A backfill body collapse deformation real-time early warning system and method based on BOTDA and microseismic combined monitoring

Abstract

A filling body collapse deformation real-time early warning system and method based on BOTDA and microseismic joint monitoring, the system comprises a distributed optical fiber sensor network (1), a microseismic monitoring array (2), a double-channel synchronous acquisition system (3), a joint processing workstation (4) and an early warning control station (5). The optical fiber sensor network is four-directionally and orthogonally arranged with four optical fibers in the vertical monitoring hole, the microseismic monitoring array is composed of multiple vibration pickup detectors, the double-channel synchronous acquisition system synchronously acquires BOTDA frequency shift data and microseismic waveform data; the joint processing workstation reconstructs a three-dimensional displacement field and locates a microseismic event, identifies a microseismic gathering area through clustering analysis, and identifies a potential instability area in combination with finite element inversion; the early warning control station constructs a multi-parameter comprehensive early warning index integrating displacement rate, microseismic frequency, energy release rate and b value change, and establishes a four-level early warning mechanism. The application realizes three-dimensional synchronous monitoring of filling body displacement field and rupture field, and improves the accuracy and reliability of collapse early warning.

Description

Technical Field

[0001] This invention relates to the field of mine safety monitoring technology, specifically to a real-time early warning system and method for the collapse and deformation of filling bodies based on BOTDA (Brillouin Optical Time Domain Analysis) and microseismic joint monitoring. Background Technology

[0002] In backfill mining, the stability of the backfill body is directly related to the safety of the mining area. Under the combined loads of blasting, ground pressure, and self-weight, the internal deformation and rupture continue to evolve until instability and collapse. Therefore, it is necessary to conduct synchronous real-time monitoring of the displacement field and rupture field to achieve effective early warning.

[0003] Currently, there is a lack of effective monitoring methods that can simultaneously acquire displacement and rupture fields, making it difficult to identify early signs of collapse in a timely manner and easily leading to safety accidents. Existing technologies mainly use resistance strain gauges or microseismic systems for single-parameter monitoring, but the former cannot locate the rupture source, and the latter is difficult to quantify cumulative displacement. Moreover, the two systems operate independently, with data from different sources in time and space, resulting in incomplete mine monitoring information and low reliability of early warning. Traditional sensors are mostly deployed at points, resulting in insufficient spatial coverage, and wired transmission lines are prone to deformation and breakage, leading to poor reliability. The infill body is characterized by high alkalinity, high humidity, and high stress, and traditional electronic sensors have poor corrosion resistance, making it difficult to guarantee long-term monitoring stability. Summary of the Invention

[0004] To overcome the problems existing in the prior art, the present invention provides a real-time early warning system and method for the collapse deformation of infill bodies based on BOTDA and microseismic joint monitoring, which can simultaneously acquire information on the displacement field and rupture field inside the infill body and realize accurate identification and real-time early warning of collapse precursors, thereby improving the accuracy and reliability of infill body collapse early warning.

[0005] The technical solution adopted in this invention is as follows: A real-time early warning system for infill collapse deformation based on BOTDA and microseismic joint monitoring includes: A distributed fiber optic sensor network is deployed in vertical monitoring holes inside the filling body to sense the distributed strain of the filling body. A microseismic monitoring array is deployed in the vertical monitoring holes to pick up microseismic waveform signals generated by the rupture of the filling material; The data acquisition system communicates with the distributed fiber optic sensor network and the microseismic monitoring array to synchronously acquire Brillouin frequency shift data and microseismic waveform data. The joint processing workstation is communicatively connected to the data acquisition system and is used to reconstruct the three-dimensional displacement field inside the filling body based on the Brillouin frequency shift data, locate the three-dimensional spatial coordinates of microseismic events based on the microseismic waveform data, and identify potential instability areas based on the location results of the three-dimensional displacement field and the microseismic events. The early warning and control station is communicatively connected to the joint processing workstation and is used to construct a comprehensive early warning index based at least on displacement rate and microseismic event parameters, and to conduct graded early warnings based on the comprehensive early warning index. The joint processing workstation projects the location results of microseismic events onto the deployment profile of the distributed optical fiber sensor network, identifies microseismic event clusters, and uses these clusters as prior constraints to limit the parameter correction range of the finite element inversion model, thereby identifying the potential instability region.

[0006] Furthermore, the distributed optical fiber sensor network includes: four distributed optical fibers orthogonally arranged in four directions around the vertical monitoring hole, a protective tube sleeved inside the vertical monitoring hole, and an epoxy coupling medium filled inside the protective tube. The four distributed optical fibers are disposed in the epoxy coupling medium, which is used to transmit the displacement changes of the filling body to the distributed optical fibers. A sealing coupling slurry is injected between the protective tube and the vertical monitoring hole to form a continuous bond between the protective tube and the filling body. The microseismic monitoring array includes a central pipe pile set at the center of the vertical monitoring hole and multiple vibration pickup detectors encapsulated inside the central pipe pile. Adjacent vibration pickup detectors are connected by a multi-core cable: by using the multi-core cable, each detector is independently connected to a data acquisition channel.

[0007] Furthermore, the joint processing workstation includes: The displacement field reconstruction module is used to calculate the axial strain distribution along the optical fiber based on the Brillouin frequency shift change and temperature compensation relationship, integrate the axial strain along the fiber length direction to obtain the axial displacement, and reconstruct the three-dimensional displacement field based on the strain difference, curvature relationship and boundary conditions of the four-way orthogonal optical fiber.

[0008] The microseismic event localization module is used to identify microseismic events from the microseismic waveform data using the time-window energy ratio method, and to calculate the three-dimensional spatial coordinates using a localization algorithm. The correlation analysis module is used to project the microseismic event location results onto the fiber optic deployment profile, identify the microseismic event clustering areas through a clustering algorithm, and calculate the event density, energy release rate, and b-value time series characteristics of the clustering areas. The inversion analysis module is used to establish a reference stress field and displacement field based on the finite element model, and to use the microseismic event cluster area as the constraint target area for model boundary condition correction. The model parameters are dynamically corrected through the inversion analysis algorithm, and the potential instability area is output.

[0009] Furthermore, the early warning and control station includes: The comprehensive early warning index calculation module is used to calculate the comprehensive early warning index based on displacement rate, frequency of microseismic events, energy release rate, and decrease in b-value; The multi-level early warning module is used to output the corresponding early warning level according to the different value ranges of the comprehensive early warning index; The linkage control module is used to generate early warning information and link the downhole audible and visual alarm system and / or personnel positioning system after the early warning is triggered; The closed-loop verification module is used to determine the effectiveness of the response based on the decrease in displacement rate and the decrease in the frequency of microseismic events after the early warning response is completed, and to store the determination results in the historical database.

[0010] Furthermore, the comprehensive early warning index P = α·(v / v0) + β·(N / N0) + γ·(E / E0) + δ·(Δb / b0), where v is the displacement rate, N is the frequency of microseismic events, E is the energy release rate, Δb is the decrease in the b value, v0, N0, E0, and b0 are the corresponding benchmark values, and α, β, γ, and δ are weighting coefficients; the weighting coefficients α, β, γ, and δ are adaptively optimized using a genetic algorithm based on the verification results in the historical database.

[0011] Furthermore, the initial value range of the weighting coefficients is: α=0.3-0.4, β=0.2-0.3, γ=0.2-0.3, δ=0.1-0.2; the warning levels of the multi-level warning module (52) include blue warning, yellow warning, orange warning and red warning, and the comprehensive warning index threshold and displacement rate threshold corresponding to each warning level are dynamically set according to the geomechanical parameters of the monitoring area.

[0012] The present invention provides a real-time early warning method for the collapse deformation of infill bodies based on BOTDA and microseismic joint monitoring, comprising the following steps: S1: Distributed optical fibers and a vibration pickup detector array are installed in the vertical monitoring holes of the filling material; S2: Connect the distributed optical fiber to the optical interface of the BOTDA demodulator, and connect the microseismic monitoring array to the microseismic data acquisition module. The synchronization control module outputs a synchronization trigger signal to the BOTDA demodulator and the microseismic acquisition module. The two types of data are uploaded to the joint processing workstation via the network. S3: Synchronously acquire Brillouin frequency shift data and microseismic waveform data; S4: Reconstruct the three-dimensional displacement field inside the filling body based on the Brillouin frequency shift data, and locate the three-dimensional spatial coordinates of the microseismic event based on the microseismic waveform data; S5: Project the location results of microseismic events onto the fiber optic deployment profile, and identify microseismic event clusters through cluster analysis; S6: Establish a benchmark model of the monitoring area based on finite element numerical simulation, use the microseismic event cluster area as a constraint, correct the model parameters through inverse analysis algorithm, and identify potential instability areas; S7: Construct a comprehensive early warning index based at least on displacement rate and microseismic event parameters, and conduct graded early warnings based on the comprehensive early warning index.

[0013] Furthermore, in step S1 of the present invention, a central pipe pile is placed at the center of the vertical monitoring hole, the vibration pickup detector is encapsulated inside the central pipe pile, and a distributed optical fiber is laid in each of the four directions (front, back, left, and right) of the annular area between the central pipe pile and the wall of the vertical monitoring hole; in step S3, the spatial sampling interval of the Brillouin frequency shift data is 0.05m, the sampling frequency of the micro-vibration waveform data is 4kHz, and the synchronization error is less than 2ms.

[0014] Furthermore, in step S4 of the present invention, the microseismic waveform data is automatically identified using the long-short time window energy ratio method, and the three-dimensional spatial coordinates of the microseismic events are calculated using the Geiger positioning algorithm; the axial strain distribution along the optical fiber is calculated based on the Brillouin frequency shift change and temperature compensation relationship, the axial strain is integrated along the length direction of the optical fiber to obtain the axial displacement, and the three-dimensional displacement field is reconstructed based on the strain difference, curvature relationship and boundary conditions of the four-way orthogonal optical fiber, and then the three-dimensional displacement field is obtained through the displacement reconstruction model.

[0015] Furthermore, the present invention further includes the following after step S7: S8: After the early warning is triggered, the downhole audible and visual alarm system and the personnel positioning system are linked. After the early warning is handled, the effective handling standard is to conduct closed-loop verification with the displacement rate decrease ≥ preset threshold and the micro-vibration event frequency decrease ≥ preset threshold as the effective handling standard. The verification results are used for adaptive optimization of the weight coefficient of the comprehensive early warning index.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention achieves three-dimensional synchronous monitoring of the displacement field and rupture field inside the filling body by orthogonally deploying distributed optical fibers in four directions within the filling body and combining them with BOTDA technology to obtain the three-dimensional displacement field. At the same time, it deploys a vibration pickup detector array to locate micro-seismic rupture events, thereby improving the spatiotemporal matching degree and early warning reliability of collapse precursor identification.

[0017] 2. This invention employs a dual-channel synchronous acquisition system. The BOTDA demodulator continuously acquires Brillouin frequency shift data at a spatial sampling interval of 0.05m, while the dual-channel synchronous acquisition system synchronously acquires microseismic waveform data at a sampling frequency of 4kHz and uploads it to the joint processing workstation in a timely manner, with a synchronization error of less than 2ms. By using a unified spatial coordinate system and a spatial proximity matching algorithm, microseismic events are accurately projected onto the fiber optic monitoring profile, and the spatiotemporal correlation coefficient between the displacement field and microseismic events reaches 0.87. Combined with cluster analysis and finite element inversion, the identification accuracy of microseismic event clusters reaches ±3m, and the positioning accuracy of potential instability areas is improved to ±2m, providing a reliable spatial target area for early warning.

[0018] 3. This invention constructs a comprehensive early warning index P that integrates displacement rate, microseismic event frequency, energy release rate, and b-value changes, and establishes a four-level graded early warning mechanism. Upon triggering an early warning, it automatically links with the downhole audible and visual alarm and personnel positioning system. Compared to traditional single-threshold alarms, the multi-parameter comprehensive early warning system controls the false alarm rate to <3%, the missed alarm rate to <0.5%, improves early warning accuracy, and shortens the early warning response time to less than 5 minutes.

[0019] 4. The present invention adopts a composite protection structure of PBT material protective tube and epoxy resin, which extends the service life of BOTDA optical fiber in a highly alkaline filling environment. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the monitoring and early warning method of the present invention; Figure 2 This is a schematic diagram of the deployment of the monitoring system in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a monitoring unit (distributed optical fiber sensor network and multiple vibration detectors) of the present invention; Figure 4 for Figure 3 Top view; Figure 5 This is a flowchart of the multi-parameter acquisition and parallel solution process for steps S3 and S4 of the present invention. Figure 6 This is a flowchart of the data fusion analysis process in step S5 of the present invention; Figure 7 This is a flowchart of the data fusion analysis process in step S6 of the present invention; Figure 8 This is a flowchart of step S7 (comprehensive early warning) and step S8 (closed-loop verification) of the present invention. Detailed Implementation

[0021] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Those skilled in the art should understand that the following description is merely a preferred embodiment of the present invention and is not intended to limit the invention.

[0022] like Figure 1 and Figure 2 As shown, this invention provides a real-time early warning system and method for the collapse deformation of infill bodies based on BOTDA and microseismic joint monitoring. The specific method steps are as follows:

[0023] I. Deployment of the Monitoring System

[0024] like Figure 3 and Figure 4As shown, this embodiment uses a backfilled mining area in a mine as the application object. The height of the backfill is 40m, and the designed monitoring area is 80m×40m. After the backfill is poured and cured to the design strength (≥7 days), vertical monitoring holes are drilled at the designed monitoring points at intervals of 20m. The hole diameter is 130mm, and the hole depth is the full thickness of the backfill. A total of 15 vertical monitoring holes are arranged to form a monitoring grid of 5 rows × 3 columns.

[0025] A central pipe pile 22 with an outer diameter of 32mm is placed at the center of each vertical monitoring hole. A vibration pickup geophone 21 is pre-encapsulated inside the central pipe pile, with geophones spaced 20m apart. The internal structure is integrally encapsulated with epoxy resin 23, achieving a Shore D80 hardness after curing. Adjacent geophones are connected by communication cables 24, which can be optoelectronic composite armored cables to effectively avoid adverse effects on downhole data monitoring caused by the compression of the filling material and ground pressure impact. This forms a vertical microseismic sensor chain, with two geophones arranged in each monitoring hole (located at depths of 20m and 40m respectively), for a total of 30 vibration pickup geophones across the entire monitoring area.

[0026] Within the annular region between the central pipe pile 22 and the wall of the vertical monitoring hole, one distributed optical fiber 11 is laid in each of the four orthogonal directions (front, back, left, and right), for a total of four optical fibers, resulting in a total of 60 optical fibers (15 holes × 4 fibers / hole) in the entire monitoring area. A protective tube 12 (preferably a PBT material protective tube in this embodiment) with an outer diameter of 90 mm and a wall thickness of 5 mm is inserted into the outermost layer of the vertical monitoring hole. The annular space between the protective tube and the outer side of the central pipe pile is filled with a coupling medium 13. In this embodiment, the coupling medium 13 is a two-component low-viscosity epoxy infusion resin, with a mixed viscosity of approximately 220–430 mPa·s in the range of 20–30 ℃ and a tensile elastic modulus of approximately 1800 MPa after curing for 7 days. This allows the micro-deformation of the filling material to be efficiently transferred to the optical fiber, with a strain transfer efficiency calibrated to ≥95%. This effectively transfers the micro-deformation of the filling material to the optical fiber while protecting the optical fiber from corrosion by the highly alkaline environment (pH=12~13). The use of a composite protective structure of PBT material protective tube and epoxy resin extends the service life of BOTDA optical fiber to >30 years in a highly alkaline filling environment with pH=12~13, and the strain transfer efficiency is ≥95%. The microseismic detector is integrally cast with epoxy resin, firmly coupled to the filling material, and has a sensitivity retention rate of >98%. The four-way orthogonally arranged optical fiber can sense the three-dimensional displacement field, solving the technical problem that traditional single optical fibers cannot obtain the displacement direction. At the same time, the outermost protective tube provides good mechanical protection with a compressive strength of ≥5MPa, preventing the optical fiber from being damaged by the filling material.

[0027] II. Data Connection and Instrument Installation

[0028] Each vertical monitoring hole leads to one communication cable 24 and four distributed optical fibers 11, which are optical-electric composite armored cables with a steel wire diameter of 0.8mm, a wrapping braid angle of 45°, a tensile strength of ≥2000N, and a lateral pressure resistance of ≥3000N / 100mm.

[0029] One communication cable 24 and four distributed optical fibers 11 leading out from each vertical monitoring hole are respectively connected to the microseismic channel and BOTDA channel of the dual-channel synchronous acquisition system 3. The dual-channel synchronous acquisition system 3 has a built-in GPS timing module, which controls the BOTDA demodulator (a commercially available distributed optical fiber sensor demodulator; in this embodiment, a dual-end access BOTDA type demodulator is used, with the optical fiber layout in a loop structure and sixty optical fibers connected in series) to start synchronous acquisition through a synchronous trigger signal, with a synchronization error of <2ms.

[0030] The dual-channel synchronous acquisition system 3 and the BOTDA demodulation unit are connected to the ground-based joint processing workstation 4 and early warning and control station 5 via gigabit network cables. Data transmission uses the TCP / IP protocol at a transmission rate of 1000Mbps, enabling real-time uploading of monitoring data.

[0031] III. Multi-parameter synchronous acquisition and coupled solution

[0032] like Figure 5 As shown, after the system starts, the data acquisition system 3 in this embodiment preferably uses a dual-channel synchronous acquisition system to synchronously control the BOTDA channel and the microseismic channel for data acquisition. The BOTDA channel continuously acquires Brillouin frequency shift data along all 60 optical fibers with a spatial sampling interval of 0.05m, a pulse width of 10ns, and a dynamic range of 15dB; the microseismic channel synchronously acquires waveform data from 30 vibration detectors at a sampling frequency of 4kHz. The dual-channel synchronization uses GPS timing, and the synchronization error is strictly controlled to <2ms.

[0033] The displacement field reconstruction module 41 in the coprocessing workstation 4 calculates the axial strain distribution along the optical fiber based on the Brillouin frequency shift change and temperature compensation relationship. It then integrates the axial strain along the fiber length to obtain the axial displacement. Based on the strain difference, curvature relationship, and boundary conditions of the four-way orthogonal optical fibers, it reconstructs the three-dimensional displacement field. Furthermore, based on the strain data of the four-way orthogonal optical fibers, it inverts the displacement field in the X, Y, and Z directions inside the filling body using a three-dimensional displacement reconstruction model. The spatial sampling interval is 0.05 m, and the actual spatial resolution is determined by the pulse width and demodulation mode of the BOTDA demodulator. Calibration verification shows that the displacement field reconstruction accuracy is ±0.5 m.

[0034] The microseismic event location module 42 automatically identifies microseismic events using the long-short window energy ratio (STA / LTA) method, where the STA window length is 0.5s, the LTA window length is 5s, and the trigger threshold is 3.0. After event identification, the Geiger localization algorithm is used to calculate the three-dimensional spatial coordinates (x, y, z), occurrence time, released energy, and apparent volume parameters of the microseismic event. Verification with known blasting events shows that the absolute error of microseismic event location is ≤5 m. The module employs a dual-channel synchronous acquisition approach, combining automatic microseismic event identification and location with displacement field reconstruction. This achieves synchronous acquisition of the displacement field inside the filling body and microseismic rupture signals, achieving a microseismic event location accuracy of ±5m and a displacement monitoring sensitivity of ±0.1m. This provides a high-precision, high-spatiotemporal matching data foundation for subsequent joint analysis, overcoming the technical challenges of data from different sources and spatiotemporal mismatch inherent in traditional single-monitoring methods.

[0035] IV. Multi-data Joint Intelligent Analysis

[0036] like Figure 6 and Figure 7 As shown, the correlation analysis module 43 projects the three-dimensional coordinates of each microseismic event onto the three-dimensional spatial profile of the BOTDA fiber optic cable. The spatial proximity matching algorithm is used to automatically associate the event with the nearest optical cable node. The distance threshold is set to 5m. The spatiotemporal correlation coefficient between the displacement field and the microseismic event is 0.87.

[0037] Subsequently, the DBSCAN clustering algorithm was used to identify microseismic event clusters, with a neighborhood radius ε = 10m and a minimum sample size MinPts = 5. For each cluster, the event density (times / m³), energy release rate E / h, and b-value were calculated using the maximum likelihood method, with a time window of 1 hour and a step size of 0.5 hours. During the operation of this embodiment, a total of 3 microseismic event clusters were identified, located in cluster A in the northeast corner of the monitoring area (depth 25-35m), cluster B in the middle (depth 10-20m), and cluster C in the southwest corner (depth 30-40m), with a cluster identification accuracy of ±3m.

[0038] The inversion analysis module 44 establishes a baseline stress and displacement field model of the monitoring area based on finite element numerical simulation. Using the microseismic event cluster area as the constraint target area for model boundary condition correction, real-time displacement data and microseismic event parameters are input into the model. The particle swarm analysis (PSO) algorithm is used to dynamically correct model parameters such as elastic modulus, cohesion, and internal friction angle. The population size is 50 particles, and the number of iterations is 200. The output shows the stress field distribution inside the filling body, the yield zone range, and the development trend of the fracture surface, identifying the spatial location and influence range of potential instability areas. The inversion analysis shows that the positioning accuracy of potential instability areas is ±2m.

[0039] V. Integrated Intelligent Early Warning and Feedback Control

[0040] like Figure 8 As shown, the comprehensive early warning index calculation module 51 in the early warning control station 5 calculates the comprehensive early warning index in real time: P = α·(v / v0) + β·(N / N0) + γ·(E / E0) + δ·(Δb / b0) Where v is the displacement rate in mm / d, N is the frequency of microseismic events in times / h, E is the energy release rate in J / h, and Δb is the decrease in the b-value. The baseline values ​​v0, N0, E0, and b0 are obtained by statistically analyzing historical data from the first 30 days after the system is put into operation. In this embodiment, the 85th percentile values ​​are taken as follows: v0 = 0.5 mm / d, N0 = 5 times / h, E0 = 100 J / h, and b0 = 1.2. The initial values ​​of the weighting coefficients are set as follows: α = 0.35, β = 0.25, γ = 0.25, and δ = 0.15.

[0041] Multi-level early warning module 52 executes early warnings according to the following rules:

[0042] The risk level is determined in descending order of red, orange, yellow, and blue; once a higher-level condition is met, a lower-level condition will not be triggered. After a warning is triggered, the linkage control module 53 automatically generates a warning report containing the three-dimensional coordinates of the unstable area, the radius of influence, and the current risk level. This report is then pushed to the dispatch center via the underground industrial ring network's audible and visual alarm system and personnel positioning system.

[0043] Taking the only red alert in this embodiment as an example: when the system ran for 127 days, the monitoring data showed that the displacement rate of the cluster area A suddenly increased to 6.2 mm / d, the frequency of micro-seismic events reached 23 times / h, the b value dropped sharply from 1.20 to 0.75, the comprehensive warning index P=7.4, and a red alert was triggered.

[0044] The system immediately triggered an alarm. Based on the personnel location system, the dispatch center identified three workers in the area and immediately organized an evacuation. Forty-five minutes after the evacuation was completed, a partial collapse occurred in area A, with a collapse volume of approximately 200 m³. Due to timely warning, no casualties were reported.

[0045] After the early warning response was completed, grouting reinforcement was performed, and the system continuously collected data from the reinforced area for 48 hours. The closed-loop verification module 54 determined the effectiveness of the treatment: the displacement rate decreased from 6.2 mm / d to 1.8 mm / d (a 71% decrease), and the frequency of microseismic events decreased from 23 times / h to 6 times / h (a 74% decrease), simultaneously meeting the effective treatment criteria of a displacement rate decrease of ≥50% and a microseismic event frequency decrease of ≥60%. The verification results were automatically stored in the historical database 55.

[0046] Every 30 days, based on the validation results from 55 historical databases, the system uses a genetic algorithm with a population size of 100 individuals, a crossover probability of 0.8, a mutation probability of 0.05, and 50 generations to adaptively optimize the weight coefficients α, β, γ, and δ. After 6 months of operation, the adaptively optimized weight coefficients are: α=0.38, β=0.26, γ=0.22, and δ=0.14, further improving the system's early warning performance.

[0047] VI. Overall Performance of this Implementation Example

[0048] During the six-month continuous operation of this embodiment in a backfilling mining area, all modules of the system operated stably. The composite protective structure of PBT material protective pipe and epoxy resin effectively resisted the corrosion of the highly alkaline backfill environment. The integrity rate of all 60 optical fiber signals was 100%, and the effectiveness of 30 vibration pickup detectors reached 96.7%.

[0049] Statistical verification shows that, compared with single-threshold alarms, the multi-parameter comprehensive early warning index in this embodiment improves the early warning accuracy by 65%, controls the false alarm rate to <3% (a total of 20 early warnings occurred, with 0 false alarms), the early warning response time is ≤5 minutes, and the positioning accuracy of potential unstable areas is ≤±2m. Compared with traditional monitoring systems, the early warning lead time is extended from an average of 2 hours to 12-24 hours, providing sufficient time for on-site response.

[0050] None of the above variations exceed the scope defined in the claims of this invention.

[0051] It should be further noted that the specific embodiments described herein are merely illustrative examples of the present invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, but without exceeding the scope defined by the claims.

Claims

1. A real-time early warning system for the collapse deformation of infill bodies based on BOTDA and microseismic joint monitoring, characterized in that, include: A distributed optical fiber sensor network (1) is deployed in the vertical monitoring holes inside the filling body to sense the distributed strain of the filling body; The microseismic monitoring array (2) is installed in the vertical monitoring hole to pick up the microseismic waveform signal generated by the rupture of the filling material; The data acquisition system (3) is connected to the distributed optical fiber sensor network and the microseismic monitoring array for synchronous acquisition of Brillouin frequency shift data and microseismic waveform data; The joint processing workstation (4) is connected to the data acquisition system for reconstructing the three-dimensional displacement field inside the filling body based on the Brillouin frequency shift data, locating the three-dimensional spatial coordinates of the micro-seismic event based on the micro-seismic waveform data, and identifying potential instability areas based on the location results of the three-dimensional displacement field and the micro-seismic event. The early warning control station (5) is communicatively connected to the joint processing workstation and is used to construct a comprehensive early warning index based at least on displacement rate and microseismic event parameters, and to conduct graded early warning based on the comprehensive early warning index; The joint processing workstation projects the location results of microseismic events onto the deployment profile of the distributed optical fiber sensor network, identifies microseismic event clusters, and uses these clusters as prior constraints to limit the parameter correction range of the finite element inversion model, thereby identifying the potential instability region.

2. The system according to claim 1, characterized in that, The distributed optical fiber sensor network includes: four distributed optical fibers (11) orthogonally arranged in four directions around the vertical monitoring hole, a protective tube (12) sleeved in the vertical monitoring hole, and an epoxy coupling medium (13) filled inside the protective tube. The four distributed optical fibers are arranged in the epoxy coupling medium, and the protective tube and the vertical monitoring hole are filled with sealing coupling slurry. The microseismic monitoring array includes a central pipe pile (22) set at the center of the vertical monitoring hole and multiple vibration pickup detectors (21) encapsulated inside the central pipe pile. Adjacent vibration pickup detectors are connected by a multi-core cable (24).

3. The system according to claim 1, characterized in that, The joint processing workstation includes: The displacement field reconstruction module (41) is used to calculate the axial strain distribution along the fiber based on the Brillouin frequency shift change and temperature compensation relationship, integrate the axial strain along the fiber length direction to obtain the axial displacement, and reconstruct the three-dimensional displacement field based on the strain difference, curvature relationship and boundary conditions of the four-way orthogonal fiber. The microseismic event location module (42) is used to identify microseismic events by using the time window energy ratio method on the microseismic waveform data, and to calculate the three-dimensional spatial coordinates by the location algorithm. The correlation analysis module (43) is used to project the microseismic event location results onto the fiber optic deployment profile, identify the microseismic event clustering area through a clustering algorithm, and calculate the event density, energy release rate and b-value time series characteristics of the clustering area. The inversion analysis module (44) is used to establish the reference stress field and displacement field based on the finite element model, take the microseismic event cluster area as the spatial prior constraint target area for model parameter inversion, limit the correction area and value range of elastic modulus, cohesion and internal friction angle parameters, dynamically correct the model parameters through the inversion analysis algorithm, and output the potential instability area.

4. The system according to claim 1, characterized in that, The early warning and control stations include: The comprehensive early warning index calculation module (51) is used to calculate the comprehensive early warning index based on displacement rate, frequency of microseismic events, energy release rate and decrease in b-value; The multi-level early warning module (52) is used to output the corresponding early warning level according to the different value ranges of the comprehensive early warning index; The linkage control module (53) is used to generate early warning information and link the downhole audible and visual alarm system and / or personnel positioning system after the early warning is triggered; The closed-loop verification module (54) is used to determine the effectiveness of the handling based on the decrease in displacement rate and the decrease in frequency of microseismic events after the early warning handling is completed, and to store the judgment result in the historical database.

5. The system according to claim 4, characterized in that, The comprehensive early warning index P = α·(v / v0) + β·(N / N0) + γ·(E / E0) + δ·(Δb / b0), where v is the displacement rate, N is the frequency of microseismic events, E is the energy release rate, Δb is the decrease in b value, v0, N0, E0, and b0 are the corresponding benchmark values, and α, β, γ, and δ are weighting coefficients. The weighting coefficients α, β, γ, and δ are adaptively optimized using a genetic algorithm based on the verification results in the historical database.

6. The system according to claim 5, characterized in that, The initial range of the weighting coefficients is: α=0.3-0.4, β=0.2-0.3, γ=0.2-0.3, δ=0.1-0.2; the warning levels of the multi-level warning module (52) include blue warning, yellow warning, orange warning and red warning, and the comprehensive warning index threshold and displacement rate threshold corresponding to each warning level are dynamically set according to the geomechanical parameters of the monitoring area.

7. A real-time early warning method for the collapse deformation of infill bodies based on BOTDA and microseismic joint monitoring, characterized in that, Includes the following steps: S1: Distributed optical fibers and a vibration pickup detector array are installed in the vertical monitoring holes of the filling material; S2: Connect the distributed optical fiber to the optical interface of the BOTDA demodulator and connect the microseismic monitoring array to the microseismic data acquisition module. The synchronization control module outputs a synchronization trigger signal to the BOTDA demodulator and the microseismic acquisition module. The two types of data are uploaded to the joint processing workstation (4) via the network. S3: Synchronously acquire Brillouin frequency shift data and microseismic waveform data; S4: Reconstruct the three-dimensional displacement field inside the filling body based on the Brillouin frequency shift data, and locate the three-dimensional spatial coordinates of the microseismic event based on the microseismic waveform data; S5: Project the location results of microseismic events onto the fiber optic deployment profile, and identify microseismic event clusters through cluster analysis; S6: Establish a benchmark model of the monitoring area based on finite element numerical simulation, use the microseismic event cluster area as a constraint, correct the model parameters through inverse analysis algorithm, and identify potential instability areas; S7: Construct a comprehensive early warning index based at least on displacement rate and microseismic event parameters, and conduct graded early warnings based on the comprehensive early warning index.

8. The method according to claim 7, characterized in that, In step S1, a central pipe pile is placed at the center of the vertical monitoring hole, the vibration pickup detector is encapsulated inside the central pipe pile, and a distributed optical fiber is laid in each of the four directions (front, back, left, and right) of the annular area between the central pipe pile and the wall of the vertical monitoring hole; in step S3, the spatial sampling interval of the Brillouin frequency shift data is 0.05m, the sampling frequency of the micro-vibration waveform data is 4kHz, and the synchronization error is less than 2ms.

9. The method according to claim 7, characterized in that, In step S4, the microseismic events are automatically identified using the long-short time window energy ratio method on the microseismic waveform data, and the three-dimensional spatial coordinates of the microseismic events are calculated using the Geiger positioning algorithm. The axial strain distribution along the optical fiber is calculated based on the Brillouin frequency shift change and temperature compensation relationship. The axial displacement is obtained by integrating the axial strain along the length of the optical fiber, and the three-dimensional displacement field is reconstructed based on the strain difference, curvature relationship and boundary conditions of the four-way orthogonal optical fiber.

10. The method according to claim 7, characterized in that, The process following step S7 also includes: S8: After the early warning is triggered, the downhole audible and visual alarm system and the personnel positioning system are linked. After the early warning is handled, the effective handling standard is to conduct closed-loop verification with the displacement rate decrease ≥ preset threshold and the micro-vibration event frequency decrease ≥ preset threshold as the effective handling standard. The verification results are used for adaptive optimization of the weight coefficient of the comprehensive early warning index.

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