A coal mine rock burst multi-parameter advanced prediction method

Abstract

The application discloses a kind of coal mine rock burst multi-parameter advanced prediction methods, it is related to coal mine rock burst disaster prediction technical field, the method includes: step 1: working face roadway space positioning and information input;Step 2: working face static impact influencing factor positioning and input;Step 3: working face two lane static impact influencing factor coupling superposition;Step 4: working face dynamic monitoring data positioning and dangerousness prediction result correction;Step 5: positioning and dangerousness prediction result correction of prevention and control measures.The application uses working face actual monitoring data, prevention and control measures implementation condition etc., to give correction to working face static evaluation result, the factors used are more comprehensive, and the prediction accuracy is higher.The application can be widely applied to the dangerousness prediction of mine threatened by coal mine rock burst disaster, and is widely applicable, strongly targeted, and can meet the needs of each mine site.

Description

Technical Field

[0001] This invention relates to the field of coal mine rockburst disaster prediction and forecasting technology, and in particular to a multi-parameter advanced prediction and forecasting method for coal mine rockburst. Background Technology

[0002] Coal mine rockburst disasters are one of the major factors affecting the safe and healthy development of the mining industry worldwide. Due to their complex mechanisms, diverse types, and the instantaneous nature of these disasters, they are often difficult to prevent and extremely destructive, causing significant loss of life and property in mines. Therefore, advanced prediction and forecasting of mine dynamic disasters is a crucial measure for preventing major rockburst accidents. While significant progress has been made in real-time early warning technology for rockbursts, there is currently no effective method for advanced prediction and forecasting at coal mining faces, further complicating the management of coal mine rockburst disasters.

[0003] Rockburst is one of the common dynamic disasters that occur during the construction of deep underground engineering projects. Currently, various methods have been proposed for monitoring and early warning of rockburst disasters, including drill cuttings monitoring, coal stress monitoring, electromagnetic radiation monitoring, and ground sound and microseismic monitoring.

[0004] 1) Drill cuttings monitoring method: By monitoring the variation of coal seam cuttings discharge and related dynamic effects, the stress state of the coal body can be understood, thereby predicting the risk of rockburst. This is currently the most commonly used monitoring method. However, this method has the disadvantages of operator error and inability to monitor continuously.

[0005] 2) Coal body stress monitoring method: By continuously monitoring the mining-induced stress within the coal body, the rock and coal impact hazard can be predicted and evaluated from the perspective of stress field. This method achieves continuous monitoring of the changes in mining-induced stress in the coal body. However, this method has a relatively small monitoring range, and the monitoring results are better for spontaneous rockbursts than for induced rockbursts.

[0006] 3) Electromagnetic radiation monitoring method: This method monitors the electromagnetic intensity and pulse number emitted during the fracturing process of coal and rock mass to determine the load-bearing capacity and fracturing strength of the coal and rock mass, thereby obtaining the degree of impact hazard. However, this method is affected by various electrical signals underground, and the results obtained are uncertain.

[0007] 4) Ground sound and microseismic monitoring method: This method monitors the vibration signals released during the fracturing of coal and rock masses, allowing analysis of different fracturing stages, understanding of the overall damage and energy release of the coal and rock masses, and enabling the prediction and early warning of impacts. However, this method only monitors the vibration signals generated by fracturing, and its monitoring effect is better for induced rockbursts than for spontaneous rockbursts.

[0008] In summary, given the complexity of the factors influencing rockbursts in coal mines, many monitoring systems have been installed in mines. However, there are prominent problems such as relatively independent early warning methods among the various monitoring systems and low efficiency of joint early warning, especially in the identification of multi-parameter dynamic hazard zones.

[0009] Regarding multi-parameter monitoring and early warning methods, Chinese invention patent CN105257339B discloses a comprehensive multi-parameter monitoring and early warning method for tunneling faces. This method primarily achieves joint early warning of multi-parameter data for tunneling faces by specifying the layout of multi-parameter monitoring points for rockburst in different regions. However, this method is limited to the tunneling face and only obtains the multi-parameter early warning index for static zoning of the tunneling face; it does not analyze the location and degree of danger of hazardous areas. Chinese invention patent CN110043317B discloses a method for identifying and warning of local hazardous areas in mines using multi-parameter data. This method identifies local hazardous areas of mine dynamic disasters through spatial positioning of monitoring points and assesses the degree of local danger based on multi-parameter monitoring results within a local area. However, this method only identifies the disaster risk of local hazardous areas and does not analyze or warn of the overall danger level of the monitored area, thus failing to fully meet the needs of on-site monitoring.

[0010] Currently, the industry's prevention and control of advanced working faces is mainly based on the results of static hazard assessments. These assessments are usually completed in advance before the working face is mined, ignoring the monitoring data and the implementation of pressure relief measures during actual mining. These problems result in the prediction results of advanced working faces not having strong guiding significance. Summary of the Invention

[0011] In view of this, the technical problem to be solved by the present invention is to provide a multi-parameter advanced prediction and forecasting method for coal mine rockburst with high advanced prediction accuracy and wide applicability.

[0012] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0013] A multi-parameter advanced prediction and forecasting method for coal mine rockburst includes:

[0014] Step 1: Spatial positioning and information entry of working face roadways

[0015] The upper and lower tunnels on the working face are spatially located and divided into analysis units, and the tunnel attributes and coordinate information of key nodes in the tunnels are entered.

[0016] Step 2: Locating and recording the static impact influencing factors at the working face

[0017] Enter the relevant geological and mining conditions influencing factors on the rockburst manifestation at the working face. The entered information includes the name of the influencing factor, the stress concentration coefficient related to the degree of influence, and the influence range of the two roadways relative to the working face. Determine whether the analysis unit is under the influence of the factor.

[0018] Step 3: Coupling and superposition of static impact influencing factors in the two roadways of the working face

[0019] The static factors entered in step 2 are superimposed to obtain the stress superposition of each analysis unit in the advanced working face roadway under the static influence factors, and the preliminary prediction and forecast results of the impact hazard of each analysis unit under the static influence factors of the two roadways of the working face are calculated.

[0020] Step 4: Location of dynamic monitoring data at the working face and correction of hazard prediction results

[0021] Using the analysis unit as a unit, the dynamic monitoring results are located and coupled to obtain the impact hazard prediction and forecast results of each analysis unit under the influence of "dynamic and static superposition";

[0022] Step 5: Determining the location of prevention and control measures and revising the results of risk prediction.

[0023] Based on whether the analysis unit has completed the depressurization construction work, the prediction results are corrected to obtain the final prediction results of the advanced working face, which serve as the basis for guiding the on-site rockburst prevention and control work.

[0024] Furthermore, in step 1, each preset distance from the opening of the working face to the stop line of the roadway is taken as an analysis unit.

[0025] Furthermore, in step 2, geological influencing factors include faults, folds, and phase transition zones; mining condition influencing factors include coal pillars, initial pressure, and the squareness of the working face.

[0026] Furthermore, in step 3, the stress superposition method includes: when there are no influencing factors, it is vertical stress; when there is one influencing factor, the superimposed stress = vertical stress + concentrated stress increment; when there are multiple influencing factors, Where n is the number of influencing factors.

[0027] Furthermore, in step 3, the preliminary prediction and forecast results of the impact hazard for each analysis unit include:

[0028] No rockburst risk: superimposed stress < 1.5 times the uniaxial compressive strength of the coal seam; weak rockburst risk: 1.5 times the uniaxial compressive strength of the coal seam ≤ superimposed stress < 1.8 times the uniaxial compressive strength of the coal seam; moderate rockburst risk: 1.8 times the uniaxial compressive strength of the coal seam ≤ superimposed stress < 2.0 times the uniaxial compressive strength of the coal seam; strong rockburst risk: 2.0 times the uniaxial compressive strength of the coal seam ≤ superimposed stress.

[0029] Furthermore, in step 4, the monitoring data is divided into two categories: local monitoring and regional monitoring. Local monitoring includes coal seam stress monitoring and drill cuttings inspection, while regional monitoring includes microseismic monitoring.

[0030] The local monitoring overlay method first determines the analysis unit to which the measuring point belongs, divides the measuring point into analysis units, calculates the average value of the measuring point monitoring within a preset time for each measuring point, and if the warning threshold is reached, the analysis unit to which the measuring point belongs is judged to be in a warning state.

[0031] The overlay method for regional monitoring first divides microseismic events into each analysis unit according to the distribution of the working face. Then, it performs statistical analysis on the microseismic energy within the analysis unit and evaluates whether the analysis unit is in a microseismic early warning state by using spatial domain weighted microseismic energy.

[0032] Furthermore, the calculation method for spatial domain weighted microseismic energy is as follows: taking the center of the roadway as the origin, a spherical spatial region with radius d is divided, and microseismic events within the spatial region are screened. The energy of the screened microseismic events is calculated using the inverse distance weighted interpolation algorithm to calculate the spatial domain weighted microseismic energy at the origin; the spatial domain weighted microseismic energy of each roadway segment is calculated using the width of the analysis unit as the step size.

[0033] Furthermore, step 4 includes:

[0034] The dynamic monitoring results are coupled, and the coupling evaluation method is as follows:

[0035] Microseismic monitoring and early warning Stress monitoring and early warning Drill cuttings monitoring and early warning Coupling results √ √ × weak impact √ √ × Medium impact √ √ √ strong impact √ × √ Medium impact × √ × weak impact × √ √ Medium impact × × √ weak impact × × × none

[0036] Furthermore, step 4 includes:

[0037] The static prediction results of the two roadways obtained in step 3 are corrected to obtain the impact hazard prediction and forecast results of each analysis unit under the influence of "dynamic and static superposition". The correction method is shown in the table below:

[0038]

[0039] Furthermore, step 5 includes:

[0040] If a certain analysis unit implements corresponding pressure relief measures, the risk level will be reduced by one level in the corresponding prediction and forecasting coupling results; if no prevention and control measures are implemented, the original prediction and forecasting results will be maintained, and the final prediction and forecasting results of the advanced working face will be obtained as the basis for guiding the on-site rockburst prevention and control work.

[0041] The present invention has the following beneficial effects:

[0042] The advantage of the multi-parameter advanced prediction and forecasting method for coal mine rockbursts of this invention lies in the fact that current industry practices for preventing and controlling rockbursts in advance mainly rely on static hazard assessment results. These assessments are typically completed before mining begins, neglecting monitoring data and the implementation of pressure relief measures during actual mining. These issues result in predictions for rockbursts lacking strong guiding significance. This invention, however, utilizes actual monitoring data and the implementation status of prevention and control measures to correct the static assessment results of the working face, employing more comprehensive factors and achieving higher prediction accuracy. Furthermore, the prediction results of this method can be dynamically adjusted as the coal mining face advances to cope with constantly changing and complex conditions. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart illustrating the principle of the multi-parameter advanced prediction and forecasting method for coal mine rockburst of the present invention.

[0045] Figure 2 This is a schematic diagram of the spatial positioning of the working face roadway in this invention;

[0046] Figure 3 This is a schematic diagram illustrating the location of factors influencing static impact on the working face in this invention.

[0047] Figure 4 This is a schematic diagram illustrating the coupled superposition of static impact influencing factors on the working face in this invention;

[0048] Figure 5 This is a schematic diagram of the spatial domain weighted microseismic energy calculation method in this invention;

[0049] Figure 6 This is a schematic diagram of the inverse distance weighted interpolation algorithm in this invention;

[0050] Figure 7This is a schematic diagram illustrating the correction of the working face hazard prediction results in this invention. Detailed Implementation

[0051] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0052] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0053] This invention presents a multi-parameter method for predicting and forecasting rockbursts in coal mines. It proposes a method to predict the rockburst hazard level and extent within the advanced mining area by employing multi-parameter analysis of the advanced influence zone of the coal mine longwall face. The parameters involved in the analysis include monitoring data, geological conditions, mining conditions, and pressure relief engineering.

[0054] Current methods for preventing and controlling rockbursts in the early stages of mining faces often rely solely on static assessments of rockburst hazard, neglecting dynamic monitoring results. This leads to a poor correlation between predicted hazards and actual manifestation, resulting in control measures failing to meet actual on-site needs. This invention addresses these shortcomings in early prediction by employing a method that combines dynamic rockburst monitoring with static environmental factors such as geological and mining conditions. This approach offers higher accuracy and wider applicability in early prediction.

[0055] This invention provides a multi-parameter advanced prediction and forecasting method for coal mine rockburst, such as... Figure 1-7 As shown, it includes:

[0056] Step 1: Spatial positioning and information entry of working face roadways

[0057] The working face and the upper and lower tunnels are spatially located and divided into analysis units, and the tunnel attributes and the coordinate information of key nodes in the tunnels are entered.

[0058] In this step, key nodes of the two lanes of the working face can be selected, and the upper and lower lanes of the working face can be spatially located. The positioning coordinates can be in the geodetic coordinate system.

[0059] Enter the roadway attributes (upper roadway, lower roadway) and the coordinate information (x, y, z) of key nodes within the roadway, which will serve as the basis for determining the relative positions of various rockburst influencing factors in step 2 and various dynamic monitoring data in step 3 in the two roadways of the working face.

[0060] For example, Figure 2 The information can be entered in the following table:

[0061]

[0062] like Figure 2 As shown, for a straight roadway, the roadway centerline coordinates of point A (the opening point) and point B (the stop line) can be selected to locate the roadway (e.g., ...). Figure 2 (The upper lane in the middle); for non-straight lanes, multiple key points can be selected for lane positioning (such as the upper lane); Figure 2 The four points C, D, E, and F in the map are located. The resulting tunnel coordinates will serve as the basis for the superposition of multiple factors in subsequent steps.

[0063] The two roadways of the working face are divided into analysis units. Specifically, each preset distance from the opening of the working face to the stop line can be considered as an analysis unit. For example... Figure 2 The upper and middle roadways are divided into 5-meter analysis units, from the working face opening to the stop line; for example... Figure 2 If the length of the roadway on the working face is 500m, then the roadway from the opening to the stop line is divided into 100 analysis units (if the last analysis unit is less than 5m, it can be regarded as a single cell).

[0064] Step 2: Locating and recording the static impact influencing factors at the working face

[0065] Enter the relevant geological and mining conditions influencing factors on the rockburst manifestation at the working face. The entered information includes the name of the influencing factor, the stress concentration coefficient related to the degree of influence, and the influence range of the two roadways relative to the working face. Determine whether the analysis unit is under the influence of the factor.

[0066] This step, building upon step 1, involves inputting relevant geological and mining condition influencing factors related to the rockburst manifestation at the working face. Geological influencing factors may include faults, folds, and facies change zones; mining condition influencing factors may include coal pillars, initial pressure, and the squareness of the working face, as shown in the table below. The input information may include the name of the influencing factor, the stress concentration factor related to its degree of influence, etc., and the input influencing factors can be expanded.

[0067]

[0068]

[0069] Furthermore, the influence range of each influencing factor relative to the two roadways of the working face is entered to determine whether the analysis unit is under the influence of that factor.

[0070] For example: The working face roadway is affected by the F1 fault structure. The coordinates of the starting point of the F1 fault are: starting point (488, 300, -100), ending point (495, 300, -100). According to the roadway information entered in step 1, its relative position to the working face roadway is determined to be within 5m-12m from the opening of the working face roadway. Based on experience, the stress concentration factor is set to 1.5. Then the information entered for this influencing factor is: Influencing factor name "F1 fault structure"; Influence range "within 5m-12m from the opening of the working face roadway"; Stress concentration factor "1.5". Figure 3 As shown, both the second and third analysis units are affected by the F1 fault structure.

[0071] Step 3: Coupling and superposition of static impact influencing factors in the two roadways of the working face

[0072] All static factors entered in step 2 are superimposed to obtain the stress superposition of each analysis unit in the advanced working face roadway under static influence factors, and the preliminary prediction and forecast results of the impact hazard of each analysis unit under static influence factors in the two roadways of the working face are calculated.

[0073] In this step, the upper and lower roadways within a certain range of the working face (based on the impact manifestation law, the default range is two roadways within 300m of the working face; the range can be adjusted according to specific application conditions) can be used as static factor superposition objects. All static factors entered in step 2 are superimposed to obtain the stress superposition situation of each analysis unit in the working face roadway under static influence factors. Stress superposition methods can include: when there are no influencing factors, it is vertical stress; when there is one influencing factor, superimposed stress = vertical stress + concentrated stress increment; when there are multiple influencing factors... Where n is the number of influencing factors. The superimposed stress is calculated for each analysis unit / cell.

[0074] For example, if the burial depth of the upper tunnel on a certain working face is 1000m and the tunnel length is 500m, then the estimated vertical stress is σ=λh=0.025*1000=25MPa. If the upper tunnel is affected by the F1 fault structure within 5m-12m from the working face opening, assuming the stress concentration factor is 1.5, then the superimposed stress of the second and third analysis units is: Superimposed stress = Vertical stress + Concentrated stress increment = 25 + 0.5*25 = 37.5MPa. The stress superposition results of each analysis unit of the upper tunnel are shown in the table below.

[0075]

[0076] Based on the superposition results, the preliminary prediction and forecast results of the impact hazard for each analysis unit under the static influence factors of the two roadways are calculated. Specifically, a multi-factor coupled evaluation method can be used. Here, the preferred preliminary prediction and forecast results of the impact hazard for each analysis unit include:

[0077] No rockburst risk: superimposed stress < 1.5 times the uniaxial compressive strength of the coal seam; weak rockburst risk: 1.5 times the uniaxial compressive strength of the coal seam ≤ superimposed stress < 1.8 times the uniaxial compressive strength of the coal seam; moderate rockburst risk: 1.8 times the uniaxial compressive strength of the coal seam ≤ superimposed stress < 2.0 times the uniaxial compressive strength of the coal seam; strong rockburst risk: 2.0 times the uniaxial compressive strength of the coal seam ≤ superimposed stress.

[0078] For example, if the single-week compressive strength of a coal seam in a certain working face is 16 MPa, the preliminary prediction results under the influence of static factors in the case are shown in the table below. Figure 4 .

[0079]

[0080] Step 4: Location of dynamic monitoring data at the working face and correction of hazard prediction results

[0081] Using the analysis unit as a unit, the dynamic monitoring results are located and coupled to obtain the impact hazard prediction and forecast results of each analysis unit under the influence of "dynamic and static superposition".

[0082] This step, based on the preliminary classification of impact hazards under static influencing factors in step 3, couples and corrects the dynamic monitoring results of the working face. That is, taking the analysis unit as the unit, the dynamic monitoring results, including stress, micro-vibration, and drill cuttings inspection, are located and coupled to obtain the impact hazard prediction and forecast results of each analysis unit under the influence of "dynamic and static superposition".

[0083] In this step, preferably, the monitoring data is divided into two categories: local monitoring and regional monitoring. Local monitoring includes monitoring data with fixed measuring point locations, such as coal seam stress monitoring and drill cuttings inspection, while regional monitoring includes microseismic monitoring, etc.

[0084] The local monitoring overlay method first determines the analysis unit to which the measuring point belongs, divides the measuring point into analysis units, calculates the average value of the measuring point monitoring within a preset time for each measuring point, and if the warning threshold is reached, the analysis unit to which the measuring point belongs is judged to be in a warning state.

[0085] Specifically, the local monitoring overlay method, taking coal seam stress monitoring as an example, firstly determines the analysis unit (grid) to which the stress measuring point belongs, divides the stress measuring point into analysis units, calculates the average value of stress measuring point monitoring on the same day (time adjustable) for each stress measuring point, and if the stress warning threshold is reached, then the analysis unit to which the stress measuring point belongs is judged to be in a stress warning state.

[0086] For example: A coal seam stress monitoring system is installed in the upper roadway of a working face, with a stress warning value of 10 MPa. Three stress gauges are installed in the upper roadway, numbered Y1, Y2, and Y3, with coordinates: Y1 (488, 293, -100); Y2 (487, 286, -100); Y3 (484, 293, -100). Based on the roadway information entered in step 1, the relative positions of the three stress gauges to the upper roadway on the working face are determined to be 12m, 13m, and 16m from the opening of the working face. Therefore, the analysis units to which the three stress gauges belong are the third, third, and fourth analysis units, respectively. Assuming the average stress values ​​for the day are 6.5 MPa, 11.5 MPa, and 12.0 MPa, the third and fourth analysis units in the upper roadway will both trigger stress monitoring warnings, as shown in the table below.

[0087]

[0088] The judgment method for drill cuttings monitoring is the same as that for coal seam stress monitoring. The analysis unit to which the drill cuttings monitoring belongs is judged to be in an early warning state by whether the amount of drill cuttings exceeds the limit.

[0089] For example: On a certain working face, three drill cuttings monitoring holes were drilled on the upper roadway that day. The drill cuttings warning index is that the maximum amount of drill cuttings per meter should not exceed 2.0 kg. The drilling locations are 12m, 18m, and 21m from the working face opening in the upper roadway. Then, the analysis units to which the three monitoring holes belong are the third, fourth, and fifth analysis units, respectively. Assuming that the maximum amount of drill cuttings per meter is 1.8 kg, 2.5 kg, and 5.1 kg, respectively, the drill cuttings monitoring warnings will be triggered in the fourth and fifth analysis units of the upper roadway, as shown in the table below.

[0090]

[0091] The overlay method for regional monitoring, taking microseismic monitoring as an example, firstly divides the distribution of microseismic events according to the working face direction into each analysis unit, statistically analyzes the microseismic energy within the analysis unit, and evaluates whether the analysis unit is in a microseismic early warning state by spatial domain weighted microseismic energy.

[0092] The spatial domain weighted microseismic energy is defined as the energy attenuation of microseismic vibration waves as they propagate in a medium, using an inverse distance weighted interpolation algorithm to calculate the impact of microseismic event energy on a point in a roadway within a certain spatial area.

[0093] The calculation method for spatial domain weighted microseismic energy can be as follows: Figure 5As shown, a spherical spatial region with radius d (e.g., tentatively set to 150m, adjustable) is divided with the center of the roadway as the origin. Microseismic events within the spatial region are then selected. The energy of the selected microseismic events is calculated using an inverse distance weighted interpolation algorithm to calculate the spatial domain weighted microseismic energy at the origin. The spatial domain weighted microseismic energy of each roadway segment is calculated using the width of the analysis unit (5m in the example above) as the step size. (Real-time data shows that the calculation distance can be the roadway 300m in front of the working face; historical data queries show that the calculation distance can be the roadway from the start time to the end time).

[0094] Among them, the inverse distance weighted interpolation algorithm is a widely used spatial interpolation algorithm, where the influence of sampling points on the interpolation result decreases as the distance increases. For example... Figure 6 As shown, let the point set be E = {E1(x1, y1, z1), E2(x2, y2, z2), E3(x3, y3, z3), ..., E...} n (x n y n , z n The microseismic energies of each point set are e1, e2, e3, ..., e n The spatial domain weighted microseismic energy of the target point is:

[0095]

[0096] In the formula: e0 is the spatial domain weighted microseismic energy at the calculation origin; e i For the i-th discrete point E i Micro-vibration energy; w i For the i-th discrete point E i The calculated weights; d i Let be the spatial distance between the i-th discrete point and the origin. If the spatial distance of a discrete point is 0 ≤ d... i If <1, then the weight of this point is taken as 1; β is a constant, taken as 1.62, and the average energy attenuation index of the four media obtained in "Microseismic Experimental Study on the Propagation Law of Impact Vibration Waves in Rock and Soil Media" is 1.62.

[0097] Evaluation method: A pre-warning threshold can be set to determine the spatial domain weighted microseismic energy of each roadway segment. The threshold needs to be determined based on the analysis of on-site monitoring data. The initial threshold can be set as follows: no-hazard threshold: 0 ≤ e0 < 5000 J; hazard threshold: 5000 J ≤ e0.

[0098] For example, based on the above budget analysis, it was found that the fourth and fifth analysis units are at risk of impact, while other locations are not at risk of impact, as shown in the table below.

[0099]

[0100] The dynamic monitoring results can be coupled together, and the coupling evaluation method can be as follows:

[0101] Microseismic monitoring and early warning Stress monitoring and early warning Drill cuttings monitoring and early warning Coupling results √ √ × weak impact √ √ × Medium impact √ √ √ strong impact √ × √ Medium impact × √ × weak impact × √ √ Medium impact × × √ weak impact × × × none

[0102] For example, based on the coal seam stress, microseismic activity, and drill cuttings monitoring results of the above cases, the dynamic coupling prediction results of each analysis unit on the working face are evaluated as shown in the table below.

[0103] Analysis Unit Microseismic prediction results Stress prediction results Drill cuttings prediction results Dynamic coupling results I, II × × × No impact danger three √ × Minor impact hazard Four √ √ √ High impact danger five × √ √ Moderate impact risk 6 to 100 × × × No impact danger

[0104] Then, based on the dynamic monitoring and coupled evaluation results of each analysis unit above, the static prediction results of the two lanes obtained in step 3 are corrected to obtain the impact hazard prediction and forecast results under the influence of "dynamic and static superposition". The correction method can be shown in the table below.

[0105]

[0106] For example: Based on the dynamic coupling prediction results of each analysis unit in the upper roadway in the above case, the static prediction results of the two roadways obtained in step 3 are corrected, and the impact hazard prediction results of each analysis unit under the influence of "dynamic and static superposition" are shown in the following table:

[0107]

[0108] Step 5: Determining the location of prevention and control measures and revising the results of risk prediction.

[0109] Based on whether the pressure relief construction work has been completed within the analysis unit (grid), the prediction results are corrected to obtain the final prediction results of the advanced working face, which serve as the basis for guiding the on-site rockburst prevention and control work.

[0110] This step, based on the impact hazard prediction and forecast results under the influence of "dynamic and static superposition" in step 4, locates the active construction and prevention measures such as pressure relief within the analysis unit. According to whether the pressure relief construction work has been completed within the analysis unit, the prediction and forecast results are corrected to obtain the final prediction and forecast results of the advanced working face, which serves as the basis for guiding the on-site rockburst prevention and control work.

[0111] This step can input the construction records of prevention and control measures, divide them into each analysis unit according to the location of the working face, and correct the impact hazard prediction results obtained in step 4 under the "dynamic and static superposition" influence based on whether prevention and control measures such as large-diameter coal seam depressurization and roof blasting have been adopted in the analysis unit.

[0112] Specifically, this step may include: if a certain analysis unit has implemented corresponding pressure relief measures, such as large-diameter coal seam pressure relief and roof blasting prevention measures, the risk level will be reduced by one level in the corresponding prediction and forecasting coupling results; if no prevention and control measures have been implemented, the original prediction and forecasting results will be maintained, and the final prediction and forecasting results of the advanced working face will be obtained as the basis for guiding the on-site rockburst prevention and control work.

[0113] For example: assuming that large-diameter coal seam depressurization construction was carried out in accordance with the rockburst prevention requirements from the opening 5-20m from the working face in the upper roadway, then the hazard level of the corresponding second to fourth analysis units would be reduced by one level. The corrected results are shown in the table below. Figure 7 .

[0114]

[0115] In summary, the advantages of the multi-parameter advanced prediction and forecasting method for coal mine rockbursts of this invention are as follows: Currently, the industry's approach to preventing and controlling rockbursts in advance mainly relies on static hazard assessment results. These assessments are typically completed before mining begins, neglecting monitoring data during actual mining operations and the implementation of pressure relief measures. These issues result in predictions for advance mining faces lacking strong guiding significance. This invention, however, utilizes actual monitoring data and the implementation of prevention and control measures to correct the static assessment results of the working face, employing more comprehensive factors and achieving higher prediction accuracy. Furthermore, the prediction results of this method can be dynamically adjusted as the coal mining face advances to cope with constantly changing and complex conditions.

[0116] This invention is widely applicable to the prediction and forecasting of mine hazards threatened by coal mine rock bursts. It has a wide range of applications, strong pertinence, and can meet the on-site needs of various mines.

[0117] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A multi-parameter advanced prediction and forecasting method for coal mine rockburst, characterized in that, include: Step 1: Spatial positioning and information entry of working face roadways The upper and lower tunnels on the working face are spatially located and divided into analysis units, and the tunnel attributes and key node coordinate information within the tunnels are entered. Step 2: Locating and recording the static impact influencing factors at the working face Enter the relevant geological and mining conditions influencing factors on the rockburst manifestation at the working face. The entered information includes the name of the influencing factor, the stress concentration coefficient related to the degree of influence, and the influence range of the two roadways relative to the working face. Determine whether the analysis unit is under the influence of the factor. Step 3: Coupling and superposition of static impact influencing factors in the two roadways of the working face The static factors entered in step 2 are superimposed to obtain the stress superposition of each analysis unit in the advanced working face roadway under the static influence factors, and the preliminary prediction and forecast results of the impact hazard of each analysis unit under the static influence factors of the two roadways of the working face are calculated. Step 4: Location of dynamic monitoring data at the working face and correction of hazard prediction results Using the analysis unit as a unit, the dynamic monitoring results are located and coupled to obtain the impact hazard prediction and forecast results of each analysis unit under the influence of "dynamic and static superposition"; Step 5: Determining the location of prevention and control measures and revising the results of risk prediction. Based on whether the analysis unit has completed the depressurization construction work, the prediction results are corrected to obtain the final prediction results of the advanced working face, which serve as the basis for guiding the on-site rockburst prevention and control work. In step 4, the monitoring data is divided into two categories: local monitoring and regional monitoring. Local monitoring includes coal seam stress monitoring and drill cuttings inspection, while regional monitoring includes microseismic monitoring. The local monitoring overlay method first determines the analysis unit to which the measuring point belongs, divides the measuring point into analysis units, calculates the average value of the measuring point monitoring within a preset time for each measuring point, and if the warning threshold is reached, the analysis unit to which the measuring point belongs is judged to be in a warning state. The overlay method for regional monitoring first divides microseismic events into each analysis unit according to the distribution of the working face. Then, it performs statistical analysis on the microseismic energy within the analysis unit and evaluates whether the analysis unit is in a microseismic early warning state by using spatial domain weighted microseismic energy. The calculation method for spatial domain weighted microseismic energy is as follows: taking the center of the roadway as the origin, a spherical spatial region with radius d is divided, and microseismic events within the spatial region are screened. The energy of the screened microseismic events is calculated using the inverse distance weighted interpolation algorithm to calculate the spatial domain weighted microseismic energy at the origin. The spatial domain weighted microseismic energy of each roadway segment is calculated using the width of the analysis unit as the step size.

2. The method according to claim 1, characterized in that, In step 1, each preset distance from the opening of the working face to the stop line of the roadway is taken as an analysis unit.

3. The method according to claim 1, characterized in that, In step 2, geological influencing factors include faults, folds, and phase transition zones; mining condition influencing factors include coal pillars, initial pressure, and the squareness of the working face.

4. The method according to claim 1, characterized in that, In step 3, the stress superposition method includes: when there are no influencing factors, it is vertical stress; when there is one influencing factor, the superimposed stress = vertical stress + concentrated stress increment; when there are multiple influencing factors... , where n is the number of influencing factors.

5. The method according to claim 1, characterized in that, In step 3, the preliminary prediction results of the impact hazard for each analysis unit include: No rockburst risk: superimposed stress < 1.5 times the uniaxial compressive strength of the coal seam; weak rockburst risk: 1.5 times the uniaxial compressive strength of the coal seam ≤ superimposed stress < 1.8 times the uniaxial compressive strength of the coal seam; moderate rockburst risk: 1.8 times the uniaxial compressive strength of the coal seam ≤ superimposed stress < 2.0 times the uniaxial compressive strength of the coal seam; strong rockburst risk: 2.0 times the uniaxial compressive strength of the coal seam ≤ superimposed stress.

6. The method according to claim 1, characterized in that, Step 4 includes: The dynamic monitoring results are coupled, and the coupling evaluation method is as follows: 。 7. The method according to claim 1, characterized in that, Step 4 includes: The static prediction results of the two roadways obtained in step 3 are corrected to obtain the impact hazard prediction and forecast results of each analysis unit under the influence of "dynamic and static superposition". The correction method is shown in the table below: 。 8. The method according to claim 1, characterized in that, Step 5 includes: If a certain analysis unit implements corresponding pressure relief measures, the risk level will be reduced by one level in the corresponding prediction and forecasting coupling results; if no prevention and control measures are implemented, the original prediction and forecasting results will be maintained, and the final prediction and forecasting results of the advanced working face will be obtained as the basis for guiding the on-site rockburst prevention and control work.

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