Intelligent advanced target non-blasting fracturing method and system for reducing rock burst

Abstract

The application discloses a method and system for reducing rock burst by intelligent advanced target non-blasting fracturing, and the method comprises the following steps: arranging a monitoring system in a rock burst tendency area within a predetermined range from a working face to obtain activity data of a rock mass; connecting the installed monitoring system to a microseismic monitoring device through a data acquisition line to collect the rock mass activity data collected by the monitoring system to obtain microseismic monitoring data; communicatively connecting a microseismic analysis device and an early warning device to the microseismic monitoring device, establishing a time series model by the microseismic analysis device, analyzing the microseismic monitoring data, and predicting a potential rock burst area in a rock burst tendency area near a tunnel construction working face based on the microseismic analysis result by the early warning device, and dividing the potential rock burst area into different grades; and according to the early warning signal, drilling a non-blasting fracturing hole in the potential rock burst area, and injecting low-wave impedance proppants into the non-blasting fracturing hole to eliminate potential rock burst disasters.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of rock engineering such as mines and tunnels, and particularly relates to a method and system for intelligent advanced targeted non-explosive fracturing to reduce rock burst. BACKGROUND

[0002] In the process of deep mine excavation, mining and deep tunnel excavation, hard surrounding rock is often in a high stress and strong disturbance environment, and dynamic disasters such as rock burst, rock burst, rock burst and mine earthquake often occur. When rock burst disaster occurs, a large amount of elastic energy is released instantaneously, and the rock is thrown out, endangering the safety of personnel and equipment, and may cause serious accidents.

[0003] Therefore, the technical personnel in the field have designed and developed various rock burst control methods, such as advanced blasting or fracturing rock pressure relief, and drilling advanced stress release holes to relieve stress concentration. By reducing rock burst hazards, the occurrence of impact accidents is reduced, and the effect of preventing and controlling disasters is achieved.

[0004] However, at present, advanced stress release or pressure relief has gone to a complex and one-size-fits-all road, and a large number of schemes are not targeted, and the effect is often good during testing, but the application enthusiasm of the construction unit is low. The main reason is that taking measures in a general way at the working face will sharply increase the cost, and in fact some areas do not need it. In addition, the process is complex, and the risk avoidance measures and ventilation process are problems, and most methods can only become "paper schemes".

[0005] Because the advanced rock burst disaster control technology at the present stage has fallen into the inertia of complex thinking, leading to more and more new methods of pressure relief (stress release) being taken, but the application is getting worse and worse, therefore, under the premise of reducing rock burst hazards, the precision of the scheme application is improved as much as possible, and the manufacturing cost and construction difficulty of the equipment are greatly reduced, so that the scheme implementation returns to simplicity and convenience. SUMMARY

[0006] The present application is directed to the problems and needs raised in the foregoing, and proposes a method and system for intelligent advanced targeted non-explosive fracturing to reduce rock burst, which can achieve the above technical purposes and bring other technical effects due to the adoption of the following technical features.

[0007] One object of the present application is to propose a method for intelligent advanced targeted non-explosive fracturing to reduce rock burst, comprising the following steps:

[0008] S10: arranging a monitoring system in a rock burst tendency area within a predetermined range from the working face to obtain rock mass activity data, wherein the monitoring system comprises a displacement sensor, a stress sensor, a temperature sensor, a sound wave sensor and a vibration sensor;

[0009] S20: connecting the installed monitoring system to the microseismic monitoring device through a data acquisition line to collect the rock mass activity data collected by the monitoring system to obtain microseismic monitoring data;

[0010] S30: communicating the microseismic analysis device and the early warning device with the microseismic monitoring device, establishing a time series model through the microseismic analysis device, analyzing the microseismic monitoring data, and predicting the potential rock burst area of the rock burst prone area near the tunnel construction face based on the microseismic analysis result through the alarm device, and dividing the grade of the potential rock burst area;

[0011] S40: according to the early warning signal, target non-bursting drilling in the potential rock burst area, and injecting low wave impedance proppant into the non-bursting drilling to eliminate the potential rock burst disaster.

[0012] In one example of the present application, in step S30, the time series model includes the following steps: S31: using the history and current data of the monitoring area microseismic single-day maximum energy as a sample, performing stationarity analysis, and if it is not stationary, performing difference operation to obtain an approximately stationary sequence; S32: selecting an appropriate model to fit the sample microseismic single-day maximum energy, and performing order determination and parameter estimation on the model, and performing adaptability test on the model; S33: using the established model to predict the future value of the difference, and then obtaining the future value of the microseismic single-day maximum energy through inverse difference operation, and in the mining process, using the established model to extrapolate and predict, and comparing with the early warning index to realize danger warning;

[0013] In one example of the present application, in the step S31, the stationarity analysis includes the following steps:

[0014] First, fit the adaptive model of the sequence, and then test whether there is a unit root in the characteristic equation composed of the autoregressive part parameters of the model, if there is no unit root, the sequence is stationary, if there is a unit root, the sequence is not stationary;

[0015] The specific process is as follows: in the autoregressive model AR (p), the random sequence {X t} satisfies the following formula:

[0016] ;

[0017] Let , make the following hypothesis test:

[0018] ;

[0019] Construct the unit root ADF test statistic: ; is the parameter The sample standard deviation of the autoregressive moving average model ARMA (p, q) is the same as the autoregressive model AR (p). If it is a sliding average model MA (q), the regression part has no parameters and must be stable.

[0020] In one example of the present invention, in step S32, determining the order of the model includes the following steps:

[0021] First, by judging whether formula (1) and formula (2) are valid, we can roughly judge the truncation of the autocorrelation coefficient of the stationary series and the truncation of the partial correlation coefficient of the stationary series and make a preliminary order determination; among them,

[0022] ;

[0023] ;

[0024] Where: For the Estimation of the autocorrelation coefficient of the period; For the The partial correlation coefficient estimation of the period; is the sequence length; is the maximum lag period of the autocorrelation coefficient;

[0025] If formula (1) is established, it is considered that the autocorrelation coefficient of the sequence is truncated at q steps, and the moving average model MA is established, and the initial order is determined to be q; if formula (2) is established, it is considered that the partial correlation coefficient of the sequence is truncated at p steps, and the autoregressive model AR is established, and the initial order is determined to be p;

[0026] Then, the AIC criterion and the BIC criterion were used to determine the final order of the model.

[0027] In one example of the present invention, the mathematical expressions of the AIC criterion and the BIC criterion are respectively:

[0028] ;

[0029] Where: is the sequence length; is the maximum likelihood estimate of the model residual variance.

[0030] In one example of the present invention, in step S32, performing adaptability test on the model includes the following steps:

[0031] use Test method, constructed statistics To test the adaptability of the model, it is only necessary to perform a white noise test on the model residuals. The statistic The expression is as follows:

[0032] ;

[0033] wherein, n is the number of samples, r is the autocorrelation coefficient of the residual sequence, p is the maximum lag number of the autocorrelation coefficient;

[0034] If the model is appropriate, the statistic approximately obeys ; at a given significant level , if , the residual sequence is considered to be a white noise sequence, and the fitting model is significant; if , the residual sequence is considered to be not a white noise sequence, and the fitting model is not significant.

[0035] In one example of the present application, when the ARMA model is selected to fit the sample microseismic single-day maximum energy in step S32, the modeling step is as follows:

[0036] It is judged whether the original microseismic time series data is stationary, that is, the stationarity of the original data is tested, and if the sequence does not satisfy the stationarity condition, it is transformed by difference, logarithm or other transformation to make it stationary;

[0037] The autocorrelation AC and partial autocorrelation PAC of the sequence are calculated to determine the order p and q values of the autoregressive moving average model ARMA of the stationary microseismic time series;

[0038] The unknown parameters of the model are estimated, the significance of the parameters and the reasonableness of the model are tested;

[0039] Diagnostic analysis is performed to confirm that the obtained model is consistent with the observed data characteristics.

[0040] In one example of the present application, before the step S30, the time series model is established, which further includes: first, white noise test is performed on the two groups of time series, if the test result is a non-white noise sequence, then the stationarity of the microseismic data is judged, and if the time series is non-stationary, it should be converted into a stationary sequence before the model is established.

[0041] Another object of the present application is to provide an intelligent advanced targeted non-explosive cracking rock burst reduction system, comprising:

[0042] The active data acquisition device is configured to arrange a monitoring system in a rock burst tendency area within a predetermined range from the working face to obtain active data of the rock mass, wherein the monitoring system comprises a displacement sensor, a stress sensor, a temperature sensor, an acoustic wave sensor and a vibration sensor.

[0043] Microseismic data acquisition device, configured to connect the installed monitoring system to the microseismic monitoring equipment through the data acquisition line to collect the rock mass activity data collected by the monitoring system to obtain microseismic monitoring data;

[0044] Microseismic data analysis device, configured to communicate the microseismic analysis equipment and the early warning equipment with the microseismic monitoring equipment, establish a time series model through the microseismic analysis equipment, analyze the microseismic monitoring data, and predict the potential rock burst area of the rock burst prone area near the tunnel construction face based on the microseismic analysis result through the alarm equipment, and divide the grade of the potential rock burst area;

[0045] Rock burst elimination device, configured to target the non-bursting drill hole in the potential rock burst area according to the early warning signal, and inject low wave impedance proppant into the non-bursting drill hole to eliminate the potential rock burst disaster.

[0046] In one example of the present application, the microseismic data analysis device comprises:

[0047] Stationarity analysis unit, configured to use the history and current data of the maximum energy of microseismic single day in the monitoring area as samples to perform stationarity analysis, and if it is not stationary, perform difference operation to obtain an approximately stationary sequence;

[0048] Adaptive test unit: configured to select an appropriate model to fit the maximum energy of the sample microseismic single day, estimate the order and parameters of the model, and perform adaptive test on the model;

[0049] Prediction and early warning unit: configured to predict the future value of the difference using the established model, then obtain the future value of the maximum energy of the microseismic single day through the inverse difference operation, and use the established model to extrapolate and predict during the mining process, and compare with the early warning index to realize the danger warning.

[0050] The optimal embodiments of the present application will be described in more detail below with reference to the accompanying drawings, so that the features and advantages of the present application can be easily understood. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings of the embodiments of the present application will be briefly introduced below. The drawings are only used to show some embodiments of the present application, and not to limit all embodiments of the present application to this.

[0052] Figure 1 The structure diagram of the intelligent advanced targeted non-bursting fracturing rock burst reduction according to the embodiment of the present application;

[0053] Figure 2 The method flow chart of the intelligent advanced targeted non-bursting fracturing rock burst reduction according to the embodiment of the present application;

[0054] Figure 3 A microseismic single-day maximum energy time series diagram according to an embodiment of the present application;

[0055] Figure 4 A sequence Energy unit root test result diagram according to an embodiment of the present application;

[0056] Figure 5 A first-order difference post-time series diagram according to an embodiment of the present application;

[0057] Figure 6 A first-order difference sequence ADF test result diagram according to an embodiment of the present application;

[0058] Figure 7 A first-order difference sequence ADF test model fitting result according to an embodiment of the present application;

[0059] Figure 8 A curve diagram of a raw sequence, a first-order difference sequence and a second-order difference sequence according to an embodiment of the present application;

[0060] Figure 9 A microseismic single-day maximum energy stationary sequence correlation diagram and a partial correlation diagram according to an embodiment of the present application;

[0061] Figure 10 A raw sequence and a lag sequence correlation scatter diagram according to an embodiment of the present application;

[0062] Figure 11 An MA(1) model regression result according to an embodiment of the present application;

[0063] Figure 12 An MA(1) model fitting effect according to an embodiment of the present application;

[0064] Figure 13 An MA(1) residual curve and a Q-Q diagram according to an embodiment of the present application;

[0065] Figure 14 A microseismic single-day maximum energy logarithmic difference transformation time series diagram according to an embodiment of the present application. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solutions and advantages of the technical solutions of the present application clearer, the technical solutions of the embodiments of the present application will be described clearly and completely below in conjunction with the drawings of the specific embodiments of the present application. The same reference signs in the drawings represent the same parts. It should be noted that the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the described embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without any creative effort fall within the scope of protection of the present application.

[0067] Unless otherwise defined, technical terms or scientific terms used herein should be understood as having the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terms "first", "second", and similar terms used in the description and the claims of the present patent application do not necessarily mean any order, number, or importance, but are only used to distinguish different components. Similarly, the terms "one" or "a" and the like do not necessarily mean a quantity limitation. The terms "including", "containing", and the like mean that the elements or objects before the term encompass the elements or objects listed after the term and their equivalents, without excluding other elements or objects. The terms "connected" or "connected" and the like do not necessarily mean physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "up", "down", "left", "right", and the like are only used to represent relative positional relationships, and when the absolute positions of the described objects change, the relative positional relationships may also change accordingly.

[0068] According to the method for intelligently reducing rock burst by advanced target non-blasting cracking according to the first aspect of the present application, the method comprises the following steps:

[0069] S10: arranging a monitoring system in a rock burst tendency area within a predetermined range from the working face to obtain activity data of the rock mass, wherein the monitoring system comprises a displacement sensor, a stress sensor, a temperature sensor, a sound wave sensor, and a vibration sensor;

[0070] S20: connecting the installed monitoring system to a microseismic monitoring device through a data acquisition line to collect the activity data of the rock mass collected by the monitoring system to obtain microseismic monitoring data;

[0071] S30: communicatively connecting a microseismic analysis device and an early warning device to the microseismic monitoring device, establishing a time series model through the microseismic analysis device, analyzing the microseismic monitoring data, and predicting potential rock burst areas in the rock burst tendency area near the working face of the tunnel construction based on the microseismic analysis results through the alarm device, and dividing the grades of the potential rock burst areas;

[0072] S40: According to the early warning signal, a non-bursting drill hole is targeted to be drilled in the potential rock burst area, and low wave impedance proppants are injected into the non-bursting drill hole to eliminate the potential rock burst disaster.

[0073] The method can not only accurately predict the potential rock burst area of the rock burst tendency area near the tunnel construction face, but also actively reduce the stress concentration of the deep hard rock roadway and the working face of the stope, weaken the stress wave disturbance, has high accuracy, greatly reduces the manufacturing cost and construction difficulty of equipment, has important guiding role for hard rock stratum tunneling and rock burst prediction and control, and has strong practicality.

[0074] In one example of the present application, in step S30, the time series model is established, including the following steps: S31: using the history and current data of the maximum energy of the microseismic single day in the monitoring area as samples, performing stationarity analysis, if not stationary, performing difference operation to obtain an approximately stationary sequence; S32: selecting an appropriate model to fit the maximum energy of the microseismic single day of the sample, and performing order determination and parameter estimation on the model, and performing adaptability test on the model; S33: using the established model to predict the future value of the difference, and then obtaining the future value of the maximum energy of the microseismic single day through inverse difference operation, in the mining process, using the established model to extrapolate and predict, and comparing with the early warning index to realize danger early warning;

[0075] In one example of the present application, in the step S31, the stationarity analysis includes the following steps:

[0076] First, fit the adaptive model of the sequence, and then test whether there is a unit root in the characteristic equation composed of the autoregressive part parameters of the model, if there is no unit root, the sequence is stationary, if there is a unit root, the sequence is not stationary;

[0077] The specific process is as follows: in the autoregressive model AR (p), the random sequence {X t} satisfies the following formula:

[0078] ;

[0079] Let , make the following hypothesis test:

[0080] ;

[0081] Construct the unit root ADF test statistic: ; is the sample standard deviation of the parameter , the autoregressive moving average model ARMA (p, q) is the same as the autoregressive model AR (p), if it is a moving average model MA (q), the regression part has no parameter, and it is definitely stationary.

[0082] In one example of the present invention, in step S32, determining the order of the model includes the following steps:

[0083] First, by judging whether formula (1) and formula (2) are valid, we can roughly judge the truncation of the autocorrelation coefficient of the stationary series and the truncation of the partial correlation coefficient of the stationary series and make a preliminary order determination; among them,

[0084] ;

[0085] ;

[0086] Where: For the Estimation of the autocorrelation coefficient of the period; For the The partial correlation coefficient estimation of the period; is the sequence length; is the maximum lag period of the autocorrelation coefficient;

[0087] If formula (1) is established, it is considered that the autocorrelation coefficient of the sequence is truncated at q steps, and the moving average model MA is established, and the initial order is determined to be q; if formula (2) is established, it is considered that the partial correlation coefficient of the sequence is truncated at p steps, and the autoregressive model AR is established, and the initial order is determined to be p;

[0088] Then, the AIC criterion and the BIC criterion were used to determine the final order of the model.

[0089] In one example of the present invention, the mathematical expressions of the AIC criterion and the BIC criterion are respectively:

[0090] ;

[0091] Where: is the sequence length; is the maximum likelihood estimate of the model residual variance.

[0092] In one example of the present invention, in step S32, performing adaptability test on the model includes the following steps:

[0093] use Test method, constructed statistics To test the adaptability of the model, it is only necessary to perform a white noise test on the model residuals. The statistic The expression is as follows:

[0094] ;

[0095] in, is the number of samples, is the autocorrelation coefficient of the residual sequence, the maximum lag number of autocorrelation coefficient;

[0096] the statistic approximately obeys at a given significance level if , the residual sequence is considered as a white noise sequence, and the fitted model is significant; if , the residual sequence is not considered as a white noise sequence, and the fitted model is not significant.

[0097] In one example of the present application, when the ARMA model is selected to fit the sample microseismic single-day maximum energy in step S32, the modeling step is as follows:

[0098] It is determined whether the original microseismic time series data is stationary, that is, the stationarity of the original data is tested, and if the sequence does not satisfy the stationarity condition, it is transformed by difference, logarithm or other transformation to make it stationary;

[0099] The autocorrelation AC and partial autocorrelation PAC of the sequence are calculated to determine the order p and q values of the autoregressive moving average model ARMA of the stationary microseismic time series;

[0100] The unknown parameters of the model are estimated, the significance of the parameters and the reasonableness of the model are tested;

[0101] Diagnostic analysis is performed to confirm that the obtained model is consistent with the observed data characteristics.

[0102] In one example of the present application, before the time series model is established in step S30, it further includes: first, white noise test is performed on the two groups of time series, if the test result is a non-white noise sequence, then the stationarity of the microseismic data is judged, and if the time series is non-stationary, it should be converted into a stationary sequence before the model is established.

[0103] The intelligent advanced target non-blasting induced fracture rock burst reduction system according to the second aspect of the present application comprises:

[0104] The activity data acquisition device is configured to arrange a monitoring system in a rock burst tendency area within a predetermined range from the working face to obtain activity data of the rock mass, wherein the monitoring system comprises a displacement sensor, a stress sensor, a temperature sensor, a sound wave sensor and a vibration sensor.

[0105] The microseismic data acquisition device is configured to connect the installed monitoring system to a microseismic monitoring device through a data acquisition line to collect the rock mass activity data collected by the monitoring system to obtain microseismic monitoring data.

[0106] The microseismic data analysis device is configured to communicate the microseismic analysis device and the early warning device with the microseismic monitoring device, establish a time series model through the microseismic analysis device, analyze the microseismic monitoring data, and predict the potential rock burst area of the rock burst tendency area near the tunnel construction face based on the microseismic analysis result through the early warning device, and divide the grade of the potential rock burst area.

[0107] The rock burst elimination device is configured to target the non-bursting drill hole in the potential rock burst area according to the early warning signal, and inject low-wave impedance proppants into the non-bursting drill hole to eliminate the potential rock burst disaster.

[0108] In one example of the present application, the microseismic data analysis device comprises:

[0109] The stationarity analysis unit is configured to use the history and current data of the maximum energy of the monitored area per day as a sample to perform stationarity analysis, and if it is not stationary, perform difference operation to obtain an approximately stationary sequence;

[0110] The adaptability test unit is configured to select an appropriate model to fit the sample maximum energy per day, estimate the order and parameters of the model, and test the adaptability of the model;

[0111] The prediction and early warning unit is configured to use the established model to predict the future value of the difference, and then obtain the future value of the maximum energy per day of the microseismic by inverse difference operation. During the mining process, the established model is used for extrapolation prediction, and the danger is warned by comparing with the early warning index.

[0112] In one example of the present application, in the step S40, the low-wave impedance proppants are injected into the non-bursting drill hole by the hydraulic fracturing device;

[0113] The hydraulic fracturing device includes a high-pressure pump, a nozzle, and a pipeline system. The precise arrangement of these devices ensures that the proppants can enter the advance drill hole at an appropriate speed and direction to ensure that the fractures and pores within the rock are filled. The proppants are usually low-wave impedance proppants, which will be described in detail in the next section.

[0114] The high-pressure pump is one of the core components of the entire process, which is responsible for injecting proppants into the advance drill hole at high pressure to achieve rock fracture. The high-pressure pump is equipped with a programmable pressure control system to allow the injection pressure of the proppants to be adjusted under different rock conditions. In addition, the design and arrangement of the nozzle ensure that the proppants enter the advance drill hole at an appropriate speed and angle, minimizing the existence of unfilled areas.

[0115] In one example of the present application, the proppant has a low wave impedance property, which helps to absorb the stress waves propagating in the rock mass and reduce the disturbance of the stress waves. The selection of the type and adjustment of the parameters of the proppant are crucial to ensure that the proppant can fill the fractures and pores in the rock to enhance the overall stability of the rock mass. In addition, the particle distribution and chemical properties of the proppant are precisely designed to ensure uniform distribution of the proppant during hydraulic fracturing and provide a lasting support effect. Furthermore, the physical properties and chemical composition of the proppant can be adjusted according to different rock mass types and engineering requirements.

[0116] Specific cases

[0117] When using the SOS microseismic monitoring system for ground pressure monitoring and early warning, the following impact risk indicators are generally used. The monitoring data is continuously analyzed and summarized during production to develop scientific and reasonable impact risk indicators.

[0118] (1) During excavation, if the vibration energy exceeds 10 4 J, it can be predicted that the area is at risk of impact.

[0119] (2) During mining, if the vibration energy exceeds 10 5 J, it can be predicted that the area is at risk of impact.

[0120] (3) When the number of vibrations and vibration energy increase simultaneously, it is a sign of rising ground pressure danger.

[0121] (4) When the number of vibrations decreases but the vibration energy increases, it is a sign of rising ground pressure danger. The initial stage of several production cycles (at least 10m of mining and excavation working face advancement) when the number of vibrations begins to decrease can be particularly dangerous, indicating energy accumulation in the rock formation. Using the KJ551 high-precision microseismic monitoring system for ground pressure monitoring and early warning, the early warning indicators are shown in Table 1:

[0122]

[0123] Note: The indicators in the table are microseismic early warning reference indicators. Actual application needs to be improved, such as further research on the relationship between each indicator, such as the total number of daily microseismic events and the average energy of daily microseismic events, and ground pressure danger; is the maximum energy of daily microseismic events.

[0124] From the above analysis, when the energy of a single microseismic event in a single day exceeds the critical value or the cumulative energy in the period increases, the possibility of occurrence of geostatic danger is higher. The change of energy accumulation in the period cannot intuitively and operatively predict the impact danger, and the single-day maximum energy of microseismic event can meet the requirement and high-energy microseismic event is also a necessary condition for the occurrence of geostatic pressure. The change (increase or decrease) of microseismic frequency plays a qualitative analysis role in the occurrence of geostatic pressure and cannot be quantitatively identified and warned, therefore, the single-day maximum energy of microseismic is selected as the impact danger early warning index of microseismic monitoring.

[0125] (1) ARIMA model building

[0126] ARIMA (p, d, q) model is the abbreviation of Auto Regressive Integrated Moving Average Model, which combines moving average, autoregressive analysis and difference together. The model needs to determine three parameters, namely autoregressive order (p), difference frequency (d) and moving average order (q). Before modeling, it is necessary to determine whether the sequence has randomness, stationarity and seasonality, then to realize the optimal fitting before prediction, and finally to predict and evaluate. The specific modeling steps are as follows:

[0127] (1) Determine whether the original microseismic time series data is stationary, that is, perform stationarity test on the original data. If the sequence does not meet the stationarity condition, it can be made stationary by difference transformation, logarithmic transformation or other transformation;

[0128] (2) Determine the order p and q value of the ARMA model of the stationary microseismic time series by calculating the statistical quantities AC (autocorrelation) and PAC (partial autocorrelation) graph describing the characteristics of the sequence;

[0129] (3) Estimate the unknown parameters of the model, test the significance of the parameters and the reasonableness of the model;

[0130] (4) Diagnose and analyze to confirm that the obtained model is consistent with the characteristics of the observed data;

[0131] (5) In steps (3) and (4), some statistical quantities and test analyses are needed to determine whether the model form in step (2) is appropriate.

[0132] (2) Analysis and processing of microseismic monitoring data

[0133] Select the microseismic monitoring data of -290 middle section working face of the mine from March 1 to July 10, and analyze and process the data. The specific data is listed in Table 3-2. Using Python software, the data is imported into the database through Pandas tool, and the results are shown in Table 2.

[0134]

[0135]

[0136]

[0137] (3) Stationary test of time series data

[0138] For time series data, the most important test is whether the time series data is white noise data, whether the time series data is stationary, and the analysis of the autocorrelation coefficient and the partial autocorrelation coefficient of the time series data. If the time series data is white noise data, it means that it has no useful information. Many analysis methods for time series data require that the time series data studied is stationary, so it is very important to judge whether the time series data is stationary and how to convert non-stationary time series data into stationary sequence data for the modeling research of time series data.

[0139] Because ARMA The premise of time series model is that the sequence to be analyzed must be stationary and non-white noise, because only non-white noise sequence can predict the future according to the historical data. Therefore, first, the white noise test of two groups of time series is carried out, if the test result is non-white noise sequence, then the stationarity of microseismic data is judged, if the time series is non-stationary, it should be converted into a stationary sequence before modeling. In this paper, the data from March 1st to July 7th is selected as the sample data, which is used Python The training set of software model, the data from July 8th to July 10th is used as the test set to test the accuracy of the model.

[0140] (1) White noise test of time series

[0141] In Python Jupyter Notebook The work file window, with the help of Matplotlib Tools can be used for graphical drawing, the data has been imported into Python , using the plot() function in Matplotlib Tools can visualize the time series to get the time series diagram of the maximum energy of microseismic per day, as shown in Figure 3 .

[0142] If a sequence is white noise (i.e. independent and identically distributed random data), then there is no need to establish a time series model for it to predict, because it is meaningless to predict random numbers. Therefore, before establishing a time series analysis, it is necessary to conduct white noise test. The commonly used white noise test method is Ljung-Box test (abbreviated as LB test), and its null hypothesis and alternative hypothesis are H0: the sequence of delay period less than or equal to m period is independent (the sequence is white noise); H1: the sequence of delay period less than or equal to m period has correlation (the sequence is not white noise). Ljung-Box test can use acorr_ljungbox() function in Python software statsmodels tool to conduct white noise test on time series, and the test results are shown in Table 3:

[0143]

[0144] From the above results, it can be seen that in the case of delay order [4, 8, 16, 32], the LB test p value of sequence Energy is less than 0.05, which means that the null hypothesis of white noise sequence can be rejected, and it is considered that the data is not random, that is, the data is not random, and there is a regular pattern to follow, which has analysis value.

[0145] (2) Stationarity test of time series

[0146] Whether the time series is stationary is very important for selecting the mathematical model of prediction. If a set of time series data is stationary, it can be directly predicted by using autoregressive moving average model (ARMA), if the data is not stationary, it needs to try to establish difference moving autoregressive average model (ARIMA) and other models for prediction.

[0147] There are two methods to judge whether the sequence is stationary: one is to make a judgment according to the characteristics displayed by the time series graph and autocorrelation graph; the other is to construct test statistics for hypothesis testing, such as unit root test. The first method is subjective, and the second method is an objective judgment method.

[0148] When a time series is a stationary sequence, its mean and variance are both constant and the time series graph always fluctuates around a certain constant. From Figure 4 , it can be seen that the maximum energy of microseismic single day decreases with the increase of time, so it is preliminarily judged that the time series of microseismic single day maximum energy is non-stationary.

[0149] ADF unit root test can be conducted by using adfuller() function of statsmodels.tsa module in Python, and the unit root test results of sequence Energy are shown in Figure 4 .

[0150] From the output of the unit root test above, we can see that the P value of the Energy series test is 0.172846, which is greater than 0.05, indicating that the Energy series is non-stationary. Combined with the previous white noise test results, it can be concluded that the Energy series is a non-white noise non-stationary time series. Therefore, it is necessary to deal with the non-stationary time series.

[0151] (3) Processing of non-stationary time series

[0152] Difference is the most common method for stabilizing non-stationary time series data, especially in ARIMA models. By using the diff() function in Python software, we can directly perform a difference operation on the Energy sequence to obtain the first-order difference sequence DEnergy. Then, using the plot() function mentioned above, we can visualize the difference sequence and generate a time series diagram of the maximum energy of a microseismic day after a single difference, as shown in the following example: Figure 5 shown.

[0153] from Figure 6 It can be seen that the time series after difference basically fluctuates around the value of "0", preliminarily determining that the time series after difference is a stationary series. The following also uses the quantitative method ADF test to further determine whether the sequence after difference is really a stationary series. The test results are as follows:

[0154] Combine Figure 7 The output results of the ADF test and the ADF test model fitting results show that the test P value of the first-order difference sequence DEnergy is 1.063001e-8, which is much smaller than 0.05. Figure 7 Comparing the value of "Test Statistic" with the value of "Critical Value (5%)" below, the result is -6.517377, which is less than -2.886151 (and also less than -3.486535 at the 1% significance level), indicating that the first-order difference sequence DEnergy under test is stationary. Combining the previous test results, we can conclude that the original sequence Energy is not stationary, while the first-order difference sequence DEnergy is stationary. In order to further confirm the most suitable number of differences for the original sequence Energy, we perform another difference based on the first-order difference and draw a curve of the second-order difference, as shown below. Figure 8 As shown in the figure, after the second-order difference is performed on the original sequence, the fluctuation range of the time series curve is obviously larger than that of the first-order difference, which will inevitably lead to a larger variance in the fitting result. Therefore, it is not appropriate to use the second-order difference for the original sequence Energy. Overall, the microseismic single-day maximum energy sequence is a first-order difference stationary sequence.

[0155] (3) Model identification and testing

[0156] (1) Model identification

[0157] Autocorrelation analysis and partial autocorrelation analysis are a method to determine the two parameters p and q in the ARMA(p, q) model. After determining that the sequence is a stationary non-white noise sequence, the truncation of the sequence can be analyzed by the size of the autocorrelation coefficient and the partial autocorrelation coefficient of the sequence.

[0158] For a time series, if the autocorrelation coefficient ACF of the sample is not equal to 0 until the lag period s = q, and the ACF is almost 0 when the lag period s > q, then the real data generation process is considered to be MA(q). If the partial autocorrelation coefficient PACF of the sample is not equal to 0 until the lag period s = p, and the PACF is almost 0 when the lag period s > p, then the real data generation process is considered to be AR(p). More generally, according to the performance of the ACF and PACF of the sample, a more appropriate ARMA(p, q) model can be fitted. Table 4 shows how to determine the parameters p and q in the model.

[0159]

[0160] For the autocorrelation coefficient and the partial autocorrelation coefficient of the time series, the plot_acf() function and the plot_pacf() function can be used for visualization. Running the following program can obtain the autocorrelation coefficient and the partial autocorrelation coefficient of the time series DEnergy, and the results are shown in Figure 9 .

[0161] As shown in Figure 9 , the autocorrelation graph is 1st-order truncated (the coefficient quickly tends to 0), and the partial autocorrelation graph is trailing (the coefficient slowly tends to 0). The preliminary identification of the microseismic single-day maximum energy time series model is the ARIMA(0, 1, 1) model, i.e. the MA(1) model. In addition, the scatter plot of the first-order difference sequence can represent the correlation of the microseismic single-day maximum energy, as shown in Figure 10 .

[0162] In Figure 10 , the vertical coordinate "Original" represents the original sequence of the first-order difference of the microseismic single-day maximum energy, and the horizontal coordinate "t-n" represents the time series with a lag of n orders based on the original sequence. The title "corr" of each scatter plot is the correlation coefficient of the original sequence and the lag sequence. The 9 scatter plots as a whole represent the correlation between the original sequence and the lag 1-9 order time series. Therefore, when n = 1, the correlation coefficient between the original sequence and the lag sequence is the largest, corr = 0.37. Therefore, it can be concluded that q = 1 in the ARIMA(p, d, q).

[0163] (2) Model verification

[0164] The regression results of the MA (1) model are as follows: Figure 11 As shown. Figure 11 It can be seen that the t-statistic of the constant term coefficient (column "z") is -1.160, with an absolute value less than 2, and the probability value corresponding to the column "P>|z|" is 0.246, which is greater than 0.05. The absolute value of the t-statistic of the explanatory variable coefficient of MA (1) is greater than 2, so there is at least a 95% probability that the true value of the coefficient is not zero. The probability value corresponding to the column "P>|z|" is 0, which is less than 0.05, indicating that the parameter estimate is valid, that is, the estimated coefficient of the explanatory variable is significantly not zero, and the MA (1) model parameters have passed the significance test.

[0165] from Figure 11 From the last line of the output, we can see that the characteristic root is 1.0896>1, which meets the stationary requirement. Using the fitted values ​​method, we can get the predicted value of the MA (1) model by fitting the ARIMA model, and then make a prediction curve. By placing the original time series curve and the prediction curve in the same coordinate system, we can intuitively show the fitting effect, as shown in the following example: Figure 12 At the same time, the resid method is used to calculate the residual value of the fitting result, and the plot() function and qqplot() function are used in Python software to draw the corresponding residual curve graph and QQ graph, as shown in the following figure: Figure 13 shown.

[0166] exist Figure 12 In the figure, the “diff” curve is the first-order difference sequence of the maximum energy value of a single microseismic day, and the “forecast_diff” curve is the prediction curve of the MA (1) model. By comparison, it can be seen that the fitting effect of the MA (1) model is average, and the prediction curve and the sequence DEnergy curve partially overlap. Figure 13 It can be seen that the residual curve of MA (1) fluctuates greatly, with the highest even exceeding 20,000. From the MA (1) residual QQ plot, it can be concluded that the model fitting quality is average, with some points located near the straight line and some points far away from the straight line. Therefore, the random error term of the model is a white noise sequence.

[0167] Although the MA (1) model passed the test, the goodness of fit value R calculated by Python software was 2 It is only 0.455, and the regression effect is poor, which needs to be improved.

[0168] (4) Model modification

[0169] From the foregoing, the single-day maximum energy time series of microseism is a non-stationary sequence, and the sequence can be stationary through simple difference operation. Further observation of the original time series shows that the single-day maximum energy decreases with time, making the variance larger and more unstable. In this case, the common analysis assumption often does not satisfy the error obeying the independent and identically distributed normal distribution, and the requirement of time series stationarity, which inevitably leads us to seek a way to make the data satisfy the assumption as much as possible, make the variance constant, and make the fluctuation relatively stable, and this purpose can be achieved through logarithmic transformation. Logarithmic transformation is a common way of data transformation, and the purpose of data transformation is to make the data presentation close to the desired premise assumption, so as to better conduct statistical inference.

[0170] The logarithmic transformation process of the single-day maximum energy time series of microseism is similar to the difference process. Here, the DLnEnergy time series graph after logarithmic transformation and difference is not demonstrated.

[0171] From Figure 14 It can be seen that the fluctuation range of the single-day maximum energy time series of microseism after taking logarithm and once difference is between [-3, 3], and compared with the time series graph directly differentiated, the fluctuation range is obviously reduced. Thus, the variance is constant, i.e. the fluctuation is relatively stable. The model single root test result is shown in Table 5, the ADF Test Statistic value is obviously smaller than the value of "Critical Value (5%)", and also smaller than the value at 1% significant level, the p value is 3.022895e-11, which is much smaller than 0.05, and even smaller than the p value of DEnergy sequence, indicating that the tested sequence is stationary, and the stationarity is better than that of DEnergy sequence.

[0172]

[0173] This method can realize rock burst early warning through microseismic monitoring system, and the following conclusions are drawn through comparative study of microseismic event sequence model and various rock burst early warning models.

[0174] (1) The history and current data of the single-day maximum energy of the monitored area microseism are used as samples for stationarity analysis. If it is not stationary, difference operation is performed to obtain an approximately stationary sequence. An appropriate model is selected to fit the sample single-day maximum energy of microseism, the parameters of the model are estimated, and the adaptability of the model is tested. If the model does not have adaptability, a new model is selected to fit the sample single-day maximum energy of microseism.

[0175] (2) Finally, the future value of the difference is predicted by using the established model, and then the future value of the single-day maximum energy of microseism is obtained through inverse difference operation. During the mining process, the established model is used for sample extrapolation and prediction, and by comparing with the early warning index, the danger early warning can be realized.

[0176] The exemplary embodiments of the method for intelligent advanced target non-blasting induced cracking to reduce rock burst proposed by the present application are described in detail above with reference to the preferred embodiments, however, those skilled in the art can understand that various modifications and changes can be made to the above specific embodiments, and various technical features and structures proposed by the present application can be combined without departing from the concept of the present application, and without exceeding the protection scope of the present application, the protection scope of the present application is determined by the appended claims.

Claims

1. A method for intelligent advanced targeted non-explosive fracturing to mitigate rockbursts, characterized in that: The steps include: S10: deploying a monitoring system in a rockburst prone area within a predetermined range from the tunnel face to obtain rock mass activity data, wherein the monitoring system includes: a displacement sensor, a stress sensor, a temperature sensor, an acoustic wave sensor, and a vibration sensor; S20: connecting the installed monitoring system to the microseismic monitoring equipment via a data acquisition line to collect the rock mass activity data acquired by the monitoring system to obtain microseismic monitoring data; S30: connect the microseismic analysis equipment and the early warning equipment to the microseismic monitoring equipment, establish a time series model through the microseismic analysis equipment, analyze the microseismic monitoring data, and use the alarm equipment to predict the potential rock burst area in the rock burst prone area near the tunnel construction face based on the microseismic analysis results, and classify the potential rock burst area; wherein, establishing the time series model includes the following steps: S31: use the historical and current data of the maximum daily energy of microseismic in the monitoring area as samples, perform stationarity analysis, and if it is not stationary, perform differential operation to obtain an approximately stationary sequence; S32: select an appropriate model to fit the maximum daily energy of the sample microseismic, determine the order and estimate the parameters of the model, and perform an adaptability test on the model; S33: use the established model to predict the future value of the difference, and then obtain the future value of the maximum daily energy of the microseismic by reverse differential operation. During the mining process, use the established model to perform sample extrapolation prediction, and achieve danger warning by comparing with the early warning index; S40: Targetedly opening non-explosive fracturing boreholes in the potential rockburst area according to the early warning signal, and injecting low-wave impedance proppant into the non-explosive fracturing boreholes to eliminate potential rockburst disasters.

2. The method for mitigating rockburst by intelligent advanced targeted non-explosive fracturing according to claim 1 is characterized in that: In step S31, the stationarity analysis includes the following steps: First fit the adaptive model of the sequence, and then test whether there is a unit root in the characteristic equation composed of the autoregressive part parameters of the model. If there is no unit root, the sequence is stationary. If there is a unit root, the sequence is non-stationary. The specific process is as follows: In the autoregressive model AR(p), the random sequence {X t } satisfies the following formula: ； make , make the following hypothesis test: ； Construct the unit root ADF test statistic: ; For parameters The sample standard deviation of the autoregressive moving average model ARMA (p, q) is the same as the autoregressive model AR (p). If it is a sliding average model MA (q), the regression part has no parameters and must be stable.

3. The method for mitigating rockburst by intelligent advanced targeted non-explosive fracturing according to claim 1 is characterized in that: In step S32, determining the order of the model includes the following steps: First, by judging whether formula (1) and formula (2) are valid, we can roughly judge the truncation of the autocorrelation coefficient of the stationary series and the truncation of the partial correlation coefficient of the stationary series and make a preliminary order determination; among them, ； ； Where: For the Estimation of the autocorrelation coefficient of the period; For the The partial correlation coefficient estimation of the period; is the sequence length; is the maximum lag period of the autocorrelation coefficient; If formula (1) is established, it is considered that the autocorrelation coefficient of the sequence is truncated at q steps, and the moving average model MA is established, and the initial order is determined to be q; if formula (2) is established, it is considered that the partial correlation coefficient of the sequence is truncated at p steps, and the autoregressive model AR is established, and the initial order is determined to be p; Then, the AIC criterion and the BIC criterion were used to determine the final order of the model.

4. The method for mitigating rockburst by intelligent advanced targeted non-explosive fracturing according to claim 3 is characterized in that: The mathematical expressions of the AIC criterion and the BIC criterion are: ； Where: is the sequence length; is the maximum likelihood estimate of the model residual variance.

5. The method for mitigating rockburst by intelligent advanced targeted non-explosive fracturing according to claim 1 is characterized in that: In step S32, the adaptability test of the model includes the following steps: use Test method, constructed statistics To test the adaptability of the model, it is only necessary to perform a white noise test on the model residuals. The statistic The expression is as follows: ； in, is the number of samples, is the autocorrelation coefficient of the residual sequence, is the maximum lag period of the autocorrelation coefficient; If the model is appropriate, the statistic Approximate obedience ; At a given significance level Next, if , then the residual sequence is considered to be a white noise sequence and the fitting model is significant; if , it is considered that the residual sequence is not a white noise sequence and the fitting model is not significant.

6. The method for mitigating rockburst by intelligent advanced targeted non-explosive fracturing according to claim 1 is characterized in that: In step S32, when the ARMA model is selected to fit the maximum daily energy of the sample microseismic events, the modeling steps are as follows: Determine whether the original microseismic time series data is stationary, that is, perform a stationarity test on the original data. If the series does not meet the stationarity condition, make it stationary through differential transformation, logarithmic transformation or other transformations; By calculating the statistics of autocorrelation AC and partial autocorrelation PAC that describe the characteristics of the sequence, the order p and q of the autoregressive moving average model ARMA of the post-stationary microseismic time series are determined. Estimate unknown parameters of the model, test the significance of the parameters and the rationality of the model; Diagnostic analyses were performed to confirm that the obtained model matched the observed data characteristics.

7. The method for mitigating rockburst by intelligent advanced targeted non-explosive fracturing according to claim 1 is characterized in that: In the step S30, before establishing the time series model, the following steps are also included: first, a white noise test is performed on the two sets of time series. If the test result is a non-white noise sequence, the stationarity of the microseismic data is judged. If the time series is non-stationary, it should be converted into a stationary sequence before establishing the model.

8. An intelligent advanced targeted non-explosive fracturing and rockburst mitigation system, characterized by: include: An activity data acquisition device is configured to arrange a monitoring system in a rockburst prone area within a predetermined range from a tunnel face to obtain activity data of the rock mass, wherein the monitoring system includes: a displacement sensor, a stress sensor, a temperature sensor, an acoustic wave sensor, and a vibration sensor; a microseismic data acquisition device configured to connect the installed monitoring system to the microseismic monitoring equipment via a data acquisition line, so as to collect the rock mass activity data acquired by the monitoring system to obtain microseismic monitoring data; A microseismic data analysis device is configured to connect a microseismic analysis device and an early warning device to a microseismic monitoring device for communication, establish a time series model through the microseismic analysis device, analyze the microseismic monitoring data, and use the alarm device to predict the potential rock burst area in the rock burst prone area near the tunnel construction face based on the microseismic analysis results, and classify the potential rock burst area; wherein, the microseismic data analysis device includes: a stationarity analysis unit, configured to use the historical and current data of the maximum daily energy of microseisms in the monitoring area as samples to perform stationarity analysis, and if it is not stationary, perform a differential operation to obtain an approximately stationary sequence; an adaptability test unit: configured to select an appropriate model to fit the maximum daily energy of the sample microseismic, determine the order and estimate the parameters of the model, and perform an adaptability test on the model; a prediction and early warning unit: configured to use the established model to predict the future value of the difference, and then obtain the future value of the maximum daily energy of the microseismic by an inverse differential operation. During the mining process, the established model is used to perform sample extrapolation prediction, and the danger warning is achieved by comparing with the early warning index; The rock burst elimination device is configured to open a non-explosion drill hole in the potential rock burst area according to the early warning signal, and inject a low-wave impedance proppant into the non-explosion fracture drill hole to eliminate the potential rock burst disaster.

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