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

Abstract

The present application relates to the technical field of mine safety monitoring and early warning, and more particularly to a method for identifying precursors of roadway instability induced by fault activation by fusing multi-parameter renormalization group, which comprises the following steps: collecting microseismic event signals and roadway convergence deformation, calculating correlation length, b value, spatial fractal dimension, and a quantitative index of roadway response to fault activation, and calculating a critical early warning index according to a constructed formula for the critical early warning index of multi-parameter fusion, identifying precursors of roadway instability induced by fault activation according to the critical early warning index, and constructing a physical model capable of linking early warning of fault activation causes and roadway instability consequences by deeply fusing multi-parameter indexes of renormalization group and roadway response information, thereby solving the problems of isolated, single, and lagging early warning of traditional methods, achieving early identification, early warning, and early disposal of roadway instability disasters induced by fault activation in deep mining, and providing unprecedented technical support for mine safety production.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mine safety monitoring and early warning, and particularly relates to a method for identifying a precursor of a roadway instability induced by a renormalization group multi-parameter fault activation. BACKGROUND

[0002] A roadway is a throat passageway of a mine, a tunnel and other underground engineering, and its stability is directly related to the safety and production efficiency of the entire engineering. In a deep high-stress environment, the excavation activity destroys the original rock stress balance, leading to stress redistribution, which easily activates the original geological structures such as faults and joints in the engineering area. A fault zone is a low-strength, low-rigidity soft zone composed of cataclastic rock and fault gouge, and under the action of mining stress, the two plates of the fault zone will slide relative to each other, i.e. "fault activation". This process not only directly shears the roadway passing through the fault, leading to sudden damage, but also releases a large amount of elastic energy, generates strong dynamic load, disturbs the surrounding rock, and causes large-scale surrounding rock instability, which is extremely dangerous. Therefore, effective monitoring of the fault zone activation process and accurate early warning have become a top priority in the prevention and control of deep engineering disasters.

[0003] At present, the main mechanism of roadway instability induced by a fault zone can be summarized as follows: (1) stress triggering mechanism. The roadway excavation or working face advancement forms a goaf, which causes stress to shift and concentrate to the surrounding area, especially the stress component perpendicular to the fault strike significantly increases, overcoming the static friction of the fault plane, thereby inducing sliding. (2) Activation-Instability Chain Effect. Engineering activities break the original stress balance of the earth's crust, and stress is highly concentrated at the end of the fault or the locking area, leading to the initiation and expansion of microcracks, and a large amount of strain energy is accumulated in the locked part of the fault before the fault slips. 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 roadway surrounding rock in the form of stress wave (dynamic load) and dislocation (static load). The static load causes the roadway surrounding rock to shear and compress and deform; the dynamic load may directly trigger rock burst, causing instantaneous catastrophic damage. When the roadway is located in the lower plate of the fault, it is particularly vulnerable to the serious impact of the downward sliding of the upper plate rock mass.

[0004] In order to prevent such disasters from causing harm to engineering facilities and personnel, monitoring and early warning become an indispensable and effective means. The microseismic monitoring technology is widely used in the industry for disaster early warning, and the main basis includes changes in statistical parameters such as microseismic event frequency, energy release rate, and b value. Existing early warning technologies mostly rely on a single parameter (such as microseismic b value, energy index) or monitor the fault and the roadway in isolation, which has the following significant defects:

[0005] (1) Isolation: Existing methods or separate monitoring of fault activation and roadway response, unable to establish the causal relationship and linkage warning mechanism between the two, unable to answer the key question of "to what extent will the fault activation pose a threat to the roadway".

[0006] (2) Singularity: Dependence on single parameters such as b value, which often accompanies hysteresis and contingency, insufficient warning reliability, and unable to verify the system critical state from multiple dimensions.

[0007] (3) Fuzziness: Parameter changes are easily affected by data integrity, external interference and other factors, with high false and missed alarm rates.

[0008] (4) Appearance and experience: Parameters are mostly based on empirical statistics, failing to reveal the physical nature of fault activation as a "critical phase transition" of rock mass system, and the warning threshold is mostly based on historical statistical experience, lacking universal criteria from the intrinsic physical evolution law of the system.

[0009] Therefore, there is an urgent need for a multi-parameter collaborative precursor recognition and warning method that can deeply integrate the "cause" of fault activation and the "effect" of roadway instability, can capture the intrinsic evolution law of the system earlier, and is based on strict physical theory. SUMMARY

[0010] The purpose of the present application is to provide a renormalization group multi-parameter fault activation induced roadway instability precursor recognition method, which solves the above technical problems.

[0011] To achieve the above purpose, the present application provides a renormalization group multi-parameter fault activation induced roadway instability precursor recognition method, the specific steps are as follows:

[0012] Step S1: Arranging a microseismic sensor network in the fault fracture zone and the roadway around the fault fracture zone, and collecting microseismic event signals in real time;

[0013] Step S2: Arranging an anti-seismic displacement monitor in the roadway, and the at least two detection points of the anti-seismic displacement monitor are not arranged in the fault fracture zone, for real-time monitoring of roadway convergence deformation;

[0014] Step S3: Preprocessing the data collected in real time in step S1;

[0015] Step S4: Draw a rolling time window and a spatial analysis area;

[0016] Step S5: Calculating the system internal critical state renormalization group parameters according to the preprocessed data of step S3 , wherein, is the correlation length, is the b value, is the spatial fractal dimension;

[0017] Step S6: Calculate the roadway deformation parameter of external macroscopic response according to the data in step S2 ;

[0018] Step S7: Construct a multi-parameter fusion critical early warning index realizing the unified measurement of internal and external state according to the system internal critical state renormalization group parameter obtained in step S5 and the roadway deformation parameter of external macroscopic response obtained in step S6 The multi-parameter fusion critical early warning index The calculation formula is as follows:

[0019] ;

[0020] Among them, , , and are weight coefficients, and satisfy The weight coefficients can be determined by historical case inversion, machine learning optimization or expert experience method; , and are the reference values of the correlation length, the b value and the spatial fractal dimension of the fault zone system in the stable background state, is the reference threshold value of the roadway displacement rate, which is obtained according to the long-term monitoring historical data of the mine.

[0021] Step S8: According to the multi-parameter fusion critical early warning index and the change speed of the multi-parameter fusion critical early warning index , identify the precursors of roadway instability induced by fault activation, obtain the early warning grade and give early warning.

[0022] Preferably, in step S1, the microseismic event signal is analyzed to obtain the data of each microseismic event, including the occurrence time, the three-dimensional spatial coordinates of the occurrence point and the released energy. The collection of all microseismic event data is , is the data of the th microseismic event, is the total number of microseismic events.

[0023] Preferably, the preprocessing includes format conversion and quality check, band pass filtering, wavelet denoising, automatic picking of P wave and S wave initial value by AIC algorithm, manual spot check and correction of picking results, and event screening based on characteristic parameters and waveform clustering.

[0024] ​​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:

[0025] ;

[0026] 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.

[0027] Preferably, in step S5, the process of calculating the association length is as follows:

[0028] Under critical conditions, the spatial correlation function obeys the following scaling law:

[0029] ;

[0030] in, For the association length;

[0031] From the above formula, we can obtain:

[0032] ;

[0033] in, To fit the length, linear least squares method is used for parameter fitting, resulting in:

[0034] ;

[0035] From the above, we can see that ,in The slope of the fitted line.

[0036] 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:

[0037] ;

[0038] 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, The average of the plurality of microseismic events is calculated.

[0039] Preferably, in step S5, the spatial fractal dimension is calculated by using the correlation integral method, and the calculation formula is as follows:

[0040] ;

[0041] Wherein, is a correlation integral function, indicating the proportion of event pairs with a distance less than , is a unit step function, is a spatial fractal dimension, representing the clustering degree of the spatial distribution of microseismic events, is the distance between the i th microseismic event and the j th microseismic event, is the three-dimensional spatial coordinate of the i th microseismic event, is the three-dimensional spatial coordinate of the j th microseismic event, is the total number of microseismic events participating in the correlation integral calculation. Preferably, in step S6, the rate of change of the roadway surface displacement or the rate of change of stress in the same time window is extracted as a quantitative index of the response of the roadway to the fault activation; The quantitative index is calculated as follows:

[0042]

[0043] ;

[0044] ;

[0045] Wherein, is the displacement change or stress change of the roadway surface in the time window, is the length of the time window.

[0046] Preferably, in step S8, the grading threshold is calibrated according to the historical data and the learning algorithm, and the grading threshold includes a first threshold , a second threshold and a third threshold ;

[0047] When , it is a blue warning, indicating that the roadway is normal, and each parameter fluctuates around the background value and is in a stable state;

[0048] When , and the growth rate of is within the first growth rate range, it is a yellow warning, indicating that at least one parameter is abnormal, the R value is continuously and slowly increasing, and the monitoring sampling frequency is increased;

[0049] When​​​ When the growth rate of the parameter is in the second growth rate range, the multiple parameters are abnormal, the fault fracture zone is being activated, and a high probability of inducing roadway instability exists, so an operation warning needs to be issued and engineering measures need to be taken. When the growth rate of the parameter is in the third growth rate range, the multiple parameters are abnormal and the roadway is deformed, which indicates that the roadway is unstable, the roadway deformation is severe, and it is determined that the instability disaster is about to occur or is occurring, so an emergency plan needs to be started immediately, and production needs to be stopped and people need to be evacuated.

[0050] When the growth rate of the parameter is in the third growth rate range, the multiple parameters are abnormal and the roadway is deformed, which indicates that the roadway is unstable, the roadway deformation is severe, and it is determined that the instability disaster is about to occur or is occurring, so an emergency plan needs to be started immediately, and production needs to be stopped and people need to be evacuated. When the growth rate of the parameter is in the third growth rate range, the multiple parameters are abnormal and the roadway is deformed, which indicates that the roadway is unstable, the roadway deformation is severe, and it is determined that the instability disaster is about to occur or is occurring, so an emergency plan needs to be started immediately, and production needs to be stopped and people need to be evacuated. Therefore, the fusion renormalization group multi-parameter fault activation precursor identification method has the beneficial effects that:

[0051] Therefore, the fusion renormalization group multi-parameter fault activation precursor identification method has the beneficial effects that:

[0052] (1) Causal linkage warning, the fault activation precursor and the roadway instability risk are associated in a unified physical framework, realizing the series warning from "cause" to "effect".

[0053] (2) Multi-parameter cross verification, multiple parameters reflecting the critical state of the system from different aspects are comprehensively utilized, and the reliability and anti-interference ability of the warning signal are greatly improved.

[0054] (3) Multiple parameters reveal the common law of the system tending to be critical instability, and the warning criterion has strong universality.

[0055] (4) By constructing a comprehensive warning index, the risk is quantified and graded, and accurate basis is provided for emergency response decisions of different levels.

[0056] The technical solutions of the present application will be further described in detail below with reference to the drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 The method flowchart of the present application is shown in the figure;

[0058] Figure 2 The laser range finder arrangement diagram of the embodiment of the present application is shown in the figure;

[0059] Figure 3 The five-point displacement meter arrangement diagram of the embodiment of the present application is shown in the figure;

[0060] Figure 4 The time sequence curve diagram of the critical warning index of the embodiment of the present application is shown in the figure.

[0061] REFERENCE NUMERALS

[0062] 1, vertical receiver; 2, laser range finder; 3, horizontal receiver; 4, five-point displacement meter; 5, roadway section. DETAILED DESCRIPTION

[0063] In the description of the present application, it should be noted that the terms "upper", "lower", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, or the orientation or positional relationship commonly used when the product of the present application is used, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the indicated device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In the description of the present application, it should be noted that, unless otherwise specified and limited, the terms "arrangement", "installation", "connection" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be connected inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0064] The embodiments of the present application will be described in detail below with reference to the drawings.

[0065] Taking the roadway of a fault fracture zone in the 3987m subsection of the east mining area of the Red Bull Copper Mine in Shangri-La City as an example, the mining depth of the mine is 450m, and at this subsection level, the F6 fault is crossed along the vein roadway. The fault zone is relatively broken, and the risk of fault activation caused by mining is high, which restricts the safe production of the mine.

[0066] As shown in Figure 1 , a method for identifying precursors of roadway instability induced by fault activation by fusing renormalization group multi-parameter, the specific steps are as follows:

[0067] Step S1: A microseismic sensor network is arranged in the fault fracture zone and the roadway around the fault fracture zone, and microseismic event signals are collected in real time.

[0068] In this embodiment, 12 microseismic sensors are arranged to form a three-dimensional monitoring network to ensure accurate coverage of the monitoring area. The sensor sampling frequency is 10kHz, which can accurately locate the microseismic event (positioning error ≤10m) and record its energy.

[0069] Step S2: An anti-seismic displacement monitor (five-point displacement meter 4) is arranged in the roadway, and at least two detection points of the anti-seismic displacement monitor are not arranged in the fault fracture zone, which is used for real-time monitoring of the convergence deformation of the roadway. As shown in Figures 2-3As 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.

[0070] 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.

[0071] Step S4: Define the rolling time window and spatial analysis area.

[0072] It automatically collects and analyzes monitoring data within a rolling time window of the past 24 hours, with a 1-hour cycle.

[0073] Step S5: Calculate the association length based on the data preprocessed in Step S3. b-value and spatial fractal dimension .

[0074] 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.

[0075] b-value: The slope of the GR relationship is calculated using the maximum likelihood method, and the current value is 0.78.

[0076] spatial fractal dimension The value is 1.85, calculated using the correlation integral method.

[0077] 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.

[0078] 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, 0.5mm / h.

[0079] Step S7: Calculate the critical early warning index .

[0080] , , and respectively take 0.4, 0.3, 0.2 and 0.1, which are determined by the historical instability case data inversion optimization of the mine.

[0081] The processed real-time data and corresponding parameters are substituted into the critical early warning index calculation formula, .

[0082] Step S8: According to the critical early warning index and the change speed of the critical early warning index , the precursors of fault activation induced roadway instability are identified, and the early warning level is obtained for early warning.

[0083] The first threshold , the second threshold and the third threshold respectively take 1.0, 1.3 and 1.5. , the system is in a critical instability state, and a disaster is about to occur. Trigger a red early warning, immediately issue an alarm, automatically notify the relevant person in charge, and start the emergency plan, requiring the dangerous area to stop production immediately and evacuate personnel.

[0084] Table 1 is a calculation example of other time windows, Figure 4 is the time sequence curve of the critical early warning index, and related early warning is also carried out in other time windows.

[0085] Table 1 Calculation example of other time windows

[0086] ;

[0087] After receiving the red early warning, the on-site engineering personnel immediately organized the workers in the 115 roadway to evacuate. About 2 hours after all personnel were evacuated, a significant slip event of the F6 fault with an earthquake level ML of 1.8 was monitored, causing about 150mm of roof subsidence and serious rib bulging near the fault zone along the vein roadway. However, due to timely early warning, no casualties were caused. This embodiment fully proves that the early warning method can effectively integrate multi-source precursor information, quantify instability risk, and provide sufficient advanced warning time, ultimately successfully avoiding a major safety accident that may occur.

[0088] It should be pointed out finally that the above examples are only used to illustrate the technical solutions of the present application but not to limit it, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can still be modified or replaced equivalently, and these modifications or equivalent replacements should not make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

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: arranging a microseismic sensor network in the fault fracture zone and the roadway around the fault fracture zone, and collecting microseismic event signals in real time; Step S2: arranging an anti-seismic displacement monitor in the roadway, and setting at least two detection points of the anti-seismic displacement monitor outside the fault fracture zone, for real-time monitoring of roadway convergence deformation; Step S3: pre-processing the data collected in real time in step S1; Step S4: demarcating a rolling time window and a spatial analysis region; Step S5: Calculate the system internal critical state renormalization group parameter according to the pre-processed data in step S3 wherein, is the correlation length, is the b-value, is the spatial fractal dimension; Step S6: Calculate the tunnel deformation parameters of the external macroscopic response according to the data in step S2 ; Step S7: Renormalization group parameter of internal critical state of system obtained according to step S5 and the obtained external macro response tunnel deformation parameter of step S6 , construct a multi-parameter fusion critical early warning index which realizes the unified measurement of internal and external states , multi-parameter fusion critical early warning index The calculation formula is as follows: ； wherein, , , and are weight coefficients, and satisfy ; , and are reference values of the correlation length, the b value and the spatial fractal dimension of the fault zone system in a stable background state, respectively, is a reference threshold value of the displacement change rate of the roadway. Step S8: determining the critical early warning index according to the multi-parameter fusion and the multi-parameter fusion critical early warning index The change speed is used to identify the precursors of roadway instability induced by fault activation, and the early warning level is obtained for early warning.

2. The method according to claim 1, wherein the method is characterized by: In step S1, the microseismic event signal is parsed to obtain data of each microseismic event, the data of the microseismic event including occurrence time, three-dimensional spatial coordinates of the occurrence point and released energy, and a collection of data of all microseismic events is , data of the first microseismic event, total number of microseismic events.

3. The method according to claim 2, wherein the method is characterized by: The pre-processing includes, in sequence, format conversion and quality inspection, band-pass filtering, wavelet denoising, automatic picking of P-wave and S-wave initial values by using AIC algorithm, manual spot check and correction of picking results, and event screening based on characteristic parameters and waveform clustering.

4. The method according to claim 3, wherein the method is characterized by: In step S5, the microseismic events within the rolling time window and the spatial analysis region are calculated by a spatial correlation function to calculate the probability of another microseismic event occurring within a set distance; the spatial correlation function As follows: ； wherein, setting a distance variable; is a distance increment; refers to the number of microseismic events existing within a spherical shell domain with a radius of from the center of the th microseismic event; is the average microseismic event density within the spatial analysis region, is the total number of events within the current time window and spatial analysis region.

5. The method according to claim 4, wherein the method is characterized by: In step S5, the correlation length is calculated as follows: Under the critical state, the spatial correlation function obeys the following scaling law: ； wherein is the correlation length; From the above formula, we have: ； wherein For fitting length, the linear least square method is used for parameter fitting, and the following is obtained: ； From the above, wherein is the slope of the fitted straight line.

6. The method according to claim 5, wherein the method is characterized by: In step S5, the b value is calculated based on the Gutenberg-Richter law by using the maximum likelihood method, and the calculation formula is as follows: ； wherein, the lower limit of the complete event energy recorded by the monitoring system, the release energy greater than the release energy of the microseismic event corresponding to, the average of a plurality of microseismic events.

7. The method according to claim 6, wherein the method is characterized by: In step S5, the spatial fractal dimension is calculated by using the correlation integral method, and the calculation formula is as follows: ； where, is the correlation integral function, representing the proportion of event pairs with distances less than is the unit step function, is the spatial fractal dimension, representing the degree of clustering of the spatial distribution of microseismic events, is the distance between the th microseismic event and the th microseismic event, is the three-dimensional spatial coordinate of the th microseismic event, is the three-dimensional spatial coordinate of the th microseismic event, is the total number of microseismic events involved in the correlation integral calculation.​ 8. The method according to claim 7, wherein the method is characterized by: In step S6, the roadway surface displacement rate or stress rate in the same time window is extracted as a quantitative index of the response of the roadway to the fault activation. Quantitative indicators The calculation formula is as follows: ； wherein, is the amount of displacement change or stress change of the roadway surface within a time window, is the length of the time window.

9. The method according to claim 8, wherein the method is characterized by: In step S8, the grading thresholds including the first threshold , the second threshold and the third threshold are calibrated according to historical data and learning algorithm. When blue pre-warning, indicating that the roadway 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 , and The growth rate of the fault fracture zone is in the second growth rate range, which is orange, and the multiple parameters are abnormal, indicating that the fault fracture zone is being activated, and there is a probability of inducing roadway instability. 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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