In the formula, u II (x, t) is the stress wave dynamic displacement field generated by fault slip; is the fault slip velocity.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] Through the organic coupling of far-field microseismic monitoring and near-field acoustic-electric monitoring, this technical solution realizes the global perception of the cross-space propagation mechanism of dynamic and static loads for the first time. Compared with single near-field monitoring or far-field monitoring technologies, this solution can synchronously capture the far-field generation mechanism and near-field failure effect of dynamic load stress waves, forming a full-chain monitoring network from seismic source rupture to surrounding rock response. This system can effectively identify various types of seismic source characteristics such as far-field roof tensile fracture, fault activation, and near-field surrounding rock rupture, expand the monitoring range, and significantly improve the spatial coverage rate of rockburst precursor information.

[0064] Aiming at the problem that microseismic monitoring signals are easily interfered, this technical solution adopts the flexible installation technology of borehole coupling agent, realizes the full-contact coupling between the sensor and the rock mass through a water-soluble base, and improves the signal-to-noise ratio. Acoustic-electric monitoring adopts a three-dimensional heterogeneous gradient layout strategy. By configuring in layers along the Z-axis and a planar non-uniform topological network, a monitoring matrix with a spatial density gradient is constructed, solving the problem of signal attenuation blind areas caused by traditional uniform layout, and greatly improving the positioning accuracy of acoustic emission events.

[0065] Based on a new method for quantitative inversion of rockburst fracture parameters, this technical solution realizes the leap from point-to-surface and qualitative description to volume and quantitative inversion in the characterization of the failure evolution process of impact coal and rock. Considering the chain evolution disaster-causing process of dynamic load generation, cross-space propagation, and surrounding rock failure in the working face, it can more realistically reproduce the whole process of rockburst disaster evolution, and helps to significantly improve the accuracy of rockburst early warning.

[0066] This technical solution establishes a global monitoring and early warning criterion for the integration of far and near fields by fusing the far-field fracture activity degree, near-field surrounding rock static load stress and energy storage state, and near-field total energy. Through a three-level early warning system, the visualization classification of risk levels is realized, and the early warning response time is shortened. Especially by dynamically correcting the near-field total energy calculation model through the attenuation coefficient, the coupling effect of dynamic load stress waves on the stability of surrounding rock can be accurately reflected.

[0067] In the following, the optimal embodiments of implementing the present invention will be described in more detail in conjunction with the accompanying drawings, so as to facilitate the understanding of the features and advantages of the present invention. Brief Description of the Drawings

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly introduced below. Among them, the accompanying drawings are only used to show some embodiments of the present invention, rather than limiting all embodiments of the present invention thereto.

[0069] Figure 1 It is a flow chart of the method for global monitoring of rockburst and collaborative early warning of dynamic and static far and near fields according to the embodiments of the present invention;

[0070] Figure 2Schematic diagram of the static and dynamic load superposition structure according to an embodiment of the present invention;

[0071] Figure 3 Schematic diagram of the structure of the microseismic sensor arrangement according to an embodiment of the present invention;

[0072] Figure 4 Schematic diagram of the layout of the acoustic-electric sensors and the transmission line layout structure on the working face according to an embodiment of the present invention;

[0073] Figure 5 Structural diagram of the multi-parameter early warning index system according to an embodiment of the present invention;

[0074] Figure 6 Schematic diagram for judging the static load stress and energy storage state of the near-field surrounding rock by evaluating the change of acoustic-electric data according to an embodiment of the present invention.

[0075] List of reference numerals:

[0076] Electromagnetic radiation antenna 1;

[0077] Acoustic emission probe 2;

[0078] Mainframe of the data acquisition instrument 3;

[0079] Monitoring sub-station 4;

[0080] Wellhead switch 5;

[0081] Server 6. Detailed implementation manners

[0082] In order to make the objectives, technical solutions and advantages of the technical solutions of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the specific embodiments of the present invention. The same reference numerals in the drawings represent the same components. It should be noted that the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0083] Unless otherwise defined, the technical terms or scientific terms used herein shall have the ordinary meanings understood by those of ordinary skill in the art to which the present invention pertains. The terms "first", "second" and similar terms used in the specification and claims of this patent application for invention do not denote any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "a" or "an" do not necessarily denote a quantity limitation. Words such as "comprising" or "including" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Terms such as "upper", "lower", "left" and "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0084] A method for global monitoring of rock bursts and collaborative early warning of dynamic and static far and near fields according to the first aspect of the present invention, as Figure 1 、 Figure 2 shown, includes the following steps:

[0085] S10: According to the disaster-causing mechanism of the generation, cross-space propagation of dynamic load stress waves in the far field of rock bursts and the coupling failure of surrounding rocks in the near field, analyze the key areas and stress wave types in the large stope structure where large-energy far-field stress waves are likely to be generated, delineate the high-stress areas within the near field of the mining and tunneling working faces and the intake and return air roadways, and clarify the propagation rock layer structure and lithological characteristics between the far field and the near field;

[0086] S20: Arrange microseismic sensors around the key areas where far-field stress waves are generated, and transmit the signals through the underground monitoring ring network to the ground monitoring host to obtain the original waveform data of the far-field stress wave generation; deploy an acoustic-electric system in the delineated near-field high-stress area, and use acoustic emission and electromagnetic radiation technologies to dynamically and real-time monitor the working face and the roadway, and obtain the characteristic parameters of near-field surrounding rock rupture and stress; form an integrated global exploration and monitoring system for the far field and the near field combining microseismic and acoustic-electric;

[0087] S30: Filter and denoise the collected microseismic stress wave data, calculate the time-frequency data of the daily microseismic frequency, energy rate, main frequency and amplitude, locate and invert the spatial position and source mechanism of the far-field stress wave generation, obtain the density cloud map, spatial fractal, tension-shear type, fracture size, attitude orientation and released energy, establish and fuse and optimize a multi-parameter index system for the far-field microseismic time, space and intensity, quantitatively evaluate the source characteristics of the far-field stress wave generation and the activity degree of far-field rock layer fracture, and identify and predict whether large-magnitude far-field dynamic load stress waves and their energy will be generated subsequently;

[0088] S40: Filter and denoise the waveform data collected by the acoustic and electrical system, calculate the real-time and cumulative intensity of electromagnetic radiation, and the real-time and cumulative pulse timing characteristic parameters, obtain the acoustic emission ring count, sound wave intensity, fractal b value, spatial position, released energy and mechanism solution parameters, quantitatively reflect the frequency and intensity of coal and rock fractures based on the acoustic and electrical index set and monitoring data, and evaluate the static load stress and energy storage state of the near-field surrounding rock according to the changes in the acoustic and electrical monitoring data;

[0089] S50: Select the same rupture data from multiple far-field microseismic and near-field acoustic emission monitoring, calculate the propagation attenuation coefficient of stress wave energy in the rock structure, and based on the principle of dynamic and static energy superposition of stress wave action, and considering the coupling effect of far-field generation, cross-space propagation and near-field surrounding rock dynamic destruction, establish a global monitoring and early warning criterion for rock burst with far and near field fusion. Combined with far-field microseismic and near-field acoustic and electrical monitoring big data, achieve advanced and accurate early warning of rock burst with the coordination of stress wave action mechanism and monitoring big data.

[0090] This early warning method realizes the global perception of the cross-space propagation mechanism of dynamic and static loads for the first time through the organic coupling of far-field microseismic monitoring and near-field acoustic and electrical monitoring. Compared with single near-field monitoring or far-field monitoring technology, this scheme can simultaneously capture the far-field generation mechanism and near-field damage effect of dynamic load stress waves, forming a full-chain monitoring network from source rupture to surrounding rock response. This system can effectively identify multiple types of source characteristics such as far-field roof tension fracture, fault activation and near-field surrounding rock rupture, expand the monitoring range, and significantly improve the spatial coverage of rock burst precursor information.

[0091] This early warning method uses a flexible installation technique of drilling coupling agent to achieve full contact coupling between the sensor and the rock mass through a water-soluble base, thereby improving the signal-to-noise ratio. Acoustic-electric monitoring adopts a three-dimensional heterogeneous gradient layout strategy, and constructs a monitoring matrix with a spatial density gradient through Z-axis layered configuration and planar non-uniform topology network, solving the signal attenuation blind area problem caused by traditional uniform layout, and greatly improving the accuracy of acoustic emission event positioning.

[0092] This early warning method is based on a new method of quantitative inversion of rock burst fracture parameters. It has achieved a leap from point-to-surface to volume characterization of the evolution process of rock burst destruction, and from qualitative description to quantitative inversion. It takes into account the chain evolution of dynamic load generation, cross-space propagation, and surrounding rock destruction at the working face. It can more realistically reproduce the entire evolution process of rock burst disasters, and help to significantly improve the accuracy of rock burst warning.

[0093] This early warning method establishes a global monitoring and early warning criterion that combines the far-field fracture activity degree, the static load stress of the near-field surrounding rock, the energy storage state, and the total near-field energy. The risk level is visually graded through a three-level early warning system, shortening the early warning response time. In particular, by dynamically modifying the total near-field energy calculation model with the attenuation coefficient, the coupling effect of dynamic stress waves on the stability of the surrounding rock can be accurately reflected.

[0094] In an example of the present invention, in the step S10, the key areas and stress wave types where large-energy far-field stress waves are likely to be generated in the large stope structure of rock bursts include: the fracture of the overlying thick rock stratum and the activation of faults in the geological structure zone:

[0095] For the fracture of the overlying thick rock stratum, its main manifestation is a rapid tensile fracture process. The dynamic displacement field is dominated by high-frequency longitudinal waves, and the energy release is concentrated and decays rapidly. Based on the theories of quantitative seismology and dynamic fracture mechanics, the expression for the distribution characteristics of the stress wave dynamic displacement field of this overlying thick rock stratum fracture is:

[0096]

[0097] In the formula, u I (x,t) is the stress wave dynamic displacement field generated by the fracture of the overlying thick rock stratum. The superscripts R, Ф, and V are the P-wave, SH-wave, and SV-wave components of the stress wave respectively; λ and μ are the Lame constants of the coal and rock; θ and are the spatial azimuth angles of the observation points; ρ is the density; c d and c s are the P-wave and S-wave velocities; ξ is the coordinate of the coal and rock rupture point; r is the distance from the rupture point ξ to the observation point x; is the crack propagation speed during the roof fracture process;

[0098] For the activation of faults in the geological structure zone, it is mainly a shear fracture process. The dynamic displacement field is dominated by low-frequency transverse waves, and the energy release is slow and the propagation distance is far. According to the theories of quantitative seismology and dynamic fracture mechanics, the spatial characteristics of the stress wave dynamic displacement field of fault slip are:

[0099]

[0100] In the formula, u II (x,t) is the stress wave dynamic displacement field generated by fault slip; is the fault slip velocity.

[0101] In an example of the present invention, in the step S20, as Figure 3 shown, the microseismic monitoring system and its layout method are:

[0102] Install sensors around the key areas in the far field. Microseismic sensors can be installed in a staggered manner at different positions and different heights, so that the microseismic sensors are in direct contact with the coal and rock mass to obtain high-quality waveform data, forming a three-dimensional microseismic network coverage. After the sensor layout is completed, connect to the mine-used DAQlink-4 type microseismic acquisition instrument through signal transmission cables, and realize the time synchronization of the collected waveforms of different channels through time synchronization of the time synchronizer. The monitoring signals are transmitted to the ground monitoring host through the underground monitoring ring network for data processing and display.

[0103] Among them, the installation method of the microseismic sensor is specifically as follows: use a hole protection casing to protect the inner wall of the drill hole, and use a reusable push pipe to transport the tamping block to the bottom of the hole to compact the water-soluble coupling agent flexible dough to form a coupling agent base. Subsequently, embed the ferrule device equipped with the microseismic sensor into the uncured coupling agent base through the same push pipe. After the base solidifies, then use this push pipe to connect to the water-filled rubber hose to inject pressure water into the cured coupling agent base to achieve direct coupling contact between the sensor and the coal and rock mass. After the sensor is used up, take out the ferrule device equipped with the microseismic sensor from the dissolved coupling agent base through the push pipe to realize the recycling of the sensor as the mining progresses.

[0104] In an example of the present invention, in the step S20, as Figure 4 shown, the acoustic-electric monitoring system and the layout method are as follows:

[0105] The acoustic-electric monitoring system includes: an electromagnetic radiation antenna, an acoustic emission probe, a data acquisition instrument host, a monitoring sub-station, an on-shaft switch, and a server; among them, the electromagnetic radiation antenna and the acoustic emission probe are both communicatively connected to the data acquisition instrument host, the data acquisition instrument host is communicatively connected to the monitoring sub-station, and the monitoring sub-station is communicatively connected to the server through the on-shaft switch;

[0106] The electromagnetic radiation antenna and the acoustic emission probe are arranged alternately along the extension direction of the coal mining face. For example, they can be symmetrically arranged on both sides of the extension direction of the coal mining face; obtain the acoustic-electric signals of the coal mining face through the electromagnetic radiation antenna and the acoustic emission probe and upload them to the server via the data acquisition instrument host, the monitoring sub-station, and the on-shaft switch.

[0107] The electromagnetic radiation antenna is directly hung on the underground anchor net for non-contact testing. Align the effective receiving direction of the electromagnetic radiation antenna to the near-field high-stress area. The distance between the antenna and the measured area is less than 50m. Arrange one at each of the boundaries of the monitoring area, and arrange one above and below the center point of the area to ensure full coverage monitoring of the near-field high-stress key area.

[0108] The acoustic emission sensors are fixed on the support bolts on the coal wall side, following the principle of three-dimensional spatial heterogeneous morphological distribution, and adopting a ten-node multi-level gradient configuration scheme. In the vertical spatial dimension, the sensor array is configured in layers along the Z-axis direction to form a longitudinal monitoring architecture with a spatial density gradient. In the horizontal projection plane, according to the characteristics of the advanced stress distribution, non-uniform discrete arrangement is carried out, with dense arrangement in the advanced stress concentration area and sparse arrangement in the virgin rock stress area, forming a complementary transverse distribution matrix to cover the high-stress areas of the working face and the roadway.

[0109] The acoustic and electrical signals of the near-field surrounding rock fracture are transmitted from the acoustic emission sensors and electromagnetic radiation antennas to the host of the GDD12 data acquisition instrument. The data acquisition instrument transmits the signals through a shielded cable to the monitoring sub-station. The sub-station converts the signals into network signals through the underground signal conversion module, and then transmits the data to the above-ground switch through the underground switch ring network. The above-ground switch converts the network signals into the original acoustic and electrical signals through the above-ground signal conversion module and inputs them into the server for storage. The acoustic emission signals and electromagnetic radiation signals recorded by the server use the same clock to ensure the consistency of the recording time.

[0110] In an example of the present invention, as Figure 5 shown, in the step S30, locating and inverting the spatial position and source mechanism of the far-field stress wave specifically includes the following steps:

[0111] Use the non-linear least squares travel-time difference source location method to determine the source location. Pick up the travel-time data of the waveforms collected by each sensor in turn, and calculate the time difference Δt between the measured travel time of the i-th and j-th sensors and the theoretical travel time ij =(t i -t j )-(t i0 -t j0 ). Take the sum of the squares of the difference between the theoretical travel time and the actual observed travel time of the ray propagation as the objective function, and the calculation result corresponding to the minimum value of the objective function is the source coordinates;

[0112] Establish a displacement discontinuity source model for the generation of far-field stress waves, and transform the displacement discontinuity tensor constraint conditions in the microseismic source model into the form of moment tensor invariants; use the Lagrange multiplier combined with the Levenberg-Marquardt optimization algorithm to iteratively solve the moment tensor components under the constraint condition that the intermediate eigenvalue of the displacement discontinuity tensor is zero, so that the least squares error between the theoretical normal displacement and the measured normal displacement of the P-wave at different sensor positions is minimized; according to the relationship between the moment tensor, the displacement discontinuity tensor and the fracture source parameters, calculate the fracture tensile-shear properties, fracture scale, occurrence orientation and other source parameters of the far-field stress wave from the solved moment tensor components.

[0113] It should be noted that the explanations for time-frequency data, density cloud maps, spatial fractals, and released energy are as follows:

[0114] Time-frequency data: It includes microseismic daily frequency, energy rate, main frequency, and amplitude. Among them, the daily frequency is the accumulation of the number of microseismic events occurring within a day; the energy rate is the accumulation of the waveform energy of the microseismic system within a day; the main frequency is the main frequency band after performing a fast Fourier transform on the microseismic waveform, which reflects the fracture scale of the far-field stress wave; the amplitude is the amplitude corresponding to the main frequency in the microseismic waveform frequency spectrum curve.

[0115] Filter and denoise the collected microseismic stress wave data, calculate the time-frequency data of microseismic daily frequency, energy rate, main frequency, and amplitude, locate and invert the spatial position and source mechanism of the far-field stress wave generation, obtain density cloud maps, spatial fractals, tension-shear types, fracture sizes, attitude orientations, released energy, and magnitude distribution b values, form a multi-parameter index system for far-field microseismic time, space, and intensity and fuse and optimize it, quantitatively evaluate the source characteristics of the far-field stress wave generation and the activity degree of far-field rock layer fractures, and identify and predict whether large-magnitude far-field dynamic load stress waves and their energies will be generated subsequently.

[0116] Density cloud map: Divide the target area into grid cells with side length s. Taking each grid point as the center, calculate the cumulative number of microseismic events within the radius r range (s ≤ r) as the spatial density characteristic value of this point; use the inverse distance weighted algorithm to perform spatial interpolation on the discrete grid data to generate a continuously distributed frequency density field; form a cloud map through color gradient mapping, and the high-density area represents the microseismic activity degree and potential impact hazard.

[0117] Spatial fractal: Quantify the spatial aggregation of microseismic fracture sources through the spatial fractal dimension. The formula for the spatial fractal dimension is:

[0118]

[0119] In the formula, N R (R < r) represents the number of occurrences of microseismic sources within a distance less than r from each other; N is the total number of microseismic sources.

[0120] Released energy: Quantitatively calculate the released energy of the far-field stress wave through the source inversion data:

[0121]

[0122] In the formula, σ t is the tensile strength; σ s is the shear strength; β is the angle between the movement direction and the normal direction of the source fracture surface; M1, M2, and M3 are the moment tensor eigenvalues; μ is the shear modulus.

[0123] In an example of the present invention, such as Figure 5As shown, in the step S30, establishing a far-field microseismic spatio-temporal intensity multi-parameter index system includes the following steps:

[0124] Set the far-field microseismic parameter set P for rockburst dynamic prediction;

[0125] Calculate the cumulative difference index. Obtain the parameter time series observation sequences Q1, Q2,..., Q n-1 , Q n (n>3) where Q n is the parameter at time series n; calculate the difference between adjacent time series parameters: ΔQ j =Q j -Q j-1 (j = 2, 3,..., N), let G j =min{ΔQ j , 0}, H j =max{ΔQ j , 0}, then:

[0126]

[0127] Let the cumulative difference R w =F1 / (F1+F2), R w ∈[0,1], calculate the cumulative difference value of each parameter in the far-field microseismic parameter P for rockburst dynamic prediction, and obtain the cumulative difference set R of microseismic evaluation parameters;

[0128] Determine that the risk level evaluation index expression is:

[0129] F p＝ R m a1+R K a2+R ΔV a3+R bVt a4+…+R w a w

[0130] In the formula, a1, a2, a3,..., a w are the weight of the far-field fracture activity degree evaluation level;

[0131] Divide the far-field fracture activity level, and through the far-field fracture activity level, construct a three-level evaluation mechanism: low activity (F p ≤θ1), medium activity (θ1<F p ≤θ2), high activity (F p(> θ2); among them, when the far field is in a low-active rupture state, it is basically in a safe state, no large-magnitude stress waves will be generated, and the released energy is small; when it is in a medium-active rupture state, large-magnitude stress waves may be generated subsequently, and the released energy is large, so timely protection should be taken; when the rupture is highly active, large-magnitude far-field dynamic load stress waves are generated, releasing a large amount of energy, and safety measures must be immediately taken for treatment.

[0132] In an example of the present invention, in step S40, as Figure 6 shown, the coal-rock rupture frequency and intensity are quantitatively reflected based on the acoustic-electric index set and monitoring data, and the static load stress and energy storage state of the near-field surrounding rock are evaluated according to the changes in the acoustic-electric monitoring data, including:

[0133] Quantitatively reflecting the coal-rock rupture frequency and intensity based on 10 parameters including the real-time and cumulative intensity of electromagnetic radiation, the real-time and cumulative pulse timing characteristic parameters, the acoustic emission ring count, the acoustic wave intensity, the fractal b value, the spatial position, the released energy, and the rupture degree, and evaluating the static load stress and energy storage state of the near-field surrounding rock according to its growth value, growth rate, and critical threshold.

[0134] In an example of the present invention, evaluating the static load stress and energy storage state of the near-field surrounding rock according to its growth value, growth rate, and critical threshold specifically includes the following:

[0135] Calculate the maximum difference of each parameter within the statistical period, denoted as a; calculate the average value and standard deviation of the growth rate of the acoustic-electric index within the statistical period, and move the standard deviation upward based on the average value of the growth rate as b; calculate the average value and standard deviation of the acoustic-electric index within the statistical period, and move the standard deviation upward based on the average value as the critical threshold C;

[0136] When the number of parameters j whose growth value exceeds a is ≤ 3, the near-field surrounding rock hardly ruptures; when 3 < j ≤ 7, further explore the growth rate;

[0137] When the number of parameters k whose growth rate exceeds b is ≤ 3, it is grade I, the surrounding rock ruptures in a small range, accompanied by a small amount of energy release; when 3 < k ≤ 7, then explore whether its value exceeds the critical threshold;

[0138] When the number of parameters l that exceed the critical threshold C is ≤ 3, it is grade II, the surrounding rock ruptures in a larger range, and more energy is released; when l > 3, it is grade III, the near-field surrounding rock ruptures in a large range, releasing huge energy. In addition, when j > 7 or k > 7, it is directly judged as grade III.

[0139] In an example of the present invention, the real-time intensity of electromagnetic radiation is calculated using the root mean square value of the electromagnetic radiation waveform signal:

[0140]

[0141] Wherein, R represents the electromagnetic radiation intensity; T is the signal duration; V(t) represents the signal amplitude;

[0142] The cumulative electromagnetic radiation intensity is the accumulation of the electromagnetic real-time intensity; the electromagnetic radiation real-time pulse is the number of times the waveform exceeds the threshold, which reflects the frequency of near-field coal and rock fractures; the cumulative pulse is the accumulation of the real-time pulse.

[0143] In an example of the present invention, the acoustic emission parameters mainly include acoustic emission ring count, acoustic wave intensity, fractal b value, spatial position, released energy, and mechanism solution parameters.

[0144] The calculation methods of the acoustic emission ring count, acoustic wave intensity, electromagnetic radiation pulse, and intensity are the same. The calculation methods of the acoustic emission spatial position, released energy, and mechanism solution parameters are the same as those of the far-field microseismic parameters. The fractal b value of acoustic emission characterizes the proportion of fractures of different scales within a period of time, and its calculation formula is:

[0145] log 10 (N(m)) = a - bm

[0146] Wherein, m is the acoustic emission magnitude, N(m) is the probability of events with a magnitude greater than m, and a and b are constants.

[0147] In an example of the present invention, in the step S50, calculating the propagation attenuation coefficient of stress wave energy in the rock stratum structure specifically includes the following steps:

[0148] Select the vibration velocity, energy, fracture scale, magnitude, and b value of the same fracture monitored by far-field microseismic and near-field acoustic emission, calculate the propagation attenuation coefficient respectively, and take the average value;

[0149] Select a large number of large-scale far-field stress wave fracture data that can be simultaneously monitored by far-field microseismic and near-field acoustic emission. According to the fracture energy E1 calculated by far-field microseismic monitoring, the energy E2 of the waveform signal collected by near-field acoustic emission, the distance r1 from the fracture source to the far-field microseismic sensor, and the distance r2 from the near-field acoustic emission sensor, calculate the propagation attenuation coefficient of stress wave energy in the rock stratum structure, and its expression is as follows:

[0150]

[0151] Calculate the energy propagation attenuation coefficient respectively according to a plurality of large-scale far-field stress wave fracture data, and take the average value of the plurality of energy propagation coefficients to obtain β as the final energy propagation attenuation coefficient.

[0152] In an example of the present invention, in the step S50, establishing the global monitoring and early warning criterion for the integration of far and near fields of rock burst includes the following steps:

[0153] S51: Combine the static load bearing and energy storage states of the near-field surrounding rock with the far-field fracture activity level, and assign scores to them respectively, denoted as K. s When the far-field fracture is in a low, medium, or high activity level, assign 2, 4, and 6 points respectively; when the near-field surrounding rock fracture and energy release are in levels I, II, and III, assign 2, 4, and 6 points respectively.

[0154] S52: According to the far-field energy E 远 , the near-field energy E 近 , and the energy attenuation coefficient, obtain the near-field total energy E 总 . Its expression is as follows:

[0155] E 总 = E 近 + βE 远

[0156] Among them, when the near-field total energy exceeds the critical energy, assign the dynamic warning score K d = 8 points, otherwise record it as 0 points.

[0157] S53: Establish a composite warning index K = K s + K d . Its theoretical threshold is [0, 20]; set up a three-level warning system: Level I warning (4 ≤ K < 8), weak rockburst danger, regularly check the stability of the surrounding rock, maintain normal operations and pay attention to abnormal signals; Level II warning (8 ≤ K < 12): medium rockburst danger, strengthen the monitoring frequency, restrict non-essential operations and formulate prevention and control plans; Level III warning (K ≥ 12): strong rockburst danger, immediately evacuate personnel.

[0158] According to an all-region monitoring and dynamic and static far-near field collaborative warning system of rockburst in the second aspect of the present invention, it includes:

[0159] A structure analysis and area delineation unit, configured to analyze the key areas and stress wave types where large-energy far-field stress waves are likely to be generated in the large stope structure according to the disaster-causing mechanism of the far-field generation, cross-space propagation, and near-field surrounding rock coupling failure of the dynamic load stress wave of rockburst, delineate the high-stress areas within the near-field range of the mining and tunneling working faces and the intake and return air roadways, and clarify the propagation rock layer structure and lithological characteristics between the far-field and the near-field;

[0160] A far-near field data monitoring and acquisition unit, configured to arrange microseismic sensors around the key areas where far-field stress waves are generated, and transmit the signals through the underground monitoring ring network to the ground monitoring host to obtain the original waveform data of the far-field stress wave generation; arrange an acoustic-electric system in the delineated near-field high-stress area, and use acoustic emission and electromagnetic radiation technologies to dynamically and real-time monitor the working face and the roadway, and obtain the near-field surrounding rock fracture and stress characteristic parameters; form an integrated all-region exploration and monitoring system combining microseismic and acoustic-electric for the far-field and the near-field;

[0161] The microseismic data processing and evaluation unit is configured to filter and denoise the collected microseismic stress wave data, calculate the time-frequency data of the daily microseismic frequency, energy rate, dominant frequency, and amplitude, locate and invert the spatial position and source mechanism of the far-field stress wave generation, obtain the density cloud map, spatial fractal, tension-shear type, fracture size, attitude orientation, and released energy, establish and fuse and optimize the multi-parameter index system of the far-field microseismic space-time intensity, quantitatively evaluate the source characteristics of the far-field stress wave generation and the activity degree of the far-field rock layer fracture, and identify and predict whether large-magnitude far-field dynamic load stress waves and their energy will be generated subsequently;

[0162] The acoustic-electric data processing and evaluation unit is configured to filter and denoise the waveform data collected by the acoustic-electric system, calculate the real-time and cumulative intensity of electromagnetic radiation, and the real-time and cumulative pulse timing characteristic parameters, obtain the acoustic emission ring count, acoustic wave intensity, fractal b value, spatial position, released energy, and mechanism solution parameters, quantitatively reflect the fracture frequency and intensity of coal and rock based on the acoustic-electric index set and monitoring data, and evaluate the static load stress and energy storage state of the near-field surrounding rock according to the changes in the acoustic-electric monitoring data;

[0163] The global monitoring and early warning unit is configured to select the same fracture data monitored by multiple far-field microseisms and near-field acoustic emissions, calculate the propagation attenuation coefficient of the stress wave energy in the rock layer structure, based on the principle of superposition of dynamic and static energies under the action of the stress wave, and consider the disaster-causing process of the coupling action of the far-field generation, cross-space propagation of the dynamic load stress wave, and dynamic failure of the near-field surrounding rock, establish the global monitoring and early warning criterion for the integration of the far and near fields of rock burst, and combine the big data of far-field microseismic and near-field acoustic-electric monitoring to achieve the advanced and accurate early warning of rock burst through the coordination of the stress wave action mechanism and monitoring big data.

[0164] Through the organic coupling of far-field microseismic monitoring and near-field acoustic-electric monitoring, this early warning system realizes the global perception of the cross-space propagation mechanism of dynamic and static loads for the first time. Compared with the single near-field monitoring or far-field monitoring technology, this solution can simultaneously capture the far-field generation mechanism and near-field failure effect of the dynamic load stress wave, and form a full-chain monitoring network from the source fracture to the surrounding rock response. This system can effectively identify various types of source characteristics such as far-field roof tensile fracture, fault activation, and near-field surrounding rock fracture, expand the monitoring range, and significantly improve the spatial coverage rate of the precursor information of rock burst.

[0165] To address the problem that the microseismic monitoring signal is vulnerable to interference, this early warning system adopts the flexible installation technology of borehole coupling agent, and realizes the full-contact coupling between the sensor and the rock mass through the water-soluble base, improving the signal-to-noise ratio. The acoustic-electric monitoring adopts a three-dimensional heterogeneous gradient layout strategy, and constructs a monitoring matrix with a spatial density gradient through the Z-axis hierarchical configuration and the plane non-uniform topological network, solving the problem of the signal attenuation blind area caused by the traditional uniform layout, and greatly improving the positioning accuracy of the acoustic emission event.

[0166] This early warning system is based on a new method for quantitative inversion of rockburst fracture parameters, achieving a leap from point-plane to volume description and from qualitative to quantitative inversion in characterizing the failure evolution process of impact coal and rock. It considers the dynamic load generation, cross-space propagation, and chain evolution disaster-causing process of the surrounding rock failure in the working face, can more realistically reproduce the entire process of rockburst disaster evolution, and helps to significantly improve the accuracy of rockburst early warning.

[0167] This early warning system establishes a global monitoring and early warning criterion for the integration of far and near fields by fusing the far-field fracture activity degree, near-field surrounding rock static load stress and energy storage state, and near-field total energy. Through a three-level early warning system, the visualization grading of risk levels is realized, and the early warning response time is shortened. In particular, by dynamically correcting the near-field total energy calculation model with the attenuation coefficient, the coupling effect of dynamic load stress waves on the stability of the surrounding rock can be accurately reflected.

[0168] In an example of the present invention, the key areas and stress wave types where large-energy far-field stress waves are likely to be generated in the large mining field structure of rockburst include: the fracture of the overlying thick rock stratum and the activation of faults in the geological structure zone:

[0169] For the fracture of the overlying thick rock stratum, it is mainly a rapid tensile fracture process. The dynamic displacement field is dominated by high-frequency longitudinal waves, and the energy release is concentrated and decays rapidly. Based on the theory of quantitative seismology and the theory of dynamic fracture mechanics, the expression for the distribution characteristics of the stress wave dynamic displacement field of this overlying thick rock stratum fracture is:

[0170]

[0171] In the formula, u I (x,t) is the stress wave dynamic displacement field generated by the fracture of the overlying thick rock stratum. The superscripts R, Ф, and V are the P-wave, SH-wave, and SV-wave components of the stress wave respectively; λ and μ are the Lame constants of coal and rock; θ and are the spatial azimuth angles of the observation points; ρ is the density; c d and c s are the P-wave and S-wave velocities; ξ is the coordinate of the coal and rock mass fracture point; r is the distance from the fracture point ξ to the observation point x; is the crack propagation speed during the roof fracture process;

[0172] For the activation of faults in the geological structure zone, it is mainly a shear fracture process. The dynamic displacement field is dominated by low-frequency transverse waves, and the energy release is slow and the propagation distance is far. According to the theory of quantitative seismology and the theory of dynamic fracture mechanics, the spatial characteristics of the stress wave dynamic displacement field of fault slip are:

[0173]

[0174] In the formula, u II (x,t) is the stress wave dynamic displacement field generated by fault slip; is the fault slip velocity.

[0175] In the foregoing, the exemplary embodiments of a method and system for global monitoring of rock burst and collaborative early warning of static and dynamic near and far fields proposed by the present invention have been described in detail with reference to preferred embodiments. However, those skilled in the art can understand that, without departing from the concept of the present invention, various modifications and variations can be made to the above specific embodiments, and various combinations of the technical features and structures proposed by the present invention can be made without exceeding the protection scope of the present invention. The protection scope of the present invention is determined by the appended claims.

Claims

1. A method for global monitoring of rock burst and collaborative early warning of static and dynamic near and far fields, characterized in that, It includes the following steps: S10: According to the disaster-causing mechanism of the far-field generation, cross-space propagation, and near-field surrounding rock coupling failure of dynamic stress waves in rock bursts, analyze the key areas and stress wave types in the large stope structure where large-energy far-field stress waves are likely to be generated, delineate the high-stress areas within the near field of the mining and tunneling working faces and the intake and return air roadways, and clarify the propagation rock layer structure and lithological characteristics between the far field and the near field; S20: Arrange microseismic sensors around the key areas where far-field stress waves are generated and obtain the original waveform data of the far-field stress wave generation; deploy an acoustic-electric system within the delineated near-field high-stress area, and use acoustic emission and electromagnetic radiation technologies to dynamically and real-timely monitor the working face and the roadway to obtain the characteristic parameters of near-field surrounding rock fracture and stress; Form an integrated full-domain exploration and monitoring system for the far field and the near field by combining microseismic and acoustic-electric technologies; S30: Calculate the time-frequency data of the daily microseismic frequency, energy rate, dominant frequency, and amplitude based on the far-field original waveform data, locate and invert the spatial position and source mechanism of the far-field stress wave generation, obtain the density cloud map, spatial fractal, tension-shear type, fracture size, occurrence orientation, and released energy, establish and fuse and optimize the multi-parameter index system of far-field microseismic spatio-temporal intensity, quantitatively evaluate the source characteristics of the far-field stress wave generation and the activity degree of far-field rock layer fracture, and identify and predict whether large-magnitude far-field dynamic stress waves and their energies will be generated subsequently; S40: Calculate the real-time and cumulative intensity of electromagnetic radiation, the real-time and cumulative pulse time-series characteristic parameters based on the characteristic parameters of near-field surrounding rock fracture and stress, obtain the acoustic emission ring count, acoustic wave intensity, fractal b value, spatial position, released energy, and mechanism solution parameters, quantitatively reflect the fracture frequency and intensity of coal and rock based on the acoustic-electric index set and monitoring data, and evaluate the static load stress and energy storage state of the near-field surrounding rock according to the changes in the acoustic-electric monitoring data; S50: Select the same fracture data monitored by multiple far-field microseismic and near-field acoustic emission, calculate the propagation attenuation coefficient of the stress wave energy in the rock layer structure, based on the static-dynamic energy superposition principle of the stress wave action, and considering the coupling disaster-causing process of the far-field generation, cross-space propagation, and near-field surrounding rock dynamic failure of the dynamic stress wave, establish a full-domain monitoring and early warning criterion for the integration of the far and near fields of rock bursts, and combine the far-field microseismic and near-field acoustic-electric monitoring big data to achieve accurate early warning of rock bursts in advance through the collaboration of the stress wave action mechanism and the monitoring big data.

2. The full-domain monitoring and static-dynamic far-near field collaborative early warning method for rock bursts according to claim 1, characterized in that In the step S10, the key areas and stress wave types in the large stope structure of rock bursts where large-energy far-field stress waves are likely to be generated include: the fracture of the overlying thick rock layer and the activation of faults in the geological structure zone: For the fracture of the overlying thick rock layer, the expression of the distribution characteristics of the dynamic displacement field of the stress wave is: where, u I (x,t) is the dynamic displacement field of the stress wave generated by the fracture of the overlying thick rock stratum. The superscripts R, Ф, and V are the P-wave, SH-wave, and SV-wave components of the stress wave respectively; λ and μ are the Lame constants of the coal and rock; θ and are the spatial azimuth angles of the observation points; ρ is the density; c d and c s are the P-wave and S-wave velocities; ξ is the coordinate of the coal and rock break point; r is the distance from the break point ξ to the observation point x; is the crack propagation velocity during the roof fracture process; For the activation of faults in the geological structure zone, according to the theory of quantitative seismology and the theory of dynamic fracture mechanics, the spatial characteristics of the dynamic displacement field of the stress wave of fault slip are obtained as: where u II (x, t) is the dynamic displacement field of the stress wave generated by fault slip; is the fault slip velocity.

3. The full-domain monitoring and static-dynamic far-near field collaborative early warning method for rock bursts according to claim 1, characterized in that In the step S30, the specific steps of locating and inverting the spatial position and source mechanism of the far-field stress wave generation include the following steps: Use the non - linear least - squares travel - time earthquake source location method to determine the source location. Pick up the travel - time data of the waveforms collected by each sensor in turn, and calculate the time difference Δt between the measured travel - time of the i - th and j - th sensors and the theoretical travel - time ij =(t i -t j )-(t i0 -t j0 ) Take the sum of the squares of the differences between the theoretical travel - time of ray propagation and the actual observed travel - time as the objective function. The calculation result corresponding to the minimum value of the objective function is the source coordinate; A displacement discontinuity source model for generating far-field stress waves is established, and the constraint conditions of the displacement discontinuity tensor in the microseismic source model are transformed into the form of moment tensor invariants; the Lagrange multiplier is combined with the Levenberg-Marquardt optimization algorithm to iteratively solve the moment tensor components under the constraint condition that the intermediate eigenvalue of the displacement discontinuity tensor is zero, so that the least square error between the theoretical normal displacement and the measured normal displacement of the P-wave at different sensor positions is minimized; according to the relationship between the moment tensor, the displacement discontinuity tensor and the rupture source parameters, the rupture tensile-shear property, the rupture scale, and the source parameters of the strike orientation of the far-field stress wave are calculated from the solved moment tensor components.

4. The full-field monitoring and dynamic-static far-near field collaborative early warning method for rock bursts according to claim 1, characterized in that In the step S30, establishing a far-field microseismic spatio-temporal intensity multi-parameter index system includes the following steps: Let the set of far-field microseismic parameters P for dynamic prediction of rock bursts; Calculate the cumulative difference index, and obtain the parameter time series observation sequences Q1, Q2, …, Q n-1 , Q n (n>3) where Q n is the parameter at time series n; calculate the difference between adjacent time series parameters: ΔQ j = Q j - Q j-1 (j = 2, 3, ..., N), let G j = min{ΔQ j , 0}, H j = max{ΔQ j , 0}, then: Let the cumulative difference be R w = F1 / (F1 + F2), R w ∈ [0, 1], calculate the cumulative difference values of each parameter in the far-field microseismic parameter P for dynamic prediction of rock burst, and obtain the cumulative difference set R of microseismic evaluation parameters; Determine that the risk level evaluation index expression is: F p＝ R m a1 + R K a2 + R ΔV a3 + R bVt a4 + … + R w a w where a1, a2, a3, …, a w are the weights of the evaluation grades for the far-field fracture activity level; Divide the far-field rupture activity level, and through the far-field rupture activity level, construct a three-level evaluation mechanism: low activity (F p ≤θ1), medium activity (θ1 < F p ≤θ2), high activity (F p >θ2); among them, when the far field is in a low-activity rupture, it is basically in a safe state, will not generate large-magnitude stress waves, and releases less energy; when the rupture degree is medium-active, large-magnitude stress waves may be generated subsequently, releasing more energy, and timely protection should be carried out; when the rupture is highly active, large-magnitude far-field dynamic load stress waves are generated, releasing a large amount of energy, and safety measures must be taken immediately for treatment.

5. The full-field monitoring and dynamic-static far-near field collaborative early warning method for rock bursts according to claim 1, characterized in that In the step S40, based on the acoustic-electric index set and the monitoring data, the coal-rock fracture frequency and intensity are quantitatively reflected, and the static load stress and energy storage state of the near-field surrounding rock are evaluated according to the changes in the acoustic-electric monitoring data, including: Based on 10 parameters of the real-time and cumulative intensity of electromagnetic radiation, the real-time and cumulative pulse time series characteristic parameters, the acoustic emission ring count, the acoustic wave intensity, the fractal b value, the spatial position, the released energy, and the fracture degree, the coal-rock fracture frequency and intensity are quantitatively reflected, and the static load stress and energy storage state of the near-field surrounding rock are evaluated according to its growth value, growth rate, and critical threshold.

6. The full-field monitoring and dynamic-static far-near field collaborative early warning method for rock bursts according to claim 5, characterized in that Evaluating the static load stress and energy storage state of the near-field surrounding rock according to its growth value, growth rate, and critical threshold specifically includes the following: Calculate the maximum difference of each parameter within the statistical period, denoted as a; calculate the average value and standard deviation of the growth rate of the acoustic-electric index within the statistical period, and move the standard deviation upward based on the average value of the growth rate as b; calculate the average value and standard deviation of the acoustic-electric index within the statistical period, and move the standard deviation upward based on the average value as the critical threshold C; When the number of parameters j whose growth value exceeds a is ≤ 3, the near-field surrounding rock hardly fractures; when 3 < j ≤ 7, further explore the growth rate; When the number of parameters k whose growth rate exceeds b is ≤ 3, it is grade I, the surrounding rock fractures in a small range, accompanied by a small amount of energy release; when 3 < k ≤ 7, then explore whether its value exceeds the critical threshold; When the number of parameters l that exceed the critical threshold C is ≤ 3, it is grade II, the surrounding rock fractures in a larger range, and more energy is released; when l > 3, it is grade III, the near-field surrounding rock fractures in a large range, releasing huge energy; in addition, when j > 7 or k > 7, it is directly judged as grade III.

7. The full-field monitoring and dynamic-static far-near field collaborative early warning method for rock bursts according to claim 1, characterized in that In the step S50, calculating the propagation attenuation coefficient of the stress wave energy in the rock layer structure specifically includes the following steps: Select the vibration velocity, energy, fracture scale, magnitude, and b-value of the same fracture monitored by far-field microseismic and near-field acoustic emission, calculate the propagation attenuation coefficient respectively, and take the average value; Select the large-scale far-field stress wave fracture data that can be simultaneously monitored by far-field microseismic and near-field acoustic emission. According to the fracture energy E1 calculated by far-field microseismic monitoring, the energy E2 of the waveform signal collected by near-field acoustic emission, the distance r1 from the fracture source to the far-field microseismic sensor, and the distance r2 from the near-field acoustic emission sensor, calculate the propagation attenuation coefficient of the stress wave energy in the rock formation structure. The expression is as follows: Calculate the energy propagation attenuation coefficient respectively according to multiple large-scale far-field stress wave fracture data, take the average value of multiple energy propagation coefficients, and obtain β as the final energy propagation attenuation coefficient.

8. The full-field monitoring and dynamic-static far-near field collaborative early warning method for rock burst according to claim 1, characterized in that In the step S50, establishing the full-field monitoring and early warning criterion for the far-near field integration of rock burst includes the following steps: S51: Combine the static load bearing and energy storage states of the near-field surrounding rock with the far-field fracture activity level, and assign scores to them respectively, denoted as K s ; When the far-field fracture is in a low, medium, or high activity level, assign 2, 4, and 6 points respectively; when the near-field surrounding rock fracture and energy release are in levels I, II, and III, assign 2, 4, and 6 points respectively; S52: According to the far-field energy E 远 , the near-field energy E 近 and the energy attenuation coefficient, the total near-field energy E 总 is obtained, and its expression is as follows: E 总 = E 近 + βE 远 Among them, when the total near-field energy exceeds the critical energy, a dynamic warning score K is assigned d = 8 points, otherwise it is recorded as 0 points; S53: Establish a composite warning index \(K = K\) s +K d , whose theoretical threshold is \([0, 20]\); Set up a three - level warning system: Level - I warning (\(4\leq K\lt8\)), weak rock burst danger, regularly check the stability of surrounding rock, maintain normal operation and pay attention to abnormal signals; Level - II warning (\(8\leq K\lt12\)): medium rock burst danger, strengthen the monitoring frequency, restrict non - essential operations and formulate prevention and control plans; Level - III warning (\(K\geq12\)): strong rock burst danger, immediately evacuate personnel.

9. A comprehensive monitoring and collaborative early warning system for rock burst in the whole area, characterized in that, Including: A structure analysis and area delineation unit configured to analyze the key areas and stress wave types where large-energy far-field stress waves are likely to be generated in the large stope structure according to the disaster-causing mechanism of far-field generation, cross-space propagation, and near-field surrounding rock coupling failure of rock burst dynamic load stress waves, delineate the high-stress areas within the near-field range of the mining and tunneling working faces and the intake and return air roadways, and clarify the propagation rock formation structure and lithology characteristics between the far field and the near field; A far-near field data monitoring and acquisition unit configured to arrange microseismic sensors around the key areas where far-field stress waves are generated and obtain the original waveform data of far-field stress wave generation; arrange an acoustic-electric system in the delineated near-field high-stress area, and use acoustic emission and electromagnetic radiation technologies to dynamically and real-time monitor the working face and roadway, and obtain the fracture and stress characteristic parameters of the near-field surrounding rock; Form an integrated full-field exploration and monitoring system for the far field and the near field by combining microseismic and acoustic-electric; A microseismic data processing and evaluation unit configured to calculate the time-frequency data of microseismic daily frequency, energy rate, main frequency, and amplitude based on the far-field original waveform data, locate and invert the spatial position and source mechanism of far-field stress wave generation, obtain the density cloud map, spatial fractal, tensile-shear type, fracture size, attitude orientation, and released energy, establish and fuse and optimize the multi-parameter index system of far-field microseismic time-space-intensity, quantitatively evaluate the source characteristics of far-field stress wave generation and the activity degree of far-field rock formation fracture, and identify and predict whether large-magnitude far-field dynamic load stress waves and their energy will be generated subsequently; An acoustic-electric data processing and evaluation unit configured to calculate the real-time and cumulative intensity of electromagnetic radiation, real-time and cumulative pulse time series characteristic parameters based on the fracture and stress characteristic parameters of the near-field surrounding rock, obtain the acoustic emission ring count, acoustic wave intensity, fractal b-value, spatial position, released energy, and mechanism solution parameters, quantitatively reflect the fracture frequency and intensity of coal and rock based on the acoustic-electric index set and monitoring data, and evaluate the static load stress and energy storage state of the near-field surrounding rock according to the changes in the acoustic-electric monitoring data; The global monitoring and early warning unit is configured to select the same fracture data for multiple far-field microseismic and near-field acoustic emission monitoring, calculate the propagation attenuation coefficient of stress wave energy in the rock formation structure, and based on the principle of superposition of static and dynamic energies under the action of stress waves, and considering the disaster-causing process of the coupling effect of the generation of far-field dynamic stress waves, cross-space propagation and dynamic failure of near-field surrounding rock, establish a global monitoring and early warning criterion for the integration of far and near fields of rock bursts, and combine the big data of far-field microseismic and near-field acoustic and electrical monitoring to achieve accurate early warning of rock bursts in advance by coordinating the stress wave action mechanism and monitoring big data.

10. The rock burst global monitoring and static-dynamic far-near field collaborative early warning system according to claim 9, wherein Key areas and stress wave types in the large stope structure of rock bursts where large-energy far-field stress waves are likely to be generated include: fracture of overlying thick rock strata and activation of faults in geological structure zones: For the fracture of the overlying thick rock strata, the expression of the distribution characteristics of the stress wave dynamic displacement field is: where, u I (x, t) is the dynamic displacement field of stress wave generated by the fracture of overlying thick strata. The superscripts R, Ф, and V are the P-wave, SH-wave, and SV-wave components of the stress wave respectively; λ and μ are the Lame constants of coal and rock; θ and are the spatial azimuth angles of the observation point; ρ is the density; c d and c s are the P-wave and S-wave velocities; ξ is the coordinate of the break point of coal and rock mass; r is the distance from the break point ξ to the observation point x; is the crack propagation velocity during the roof fracture process; For the activation of faults in geological structure zones, according to the theory of quantitative seismology and the theory of dynamic fracture mechanics, the spatial characteristics of the stress wave dynamic displacement field of fault slip are obtained as: where u II (x, t) is the dynamic displacement field of the stress wave generated by fault slip; is the fault slip velocity.

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