Fusion reforming group multi-parameter fault activation induced roadway instability precursor identification method

Abstract

The invention relates to the technical field of mine safety monitoring and early warning, in particular to a fusion reforming group multi-parameter fault activation induced roadway instability precursor identification method. The method comprises the following specific steps: collecting a microseismic event signal and roadway convergence deformation, calculating a correlation length, a b value, a spatial fractal dimension and quantitative indexes of roadway activation response to fault, and calculating a critical early warning index according to a constructed multi-parameter fused critical early warning index calculation formula, according to the method, a fault activation induced roadway instability precursor is identified according to a critical early warning index, a physical model capable of realizing linkage early warning of fault activation factors and roadway instability fruits is constructed through deep fusion of multi-parameter indexes of a reformed group and roadway response information, the problems of isolation, singleness and lagging of early warning of a traditional method are solved, and the early warning efficiency is improved. Early recognition, early warning and early disposal of roadway instability disasters induced by fault activation in deep mining are achieved, and unprecedented technical guarantee is provided for mine safety production.

Description

Technical Field

[0001] This invention relates to the field of mine safety monitoring and early warning technology, and in particular to a method for identifying precursors of roadway instability induced by activation of multi-parameter faults in fusion renormalization groups. Background Technology

[0002] Tunneling is the lifeblood of underground engineering projects such as mines and tunnels, and its stability directly affects the safety and production efficiency of the entire project. In deep, high-stress environments, excavation activities disrupt the original rock stress balance, leading to stress redistribution and easily activating existing geological structures such as faults and joints within the project area. Fault zones are weak zones composed of fractured rock and fault gouge, characterized by low strength and low stiffness. Under mining stress, the two sides of these faults will experience relative slippage, a process known as "fault activation." This process not only directly shears through the fault-crossing tunnel, causing abrupt damage, but also releases a large amount of elastic energy, generating strong dynamic loads, disturbing the surrounding rock mass, and triggering widespread rock instability, posing a significant hazard. Therefore, effective monitoring and accurate early warning of fault zone activation processes have become paramount for disaster prevention and control in deep engineering projects.

[0003] Currently, the mainstream fault-induced tunnel instability mechanism can be summarized in two aspects: (1) Stress triggering mechanism. The excavation of the tunnel or the advancement of the working face forms a goaf, which causes stress to transfer and concentrate in all directions. In particular, the stress component perpendicular to the fault strike increases significantly, which overcomes the static friction of the fault plane and thus induces sliding. (2) Activation-instability chain effect. Engineering activities break the original stress balance of the crust. The stress is highly concentrated at the fault end or locked area, which leads to the initiation and expansion of microcracks. The locked part of the two sides of the fault accumulates a large amount of strain energy before the displacement. When the stress reaches the critical state, the energy is suddenly released in the form of brittle fracture (microseismic event) and macroscopic slip. The released energy acts on the surrounding rock of the tunnel in the form of stress wave (dynamic load) and dislocation (static load). Static load causes shear and compression deformation of the surrounding rock of the tunnel; dynamic load may directly trigger rockburst, causing instantaneous catastrophic damage. When the tunnel is located on the footwall of the fault, it is particularly susceptible to the serious impact of the sliding of the footwall rock.

[0004] To prevent such disasters from harming engineering facilities and personnel, monitoring and early warning have become indispensable and effective means. The industry widely uses microseismic monitoring technology for disaster early warning, primarily based on changes in statistical parameters such as microseismic event frequency, energy release rate, and b-value. Existing early warning technologies often rely on single parameters (such as microseismic b-value or energy index) or involve isolated monitoring of faults and tunnels, exhibiting the following significant drawbacks: (1) Isolation: Existing methods may monitor fault activation and roadway response separately, failing to establish a causal relationship and linkage early warning mechanism between the two, and thus failing to answer the key question of "to what extent fault activation will pose a threat to the roadway".

[0005] (2) Uniqueness: It relies on a single parameter such as b value, and its changes are often accompanied by lag and randomness, resulting in insufficient reliability of early warning and inability to cross-verify the critical state of the system from multiple dimensions.

[0006] (3) Ambiguity: Parameter changes are easily affected by factors such as data integrity and external interference, resulting in a high false alarm rate and a high false alarm rate.

[0007] (4) Representational and empirical: The parameters are mostly based on empirical statistics, failing to deeply reveal the physical essence of fault activation as a "critical phase transition" in the rock mass system. The warning thresholds are mostly based on historical statistical experience, lacking universal criteria based on the inherent physical evolution law of the system.

[0008] Therefore, there is an urgent need for a method that can deeply integrate the "cause" of fault activation and the "effect" of roadway instability, capture the inherent evolutionary laws of the system earlier, and identify and warn of multi-parameter coordinated precursors based on rigorous physical theory. Summary of the Invention

[0009] The purpose of this invention is to provide a method for identifying precursors of roadway instability induced by multi-parameter fault activation in fused renormalization groups, thereby solving the aforementioned technical problems.

[0010] To achieve the above objectives, this invention provides a method for identifying precursors of roadway instability induced by multi-parameter fault activation in renormalization groups, the specific steps of which are as follows: Step S1: Deploy a network of microseismic sensors in the fault rupture zone and the surrounding roadways, and collect microseismic event signals in real time; Step S2: Install seismic-resistant displacement monitoring instruments in the roadway. At least two detection points of the seismic-resistant displacement monitoring instruments are not set in the fault rupture zone, and are used for real-time monitoring of roadway convergence deformation. Step S3: Preprocess the data collected in real time in step S1; Step S4: Define the rolling time window and spatial analysis area; Step S5: Calculate the renormalization group parameters of the critical state of the system based on the data preprocessed in step S3. ,in, For the association length, For the value of b, The spatial fractal dimension; Step S6: Calculate the tunnel deformation parameters of the external macroscopic response based on the data in Step S2. ; Step S7: Based on the renormalization group parameters of the system's internal critical state obtained in step S5 The tunnel deformation parameters of the external macroscopic response obtained in step S6 Construct a multi-parameter fusion critical early warning index to achieve unified measurement of internal and external states. Multi-parameter fusion critical early warning index The calculation formula is as follows: ; in, , , as well as All are weighting coefficients, and satisfy the following conditions: The weighting coefficients can be determined through historical case inversion, machine learning optimization, or expert experience methods. , as well as These are the reference values ​​for the correlation length, b-value, and spatial fractal dimension of the fault zone system under a stable background state, respectively. This is a reference threshold for the rate of change of roadway displacement, which is obtained by retrospectively analyzing historical data from long-term mine monitoring.

[0011] Step S8: Based on the multi-parameter fusion critical early warning index and multi-parameter fusion critical early warning index The rate of change is used to identify the precursors of roadway instability induced by fault activation, and an early warning level is obtained for early warning.

[0012] Preferably, in step S1, the microseismic event signals are analyzed to obtain data for each microseismic event. The data for each microseismic event includes the occurrence time, the three-dimensional spatial coordinates of the occurrence point, and the released energy. The set of data for all microseismic events is... , For the first Data on microseismic events, The total number of microseismic events.

[0013] Preferably, the preprocessing includes, in sequence, format conversion and quality check, bandpass filtering, wavelet denoising, automatic picking of initial values ​​for P-waves and S-waves using the AIC algorithm, manual sampling to check the picking results, and event filtering based on feature parameters and waveform clustering.

[0014] Preferably, in step S5, the probability of another microseismic event occurring within a set distance is calculated for microseismic events within the rolling time window and spatial analysis area using a spatial correlation function; the spatial correlation function... as follows: ; in, Set the distance variable; It is the distance increment; It refers to the first Centered on a microseismic event, at a distance of The number of other microseismic events existing within the spherical shell domain; The average number density of microseismic events within the spatial analysis area. It represents the total number of events within the current time window and spatial analysis area.

[0015] Preferably, in step S5, the process of calculating the association length is as follows: Under critical conditions, the spatial correlation function obeys the following scaling law: ; in, For the association length; From the above formula, we can obtain: ; in, To fit the length, linear least squares method is used for parameter fitting, resulting in: ; From the above, we can see that ,in The slope of the fitted line.

[0016] Preferably, in step S5, the value of b is calculated using the maximum likelihood method based on the Gutenberg-Richter law, and the calculation formula is as follows: ; in, To monitor the lower limit of the complete event energy recorded by the system, To release energy greater than The energy released by the corresponding microseismic events, To calculate the average value of multiple microseismic events.

[0017] Preferably, in step S5, the spatial fractal dimension is calculated using the correlation integral method, and the calculation formula is as follows: ; in, Let be the relevant integral function, representing a distance less than 1 / 2. The proportion of events, For unit step function, The spatial fractal dimension represents the degree of clustering in the spatial distribution of microseismic events. For the first The micro-seismic event and the first The distance of a micro-seismic event For the first Three-dimensional spatial coordinates of a microseismic event For the first Three-dimensional spatial coordinates of a microseismic event This represents the total number of microseismic events involved in the relevant integral calculation.

[0018] Preferably, in step S6, the displacement change rate or stress change rate of the roadway surface within the same time window is extracted as a quantitative indicator of the roadway's response to fault activation. Quantitative indicators The calculation formula is as follows: ; in, This refers to the change in surface displacement or stress within a time window. This represents the length of the time window.

[0019] Preferably, in step S8, a classification threshold is determined based on historical data and a learning algorithm. The classification threshold includes a first threshold. Second threshold and the third threshold ; when A blue alert indicates that the tunnel is normal, and all parameters are fluctuating around the background value and are in a stable state. when At that time, and A yellow alert is issued if the growth rate is within the first growth rate range, indicating that at least one parameter is abnormal. The R value continues to grow slowly, and the monitoring sampling frequency should be increased. when At that time, and The growth rate is in the second growth rate range (orange), indicating multiple parameters are abnormally correlated, which indicates that the fault rupture zone is being activated and there is a high probability of inducing roadway instability. An operational warning should be issued and engineering measures should be taken. It is recommended to take engineering measures such as strengthening support and optimizing the mining sequence.

[0020] when At that time, and The growth rate is within the third growth rate range, which is a red alert. Multiple parameters are abnormally coordinated and the roadway is deformed, indicating roadway instability. The roadway deformation is severe, and it is determined that an instability disaster is about to occur or is already occurring. The emergency plan should be activated immediately, and production should be stopped and personnel evacuated.

[0021] Therefore, the present invention employs the above-mentioned method for identifying precursors of roadway instability induced by multi-parameter fault activation in fusion renormalization groups, which has the following beneficial effects: (1) Causal linkage early warning: the precursors of fault activation and the risk of roadway instability are linked in a unified physical framework, realizing a series of early warnings from "cause" to "effect".

[0022] (2) Multi-parameter cross-validation: Multiple parameters that reflect the critical state of the system from different perspectives are used to verify each other, which greatly improves the reliability and anti-interference ability of the early warning signal.

[0023] (3) Multiple parameters reveal the common laws of the system tending to become critically unstable, and the early warning criteria have strong universality.

[0024] (4) By constructing a comprehensive early warning index, the risk is quantitatively classified, providing a precise basis for emergency response decisions at different levels.

[0025] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0026] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a diagram showing the arrangement of the laser rangefinder in an embodiment of the present invention; Figure 3 This is a diagram showing the arrangement of five displacement gauges according to an embodiment of the present invention; Figure 4 This is a time-series curve of the critical early warning index in an embodiment of the present invention.

[0027] Figure Labels 1. Vertical receiver; 2. Laser rangefinder; 3. Horizontal receiver; 4. Five-point displacement meter; 5. Tunnel cross-section. Detailed Implementation

[0028] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product is in use. They are used only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," and "connect" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0029] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0030] Taking the 3987m section of the East Mining Area of ​​Hongniu Copper Mine in Shangri-La City as an example, the mine has a mining depth of 450 meters. At this section level, the roadway crosses the F6 fault. The source material of the fault zone is relatively broken, and the risk of fault activation due to mining activities is high, which restricts the safe production of the mine.

[0031] like Figure 1 As shown, a method for identifying precursors of roadway instability induced by multi-parameter fault activation in renormalization groups is described, with the following specific steps: Step S1: Deploy a network of microseismic sensors in the fault rupture zone and the surrounding roadways, and collect microseismic event signals in real time.

[0032] This embodiment deploys 12 microseismic sensors to form a three-dimensional monitoring network, ensuring accurate coverage of the monitoring area. The sensors have a sampling frequency of 10kHz, enabling them to accurately locate microseismic events (location error ≤10m) and record their energy.

[0033] Step S2: Install seismic-resistant displacement monitoring instruments (five-point displacement gauge 4) in the roadway. At least two detection points of the seismic-resistant displacement monitoring instruments should not be located within the fault rupture zone, for real-time monitoring of roadway convergence deformation. For example... Figures 2-3 As shown, three five-point displacement meters 4 and two laser rangefinders 2 are installed on the roof and sides of the 115 roadway. The vertical receiver 1 and the horizontal receiver 3 are used in conjunction with the laser rangefinders 2 to monitor the convergence deformation of the roadway surface in real time, and the data is automatically transmitted to the ground server.

[0034] Step S3: Preprocess the data acquired in real time in Step S1, analyze the microseismic event signals to obtain data for each microseismic event. The data for each microseismic event includes the occurrence time, the three-dimensional spatial coordinates of the occurrence point, and the released energy. The set of data for all microseismic events is as follows: , For the first Data on microseismic events, The total number of microseismic events. Preprocessing includes format conversion and quality check, bandpass filtering, wavelet denoising, automatic picking of initial P-wave and S-wave values ​​using the AIC algorithm, manual sampling to verify the picking results, and event filtering based on feature parameters and waveform clustering.

[0035] Step S4: Define the rolling time window and spatial analysis area.

[0036] It automatically collects and analyzes monitoring data within a rolling time window of the past 24 hours, with a 1-hour cycle.

[0037] Step S5: Calculate the association length based on the data preprocessed in Step S3. b-value and spatial fractal dimension .

[0038] Association length ( ): By calculating the spatial correlation function of microseismic events The fitting results show that the calculated value for the current time window is 65 meters.

[0039] b-value: The slope of the GR relationship is calculated using the maximum likelihood method, and the current value is 0.78.

[0040] spatial fractal dimension The value is 1.85, calculated using the correlation integral method.

[0041] By analyzing monitoring data from historically stable periods in the region, background reference values ​​for each parameter were determined. , as well as The values ​​are 20m, 1.2m, and 2.5m respectively.

[0042] Step S6: Calculate the quantitative index of the roadway's response to fault activation based on the data from Step S2. The roof subsidence and sidewall convergence of the roadway were extracted from the roadway deformation monitoring system, and their overall deformation rate was calculated. Based on the mine's support design specifications, It is 0.5 mm / h.

[0043] Step S7: Calculate the critical early warning index .

[0044] , , as well as The values ​​were set to 0.4, 0.3, 0.2, and 0.1 respectively, and were determined through inversion and optimization using historical instability case data of the mine.

[0045] Substitute the processed real-time data and corresponding parameters into the critical early warning index. Calculation formula .

[0046] Step S8: Based on the critical early warning index and critical early warning index The rate of change is used to identify the precursors of roadway instability induced by fault activation, and an early warning level is obtained for early warning.

[0047] First threshold Second threshold and the third threshold We set the values ​​to 1.0, 1.3, and 1.5 respectively. The system is in a critical instability state, and a disaster is imminent. A red alert has been triggered, an alarm has been immediately issued, relevant responsible persons have been automatically notified, and the emergency response plan has been activated, requiring the immediate cessation of production and evacuation of personnel from the danger zone.

[0048] Table 1 shows calculation examples for other time windows. Figure 4 This is a time series curve of the critical warning index, and related warnings are also issued in other time windows.

[0049] Table 1 Calculation Examples of Other Time Windows ;

[0050] Upon receiving the red alert, on-site engineers immediately organized the evacuation of workers in roadway 115. Approximately two hours after the evacuation, a significant slip event with a magnitude (ML) of 1.8 was detected on the F6 fault, causing approximately 150 mm of roof subsidence and severe rib bulging in the roadway near the fault zone. However, due to the timely warning, no casualties were reported. This example fully demonstrates that the warning method can effectively integrate multi-source precursor information, quantify instability risks, and provide sufficient advance warning time, ultimately successfully preventing a potential major safety accident.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for identifying precursors of roadway instability induced by multi-parameter fault activation in renormalization groups, characterized in that, The specific steps are as follows: Step S1: Deploy a network of microseismic sensors in the fault rupture zone and the surrounding roadways, and collect microseismic event signals in real time; Step S2: Install seismic-resistant displacement monitoring instruments in the roadway. At least two detection points of the seismic-resistant displacement monitoring instruments are not set in the fault rupture zone, and are used for real-time monitoring of roadway convergence deformation. Step S3: Preprocess the data collected in real time in step S1; Step S4: Define the rolling time window and spatial analysis area; Step S5: Calculate the renormalization group parameters of the critical state of the system based on the data preprocessed in step S3. ,in, For the association length, For the value of b, The spatial fractal dimension; Step S6: Calculate the tunnel deformation parameters of the external macroscopic response based on the data in Step S2. ; Step S7: Based on the renormalization group parameters of the system's internal critical state obtained in step S5 The tunnel deformation parameters of the external macroscopic response obtained in step S6 Construct a multi-parameter fusion critical early warning index to achieve unified measurement of internal and external states. Multi-parameter fusion critical early warning index The calculation formula is as follows: ； in, , , as well as All are weighting coefficients, and satisfy the following conditions: ; , as well as These are the reference values ​​for the correlation length, b-value, and spatial fractal dimension of the fault zone system under a stable background state, respectively. This serves as a reference threshold for the rate of change of roadway displacement. Step S8: Based on the multi-parameter fusion critical early warning index and multi-parameter fusion critical early warning index The rate of change is used to identify the precursors of roadway instability induced by fault activation, and an early warning level is obtained for early warning.

2. The method for identifying precursors of roadway instability induced by multi-parameter fault activation in fusion renormalization groups according to claim 1, characterized in that: In step S1, the microseismic event signals are analyzed to obtain data for each microseismic event. The data for each microseismic event includes the occurrence time, the three-dimensional spatial coordinates of the occurrence point, and the released energy. The set of data for all microseismic events is... , For the first Data on microseismic events, The total number of microseismic events.

3. The method for identifying precursors of roadway instability induced by multi-parameter fault activation in fusion renormalization groups according to claim 2, characterized in that: The preprocessing process includes format conversion and quality check, bandpass filtering, wavelet denoising, automatic picking of initial values ​​for P-waves and S-waves using the AIC algorithm, manual checking and verification of the picking results, and event filtering based on feature parameters and waveform clustering.

4. The method for identifying precursors of roadway instability induced by multi-parameter fault activation in fusion renormalization groups according to claim 3, characterized in that: In step S5, the probability of another microseismic event occurring within a set distance is calculated for microseismic events within the rolling time window and spatial analysis area using a spatial correlation function; the spatial correlation function... as follows: ； in, Set the distance variable; It is the distance increment; It refers to the first Centered on a microseismic event, at a distance The number of other microseismic events existing within the spherical shell domain; The average number density of microseismic events within the spatial analysis area. It represents the total number of events within the current time window and spatial analysis area.

5. The method for identifying precursors of roadway instability induced by multi-parameter fault activation in fusion renormalization groups according to claim 4, characterized in that: In step S5, the process of calculating the association length is as follows: Under critical conditions, the spatial correlation function obeys the following scaling law: ； in, For the association length; From the above formula, we can obtain: ； in, To fit the length, linear least squares method is used for parameter fitting, resulting in: ； From the above, we can see that ,in The slope of the fitted line.

6. The method for identifying precursors of roadway instability induced by multi-parameter fault activation in fusion renormalization groups according to claim 5, characterized in that: In step S5, the value of b is calculated using the maximum likelihood method based on the Gutenberg-Richter law. The calculation formula is as follows: ； in, To monitor the lower limit of the complete event energy recorded by the system, To release energy greater than The energy released by the corresponding microseismic events, To calculate the average value of multiple microseismic events.

7. The method for identifying precursors of roadway instability induced by multi-parameter fault activation in fusion renormalization groups according to claim 6, characterized in that: In step S5, the spatial fractal dimension is calculated using the correlation integral method, and the calculation formula is as follows: ； in, Let be the relevant integral function, representing a distance less than 1 / 2. The proportion of events, For unit step function, The spatial fractal dimension represents the degree of clustering in the spatial distribution of microseismic events. For the first The micro-seismic event and the first The distance of a micro-seismic event For the first Three-dimensional spatial coordinates of a microseismic event For the first Three-dimensional spatial coordinates of a microseismic event This represents the total number of microseismic events involved in the relevant integral calculation.

8. The method for identifying precursors of roadway instability induced by multi-parameter fault activation in fusion renormalization groups according to claim 7, characterized in that: In step S6, the displacement change rate or stress change rate of the roadway surface within the same time window is extracted as a quantitative indicator of the roadway's response to fault activation. Quantitative indicators The calculation formula is as follows: ； in, This refers to the change in surface displacement or stress within a time window. This represents the length of the time window.

9. The method for identifying precursors of roadway instability induced by multi-parameter fault activation in fusion renormalization groups according to claim 8, characterized in that: In step S8, the grading thresholds are determined based on historical data and the learning algorithm. The grading thresholds include a first threshold. Second threshold and the third threshold ; when A blue alert indicates that the tunnel is normal and in a stable state. when At that time, and A yellow alert is issued if the growth rate is within the first growth rate range, indicating that at least one parameter is abnormal, and the monitoring sampling frequency should be increased. when At that time, and The growth rate is in the second growth rate range (orange), and multiple parameters are abnormally correlated, indicating that the fault rupture zone is being activated, which may induce roadway instability. An early warning should be issued and engineering measures should be taken. when At that time, and The growth rate is within the third growth rate range, which is a red alert. Multiple parameters are abnormally coordinated and the roadway is deformed, indicating roadway instability. The emergency plan should be activated immediately, production should be stopped and personnel evacuated.

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