A method and system for monitoring and early warning of rock burst in a global range

Abstract

The application discloses a rock burst global monitoring and dynamic-static near and far field cooperative early warning method and system, and the early warning method comprises the following steps: based on the far field generation and propagation mechanism of dynamic load stress wave, determining the key monitoring area of the stope space and the stress wave type, delineating the high stress area of the working face surrounding rock and analyzing the structure characteristics of the rock stratum; setting a microseismic sensor in the far field key area, obtaining parameters such as the source rupture size, energy and magnitude through waveform inversion; arranging an acoustic-electric system in the near field high stress area, dynamically monitoring the coal rock rupture characteristics and quantitatively analyzing the static load energy storage state of the surrounding rock. Combined with the stress wave propagation attenuation coefficient, the near and far field fusion early warning criterion under the superposition action of dynamic and static energy is established, and through the cooperative analysis of multi-source monitoring data, the rock burst advanced precise early warning is realized. The application proposes a disaster gestating space global monitoring technology fusing seismic, acoustic and electric, considers the chain evolution disaster-causing process of dynamic load generation, cross-space propagation and working face surrounding rock damage, and is helpful to significantly improve the accuracy of rock burst early warning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of rock burst, in particular to a rock burst global monitoring and dynamic-static near-field and far-field collaborative early warning method and system. BACKGROUND

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

[0003] The dynamic coupling disaster mechanism of the multi-source stress field in the current deep stope has not been fully understood, which specifically manifests in that: the whole process evolution law of coal and rock mass damage-fracture-destabilization under the combined action of dynamic and static loads still has a cognitive blind spot; the spatiotemporal correlation characteristics of multi-field coupling disaster precursor information lack systematic characterization; the existing monitoring technology has the dual defects of insufficient spatial coverage and low multi-source information fusion degree. Although patent CN118859311A proposes a mine disaster comprehensive monitoring and early warning method and system based on microseismic monitoring technology, which determines the source location and energy by using surface microseismic wave data to complete disaster monitoring; patent CN114017121A realizes disaster monitoring and early warning by real-time monitoring of the impact of the strain field on rock burst; patent CN106437854A synchronously and real-timely monitors and collects the sound wave signals and electromagnetic radiation signal waveforms generated by the loaded coal and rock mass, reflects the coal and rock dynamic disaster evolution process according to the signal changes, and performs early warning, real-time positioning of the sound wave source location and electromagnetic anomaly area, and determination of the dangerous area according to the distribution; patent CN114137600A inverts the fracture mechanism through microseismic monitoring data, and establishes a destabilization prediction index to perform disaster monitoring and early warning. However, there are the following significant limitations: the single index has insufficient sensitivity to complex disaster precursors, and has a high false alarm rate; the single monitoring dimension limits the spatial resolution, and it is difficult to realize kilometer-level stope three-dimensional dynamic perception; the lack of spatiotemporal correlation analysis of multi-source information cannot reveal the disaster chain evolution law. SUMMARY

[0004] The present application proposes a rock burst global monitoring and dynamic-static near-field and far-field collaborative early warning method and system to solve the problems and meet the needs mentioned above. The technical purposes mentioned above can be achieved and other technical effects can be brought about due to the adoption of the following technical features.

[0005] One object of the present application is to propose a rock burst global monitoring and dynamic-static near-field and far-field collaborative early warning method, which comprises the following steps:

[0006] S10: According to the mechanism of the far-field generation, cross-space propagation and near-field surrounding rock coupling damage of the rock burst dynamic load stress wave, the key area and stress wave type of the large energy far-field stress wave in the large mining structure are analyzed, the high stress area in the near-field range of the mining and tunneling working face and the air inlet and outlet roadway is circled, and the propagation rock structure and lithology characteristics between the far-field and the near-field are determined;

[0007] S20: The microseismic sensor is arranged around the key area of the far-field stress wave generation, and the original waveform data of the far-field stress wave generation is obtained; the acoustic-electric system is arranged in the circled near-field high stress area, the dynamic real-time monitoring of the working face and the roadway is carried out by using the acoustic emission and electromagnetic radiation technology, the near-field surrounding rock fracture and stress characteristic parameters are obtained, and the far-field and near-field integrated global monitoring and monitoring system combining microseismic and acoustic-electric is formed;

[0008] S30: Based on the far-field original waveform data, the time-frequency data of the microseismic daily frequency, energy rate, main frequency and amplitude are calculated, the far-field stress wave generation space position and source mechanism are located and inverted, the density cloud picture, spatial fractal, tensile-shear type, fracture size, occurrence orientation and released energy are obtained, the far-field microseismic time and space strong multi-parameter index system is established and is fused and optimized, the source characteristics of the far-field stress wave generation and the near-field rock layer fracture activity degree are quantitatively evaluated, and whether large-magnitude far-field dynamic load stress wave and energy are generated subsequently is identified and predicted;

[0009] S40: Based on the near-field surrounding rock fracture and stress characteristic parameters, the real-time and cumulative intensity of electromagnetic radiation, real-time and cumulative pulse time sequence characteristic parameters are calculated, the acoustic emission ringing count, acoustic wave intensity, fractal b value, spatial position, released energy and mechanism solution parameters are obtained, the coal rock fracture frequency and intensity are quantitatively reflected based on the acoustic-electric index set and monitoring data, and the near-field surrounding rock static load stress and energy storage state are evaluated according to the acoustic-electric monitoring data change;

[0010] S50: The same fracture data of the far-field microseismic and the near-field acoustic emission monitoring are selected, the stress wave energy propagation attenuation coefficient in the rock structure is calculated, the dynamic and static energy superposition principle based on the stress wave effect is considered, the coupling effect disaster process of the dynamic load stress wave far-field generation, cross-space propagation and near-field surrounding rock dynamic damage is considered, the global monitoring and early warning criterion of the rock burst far-field and near-field fusion is established, the stress wave action mechanism and the monitoring big data are cooperated, and the rock burst advanced and accurate early warning is realized.

[0011] In addition, according to the rock burst global monitoring and dynamic and static far-field and near-field cooperative early warning method, the following technical features can also be had:

[0012] In one example of the present invention, in step S10, the key areas and stress wave types prone to generating high-energy far-field stress waves within the rockburst mining structure include: fractures in the overlying thick rock strata and activation of faults in geological structural zones.

[0013] For a fracture in an overlying, thick rock stratum, the expression for the dynamic displacement field distribution characteristics of the stress wave is:

[0014]

[0015] In the formula, u I (x,t) represents the dynamic displacement field of stress waves generated by the fracture of the overlying thick rock strata, with superscripts R, Ф, and V representing the P-wave, SH-wave, and SV-wave components of the stress waves, respectively; λ and μ are the Lamé constants of the coal and rock; θ and ρ is the spatial azimuth of the observation point; c is the density; d and c s ξ represents the wave velocity of P-wave and S-wave; ξ represents the coordinates of the fracture point of the coal and rock mass; r represents the distance from the fracture point ξ to the observation point x. This refers to the crack propagation rate during the top plate fracture process;

[0016] For fault activation in geological structural zones, based on quantitative seismology and dynamic fracture mechanics, the spatial characteristics of the stress wave dynamic displacement field of fault slip are obtained as follows:

[0017]

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

[0019] In one example of the present invention, step S30, locating and inverting the spatial location and source mechanism of the far-field stress wave generation specifically includes the following steps:

[0020] The source location is determined using a nonlinear least squares travel time difference source localization method. Travel time data from each sensor is sequentially collected, and the travel time difference Δt between the measured and theoretical travel times of the i-th and j-th sensors is calculated. ij =(t i -t j )-(t i0 -t j0 The objective function is the sum of the squares of the differences between the theoretical travel time and the actual observed travel time of the ray propagation. The calculation result corresponding to the minimum value of the objective function is the source coordinates.

[0021] A displacement discontinuity source model for far-field stress waves was established, and the displacement discontinuity tensor constraints in the microseismic source model were transformed into moment tensor invariant forms. The moment tensor components satisfying the constraint condition of zero intermediate eigenvalues ​​of the displacement discontinuity tensor were iteratively solved using Lagrange multipliers combined with the Levenberg-Marquardt optimization algorithm, minimizing the least squares error between the theoretical and measured normal displacements of the P-wave at different sensor locations. Based on the relationship between the moment tensor, the displacement discontinuity tensor, and the rupture source parameters, the source parameters of the far-field stress wave, including rupture tensile-shear properties, fracture scale, and orientation, were calculated from the solved moment tensor components.

[0022] In one example of the present invention, step S30, establishing a multi-parameter index system for far-field microseismic spatiotemporal intensity, includes the following steps:

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

[0024] Calculate the cumulative difference index and obtain the parameter time series observation sequence Q1, Q2, ..., Q within the sliding time window. n-1 Q n (n>3) where Q n The parameters are given when the timing sequence is n; calculate the difference between adjacent timing parameters: ΔQ j =Q j -Q j-1 (j = 2, 3, ..., N), let G j =min{ΔQ j ,0},H j =max{ΔQ j If , 0}, then:

[0025]

[0026] 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 of dynamic prediction of rockburst, and obtain the cumulative difference set R of microseismic evaluation parameters;

[0027] The expression for the risk level assessment indicator is as follows:

[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 weight of the evaluation level of the far-field fracture activity degree;

[0030] 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-activity fracture, it is basically in a safe state, will not generate large-magnitude stress waves, and releases less energy; when the fracture degree is medium-active, 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 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:

[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, and evaluate the static load stress 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 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:

[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 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.

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

[0039] The vibration velocity, energy, rupture size, magnitude, and b-value of the same rupture were selected from far-field microseismic and near-field acoustic emission monitoring, and the propagation attenuation coefficient was calculated and the average value was taken.

[0040] Large-scale far-field stress wave rupture data that can be simultaneously monitored by far-field microseismic and near-field acoustic emission are selected. Based on the rupture energy E1 calculated from far-field microseismic monitoring, the energy E2 of the waveform signal acquired by near-field acoustic emission, the distance r1 from the rupture source to the far-field microseismic sensor, and the distance r2 from the near-field acoustic emission sensor, the propagation attenuation coefficient of stress wave energy in the rock structure is calculated, and its expression is as follows:

[0041]

[0042] The energy propagation attenuation coefficients were calculated based on multiple large-scale far-field stress wave rupture data. The average value of the multiple energy propagation coefficients was then taken to obtain β, which is the final energy propagation attenuation coefficient.

[0043] In one example of the present invention, in step S50, establishing the global monitoring and early warning criteria for rockburst far-field fusion includes the following steps:

[0044] S51: Combining the far-field fracturing activity level with the near-field surrounding rock static load and energy storage state, a score is assigned to each, denoted as K. s When the far-field fracturing is at a low, medium, or high level of activity, it is assigned 2, 4, or 6 points respectively; when the near-field surrounding rock fracturing and energy release are at levels I, II, or III, it is assigned 2, 4, or 6 points respectively.

[0045] S52: Based on the far-field energy E 远 Near-field energy E 近 The total near-field energy E is obtained from the energy attenuation coefficient. 总 Its expression is as follows:

[0046] E 总 =E 近 +βE 远

[0047] Among them, a dynamic warning score K is assigned when the total near-field energy exceeds the critical energy. d =8 points, otherwise 0 points;

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

[0049] Another objective of this invention is to provide a comprehensive monitoring and dynamic / static near-field and far-field coordinated early warning system for rockbursts, comprising:

[0050] The structural analysis and regional delineation unit is configured to analyze the key areas and stress wave types that are prone to generating high-energy far-field stress waves in the large mining area structure, based on the mechanism of far-field generation, cross-space propagation and near-field surrounding rock coupling failure of rockburst dynamic load stress waves. It also delineates high-stress areas within the near-field range of mining and tunneling faces and intake and return airway, and clarifies the propagation strata structure and lithological characteristics between the far-field and near-field.

[0051] The near-field and far-field data monitoring and acquisition unit is configured to deploy microseismic sensors around key areas where far-field stress waves are generated and obtain the raw waveform data of the far-field stress waves; and to deploy an acoustic-electric system in the delineated high-stress area in the near field to dynamically monitor the working face and roadway in real time using acoustic emission and electromagnetic radiation technologies, and to obtain near-field surrounding rock fracture and stress characteristic parameters; thus forming an integrated far-field and near-field full-domain detection and monitoring system that combines microseismic and acoustic-electric technologies.

[0052] The microseismic data processing and evaluation unit is configured to calculate time-frequency data of daily frequency, energy rate, dominant frequency and amplitude of microseismic events based on far-field raw waveform data, locate and invert the spatial location and source mechanism of far-field stress wave generation, obtain density cloud map, spatial fractal, tension-shear type, fracture size, orientation and released energy, establish and optimize a multi-parameter index system of far-field microseismic spatiotemporal intensity, quantitatively evaluate the source characteristics of far-field stress wave generation and the degree of far-field rock strata fracture activity, and identify and predict whether large-magnitude far-field dynamic load stress waves and their energy will be generated subsequently.

[0053] The acoustic and electrical data processing and evaluation unit is configured to calculate the real-time and cumulative intensity of electromagnetic radiation and the real-time and cumulative pulse timing characteristic parameters based on the near-field surrounding rock fracture and stress characteristic parameters, obtain acoustic emission ringing count, sound wave intensity, fractal b-value, spatial location, released energy and mechanism solution parameters, quantitatively reflect the frequency and intensity of coal and rock fracture based on the acoustic and electrical index set and monitoring data, and evaluate the near-field surrounding rock static load stress and energy storage state based on changes in acoustic and electrical monitoring data;

[0054] The comprehensive monitoring and early warning unit is configured to 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 superposition of dynamic and static energy of stress wave action, and considering the disaster-causing process of the coupling effect of far-field generation, cross-space propagation and near-field dynamic damage of dynamic stress wave, establish a comprehensive monitoring and early warning criterion for rockburst that integrates far-field and near-field acoustic emission monitoring big data, to achieve advanced and accurate early warning of rockburst by synergistic effect of stress wave action mechanism and monitoring big data.

[0055] In one example of the present invention, the key areas and stress wave types prone to generating high-energy far-field stress waves within the rockburst mining structure include: fractures in the overlying thick rock strata and activation of faults in geological structural zones.

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

[0057]

[0058] In the formula, u I (x,t) represents the dynamic displacement field of stress waves generated by the fracture of the overlying thick rock strata, with superscripts R, Ф, and V representing the P-wave, SH-wave, and SV-wave components of the stress waves, respectively; λ and μ are the Lamé constants of the coal and rock; θ and ρ is the spatial azimuth of the observation point; c is the density; d and c s ξ represents the wave velocity of P-wave and S-wave; ξ represents the coordinates of the fracture point of the coal and rock mass; r represents the distance from the fracture point ξ to the observation point x. This refers to the crack propagation rate during the top plate fracture process;

[0059] For fault activation in geological structural zones, based on quantitative seismology and dynamic fracture mechanics, the spatial characteristics of the stress wave dynamic displacement field of fault slip are obtained as follows:

[0060]

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

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

[0063] This technical solution, through the organic coupling of far-field microseismic monitoring and near-field acoustic-electric monitoring, achieves for the first time a comprehensive perception of the propagation mechanism of dynamic and static loads across space. Compared to single near-field or far-field monitoring technologies, this solution can simultaneously capture the far-field generation mechanism and near-field damage effect of dynamic 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 tensional fracturing, fault activation, and near-field surrounding rock rupture, expanding the monitoring range and significantly improving the spatial coverage of rockburst precursor information.

[0064] This technical solution addresses the challenge of interference-prone microseismic monitoring signals by employing a flexible installation technique using borehole coupling agent. A water-soluble base enables full-contact coupling between the sensor and the rock mass, improving the signal-to-noise ratio. The acoustic-electric monitoring utilizes a three-dimensional heterogeneous gradient layout strategy. Through Z-axis layered configuration and a planar non-uniform topology network, a monitoring matrix with spatial density gradients is constructed, resolving the signal attenuation blind zone problem caused by traditional uniform deployment and significantly improving the accuracy of acoustic emission event localization.

[0065] This technical solution is based on a new quantitative inversion method for rockburst fracturing parameters. It achieves a leap from point-to-surface to volumetric characterization of the rockburst failure evolution process, and from qualitative description to quantitative inversion. It considers the chain evolution of dynamic load generation, cross-space propagation, and working face surrounding rock failure, which can more realistically reproduce the entire rockburst disaster evolution process and help to significantly improve the accuracy of rockburst early warning.

[0066] This technical solution establishes a comprehensive monitoring and early warning criterion that integrates far-field fracturing activity, near-field static load stress and energy storage status of the surrounding rock, and near-field total energy. A three-tiered early warning system enables visualized risk level classification, shortening the early warning response time. In particular, by dynamically correcting the near-field total energy calculation model using an attenuation coefficient, it can accurately reflect the coupling effect of dynamic stress waves on the stability of the surrounding rock.

[0067] The preferred embodiments of the invention will be described in more detail below with reference to the accompanying drawings, so as to facilitate an understanding of the features and advantages of the invention. Attached Figure Description

[0068] 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 described below. The drawings are merely illustrative of some embodiments of the present invention and are not intended to limit the scope of the present invention to all embodiments.

[0069] Figure 1 This is a flowchart of a method for comprehensive monitoring and coordinated near-field and dynamic / static far-field early warning of rockburst according to an embodiment of the present invention;

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

[0071] Figure 3 This is a schematic diagram of the arrangement of micro-vibration sensors according to an embodiment of the present invention;

[0072] Figure 4 This is a schematic diagram of the arrangement of acoustic and electrical sensors and the transmission line arrangement on the working surface according to an embodiment of the present invention;

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

[0074] Figure 6 This is a schematic diagram illustrating the assessment of near-field surrounding rock static load stress and energy storage status based on acoustic and electrical data changes according to an embodiment of the present invention.

[0075] List of reference numerals in the attached diagram:

[0076] Electromagnetic radiation antenna 1;

[0077] Acoustic emission probe 2;

[0078] Data acquisition unit host 3;

[0079] Monitoring substation 4;

[0080] Well-mounted switch 5;

[0081] Server 6. Detailed Implementation

[0082] To make the objectives, technical solutions, and advantages 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. The same reference numerals in the drawings represent the same components. It should be noted that the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0083] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms “first,” “second,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, “an” or “a” and similar terms do not necessarily indicate a quantity limitation. Terms such as “comprising” or “including” mean that the element or object preceding the word encompasses the element or object listed following the word and its equivalents, without excluding other elements or objects. Terms such as “connected” or “linked” are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as “upper,” “lower,” “left,” and “right” are used only to indicate relative positional relationships; these relative positional relationships may change accordingly when the absolute position of the described object changes.

[0084] According to a first aspect of the present invention, a method for comprehensive monitoring and coordinated early warning of rockbursts in both static and dynamic near-field fields is provided, such as... Figure 1 , Figure 2 As shown, it includes the following steps:

[0085] S10: Based on the mechanism of the far-field generation, cross-space propagation and near-field surrounding rock coupling failure of rockburst dynamic stress waves, analyze the key areas and stress wave types that are prone to generating high-energy far-field stress waves in the large mining structure, delineate the high-stress areas in the near-field range of mining and tunneling faces and intake and return airways, and clarify the propagation rock strata structure and lithological characteristics between the far field and the near field.

[0086] S20: Microseismic sensors are deployed around key areas where far-field stress waves are generated. The signals are transmitted to the ground monitoring host through the downhole monitoring ring network to obtain the raw waveform data of the far-field stress waves. An acoustic-electric system is deployed in the delineated near-field high-stress area to dynamically monitor the working face and roadway in real time using acoustic emission and electromagnetic radiation technologies, and to obtain near-field surrounding rock fracture and stress characteristic parameters. This forms an integrated far-field and near-field full-domain detection and monitoring system that combines microseismic and acoustic-electric technologies.

[0087] S30: The collected microseismic stress wave data is filtered and denoised, and the time-frequency data of daily frequency, energy, dominant frequency and amplitude of microseismic events are calculated. The spatial location and source mechanism of far-field stress wave generation are located and inverted. Density cloud map, spatial fractal, tension-shear type, fracture size, orientation and released energy are obtained. A multi-parameter index system of far-field microseismic spatiotemporal intensity is established and optimized. The source characteristics of far-field stress wave generation and the degree of far-field rock strata fracture activity are quantitatively evaluated. It is determined and predicted whether a large-magnitude far-field dynamic load stress wave and its energy will be generated in the future.

[0088] S40: 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 acoustic emission ringing count, sound wave intensity, fractal b-value, spatial location, released energy and mechanism solution parameters, quantify the frequency and intensity of coal and rock fracture based on the acoustic-electric index set and monitoring data, and evaluate the static load and energy storage status of the near-field surrounding rock based on changes in acoustic-electric 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 dynamic and static energy superposition principle of stress wave action, and considering the disaster-causing process of the coupling effect of far-field generation of dynamic stress wave, cross-space propagation and near-field surrounding rock dynamic damage, establish a comprehensive monitoring and early warning criterion for rockburst with far-field and near-field fusion, and combine far-field microseismic and near-field acoustic emission monitoring big data to achieve advanced and accurate early warning of rockburst through the synergy of stress wave action mechanism and monitoring big data.

[0090] This early warning method, through the organic coupling of far-field microseismic monitoring and near-field acoustic-electric monitoring, achieves for the first time a comprehensive perception of the cross-space propagation mechanism of dynamic and static loads. Compared with single near-field or far-field monitoring technologies, this scheme can simultaneously capture the far-field generation mechanism and near-field damage effect of dynamic 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 tensional fracturing, fault activation, and near-field surrounding rock rupture, expanding the monitoring range and significantly improving the spatial coverage of rockburst precursor information.

[0091] This early warning method addresses the challenge of interference to microseismic monitoring signals by employing a flexible installation technique using borehole coupling agent. A water-soluble base enables full-contact coupling between the sensor and the rock mass, improving the signal-to-noise ratio. The acoustic-electric monitoring utilizes a three-dimensional heterogeneous gradient layout strategy. Through Z-axis layered configuration and a non-uniform planar topology network, a monitoring matrix with spatial density gradients is constructed, resolving the signal attenuation blind zone problem caused by traditional uniform deployment and significantly improving the accuracy of acoustic emission event location.

[0092] This early warning method is based on a new quantitative inversion method for rockburst fracturing parameters. It achieves a leap from point-to-surface to volume-to-volume characterization of the rockburst failure evolution process, and from qualitative description to quantitative inversion. It considers the chain evolution of the disaster-causing process of dynamic load generation, cross-space propagation, and working face surrounding rock failure. It can more realistically reproduce the entire process of rockburst disaster evolution and help to significantly improve the accuracy of rockburst early warning.

[0093] This early warning method establishes a comprehensive monitoring and early warning criterion by integrating far-field fracturing activity, near-field static load stress and energy storage status of the surrounding rock, and near-field total energy. A three-tiered early warning system enables visualized risk level classification, shortening the early warning response time. In particular, by dynamically correcting the near-field total energy calculation model using an attenuation coefficient, it can accurately reflect the coupling effect of dynamic stress waves on the stability of the surrounding rock.

[0094] In one example of the present invention, in step S10, the key areas and stress wave types prone to generating high-energy far-field stress waves within the rockburst mining structure include: fractures in the overlying thick rock strata and activation of faults in geological structural zones.

[0095] For fractures in overlying thick rock strata, the main manifestation is a rapid tensile fracturing process. The dynamic displacement field is dominated by high-frequency P-waves, with concentrated energy release and rapid attenuation. Based on quantitative seismology theory and dynamic fracture mechanics theory, the expression for the stress wave dynamic displacement field distribution characteristics of this overlying thick rock strata fracture is obtained as follows:

[0096]

[0097] In the formula, u I (x,t) represents the dynamic displacement field of stress waves generated by the fracture of the overlying thick rock strata, with superscripts R, Ф, and V representing the P-wave, SH-wave, and SV-wave components of the stress waves, respectively; λ and μ are the Lamé constants of the coal and rock; θ and ρ is the spatial azimuth of the observation point; c is the density; d and c s ξ represents the wave velocity of P-wave and S-wave; ξ represents the coordinates of the fracture point of the coal and rock mass; r represents the distance from the fracture point ξ to the observation point x. This refers to the crack propagation rate during the top plate fracture process;

[0098] For fault activation in geological structural zones, the main process is shear fracturing. The dynamic displacement field is dominated by low-frequency shear waves, which release energy slowly and propagate over long distances. Based on quantitative seismology theory and dynamic fracture mechanics theory, the spatial characteristics of the stress wave dynamic displacement field of fault slip are as follows:

[0099]

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

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

[0102] Sensors are installed around key areas in the far field. Microseismic sensors can be installed in a staggered manner at different locations and heights, allowing them to directly contact the coal and rock mass to acquire high-quality waveform data, forming a three-dimensional microseismic network coverage. After the sensors are deployed, they are connected to a mining DAQlink-4 microseismic acquisition instrument via signal transmission cables. A time synchronizer ensures time synchronization of waveforms acquired from different channels. The monitoring signals are transmitted through the underground monitoring ring network to the surface monitoring host for data processing and display.

[0103] The specific installation method for the microseismic sensor is as follows: A borehole casing is used to protect the inner wall of the borehole. A reusable pusher tube is used to transport compacted blocks to the bottom of the borehole to compact the flexible dough of water-soluble coupling agent, forming a coupling agent base. Subsequently, a ferrule containing the microseismic sensor is embedded into the uncured coupling agent base through the same pusher tube. After the base solidifies, a water-filled hose is connected to the pusher tube to pressurize and inject water into the solidified coupling agent base, achieving direct coupling contact between the sensor and the coal and rock mass. After the sensor is no longer in use, the ferrule containing the microseismic sensor is removed from the dissolved coupling agent base through the pusher tube, allowing for the sensor to be recycled during mining operations.

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

[0105] The acoustic monitoring system includes: an electromagnetic radiation antenna, an acoustic emission probe, a data acquisition unit, monitoring substations, a surface switch, and a server; among them, the electromagnetic radiation antenna and the acoustic emission probe are both connected to the data acquisition unit, the data acquisition unit is connected to the monitoring substations, and the monitoring substations are connected to the server through the surface switch.

[0106] Electromagnetic radiation antennas and acoustic emission probes are arranged alternately along the extension direction of the longwall face. For example, they can be arranged symmetrically on both sides of the extension direction of the longwall face. The acoustic and electrical signals of the longwall face are acquired by the electromagnetic radiation antennas and acoustic emission probes and uploaded to the server via the data acquisition host, monitoring substation and surface switch.

[0107] Electromagnetic radiation antennas are directly suspended on the underground anchor net for non-contact testing. The effective receiving direction of the electromagnetic radiation antennas is aimed at the near-field high-stress area. The distance between the antennas and the area being measured is less than 50m. One antenna is placed at each boundary of the monitoring area, and one antenna is placed above and below the center point of the area to ensure full coverage monitoring of key near-field high-stress areas.

[0108] Acoustic emission sensors are fixed to the side support anchors of the coal face, following the principle of heterogeneous morphological distribution in three-dimensional space, and adopting a ten-node multi-level gradient configuration scheme. In the vertical spatial dimension, the sensor array is layered along the Z-axis, forming a longitudinal monitoring architecture with spatial density gradient. In the horizontal projection plane, the sensors are non-uniformly discretized according to the characteristics of the pre-stress distribution, with dense arrangement in the pre-stress concentration area and sparse arrangement in the original rock stress area, forming a complementary lateral distribution matrix covering the high-stress areas of the working face and roadway.

[0109] The acoustic and electrical signals from near-field rock fracturing are transmitted to the GDD12 data acquisition unit via an acoustic emission sensor and an electromagnetic radiation antenna. The data acquisition unit then transmits the signals to a monitoring substation via a shielded cable. At the substation, the signal is converted into a network signal by a downhole signal conversion module, and then transmitted to a surface switch via a downhole switch ring network. The surface switch converts the network signal back into the original acoustic and electrical signals via a surface signal conversion module and saves them to the server. The acoustic emission and electromagnetic radiation signals recorded by the server use the same clock to ensure consistency in recording time.

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

[0111] The seismic source location is determined using a nonlinear least squares travel time difference method. Travel time data from each sensor is sequentially acquired, and the travel time difference Δt between the measured and theoretical travel times of the i-th and j-th sensors is calculated. ij =(t i -t j )-(t i0 -t j0 The objective function is the sum of the squares of the differences between the theoretical travel time and the actual observed travel time of the ray propagation. The calculation result corresponding to the minimum value of the objective function is the source coordinates.

[0112] A displacement discontinuity source model for far-field stress waves was established, and the displacement discontinuity tensor constraints in the microseismic source model were transformed into moment tensor invariant forms. The moment tensor components satisfying the constraint condition of zero intermediate eigenvalues ​​of the displacement discontinuity tensor were iteratively solved using Lagrange multipliers combined with the Levenberg-Marquardt optimization algorithm, minimizing the least squares error between the theoretical and measured normal displacements of the P-wave at different sensor locations. Based on the relationship between the moment tensor, the displacement discontinuity tensor, and the rupture source parameters, the rupture tensile-shear properties, fracture scale, and orientation of the far-field stress waves were calculated 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, dominant 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 dominant frequency is the main frequency band after performing a fast Fourier transform on the microseismic waveform, which reflects the fracture scale of far-field stress waves; the amplitude is the amplitude corresponding to the dominant frequency in the microseismic waveform spectrum curve.

[0115] Filter and denoise the collected microseismic stress wave data, calculate the time-frequency data of microseismic daily frequency, energy rate, dominant 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, tensile-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 microseismic source occurrences 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 step S30, establishing a multi-parameter index system for far-field microseismic spatiotemporal intensity includes the following steps:

[0124] Let P be the set of far-field microseismic parameters for dynamic prediction of rockburst;

[0125] Calculate the cumulative difference index and obtain the parameter time series observation sequence Q1, Q2, ..., Q within the sliding time window. n-1 Q n (n>3) where Q n The parameters are given when the timing sequence is n; calculate the difference between adjacent timing parameters: ΔQ j =Q j -Q j-1 (j = 2, 3, ..., N), let G j =min{ΔQ j ,0},H j =max{ΔQ j If , 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 of dynamic prediction of rockburst, and obtain the cumulative difference set R of microseismic evaluation parameters;

[0128] The expression for the risk level assessment indicator is as follows:

[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 Weighting of the evaluation level for the degree of far-field fracturing activity;

[0131] The activity level of far-field fracturing is classified into different levels, and a three-level evaluation mechanism is constructed based on these levels: low activity (F...). p ≤θ1), active in the middle (θ1) <F p ≤θ2), High activity (F) p(>θ2); where, 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 degree, large-magnitude stress waves may be generated subsequently, and the released energy is large, so protection should be taken in time; 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.

[0132] In an example of the present invention, in the step S40, as Figure 6 shown, the quantification of the coal and rock rupture frequency and intensity based on the acoustic and electrical index set and the monitoring data, and the evaluation of the static load force and energy storage state of the near-field surrounding rock according to the change of the acoustic and electrical monitoring data include:

[0133] Quantitatively reflecting the coal and 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 time sequence 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 force 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 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:

[0135] Calculating the maximum difference of each parameter within the statistical period, denoted as a; calculating the average value and standard deviation of the growth rate of the acoustic and electrical indexes within the statistical period, and moving the standard deviation upward based on the average value of the growth rate as b; calculating the average value and standard deviation of the acoustic and electrical indexes within the statistical period, and moving 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, and 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, and the surrounding rock ruptures in a larger range, with more energy release; when l > 3, it is grade III, and 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] In the formula, R represents the electromagnetic radiation intensity; T is the signal duration; and V(t) represents the signal amplitude.

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

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

[0144] The methods for calculating acoustic emission ringing counts, acoustic wave intensity, and electromagnetic radiation pulses and intensities are consistent. The methods for calculating the spatial location, released energy, and mechanism solution parameters of acoustic emissions are consistent with those for calculating far-field microseismic parameters. The fractal b-value of acoustic emissions characterizes the proportion of large-scale and small-scale fractures over a period of time; its calculation formula is as follows:

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

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

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

[0148] The vibration velocity, energy, rupture size, magnitude, and b-value of the same rupture were selected from far-field microseismic and near-field acoustic emission monitoring, and the propagation attenuation coefficient was calculated and the average value was taken.

[0149] Large-scale far-field stress wave rupture data that can be simultaneously monitored by far-field microseismic and near-field acoustic emission are selected. Based on the rupture energy E1 calculated from far-field microseismic monitoring, the energy E2 of the waveform signal acquired by near-field acoustic emission, the distance r1 from the rupture source to the far-field microseismic sensor, and the distance r2 from the near-field acoustic emission sensor, the propagation attenuation coefficient of stress wave energy in the rock structure is calculated, and its expression is as follows:

[0150]

[0151] The energy propagation attenuation coefficients were calculated based on multiple large-scale far-field stress wave rupture data. The average value of the multiple energy propagation coefficients was then taken to obtain β, which is the final energy propagation attenuation coefficient.

[0152] In one example of the present invention, in step S50, establishing the global monitoring and early warning criteria for rockburst far-field fusion includes the following steps:

[0153] S51: Combining the far-field fracturing activity level with the near-field surrounding rock static load and energy storage state, a score is assigned to each, denoted as K. s When the far-field fracturing is at a low, medium, or high level of activity, it is assigned 2, 4, or 6 points respectively; when the near-field surrounding rock fracturing and energy release are at levels I, II, or III, it is assigned 2, 4, or 6 points respectively.

[0154] S52: Based on the far-field energy E 远 Near-field energy E 近 The total near-field energy E is obtained from the energy attenuation coefficient. 总 Its expression is as follows:

[0155] E 总 =E 近 +βE 远

[0156] Among them, a dynamic warning score K is assigned when the total near-field energy exceeds the critical energy. d =8 points, otherwise 0 points;

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

[0158] According to a second aspect of the present invention, a comprehensive monitoring and dynamic / static near-field and far-field coordinated early warning system for rockburst includes:

[0159] The structural analysis and regional delineation unit is configured to analyze the key areas and stress wave types that are prone to generating high-energy far-field stress waves in the large mining area structure, based on the mechanism of far-field generation, cross-space propagation and near-field surrounding rock coupling failure of rockburst dynamic load stress waves. It also delineates high-stress areas within the near-field range of mining and tunneling faces and intake and return airway, and clarifies the propagation strata structure and lithological characteristics between the far-field and near-field.

[0160] The near-field and far-field data monitoring and acquisition unit is configured to deploy microseismic sensors around key areas where far-field stress waves are generated. The signals are transmitted to the ground monitoring host through the downhole monitoring ring network to obtain the raw waveform data of the far-field stress waves. An acoustic-electric system is deployed in the delineated near-field high-stress area to dynamically monitor the working face and roadway in real time using acoustic emission and electromagnetic radiation technologies, and to obtain near-field surrounding rock fracture and stress characteristic parameters. This forms an integrated far-field and near-field full-domain detection and monitoring system that combines microseismic and acoustic-electric technologies.

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

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

[0163] The comprehensive monitoring and early warning unit is configured to 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 superposition of dynamic and static energy of stress wave action, and considering the disaster-causing process of the coupling effect of far-field generation, cross-space propagation and near-field dynamic damage of dynamic stress wave, establish a comprehensive monitoring and early warning criterion for rockburst that integrates far-field and near-field acoustic emission monitoring big data, to achieve advanced and accurate early warning of rockburst by synergistic effect of stress wave action mechanism and monitoring big data.

[0164] This early warning system, through the organic coupling of far-field microseismic monitoring and near-field acoustic-electric monitoring, achieves for the first time a comprehensive perception of the propagation mechanism of dynamic and static loads across space. Compared to single near-field or far-field monitoring technologies, this scheme can simultaneously capture the far-field generation mechanism and near-field damage effect of dynamic 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 tensional fracturing, fault activation, and near-field surrounding rock rupture, expanding the monitoring range and significantly improving the spatial coverage of rockburst precursor information.

[0165] This early warning system addresses the challenge of interference to microseismic monitoring signals by employing a flexible installation technology using borehole coupling agent. A water-soluble base enables full-contact coupling between the sensor and the rock mass, improving the signal-to-noise ratio. The acoustic-electric monitoring utilizes a three-dimensional heterogeneous gradient layout strategy. Through Z-axis layered configuration and a non-uniform planar topology network, a monitoring matrix with spatial density gradients is constructed, resolving the signal attenuation blind zone problem caused by traditional uniform deployment and significantly improving the accuracy of acoustic emission event location.

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

[0167] This early warning system establishes a comprehensive monitoring and early warning criterion by integrating far-field fracturing activity, near-field static load stress and energy storage status of the surrounding rock, and near-field total energy. A three-tiered early warning system enables visualized risk level classification, shortening the early warning response time. In particular, by dynamically correcting the near-field total energy calculation model using an attenuation coefficient, it can accurately reflect the coupling effect of dynamic stress waves on the stability of the surrounding rock.

[0168] In one example of the present invention, the key areas and stress wave types prone to generating high-energy far-field stress waves within the rockburst mining structure include: fractures in the overlying thick rock strata and activation of faults in geological structural zones.

[0169] For fractures in overlying thick rock strata, the main manifestation is a rapid tensile fracturing process. The dynamic displacement field is dominated by high-frequency P-waves, with concentrated energy release and rapid attenuation. Based on quantitative seismology theory and dynamic fracture mechanics theory, the expression for the stress wave dynamic displacement field distribution characteristics of this overlying thick rock strata fracture is obtained as follows:

[0170]

[0171] In the formula, u I (x,t) represents the dynamic displacement field of stress waves generated by the fracture of the overlying thick rock strata, with superscripts R, Ф, and V representing the P-wave, SH-wave, and SV-wave components of the stress waves, respectively; λ and μ are the Lamé constants of the coal and rock; θ and ρ is the spatial azimuth of the observation point; c is the density; d and c s ξ represents the wave velocity of P-wave and S-wave; ξ represents the coordinates of the fracture point of the coal and rock mass; r represents the distance from the fracture point ξ to the observation point x. This refers to the crack propagation rate during the top plate fracture process;

[0172] For fault activation in geological structural zones, the main process is shear fracturing. The dynamic displacement field is dominated by low-frequency shear waves, which release energy slowly and propagate over long distances. Based on quantitative seismology theory and dynamic fracture mechanics theory, the spatial characteristics of the stress wave dynamic displacement field of fault slip are as follows:

[0173]

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

[0175] The foregoing description, with reference to preferred embodiments, details an exemplary implementation of the proposed method and system for comprehensive monitoring and coordinated early warning of rockbursts in both static and dynamic near and far fields. However, those skilled in the art will understand that various modifications and alterations can be made to the above specific embodiments without departing from the concept of the present invention, and various combinations can be made to the various technical features and structures proposed in the present invention without exceeding the protection scope of the present invention, which is determined by the appended claims.

Claims

1. A method for comprehensive monitoring and coordinated early warning of rockbursts in both static and dynamic near-field and long-field environments, characterized in that, Includes the following steps: S10: Based on the mechanism of the far-field generation, cross-space propagation and near-field surrounding rock coupling failure of rockburst dynamic stress waves, analyze the key areas and stress wave types that are prone to generating high-energy far-field stress waves in the large mining structure, delineate the high-stress areas in the near-field range of mining and tunneling faces and intake and return airways, and clarify the propagation rock strata structure and lithological characteristics between the far field and the near field. S20: Deploy microseismic sensors around the key areas where far-field stress waves are generated and obtain the original waveform data of the far-field stress waves; deploy an acoustic-electric system in the delineated near-field high-stress area and use acoustic emission and electromagnetic radiation technology to dynamically monitor the working face and roadway in real time, and obtain near-field surrounding rock fracture and stress characteristic parameters. A comprehensive monitoring system integrating far-field and near-field detection, combining microseismic and acoustic-electrical technologies, has been established. S30: Based on the original waveform data of far-field earthquakes, calculate the time-frequency data of daily frequency, energy rate, dominant frequency and amplitude of microseismic events, locate and invert the spatial location and source mechanism of far-field stress wave generation, obtain density cloud map, spatial fractal, tension-shear type, fracture size, orientation and released energy, establish and optimize the spatiotemporal intensity multi-parameter index system of far-field microseismic events, quantitatively evaluate the source characteristics of far-field stress wave generation and the degree of far-field rock strata fracture activity, and identify and predict whether large-magnitude far-field dynamic load stress waves and their energy will be generated in the future. S40: Based on the near-field surrounding rock fracture and stress characteristic parameters, calculate the real-time and cumulative intensity of electromagnetic radiation, and the real-time and cumulative pulse timing characteristic parameters. Obtain acoustic emission ringing count, sound wave intensity, fractal b-value, spatial location, released energy, and mechanism solution parameters. Based on the acoustic and electrical index set and monitoring data, quantitatively reflect the frequency and intensity of coal and rock fracture. Evaluate the near-field surrounding rock static load stress and energy storage status based on changes in acoustic and electrical monitoring data. 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 dynamic and static energy superposition principle of stress wave action, and considering the disaster-causing process of the coupling effect of far-field generation of dynamic stress wave, cross-space propagation and near-field surrounding rock dynamic damage, establish a comprehensive monitoring and early warning criterion for rockburst with far-field and near-field fusion, and combine far-field microseismic and near-field acoustic emission monitoring big data to achieve advanced and accurate early warning of rockburst through the synergy of stress wave action mechanism and monitoring big data.

2. The method for comprehensive monitoring and coordinated early warning of rockbursts in both static and dynamic near-field fields according to claim 1, characterized in that, In step S10, the key areas and types of stress waves that are prone to generating high-energy far-field stress waves within the rockburst mining structure include: fractures in the overlying thick rock strata and activation of faults in geological structural zones. For a fracture in an overlying, thick rock stratum, the expression for the dynamic displacement field distribution characteristics of the stress wave is: In the formula, u I (x,t) represents the dynamic displacement field of stress waves generated by the fracture of the overlying thick rock strata, with superscripts R, Ф, and V representing the P-wave, SH-wave, and SV-wave components of the stress waves, respectively; λ and μ are the Lamé constants of the coal and rock; θ and ρ is the spatial azimuth of the observation point; c is the density; d and c s ξ represents the wave velocity of P-wave and S-wave; ξ represents the coordinates of the fracture point of the coal and rock mass; r represents the distance from the fracture point ξ to the observation point x. This represents the crack propagation rate during the top plate fracture process. For fault activation in geological structural zones, based on quantitative seismology and dynamic fracture mechanics, the spatial characteristics of the stress wave dynamic displacement field of fault slip are obtained as follows: In the formula, u II (x,t) represents the dynamic displacement field of the stress wave generated by fault slip; This represents the fault slip velocity.

3. The method for comprehensive monitoring and coordinated early warning of rockbursts in both static and dynamic near-field fields according to claim 1, characterized in that, In step S30, locating and inverting the spatial location and source mechanism of far-field stress wave generation specifically includes the following steps: The source location is determined using a nonlinear least squares travel time difference source localization method. Travel time data from each sensor is sequentially collected, and the travel time difference Δt between the measured and theoretical travel times of the i-th and j-th sensors is calculated. ij =(t i -t j )-(t i0 -t j0 The sum of the squares of the differences between the theoretical travel time and the actual observed travel time of the ray propagation is taken as the objective function. The calculation result corresponding to the minimum value of the objective function is the source coordinates. 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 intermediate 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 rupture source parameters, the rupture 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.

4. The full-field monitoring and dynamic-static near-far field collaborative early warning method for rock burst according to claim 1, wherein in the step S30, establishing a far-field microseismic spatio-temporal intensity multi-parameter index system includes the following steps: Let the far-field microseismic parameter set P for dynamic prediction of rock burst; Calculate the cumulative difference index and obtain the parameter time series observation sequence Q1, Q2, ..., Q within the sliding time window. n-1 Q n , n>3, where Q n The parameters are given when the timing sequence is n; calculate the difference between adjacent timing parameters: ΔQ j =Q j -Q j-1 Let j = 2, 3, ..., N, and let G j =min{ΔQ j ,0},H j =max{ΔQ j If , 0}, then: 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 of dynamic prediction of rockburst, 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 In the formula, a1, a2, a3, ..., a w Weighting of the evaluation level for the degree of far-field fracturing activity; The activity level of far-field fracture is classified into different levels, and a three-level evaluation mechanism is constructed based on these levels: low activity (F). p ≤θ1, Medium active θ1 <F p ≤θ2, Highly active F p >θ2; Among them, when the far field is in a low-activity rupture state, it is basically in a safe state, and no large-magnitude stress wave will be generated, and the energy released is relatively small; when it is in a medium-activity rupture state, a large-magnitude stress wave may be generated subsequently, and a large amount of energy will be released, so timely protection should be provided; when the rupture is highly active, a large-magnitude far-field dynamic load stress wave will be generated, and a large amount of energy will be released, so safety measures must be taken immediately for treatment.

5. The full-field monitoring and dynamic-static near-far field collaborative early warning method for rock burst according to claim 1, wherein in the step S40, based on the acoustic-electricity index set and the monitoring data, quantitatively reflecting the coal and rock fracture frequency and intensity, and evaluating the static load bearing and energy storage state of the near-field surrounding rock according to the change of the acoustic-electricity monitoring data includes: Quantitatively reflecting the coal and 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, and evaluating the static load bearing and energy storage state of the near-field surrounding rock according to its growth value, growth rate and critical threshold.

6. The full-field monitoring and dynamic-static near-far field collaborative early warning method for rock burst according to claim 5, wherein evaluating the static load bearing 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-electricity index within the statistical period, and move the standard deviation upward based on the average value of the growth rate as b; the average value and standard deviation of the acoustic-electricity 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 discuss 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 large 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 near-far field collaborative early warning method for rock burst according to claim 1, wherein in the step S50, calculating the propagation attenuation coefficient of the stress wave energy in the rock formation structure specifically includes the following steps: The vibration velocity, energy, rupture size, magnitude, and b-value of the same rupture were selected from far-field microseismic and near-field acoustic emission monitoring, and the propagation attenuation coefficient was calculated and the average value was taken. Large-scale far-field stress wave rupture data that can be simultaneously monitored by far-field microseismic and near-field acoustic emission are selected. Based on the rupture energy E1 calculated from far-field microseismic monitoring, the energy E2 of the waveform signal acquired by near-field acoustic emission, the distance r1 from the rupture source to the far-field microseismic sensor, and the distance r2 from the near-field acoustic emission sensor, the propagation attenuation coefficient of stress wave energy in the rock structure is calculated, and its expression is as follows: The energy propagation attenuation coefficients were calculated based on multiple large-scale far-field stress wave rupture data. The average value of the multiple energy propagation coefficients was then taken to obtain β, which is the final energy propagation attenuation coefficient.

8. The method for comprehensive monitoring and coordinated early warning of rockbursts in both static and dynamic near-field fields according to claim 1, characterized in that, In step S50, establishing a comprehensive monitoring and early warning criterion for rockburst with near-field and far-field fusion includes the following steps: S51: Combining the far-field fracturing activity level with the near-field surrounding rock static load and energy storage state, a score is assigned to each, denoted as K. s When the far-field fracturing is at a low, medium, or high level of activity, it is assigned 2, 4, or 6 points respectively; when the near-field surrounding rock fracturing and energy release are at levels I, II, or III, it is assigned 2, 4, or 6 points respectively. S52: Based on the far-field energy E 远 Near-field energy E 近 The total near-field energy E is obtained from the energy attenuation coefficient. 总 Its expression is as follows: It is 总 =E 近 +βE 远 Among them, a dynamic warning score K is assigned when the total near-field energy exceeds the critical energy. d =8 points, otherwise 0 points; S53: Establish a composite early warning index K = K s +K d Its theoretical threshold is [0,20]; a three-level early warning system is set up: when 4≤K<8, it is a Level I warning, a weak rockburst hazard, and the stability of the surrounding rock is checked regularly, normal operations are maintained and abnormal signals are monitored; when 8≤K<12, it is a Level II warning, a moderate rockburst hazard, and the monitoring frequency is increased, non-essential operations are restricted and prevention and control plans are formulated; when K≥12, it is a Level III warning, a strong rockburst hazard, and personnel evacuation is carried out immediately.

9. A comprehensive monitoring and dynamic / static near-field and far-field coordinated early warning system for rockburst, characterized in that, include: The structural analysis and regional delineation unit is configured to analyze the key areas and stress wave types that are prone to generating high-energy far-field stress waves in the large mining area structure, based on the mechanism of far-field generation, cross-space propagation and near-field surrounding rock coupling failure of rockburst dynamic load stress waves. It also delineates high-stress areas within the near-field range of mining and tunneling faces and intake and return airway, and clarifies the propagation strata structure and lithological characteristics between the far-field and near-field. The near-field and far-field data monitoring and acquisition unit is configured to deploy micro-seismic sensors around the key areas where far-field stress waves are generated and obtain the original waveform data of far-field stress waves; and to deploy an acoustic-electric system in the delineated near-field high-stress area to dynamically monitor the working face and roadway in real time using acoustic emission and electromagnetic radiation technology, and obtain near-field surrounding rock fracture and stress characteristic parameters. A comprehensive monitoring system integrating far-field and near-field detection, combining microseismic and acoustic-electrical technologies, has been established. The microseismic data processing and evaluation unit is configured to calculate time-frequency data of daily frequency, energy rate, dominant frequency and amplitude of microseismic events based on far-field raw waveform data, locate and invert the spatial location and source mechanism of far-field stress wave generation, obtain density cloud map, spatial fractal, tension-shear type, fracture size, orientation and released energy, establish and optimize a multi-parameter index system of far-field microseismic spatiotemporal intensity, quantitatively evaluate the source characteristics of far-field stress wave generation and the degree of far-field rock strata fracture activity, and identify and predict whether large-magnitude far-field dynamic load stress waves and their energy will be generated subsequently. The acoustic and electrical data processing and evaluation unit is configured to calculate the real-time and cumulative intensity of electromagnetic radiation and the real-time and cumulative pulse timing characteristic parameters based on the near-field surrounding rock fracture and stress characteristic parameters, obtain acoustic emission ringing count, sound wave intensity, fractal b-value, spatial location, released energy and mechanism solution parameters, quantitatively reflect the frequency and intensity of coal and rock fracture based on the acoustic and electrical index set and monitoring data, and evaluate the near-field surrounding rock static load stress and energy storage state based on changes in acoustic and electrical monitoring data; The comprehensive monitoring and early warning unit is configured to 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 disaster-causing process of the coupling effect of far-field generation, cross-space propagation and near-field surrounding rock dynamic damage of dynamic stress wave, establish a comprehensive monitoring and early warning criterion for rockburst with far-field and near-field fusion. By combining far-field microseismic and near-field acoustic emission monitoring big data, it can achieve advanced and accurate early warning of rockburst by synergistic effect of stress wave action mechanism and monitoring big data.

10. The rockburst global monitoring and dynamic / static near-field and far-field coordinated early warning system according to claim 9, characterized in that, High-energy far-field stress waves are easily generated within the structure of large-scale rockburst mining areas. Key areas and stress wave types include: fractures in overlying thick rock strata and activation of faults in geological structural zones. For a fracture in an overlying, thick rock stratum, the expression for the dynamic displacement field distribution characteristics of the stress wave is: In the formula, u I (x,t) represents the dynamic displacement field of stress waves generated by the fracture of the overlying thick rock strata, with superscripts R, Ф, and V representing the P-wave, SH-wave, and SV-wave components of the stress waves, respectively; λ and μ are the Lamé constants of the coal and rock; θ and ρ is the spatial azimuth of the observation point; c is the density; d and c s ξ represents the wave velocity of P-wave and S-wave; ξ represents the coordinates of the fracture point of the coal and rock mass; r represents the distance from the fracture point ξ to the observation point x. This represents the crack propagation rate during the top plate fracture process. For fault activation in geological structural zones, based on quantitative seismology and dynamic fracture mechanics, the spatial characteristics of the stress wave dynamic displacement field of fault slip are obtained as follows: In the formula, u II (x,t) represents the dynamic displacement field of the stress wave generated by fault slip; This represents the fault slip velocity.

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