Tunnel rockburst early warning method based on micro-seismic activity space aggregation degree

Abstract

The invention relates to the technical field of rockburst early warning, and provides a tunnel rockburst early warning method based on micro-seismic activity space aggregation degree, which comprises the following steps: 1) arranging micro-seismic monitoring sensors, and building a micro-seismic monitoring system; 2) counting spatial distribution characteristics between a position where rockburst occurs and a tunnel face in a tunnel excavation process, and delimiting a calculation area of a micro-seismic activity spatial aggregation degree; 3) exporting the occurrence position, the occurrence time and the released energy of the micro-seismic monitoring signal in the calculation area to calculate the time-space gathering degree of the micro-seismic activity; 4) counting a micro-seismic activity space-time gathering degree evolution law of different levels of rockburst, establishing a mapping relation between the excavation tunnel rockburst occurrence risk level and the micro-seismic activity space-time gathering degree, and constructing a rockburst risk early warning model; and 5) calculating the gathering degree value of the microseismic activity in the microseismic activity space-time gathering degree calculation area in real time, and calculating and determining the potential rockburst occurrence probability and grade in the early warning area. According to the invention, tunnel rockburst early warning can be well carried out.

Description

Technical Field

[0001] The invention relates to the technical field of rock burst warning, and in particular to a tunnel rock burst warning method based on the spatial aggregation degree of microseismic activities. Background Art

[0002] Rockburst is a common geological disaster during the excavation of deep hard rock tunnels under high ground stress conditions, which often causes heavy casualties and property losses. The prevention, early warning and control of rockburst are global problems. Microseismic monitoring is a mature and effective means of rockburst monitoring. It is the basis for early warning and control of rockburst by summarizing and analyzing the characteristics of microseismic activity of rock mass during the process of rockburst incubation.

[0003] Many studies have mentioned that within a certain area, the more microseismic events occur, the greater the degree of clustering, indicating that the stress state of the area is much higher than the stress level of the original rock, and the higher the risk of rock burst. However, the degree of microseismic clustering is mostly determined by observation and subjective determination. There is no quantitative way to measure the degree of spatial clustering of microseismic activities, let alone the corresponding relationship analysis between the degree of clustering and whether a rock burst occurs, or between rock bursts of different grades. Summary of the invention

[0004] The present invention provides a tunnel rockburst early warning method based on the spatial aggregation degree of microseismic activity, which solves the problem that the existing method cannot objectively quantify the aggregation degree of microseismic activity and establish the relationship between the aggregation degree of microseismic activity and the rockburst risk level.

[0005] According to the present invention, a tunnel rockburst early warning method based on the spatial aggregation degree of microseismic activity comprises the following steps:

[0006] (1) Deploy microseismic monitoring sensors and build a microseismic monitoring system based on the structural dimensions and excavation characteristics of the tunnel. As the excavation of the tunnel face progresses, the microseismic monitoring system is moved when it exceeds the effective monitoring range.

[0007] (2) Count the spatial distribution characteristics between the locations where rockbursts have occurred during tunnel excavation and the tunnel face, and define the calculation area for the spatial concentration of microseismic activity;

[0008] (3) Derive the occurrence location, occurrence time and released energy of the microseismic monitoring signal in the calculation area to calculate the spatiotemporal concentration of microseismic activity;

[0009] (4) Statistically analyze the evolution law of the spatiotemporal clustering of microseismic activities of different levels of rockburst, establish a mapping relationship between the risk level of rockburst in tunnel excavation and the spatiotemporal clustering of microseismic activities, and construct a rockburst risk warning model;

[0010] (5) Following the excavation process of the tunnel face, the temporal and spatial concentration of microseismic activity in the calculation area is calculated in real time, and the probability and level of potential rockburst in the warning area are determined by combining the early warning model.

[0011] Preferably, in step (2), the calculation area of ​​the spatial concentration degree of microseismic activity comprehensively considers the spatial location of previous rock bursts and the scope of the excavation damage area, and determines the potential high-incidence area of ​​immediate rock bursts with the greatest danger during tunnel excavation as the concentration calculation area.

[0012] Preferably, in step (2), the spatial distribution characteristics between the location where the rock burst has occurred and the tunnel face refer to specific locations in a spatial coordinate system with the center of the bottom of the tunnel excavation face as the origin.

[0013] Preferably, in step (3), the deriving of the occurrence position, occurrence time and release energy of the microseismic monitoring signal refers to multiple source parameters of the microseismic monitoring signal, which are comprehensively determined by considering the difficulty of parameter acquisition, the significance of convergence calculation and the degree of direct impact on rock burst;

[0014] The occurrence location is the spatial distribution of microseismic events, which is confirmed by calculating the average spatial point distance between each event point; the occurrence time is the time consumed to generate the same number of microseismic events and microseismic energy release in the calculation area. The shorter the gestation time of the microseismic event, the more drastic the adjustment of the surrounding rock stress state, that is, the higher the rock burst risk; the released energy is the embodiment of the rock mass fracture strength.

[0015] Preferably, in step (3), the microseismic activity spatiotemporal clustering degree is calculated by determining the weight distribution of each calculation index through the analytic hierarchy process AHP, and determining the calculation formula of the microseismic activity spatial clustering degree JLD based on the weight combination:

[0016]

[0017] Where W1~W3 and X1~X3 are the weight coefficients and monitoring values ​​corresponding to the gestation time of microseismic events, the cumulative release energy of microseismic events, and the average spatial distribution point distance of microseismic events in the calculation area, respectively.

[0018] Preferably, in step (4), the risk levels of rock burst during tunnel excavation include no rock burst, slight rock burst, moderate rock burst and severe rock burst.

[0019] Preferably, in step (4), statistically analyzing the spatiotemporal clustering evolution law of microseismic activities of rockbursts of different levels means: selecting typical rockburst cases of different levels in the target tunnel or similar projects for quantitative calculation of clustering, and the rockburst selection follows the criteria of immediate rockburst, no control measures, no complex damage, no overlapping of multiple rockbursts, and no rockburst occurring during the risk reduction process.

[0020] Preferably, in step (4), a mapping relationship between the rockburst risk level and the spatiotemporal concentration of microseismic activity is established. Constructing a rockburst risk warning model means: taking the spatial concentration characteristic value of microseismic activity as the center, using a probability distribution function to express the quantitative relationship between microseismic information and rockburst, specifically as follows:

[0021] Note: H ,P M ,P S and P N are the occurrence probabilities of strong rockburst H, moderate rockburst M, slight rockburst S and no rockburst N, respectively. The calculated value of the spatial concentration of microseismic activity is s. The specific functional relationship is:

[0022] ① When there is no rock burst risk in the early warning,

[0023]

[0024] ② When the risk of a minor rock burst is warned,

[0025]

[0026] ③When the warning indicates a medium rockburst risk,

[0027]

[0028] ④ When the risk of strong rock burst is warned,

[0029]

[0030] Preferably, in step (5), determining the probability and level of potential rock burst in the warning area by combining the early warning model means: in the subsequent excavation process, the spatial aggregation degree of microseismic activity in the warning area is calculated in real time, the calculated characteristic values ​​are substituted into the early warning model for calculation, and the potential rock burst risk level is finally determined based on the results.

[0031] The present invention first determines the calculation area of ​​the spatial aggregation degree of microseismic activity, and determines the calculation method of the spatial aggregation degree of microseismic activity, and then performs a real-time warning of rock burst risk during excavation by establishing a rock burst risk warning model through statistics of the spatiotemporal aggregation degree of microseismic activity of rock bursts of different levels. The rock burst warning method based on the spatial aggregation degree of microseismic activity provided by the present invention can scientifically quantify the spatial distribution state of microseismic activity, and determine the corresponding relationship between the spatial aggregation degree of microseismic activity and whether a rock burst occurs, and between rock bursts of different levels, so as to realize the instant warning of rock burst risk during excavation according to the spatial aggregation degree of microseismic activity, and can provide a strong basis and reference for rock burst risk warning based on microseismic activity. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1It is a flow chart of a tunnel rockburst early warning method based on the spatial aggregation degree of microseismic activities in an embodiment;

[0033] Figure 2 Schematic diagram of the frequency of rock burst at different positions from the tunnel face in the embodiment;

[0034] FIG3( a ) is a schematic diagram of the number of microseismic events along the x direction of the tunnel axis relative to the tunnel face in the embodiment;

[0035] FIG3( b ) is a schematic diagram of the number of microseismic events along the y direction of the tunnel axis relative to the tunnel face in the embodiment;

[0036] FIG3( c ) is a schematic diagram of the number of microseismic events along the z direction of the tunnel axis relative to the tunnel face in the embodiment;

[0037] Figure 4 Schematic diagram of the spatial convergence degree calculation area in the embodiment, in which the spatial warning unit is the spatial convergence degree calculation area;

[0038] FIG5( a ) is a schematic diagram of the distribution characteristics of the accumulated microseismic energy released by a minor rockburst in an embodiment;

[0039] FIG5( b ) is a schematic diagram of the distribution characteristics of the accumulated microseismic release energy of a moderate rockburst in an embodiment;

[0040] FIG5( c ) is a schematic diagram of the distribution characteristics of the accumulated microseismic energy released by a strong rockburst in an embodiment;

[0041] Figure 6 A distribution state diagram of microseismic events in the calculation space in the embodiment;

[0042] Figure 7 Schematic diagram of the hierarchical analysis model of quantification indexes of spatial aggregation of microseismic activity in the embodiment;

[0043] Figure 8 Schematic diagram of the evolution characteristics of the spatial concentration degree of average microseismic activities of rock bursts of different levels in the embodiment;

[0044] Fig. 9 Schematic diagram of the quantitative relationship between the spatial concentration of microseismic activity and rock burst risk in the embodiment. DETAILED DESCRIPTION

[0045] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0046] Example

[0047] like Figure 1As shown, this embodiment provides a tunnel rockburst early warning method based on the spatial aggregation degree of microseismic activities, which includes the following steps:

[0048] (1) Deploy microseismic monitoring sensors and build a microseismic monitoring system based on the structural dimensions and excavation characteristics of the tunnel. As the excavation of the tunnel face progresses, the microseismic monitoring system is moved when it exceeds the effective monitoring range.

[0049] (2) Count the spatial distribution characteristics between the locations where rockbursts have occurred during tunnel excavation and the tunnel face, and define the calculation area for the spatial concentration of microseismic activity;

[0050] (3) Derive the occurrence location, occurrence time and released energy of the microseismic monitoring signal in the calculation area to calculate the spatiotemporal concentration of microseismic activity;

[0051] (4) Statistically analyze the evolution law of the spatiotemporal clustering of microseismic activities of different levels of rockburst, establish a mapping relationship between the risk level of rockburst in tunnel excavation and the spatiotemporal clustering of microseismic activities, and construct a rockburst risk warning model; the risk level of rockburst in tunnel excavation includes no rockburst, slight rockburst, moderate rockburst and severe rockburst, which can realize rockburst risk warning with quantitative standards;

[0052] (5) Following the excavation process of the tunnel face, the temporal and spatial concentration of microseismic activity in the calculation area is calculated in real time, and the probability and level of potential rockburst in the warning area are determined by combining the early warning model.

[0053] In step (2), the calculation area of ​​the spatial concentration degree of microseismic activity comprehensively considers the spatial location of previous rock bursts and the scope of the excavation damage area, and determines that the potential high-incidence area of ​​immediate rock bursts with the greatest danger during tunnel excavation is the concentration calculation area. The concentration calculation area is accurately determined to avoid an overly large warning area, a large amount of calculation, and inaccurate calculation results.

[0054] In step (2), the spatial distribution characteristics between the location where the rockburst has occurred and the tunnel face refer to the specific location in the spatial coordinate system with the center of the bottom of the tunnel excavation face as the origin, so as to facilitate the identification of high-incidence rockburst warning areas during the excavation process.

[0055] The calculation of spatial concentration should include potential rockburst risk areas and all microseismic events related to potential rockbursts. The research results show that microseismic activity is positively correlated with rockburst occurrence, and the microseismic activity core area is basically consistent with the rockburst occurrence location. Therefore, the definition of the calculation area should comprehensively consider the spatial distribution characteristics of rockburst and microseismic activity. Figure 2 Figure 3(a), Figure 3(b), and Figure 3(c) show the distribution characteristics of tunnel rockburst and microseismic activities relative to the tunnel face along the tunnel axis.

[0056] Depend on Figure 2 It can be seen that tunnel rock bursts mainly occur in the current face and within the 10m excavation range behind it. Correspondingly, the main distribution range of microseismic events in the spatial X, Y and Z directions is the area from 25m excavated behind the face to 10m unexcavated in front, 30m to the left and right of the tunnel axis, and 25m up to 40m down from the tunnel axis. At the same time, research results show that the unloading disturbance zone of tunnel excavation is mainly distributed in the area from 2 times the tunnel diameter behind the face to 0.5 times the tunnel diameter in front, and the average daily advance of a tunnel excavation by drilling and blasting is about 6m / day. The calculation area of ​​spatial concentration should comprehensively consider the above characteristics.

[0057] Based on the above analysis, combined with the calculation of convergence and its engineering guidance significance, the calculation area of ​​convergence of a tunnel space is finally determined as follows: Figure 4 As shown, the dimensional parameters are as follows: 25m has been excavated behind the heading face to 10m is planned to be excavated in front, 30m to the left and right of the tunnel axis, and 25m upward to 40m downward from the tunnel axis.

[0058] In step (3), the occurrence location, occurrence time and release energy of the microseismic monitoring signal are derived, which refers to multiple source parameters of the microseismic monitoring signal, which are comprehensively determined by considering the difficulty of parameter acquisition, the significance of convergence calculation and the degree of direct impact on rock burst;

[0059] The occurrence location is the spatial distribution of microseismic events, which is confirmed by calculating the average spatial point distance between each event point; the occurrence time is the time consumed to generate the same number of microseismic events and microseismic energy release in the calculation area. The shorter the gestation time of the microseismic event, the more drastic the adjustment of the surrounding rock stress state, that is, the higher the risk of rock burst; the released energy is the simplest and most intuitive manifestation of the rock mass fracture strength.

[0060] The location, time and released energy of microseismic signals are all directly obtained objective data. The time, space and intensity characteristics of microseismic activities can be fully considered by selecting three typical parameters.

[0061] Microseismic monitoring signals have multiple source parameters such as microseismic release energy, energy index, amplitude, apparent volume, seismic moment, gestation time, etc. According to the research purpose, combined with the significance of microseismic signal parameters, comprehensive consideration of the difficulty of parameter acquisition, the significance of convergence calculation and the direct impact on rock burst, the gestation time of microseismic events, the accumulated microseismic release energy, and the spatial distribution point distance of microseismic events were selected as calculation indicators.

[0062] In this embodiment, the incubation time Δt refers to the time t when the first microseismic event occurs in the convergence calculation area. 起 The time t when the last microseismic event occurs is 止 , the calculation formula is as follows:

[0063] Δt=t 止 -t起

[0064] Usually, when the same number of microseismic events and microseismic release energy are generated in the calculation area, the shorter the incubation time, the more drastic the adjustment of the surrounding rock stress state, that is, the higher the rock burst risk, indicating that there is a negative correlation between the incubation time of microseismic events and the rock burst risk.

[0065] Microseismic release energy is the energy released by rock mass during cracking or friction activities. It is the result of the transformation of rock mass from elastic deformation to inelastic deformation. Therefore, microseismic release energy is the simplest and most intuitive embodiment of rock mass fracture strength. The calculation of the cumulative microseismic release energy in this embodiment is calculated by JDI visualization software. The microseismic events in the calculation area are represented in the form of spheres. The size of the spheres represents the size of the microseismic release energy. The cumulative microseismic release energy in the calculation area is indirectly measured by calculating the cumulative volume of the spheres.

[0066] The accumulated microseismic release energy can also characterize the storage and release of rock mass strain energy in the source area, which is directly related to the working state of the rock mass under the external excavation disturbance. Figure 5(a), Figure 5(b), and Figure 5(c) show the energy distribution characteristics of accumulated microseismic events of rockbursts of different levels. With the increase of rockburst level, the accumulated microseismic release energy corresponding to rockburst increases significantly, and there is a positive correlation between microseismic release energy and rockburst risk.

[0067] The distribution of microseismic events in the computational space is as follows: Figure 6 As shown in the figure, microseismic events are represented in the form of spatial points. The quantification of spatial clustering is closely related to its distribution state. The average spatial point distance is used to measure the spatial distribution state of event points. The calculation of spatial point distance is the average value of the distance between each microseismic event point and the nearest adjacent event point in the calculation area. This calculation method can offset the inherent positioning error to a certain extent.

[0068] In order to reasonably reflect the influence of various indicators on the spatial clustering of microseismic activity, the analytic hierarchy process (AHP) method is used to determine the weight distribution of each indicator. A hierarchical analysis model of quantitative indicators of spatial clustering of microseismic activity is constructed, such as Figure 7 As shown in the figure, the importance of each indicator is compared pairwise by using the expert scoring method to construct a judgment matrix. Finally, the weight w of each factor is calculated according to the judgment matrix, and the consistency and random tests are performed. The random consistency ratio CR=0.0825≤0.1, and the calculation results have satisfactory consistency. Finally, the weight distribution of each influencing factor indicator can be obtained, and the weight vector of each factor to the target W=(W1,W2,W3,)=(0.10,0.23,0.67), as shown in Table 1.

[0069] Table 1 Weight judgment matrix and weight distribution of microseismic activity spatial clustering index

[0070]

[0071] Based on the relationship between each quantitative index and rock burst risk, each index is combined according to a certain weight to comprehensively quantify the spatial concentration degree of microseismic activity JLD, that is:

[0072]

[0073] Where W1~W3 and X1~X3 are the weight coefficients and monitoring values ​​corresponding to the gestation time of microseismic events, the cumulative release energy of microseismic events, and the average spatial distribution point distance of microseismic events in the calculation area, respectively.

[0074] In step (4), the evolution law of spatiotemporal clustering of microseismic activities of rockbursts of different levels is statistically analyzed, which means: selecting typical rockburst cases of different levels in the target tunnel or similar projects for quantitative calculation of clustering. The selection of rockbursts follows the criteria of immediate rockburst, no control measures, no complex damage, no overlapping of multiple rockbursts, and rockbursts that occur during the process of risk reduction. The calculation results are shown in Table 2:

[0075] Table 2 Calculation results of spatial aggregation of typical rockburst cases of different levels

[0076]

[0077]

[0078] It can be seen from Table 2 that the spatial clustering degree of rockburst microseismic events of the same level is basically the same, and the spatial clustering degree of rockburst microseismic events of different levels is obviously different. Figure 8 These are the evolution characteristics of the spatial clustering degree of average microseismic activities of rockbursts of different levels.

[0079] In step (4), a mapping relationship between the rockburst risk level and the spatiotemporal concentration of microseismic activity is established, and a rockburst risk warning model is constructed:

[0080] The mean of the microseismic information of different levels of rockburst incubation process in typical cases is calculated and recorded as the microseismic characteristic value. The microseismic characteristic value is the central value of the microseismic information of the rockburst incubation process. Under the same tunnel project, the same microseismic monitoring system and sensor layout conditions, if the monitored microseismic information is similar to the characteristic value of a certain type and level of rockburst, based on the historical experience of the project, this type of rockburst will most likely occur, while the possibility of other types and levels of rockburst will be smaller. The closer the monitored microseismic information is to the characteristic value of a certain type and level of rockburst, the greater the possibility of a rockburst of this type and level. On the contrary, the greater the difference between the monitored microseismic information and the characteristic value of a certain type and level of rockburst, the smaller the possibility of a rockburst of this type and level. According to the above ideas, with the characteristic value of the spatial concentration of microseismic activity as the center, a suitable probability distribution function is used to express the quantitative relationship between microseismic information and rockburst.

[0081] The selection of probability distribution function is based on the standard of being practical, concise and practical. When the monitored microseismic information is less than the microseismic characteristic value of the lowest level rockburst incubation process, the possibility of the lowest level rockburst is still the greatest, because there is no rockburst lower than the lowest level rockburst; similarly, it also applies to the highest level rockburst.

[0082] In summary, taking the spatial concentration characteristic value of microseismic activity calculated in Table 2 as the center, a concise linear triangular fuzzy number form is used to express the quantitative relationship between microseismic concentration and rock burst risk, as follows: Fig. 9 shown.

[0083] The details are as follows:

[0084] Note: H ,P M ,P S and P N are the occurrence probabilities of strong rockburst H, moderate rockburst M, slight rockburst S and no rockburst N, respectively. The calculated value of the spatial concentration of microseismic activity is s. The specific functional relationship is:

[0085] ① When there is no rock burst risk in the early warning,

[0086]

[0087] ②When the risk of a minor rock burst is warned,

[0088]

[0089] ③When the warning indicates a medium rockburst risk,

[0090]

[0091] ④ When the risk of strong rock burst is warned,

[0092]

[0093] According to the above warning method, the rock burst risk warning was carried out for the tunnel section 195+050-195+220. The warning results are shown in Table 3 and compared with the actual situation on site.

[0094] Table 3 Comparison between rock burst risk warning and actual occurrence

[0095]

[0096]

[0097] It can be seen from the table that rockburst warning based on the spatial clustering of microseismic events is feasible, and the warning results are highly consistent with the actual occurrence of rockburst on site.

[0098] In step (5), determining the probability and level of potential rock burst in the warning area by combining the early warning model means: in the subsequent excavation process, the spatial aggregation degree of microseismic activity in the warning area is calculated in real time, the calculated characteristic values ​​are substituted into the early warning model for calculation, and the potential rock burst risk level is finally determined based on the results.

[0099] The rockburst warning method based on the spatial aggregation degree of microseismic activities provided in this embodiment can scientifically quantify the spatial distribution state of microseismic activities, and determine the corresponding relationship between the spatial aggregation degree of microseismic activities and whether a rockburst occurs, and between rockbursts of different grades, thereby realizing real-time warning of rockburst risks during excavation according to the spatial aggregation degree of microseismic activities, and can provide a strong basis and reference for rockburst risk warning based on microseismic activities.

[0100] The present invention and its embodiments are described schematically above, and the description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. Therefore, if a person skilled in the art is inspired by it and designs a structural method and an embodiment similar to the technical solution without creativity without departing from the purpose of the invention, they shall all fall within the protection scope of the present invention.

Claims

1. A tunnel rockburst early warning method based on the spatial aggregation degree of microseismic activity, characterized by: The following steps are involved: (1) Deploy microseismic monitoring sensors and build a microseismic monitoring system based on the structural dimensions and excavation characteristics of the tunnel. As the excavation of the tunnel face progresses, the microseismic monitoring system is moved when it exceeds the effective monitoring range. (2) Count the spatial distribution characteristics between the locations where rockbursts have occurred during tunnel excavation and the tunnel face, and define the calculation area for the spatial concentration of microseismic activity; (3) Derive the occurrence location, occurrence time and released energy of the microseismic monitoring signal in the calculation area to calculate the spatiotemporal concentration of microseismic activity; (4) Statistically analyze the evolution law of the spatiotemporal clustering of microseismic activities of different levels of rockburst, establish a mapping relationship between the risk level of rockburst in tunnel excavation and the spatiotemporal clustering of microseismic activities, and construct a rockburst risk warning model; (5) Following the excavation process of the tunnel face, the temporal and spatial concentration of microseismic activity in the calculation area is calculated in real time, and the probability and level of potential rockburst in the warning area are determined by combining the early warning model.

2. The tunnel rockburst early warning method based on the spatial concentration of microseismic activity according to claim 1 is characterized by: In step (2), the calculation area of ​​the spatial concentration degree of microseismic activity comprehensively considers the spatial location of previous rock bursts and the scope of the excavation damage area, and determines the potential high-incidence area of ​​immediate rock bursts with the greatest danger during tunnel excavation as the concentration degree calculation area.

3. The tunnel rockburst early warning method based on the spatial aggregation degree of microseismic activity according to claim 2 is characterized by: In step (2), the spatial distribution characteristics between the location where the rock burst has occurred and the tunnel face refer to the specific location in the spatial coordinate system with the center of the bottom of the tunnel excavation face as the origin.

4. The tunnel rockburst early warning method based on the spatial concentration of microseismic activity according to claim 3 is characterized by: In step (3), the occurrence location, occurrence time and release energy of the microseismic monitoring signal are derived, which refers to multiple source parameters of the microseismic monitoring signal, which are comprehensively determined by considering the difficulty of parameter acquisition, the significance of convergence calculation and the degree of direct impact on rock burst; The occurrence location is the spatial distribution of microseismic events, which is confirmed by calculating the average spatial point distance between each event point; the occurrence time is the time consumed by the same number of microseismic events and microseismic energy release in the calculation area. The shorter the gestation time of microseismic events, the more drastic the adjustment of the surrounding rock stress state, that is, the higher the risk of rock burst; The released energy is a reflection of the fracture strength of the rock mass.

5. The tunnel rockburst early warning method based on the spatial concentration of microseismic activity according to claim 4 is characterized by: In step (3), the temporal and spatial clustering of microseismic activity is calculated by determining the weight distribution of each calculation index through the analytic hierarchy process (AHP), and then determining the calculation formula of the spatial clustering of microseismic activity JLD based on the weight combination: Where W1~W3 and X1~X3 are the weight coefficients and monitoring values ​​corresponding to the gestation time of microseismic events, the cumulative release energy of microseismic events, and the average spatial distribution point distance of microseismic events in the calculation area, respectively.

6. The tunnel rockburst early warning method based on the spatial concentration of microseismic activity according to claim 5 is characterized by: In step (4), the risk levels of rock burst during tunnel excavation include no rock burst, slight rock burst, moderate rock burst and severe rock burst.

7. The tunnel rockburst early warning method based on the spatial concentration of microseismic activities according to claim 6 is characterized by: In step (4), the evolution law of spatiotemporal clustering of microseismic activities of rockbursts of different levels is statistically analyzed, which means: selecting typical rockburst cases of different levels in the target tunnel or similar projects for quantitative calculation of clustering, and the rockburst selection follows the criteria of immediate rockburst, no control measures, no complex damage, no overlapping of multiple rockbursts, and rockburst occurring in the process of no risk reduction.

8. The tunnel rockburst early warning method based on the spatial concentration of microseismic activities according to claim 7 is characterized by: In step (4), a mapping relationship between the rockburst risk level and the spatiotemporal concentration of microseismic activity is established. The construction of a rockburst risk warning model means that the quantitative relationship between microseismic information and rockburst is expressed by a probability distribution function with the spatial concentration characteristic value of microseismic activity as the center, as follows: Note: H ,P M ,P S and P N are the occurrence probabilities of strong rockburst H, moderate rockburst M, slight rockburst S and no rockburst N, respectively. The calculated value of the spatial concentration of microseismic activity is s. The specific functional relationship is: ① When there is no rock burst risk in the early warning, ②When the risk of a minor rock burst is warned, ③When the warning indicates a medium rockburst risk, ④ When the risk of strong rock burst is warned, 9. The tunnel rockburst early warning method based on the spatial concentration of microseismic activities according to claim 8, characterized in that: In step (5), determining the probability and level of potential rock burst in the warning area by combining the early warning model means: in the subsequent excavation process, the spatial aggregation degree of microseismic activity in the warning area is calculated in real time, the calculated characteristic values ​​are substituted into the early warning model for calculation, and the potential rock burst risk level is finally determined based on the results.

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