A deep well tunneling active-passive fusion rock burst monitoring system and method

Abstract

The application discloses a kind of active-passive fusion rock burst monitoring systems and methods for deep well roadway driving, and the system is composed of regional active detection layer, local passive monitoring layer, data processing center and physically isolated dual-fiber ring network.The regional active detection layer uses splicing annular track and electrically sliding controllable seismic source / seismic pickup, and the sliding interval is dynamically adjusted according to the real-time measured value of the loose circle;The local passive monitoring layer arranges microseismic sensors using pentahedron topology support, and the node depth changes adaptively with the loose circle.The data processing center integrates lightweight dual-source CT inversion module, confidence-weighted double-difference positioning module and excavation step-model refresh mapping table.Each time the roadway is excavated by 50m, a 1m×1m×1m grid velocity model local update can be completed under the condition of no downtime, the microseismic event positioning error ellipsoid volume is reduced by ≥30%, and high-precision, blind area-free and continuous monitoring of rock burst danger zones is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mine engineering safety monitoring, in particular to a kind of active-passive fusion rock burst monitoring system and method for deep well roadway excavation engineering (burial depth ≥800m). BACKGROUND

[0002] In the process of deep well roadway engineering excavation, the elastic energy accumulated in the surrounding rock is prone to sudden release, inducing rock burst disasters. Rock burst can produce violent vibration and high-speed rock fragments, directly threatening personnel and equipment safety, and inducing secondary disasters such as support structure failure. To reduce the risk, the stress and fracture evolution process in the rock mass must be monitored throughout the process and in detail to provide reliable basis for support design optimization, construction parameter adjustment and personnel safety.

[0003] Existing monitoring methods mainly include active detection and passive monitoring. Active detection uses artificial seismic sources to excite elastic waves and reconstruct regional wave velocity fields, which can macroscopically depict stress field distribution. However, due to limitations of seismic source energy, sensor layout density and wave velocity inversion algorithm accuracy, the resolution cannot meet the local rock mass fracture positioning requirements. Passive monitoring captures weak signals generated by rock mass fracture through a microseismic sensor array, which can locate micro-fracture events in real time. However, due to the lack of accurate initial velocity model, the positioning error is large and the depth information is missing.

[0004] Existing technology one: Chinese invention patent with authorization announcement number CN116400413B provides a data fusion inversion algorithm, but does not involve the core engineering problem of how to layout sensors in complex well roadway environment, and the method lacks specific engineering realizability in this technical field.

[0005] Existing technology two: Chinese invention patent with authorization announcement number CN104335072B mentions multiple array layouts, but its scenario is large-area exploration on the ground or sea, and the array form (such as streamer, seabed cable) cannot be directly applied to narrow, dynamically extended and harsh mine roadway. This patent does not provide any guidance on how to specifically layout in the mine and how to adjust with excavation.

[0006] Existing technology three: Chinese invention patent application with publication number CN114512983A fixes microseismic sensors at the end of anchor rods, which is convenient for installation, but due to the length limitation of anchor rods, it is difficult to penetrate the complete rock mass outside the loosening circle, and the signal is easily disturbed by roadway blasting vibration.

[0007] In addition, existing technologies generally separate the implementation of active detection and passive monitoring, with two independent systems and overlapping data transmission paths, causing resource waste, maintenance difficulties, and no unified velocity model updating mechanism, resulting in large positioning result time lag and obvious drift.

[0008] In view of the above technical problems, the skilled in the art urgently needs to develop a kind of active-passive fusion rock burst monitoring system and method for deep well roadway excavation. SUMMARY

[0009] The purpose of the present application is to provide a kind of active-passive fusion rock burst monitoring system and method for deep well roadway excavation, the system obtains high-precision initial wave velocity field through regional active detection layer, and provides real-time speed model support for local passive monitoring layer;Realize sensor fast migration, data efficient transmission and rock mass fracture accurate positioning, so as to overcome the defects such as insufficient resolution, large blind area, system fragmentation and high maintenance cost.

[0010] In order to achieve the above purpose, the present application provides the following technical scheme:

[0011] A kind of active-passive fusion rock burst monitoring system for deep well roadway excavation of the present application, the system includes regional active detection component and local passive monitoring component;

[0012] The regional active detection component includes:

[0013] The seismic source controller adopts GW-SC2000 type seismic source controller, and the time interval of controllable seismic source signal is set to realize automatic excitation of signal;

[0014] The spliced track is arranged along the outer wall of the roadway, and the spliced track extends along the driving direction;And

[0015] The controllable seismic source and the seismic sensor are installed on the spliced track, the controllable seismic source selects GW-EVS1000 type electric controllable seismic source, the seismic sensor selects CMG-40T type broadband seismic sensor, the controllable seismic source and the seismic sensor are connected with power module, and the data obtained by the seismic sensor is transmitted to the data processing center through the first fiber ring network;

[0016] The local passive monitoring component includes SinoSeiSm microseismic monitoring system, GU (T)-10 speed type microseismic sensor and second fiber ring network connected with microseismic sensor node.

[0017] Further, the system further includes data processing center and loose circle real-time determination module;

[0018] The loose circle real-time determination module adopts mine intrinsic safety type acoustic borehole instrument, and the acoustic travel time data is obtained by "single hole one shot double collection" mode, combined with the empirical formula of wave velocity-stress of surrounding rock, the depth D of surrounding rock loose circle is calculated.The parameter is used as the basic input of system layout and model inversion.The distance L between the controllable seismic source and the seismic sensor is 1.5D~2.0D;

[0019] The data processing center includes:

[0020] A lightweight dual-source CT inversion module based on joint tomography algorithm is used to generate an initial velocity field in a 3m*3m*3m grid and to update locally in a 1m*1m*1m grid;

[0021] A confidence-weighted double-difference location module introduces a confidence weight mechanism to reduce the positioning error ellipsoid volume of the collected microseismic events and improve the positioning accuracy of clustered events using the initial velocity field.

[0022] A driving distance-model refresh mapping table is a dynamically updated relational table that records the mapping relationship between driving distance and corresponding velocity model, and is used to automatically refresh the velocity model after driving 50m.

[0023] Further, the spliced track is spliced by multiple arc-shaped aluminum alloy tracks.

[0024] Further, the seismic source controller adopts a random coding excitation strategy, and the excitation signal is inconsistent with the vibration spectrum of the mining machine, and the autocorrelation sidelobe of the excitation signal is lower than -25dB.

[0025] Further, the microseismic monitoring system is arranged 150m behind the working face, and a wave velocity processing center is integrated on the microseismic monitoring system.

[0026] The application also discloses a deep well and roadway driving active-passive fusion rock burst monitoring method based on the monitoring system.

[0027] S1, the roadway is driven every 50m, the loose circle real-time measurement module tests the surrounding rock by sound wave, and outputs the loose circle depth D;

[0028] S2, the regional active detection assembly is arranged in a ring-shaped three-dimensional configuration, and the distance L between the controllable seismic source and the seismic sensor on the ring-shaped track is adjusted according to the loose circle depth D, that is, L=1.5D~2.0D;

[0029] S3, the microseismic sensor installation method adopts a pentahedral topological structure, and the microseismic sensor installation depth H is H=D+ (0.5m~1.0m);

[0030] S4, the seismic source controller drives the controllable seismic source to emit an active signal in a random coding excitation strategy, the seismic sensor receives the active signal, and the first optical fiber ring network transmits the active signal to the data processing center, and generates an initial velocity field in a 3m*3m*3m grid;

[0031] S5, the microseismic sensor passively collects microseismic events, and transmits the microseismic events to the data processing center through the second optical fiber ring network;

[0032] S6, the confidence-weighted double-difference positioning module uses the initial velocity field to position the collected microseismic events, so that the error ellipsoid volume is reduced by at least 30%;

[0033] S7, the lightweight dual-source CT inversion module locally updates the velocity field with a 1m×1m×1m grid and stores it in the driving distance-model refresh mapping table;

[0034] S8, the velocity field stored in the driving distance-model refresh mapping table is reused for microseismic event positioning, thereby improving the positioning accuracy. When the working face advances 50m again, repeat the above operation, update the wave velocity result, and perform microseismic event positioning, finally realize the continuous acquisition of high-precision wave velocity to ensure the accuracy of microseismic event positioning.

[0035] Further, in step S2, the annular three-dimensional configuration is configured to arrange a spliced track capable of extending with the driving near the roadway in an annular area with a radial distance of 150m-300m outside the roadway profile, and a controllable source and a pick-up sensor capable of moving in the extension direction of the spliced track to adjust the position are arranged on the spliced track.

[0036] Further, in step S3, the pentahedral topology structure arranges the microseismic sensors in five directions;

[0037] The five directions of the pentahedral topology structure are:

[0038] 12 o'clock direction, 10 o'clock direction, 2 o'clock direction, 7 o'clock direction and 5 o'clock direction, and one microseismic sensor is arranged in each of the five directions;

[0039] Each group of adjacent microseismic sensors is arranged with a 1m staggered arrangement in the driving direction of the working face, and the number of arranged end faces is not less than 2, and one group of microseismic sensors is arranged at each end face.

[0040] Further, in step S4, the random coding excitation strategy is an m sequence or a Gold code sequence, and the code length is greater than or equal to 1023, and the correlation sidelobe suppression is greater than or equal to 30dB.

[0041] Further, in step S6, the weight of the confidence-weighted double-difference positioning module is an exponential function ;

[0042] In the formula:

[0043] v i is the error ellipsoid volume of the i th passive source event;

[0044] v 0 is the average value of the active source event error ellipsoid volume.

[0045] In the above technical solution, the active-passive fusion rock burst monitoring system and method for deep well and roadway excavation provided by the application has the following beneficial effects:

[0046] The system of the application realizes dynamic migration with excavation by the splicable track and the controlled source / receiving sensor, completes 50m step adjustment within 30min without stopping work; the hole depth and interval are adaptively adjusted by using the real-time measurement value of the loosening circle, so that the sensor is always located in the complete rock mass; the confidence-weighted double difference positioning algorithm is used to reduce the error ellipsoid volume of the microseismic event by at least 30%; the 1m*1m*1m grid local refresh model is refreshed within 5min, which significantly improves the accuracy of wave velocity field inversion; the double optical fiber physical isolation and random code excitation are used to resist interference, so that high-precision continuous acquisition of wave velocity is realized without stopping production. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art according to these drawings.

[0048] Figure 1 A microseismic monitoring overall distribution diagram of the active-passive fusion rock burst monitoring system for deep well and roadway excavation provided by the embodiment of the present application;

[0049] Figure 2 A track structure schematic diagram of the active-passive fusion rock burst monitoring system for deep well and roadway excavation provided by the embodiment of the present application;

[0050] Figure 3 A microseismic sensor arrangement formal drawing of the active-passive fusion rock burst monitoring system for deep well and roadway excavation provided by the embodiment of the present application;

[0051] Figure 4 A microseismic sensor arrangement plan view of the active-passive fusion rock burst monitoring system for deep well and roadway excavation provided by the embodiment of the present application;

[0052] Figure 5 A control module principle block diagram of the active-passive fusion rock burst monitoring system for deep well and roadway excavation provided by the embodiment of the present application.

[0053] Explanation of reference signs:

[0054] 1, pick-up sensor; 2, controllable seismic source; 3, seismic source controller; 4, first fiber ring network; 5, spliced track; 6, second fiber ring network; 7, working face; 8, microseismic sensor; 9, microseismic monitoring system; 10, wave velocity processing center; 11, connecting plate; 12, connecting plate screw; 13, sliding plate; 14, fastening plate; 15, fastening plate screw; 16, track fastening screw. DETAILED DESCRIPTION

[0055] In order for those skilled in the art to better understand the technical solutions of the present application, the present application will be further described in detail below with reference to the accompanying drawings.

[0056] Referring to Figures 1 to 5 as shown;

[0057] The embodiment discloses a deep well and roadway driving active-passive fusion rock burst monitoring system, which comprises a regional active detection assembly and a local passive monitoring assembly.

[0058] The regional active detection assembly of the embodiment comprises:

[0059] The seismic source controller 3 of the embodiment adopts a GW-SC2000 type seismic source controller, which realizes automatic excitation of signals by setting the signal excitation time interval of the controllable seismic source 2.

[0060] The spliced track 5 is arranged along the outer wall of the roadway and extends along the driving direction; and

[0061] The controllable seismic source 2 and the pick-up sensor 1 are installed on the spliced track 5, wherein the controllable seismic source 2 of the embodiment selects a GW-EVS1000 type electric controllable seismic source, and the pick-up sensor 1 of the embodiment selects a CMG-40T type wideband pick-up sensor; the controllable seismic source 2 and the pick-up sensor 1 are connected with a power module, and the data acquired by the pick-up sensor 1 is transmitted to the data processing center through the first fiber ring network 4.

[0062] The local passive monitoring assembly comprises a SinoSeiSm microseismic monitoring system 9, a GU(T)-10 velocity type microseismic sensor 8, and a second fiber ring network 6 connected with the nodes of the microseismic sensor 8.

[0063] Referring to Figure 5 as shown, the system of the embodiment further comprises a data processing center and a loose circle real-time determination module.

[0064] Wherein, the loose circle real-time measurement module of the embodiment adopts a mine intrinsic safety type acoustic borehole instrument, preferably a KBA3.7 type mine intrinsic safety type acoustic borehole instrument produced by Chongqing Research Institute of Coal Science and Technology Group, and is used to measure the surrounding rock loose circle depth D. The arrangement positions of the controllable seismic source 2 and the seismic pick-up sensor 1 need to be set according to the measured surrounding rock loose circle depth D, specifically: the distance L between the controllable seismic source 2 and the seismic pick-up sensor 1 is 1.5D~2.0D.

[0065] Preferably, the data processing center of the embodiment comprises:

[0066] A lightweight dual-source CT inversion module, which is based on the dual-source travel time data of the active source and passive microseismic events and adopts a joint tomography algorithm. The lightweight dual-source CT inversion module is used to generate an initial velocity field in a roadway scale 3m×3m×3m grid and locally update in a 1m×1m×1m grid.

[0067] A confidence-weighted double-difference location module, which introduces a "confidence weight" mechanism, uses the initial velocity field to reduce the positioning error ellipsoid volume of the collected microseismic events, and improves the positioning accuracy of clustered events.

[0068] A driving step-model refresh mapping table, which is a dynamically updated relationship table recording the mapping relationship between the driving distance and the corresponding velocity model. The driving step-model refresh mapping table is used to automatically refresh the velocity model after driving 50m.

[0069] Preferably, the spliced track 5 of the embodiment is spliced by multiple arc-shaped aluminum alloy tracks. The embodiment uses fasteners such as clamping grooves and positioning pins to realize the connection between the arc-shaped aluminum alloy tracks, which can ensure that the operator completes the extension installation within 30 minutes. Referring to Figure 2 As shown in the figure, a connection structure of a spliced track 5 and a sliding structure of a seismic pick-up sensor 1 are shown. First, a sliding plate 13 is arranged inside the track, and the seismic pick-up sensor 1 is installed in the sliding plate 13 through a fastening plate 14 and a fastening plate screw 15. Secondly, a connecting plate 11 is also installed on the surface of the track through a connecting plate screw 12, and the adjacent tracks of the embodiment can be connected into one through the connecting plate 11.

[0070] Preferably, the seismic source controller 3 of the embodiment adopts a random coding excitation strategy, and the excitation signal is inconsistent with the vibration spectrum of the mining machine. The autocorrelation sidelobe of the excitation signal is lower than -25dB.

[0071] Preferably, the microseismic monitoring system 9 of the embodiment is arranged 150m behind the working face, and a wave velocity processing center is integrated on the microseismic monitoring system 9.

[0072] The application further discloses a rock burst monitoring method for deep well and roadway driving active-passive fusion, which is based on the monitoring system and mainly comprises the following steps.

[0073] S1, the roadway is excavated by 50 m each time, the loose circle real-time measurement module performs sound wave testing on the surrounding rock, and outputs the loose circle depth D;

[0074] S2, the active detection assembly is arranged in a ring-shaped three-dimensional configuration, and the interval L between the controllable seismic source 2 and the seismic sensor 1 on the ring-shaped track is adjusted to be 1.5D-2.0D according to the loose circle depth D;

[0075] S3, the microseismic sensor 8 is installed by adopting a five-face topological structure, and the installation depth H of the microseismic sensor 8 is D+(0.5m-1.0m);

[0076] S4, the seismic source controller 3 drives the controllable seismic source 2 to emit an active signal by using a random coding excitation strategy, the seismic sensor 1 receives the active signal, and the active signal is transmitted to the data processing center through the first optical fiber ring network 4 and an initial velocity field of a 3m*3m*3m grid is generated;

[0077] S5, the microseismic sensor 8 passively collects microseismic events, and transmits the microseismic events to the data processing center through the second optical fiber ring network 6;

[0078] S6, the confidence-weighted double-difference positioning module positions the collected microseismic events by using the initial velocity field, so that the error ellipsoid volume is reduced by at least 30%;

[0079] S7, the lightweight double-source CT inversion module locally updates the velocity field in a 1m*1m*1m grid and stores the velocity field in the driving step-distance-model refreshing mapping table;

[0080] S8, the velocity field stored in the driving step-distance-model refreshing mapping table is used for microseismic event positioning again, so as to improve the positioning accuracy, and the above operation is repeated when the working face is excavated by 50 m again, the wave velocity result is updated, the microseismic event is positioned, and finally the high-precision continuous wave velocity is obtained to ensure the accuracy of the microseismic event positioning.

[0081] Based on the monitoring method, in step S2, the ring-shaped three-dimensional configuration is configured to arrange the spliced track 5 capable of extending with the driving near the roadway in a ring-shaped area with a radial distance of 150m-300m from the outer diameter of the roadway profile, and the controllable seismic source 2 and the seismic sensor 1 capable of moving in the extension direction of the spliced track 5 are arranged on the spliced track 5.

[0082] Based on the monitoring method, in step S3, the five-face topological structure is used to arrange the microseismic sensor 8 in five directions;

[0083] The five directions of the pentahedron topology are respectively:

[0084] 12 o'clock direction, 10 o'clock direction, 2 o'clock direction, 7 o'clock direction and 5 o'clock direction, and one microseismic sensor 8 is arranged in each of the five directions;

[0085] Each group of microseismic sensors 8 arranged adjacently are staggered along the driving direction of the working face 7 with an interval of 1 m, and the number of end faces arranged is not less than 2, and one group of microseismic sensors 8 is arranged at each end face.

[0086] Based on the monitoring method of the embodiment, in step S4, the random coding excitation strategy is an m sequence or a Gold code sequence, and the code length is greater than or equal to 1023, and the correlation sidelobe suppression is greater than or equal to 30 dB.

[0087] Based on the monitoring method of the embodiment, in step S6, the weight of the confidence-weighted double-difference positioning module is an exponential function ;

[0088] In the formula:

[0089] v i is the error ellipsoid volume of the i th passive source event;

[0090] v 0 is the average value of the error ellipsoid volume of the active source event;

[0091] represents the base of the natural logarithm, which is approximately equal to 2.71828, and in this formula, is the base of the exponential function.

[0092] Embodiment one further explains and describes the system and invention of the present application;

[0093] A mine roadway has a buried depth of 1200 m, and the roadway end face is a city gate type with a size of 5 m (width) x 4 m (height), and the driving speed is 2 m / d. The surrounding rock is fine-grained granite, the uniaxial compressive strength is , and the integrity coefficient is .

[0094] The field loose circle real-time measurement module adopts a mine intrinsically safe acoustic borehole instrument, adopts a "single hole one shot double collection" mode, calculates an average D = 2.0 m, and uses the same as an input quantity for subsequent all automatic adjustments. A track, a controllable source 2 and a seismic sensor 1 are arranged in a ring-shaped area near a roadway at a radial distance of 150 m to 300 m from a current working face. A 12-segment 2 m long arc-shaped aluminum alloy track is selected in the field, a single segment weighs ≤8 kg, and a person can manually carry the same. A "clamping groove + 8 mm positioning pin" structure is used between track segments, the shear strength of the pin is ≥50 kN, and the same meets the underground impact requirements. For the first time, 24 m (12 segments) of the track is installed in the roadway surrounding rock, and subsequently, every 50 m of excavation, 2 segments (4 m) of extension are added, and a single person can complete the same in 26 min, which meets the requirement of "completing an extension within 30 min". A distance L between the controllable source 2 and the seismic sensor 1 on the track is calculated based on D = 2.0 m, and the specific distance is L = 2D = 4.0 m, and the controllable source 2 and the seismic sensor 1 are alternately arranged. The controllable source 2 is automatically excited by setting a source controller 3, the source controller 3 generates an m sequence, a code length N = 1023, a chip width Δt = 1 ms, a signal bandwidth 1 kHz, and a duration 1.023 s. A self-correlation sidelobe is measured to be -30.2 dB, there is no overlap with a main frequency (80-120 Hz) of a mining machine vibration, continuous acquisition can be realized without stopping work, the controllable source 2 is excited at a 3.2 m interval, the seismic sensor receives, a recording length is 2 s, a sampling rate is 4 kHz, a signal-to-noise ratio is measured to be 42 dB, the same is transmitted to a data processing center through a first optical fiber ring network 4, an initial velocity field of a 3 m x 3 m x 3 m grid is generated, a straight ray tracing + LSQR is used for velocity inversion, a travel time residual is reduced from an initial value 48 ms to 6 ms, and , is obtained as an initial velocity model, that is, an initial velocity field obtained through active monitoring, represents an initial P-wave velocity (a longitudinal wave) of 5.56 km / s, represents an initial S-wave velocity of 3.1 km / s.

[0095] The microseismic monitoring system 9 is arranged 150 m behind the working face 7. The microseismic sensor 8 is arranged in the roadway excavation influence area, i.e. in the range of 80 m in front of the working face 7 to 200 m behind the working face 7, as a local passive monitoring component. The microseismic sensor 8 is arranged closely behind the working face 7 in a three-dimensional topological configuration. The microseismic sensor 8 is arranged 80-130 m behind the working face 7. The number of cross sections is not less than 2. Each cross section is a row of sensors. The microseismic sensor 8 is arranged in a five-faced structure, i.e. one sensor is arranged at 12 o'clock, one sensor is arranged at 10 o'clock and 2 o'clock respectively, and one sensor is arranged at 7 o'clock and 5 o'clock respectively. The microseismic sensor 8 is arranged in a staggered manner along the heading direction of the working face 7 with an interval of 1 m. The required drilling depth H of the microseismic sensor 8 is D+(0.5 m-1.0 m), i.e. H=2.5 m. The drilling hole is required to be perpendicular to the axis of the roadway. The microseismic sensor 8 is arranged at the bottom of the hole. Every 50 m of the working face 7, the row of sensors away from the working face 7 is recovered and reinstalled at 80 m-130 m of the working face 7. The microseismic events passively collected by the microseismic sensor 8 are transmitted to the microseismic monitoring system 9 through the second optical fiber ring network 6.

[0096] The field microseismic monitoring system 9 records 18 microseismic events in 24 hours. The P-wave first arrival is clear, and the average signal-to-noise ratio is 28 dB. m 3 When the error ellipsoid volume of the ith microseismic event is m 3 , When m 3 , After weighting, the error of the microseismic event cluster center is reduced from ±8.2 m to ±5.6 m, and the ellipsoid volume is reduced from 45 m 3 to 28 m 3 , with a reduction of 37.8%≥30%, which meets the requirements. With the microseismic event cluster center as the core, a 10 m cubic region is regridded with a 1 m grid. Through the lightweight dual-source CT inversion module, the initial velocity model correction result is , , which means that the initial velocity field model obtained by active monitoring is further corrected to obtain the initial velocity model correction result (the final wave velocity result). Here, is the corrected P-wave velocity, i.e. is the corrected wave velocity, is the corrected S-wave velocity, i.e. is the corrected wave velocity. The correction result is automatically written into the driving step-model refresh mapping table and is used again for microseismic event positioning calculation of the microseismic monitoring system 9. When the working face is excavated again by 50 m, the above operation is repeated, the wave velocity result is updated, and the microseismic event positioning is performed, so as to finally realize continuous acquisition of high-precision wave velocity and ensure the accuracy of microseismic event positioning.

[0097] In the above technical solution, the active-passive fusion rock burst monitoring system and method for deep well and roadway tunneling provided by the application has the following beneficial effects:

[0098] The system of the application realizes dynamic migration with tunneling by the splicable track 5 and the controllable source 2 / vibration pickup sensor 1, 50m step adjustment is completed within 30min without stopping work; the hole depth and interval are adaptively adjusted by using the real-time measurement value of the loosening circle, so that the sensor is always located in the complete rock mass; the confidence-weighted double difference positioning algorithm is used, so that the error ellipsoid volume of the microseismic event is reduced by ≥30%; the 1m×1m×1m grid local refresh model is <5min, and the wave velocity field inversion accuracy is significantly improved; the anti-interference is realized by double optical fiber physical isolation and random code excitation, without stopping production, and high-precision continuous acquisition of wave velocity is realized.

[0099] The above only describes some exemplary embodiments of the application by way of illustration, and it is needless to say that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the application. Therefore, the above drawings and descriptions are illustrative in nature and should not be understood as limiting the scope of protection of the claims of the application.

Claims

1. A combined active-passive rockburst monitoring system for deep tunnel excavation, characterized in that, The system includes regional active detection components and local passive monitoring components; The active regional detection component includes: The source controller (3) achieves automatic signal excitation by setting a controllable source signal excitation time interval; A modular track (5) is arranged along the outer wall of the tunnel, and the modular track (5) extends in the direction of excavation; and The controllable vibration source (2) and the vibration sensor (1) are installed on the spliced ​​track (5). The controllable vibration source (2) and the vibration sensor (1) are connected to the power supply module, and the data acquired by the vibration sensor (1) is transmitted to the data processing center through the first optical fiber ring network (4). The local passive monitoring component includes a microseismic monitoring system (9), a microseismic sensor (8), and a second optical fiber ring network (6) connected to the nodes of the microseismic sensor (8). The microseismic monitoring system (9) is located 150m behind the working face (7), and the microseismic monitoring system (9) integrates a wave velocity processing center (10). A method for monitoring rockburst in deep tunnel excavation based on an active-passive fusion rockburst monitoring system mainly includes the following steps: S1. Every 50m of tunnel excavation, the loosening zone real-time measurement module performs acoustic wave testing on the surrounding rock and outputs the loosening zone depth D. S2. The active detection components in the area are arranged in a ring-shaped three-dimensional configuration. At the same time, the distance between the controllable source (2) and the vibration sensor (1) on the ring track is adjusted according to the loosening zone depth D, L=1.5D~2.0D. S3. The micro-vibration sensor (8) is installed using a pentahedral topology, and the installation depth of the micro-vibration sensor (8) is H = D + (0.5m ~ 1.0m). S4. The source controller (3) drives the controllable source to emit active signals using a random coding excitation strategy. After receiving the signals, the seismic sensor (1) transmits them to the data processing center through the first optical fiber ring network (4) and generates an initial velocity field of 3m×3m×3m grid. S5. The micro-seismic sensor (8) passively collects micro-seismic events and transmits them to the data processing center via the second optical fiber ring network (6). S6. The confidence-weighted double-difference positioning module uses the initial velocity field to locate the acquired microseismic events, reducing the error ellipsoid volume by at least 30%. S7, the lightweight dual-source CT inversion module updates the velocity field locally with a 1m×1m×1m grid and stores it in the tunneling distance-model refresh mapping table; S8. The velocity field stored in the tunneling distance-model refresh mapping table is reused for microseismic event localization, thereby improving localization accuracy; S9. When the working face (7) is excavated for another 50m, repeat the operation from step S1 to step S8 to finally achieve high-precision continuous acquisition of wave velocity to ensure the accuracy of micro-seismic event location.

2. The active-passive integrated rockburst monitoring system for deep tunnel excavation according to claim 1, characterized in that, The system also includes a data processing center and a real-time loosening zone measurement module; The real-time loosening zone measurement module is used to measure the depth D of the loosening zone of the surrounding rock. The distance L between the controllable seismic source (2) and the seismic sensor (1) is 1.5D~2.0D. The data processing center includes: A lightweight dual-source CT inversion module is used to generate an initial velocity field within a 3m×3m×3m grid at the roadway scale and to locally update it within a 1m×1m×1m grid. A confidence-weighted double-difference positioning module, wherein the confidence-weighted double-difference positioning module utilizes the initial velocity field to reduce the volume of the ellipsoid of the acquired microseismic event positioning error; and The tunneling distance-model refresh mapping table is used to automatically refresh the speed model after tunneling 50m.

3. The active-passive integrated rockburst monitoring system for deep tunnel excavation according to claim 1, characterized in that, The spliced ​​track (5) is made by splicing together multiple arc-shaped aluminum alloy tracks.

4. The active-passive integrated rockburst monitoring system for deep tunnel excavation according to claim 1, characterized in that, The source controller (3) adopts a random coding excitation strategy, and the excitation signal is inconsistent with the vibration spectrum of the mining machine. The autocorrelation sidelobe of the excitation signal is less than -25dB.

5. The active-passive integrated rockburst monitoring system for deep tunnel excavation according to claim 1, characterized in that, In step S2, the annular three-dimensional configuration is configured such that, with the working face (7) as the center, a spliced ​​track (5) that can extend with the excavation is laid in the annular area with a radial distance of 150m to 300m outside the roadway outline, and a controllable seismic source (2) and a seismic sensor (1) that can move along the extension direction of the spliced ​​track (5) to adjust the position are arranged on the spliced ​​track (5).

6. The active-passive integrated rockburst monitoring system for deep tunnel excavation according to claim 1, characterized in that, In step S3, the pentahedral topology structure employs a five-directional arrangement of the micro-vibration sensor (8). The five directions of the pentahedral topology are as follows: A micro-vibration sensor is arranged in each of the five directions: 12 o'clock, 10 o'clock, 2 o'clock, 7 o'clock and 5 o'clock (8). Each set of adjacent micro-vibration sensors (8) is staggered by 1m along the tunneling direction of the working face, and the number of end faces is not less than 2, with a set of micro-vibration sensors (8) arranged at each end face.

7. The active-passive integrated rockburst monitoring system for deep tunnel excavation according to claim 1, characterized in that, In step S4, the random coding excitation strategy is an m-sequence or a Gold code sequence, with a code length ≥ 1023 and a related sidelobe suppression ≥ 30dB.

8. The active-passive integrated rockburst monitoring system for deep tunnel excavation according to claim 1, characterized in that, In step S6, the weights of the confidence-weighted double-difference localization module are exponential functions. ; In the formula: v i For the first i The error ellipsoidal volume of a passive source event; v 0 represents the average volume of the error ellipsoid of the active source event.

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