Rockburst risk identification and early warning system based on micro-seismic monitoring data

Abstract

The invention relates to the technical field of rockburst risk identification, and particularly discloses a rockburst risk identification and early warning system based on micro-seismic monitoring data. Comprising a rock mass fracture energy monitoring module, a rock mass magnitude parameter monitoring module, a rockburst energy magnitude risk analysis module, a rock mass micro-seismic state monitoring module, a rockburst amplitude-frequency magnitude risk analysis module, a rockburst risk comprehensive assessment analysis module, a rockburst type prediction analysis module and a control terminal. Through multi-dimensional and multi-level comprehensive analysis, the rockburst risk is comprehensively evaluated, and the accuracy and reliability of risk identification are improved; a scatter diagram is drawn based on the direction consistency parameter, the accumulated energy value, the energy release rate change rate and the like of a micro-seismic event, the rockburst type can be effectively predicted, a basis is provided for taking targeted prevention and control measures, various data and analysis results are integrated and processed, corresponding signals are generated, the rockburst type is judged, and the rockburst prediction accuracy is improved. Intellectualization and automation of the whole system are achieved, and rockburst disasters can be prevented in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of rockburst risk identification, and in particular to a rockburst risk identification and early warning system based on microseismic monitoring data. Background Art

[0002] In the field of underground engineering, rockburst is an extremely destructive disaster phenomenon. Its sudden occurrence often causes serious damage to engineering facilities and even endangers the lives of construction workers. Traditional rockburst monitoring methods mainly rely on empirical judgment and monitoring of single parameters, such as focusing only on individual indicators such as energy release or magnitude. This method has obvious limitations. Since the occurrence of rockburst is a complex physical process and is affected by a variety of factors, a single parameter cannot fully and accurately reflect the gestation and development status of rockburst, resulting in inaccurate assessment of rockburst risks and untimely warnings, which makes it difficult to meet the high requirements of modern underground engineering for safe construction.

[0003] At present, the rockburst risk identification system based on microseismic monitoring still has some shortcomings. On the one hand, some systems are relatively simple in data processing and fail to fully tap the rich information contained in microseismic signals. For example, they only perform simple statistics on the energy or magnitude of microseismic events, while ignoring the inherent relationship between key information such as energy release rate, magnitude distribution characteristics, and frequency; on the other hand, in terms of risk assessment and early warning, there is a lack of systematic and multi-dimensional comprehensive analysis models, which makes it impossible to accurately quantify the degree of rockburst risk, and it is difficult to issue effective early warning signals in a timely manner according to different risk levels, making it difficult for engineers to make scientific and reasonable decisions when facing rockburst risks. Summary of the invention

[0004] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a rockburst risk identification and early warning system based on microseismic monitoring data, in order to solve the above-mentioned technical defects.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a rockburst risk identification and early warning system based on microseismic monitoring data, including a rock mass fracture energy monitoring module, a rock mass magnitude parameter monitoring module, a rockburst energy magnitude risk analysis module, a rock mass microseismic state monitoring module, a rockburst amplitude-frequency magnitude risk analysis module, a rockburst risk comprehensive assessment and analysis module, a rockburst type prediction and analysis module and a control terminal;

[0006] The rock mass fracture energy monitoring and analysis module detects and collects the number of microseismic events in each monitoring period at each distributed monitoring point in the rockburst area and the energy released by each microseismic event based on microseismic sensors, and obtains the energy rate value of each monitoring period at each distributed monitoring point in the rockburst area;

[0007] The rock mass magnitude parameter monitoring module is used to obtain the vibration waveform and duration of each microseismic event in each monitoring period at each distributed monitoring point corresponding to the rockburst area based on the microseismic sensor;

[0008] The rockburst energy magnitude risk analysis module is used to perform comprehensive calculation and analysis based on the energy rate value and magnitude centroid value of each monitoring period in each distributed monitoring point corresponding to the standardized rockburst area, and the rockburst magnitude risk coefficient of each monitoring period in each distributed monitoring point corresponding to the rockburst area is obtained.

[0009] The rock mass microseismic state monitoring module continuously records microseismic signals at a set high-frequency sampling rate based on microseismic sensors, converts the physical changes caused by vibration into electrical signals and transmits them to the ground control terminal in real time. The amplitude and frequency of the collected microseismic signals are comprehensively analyzed to obtain the rockburst amplitude-frequency risk coefficient ZF for each monitoring period in each distributed monitoring point in the rockburst area based on the amplitude of the microseismic signal.

[0010] Furthermore, the energy released by each microseismic event in each monitoring period of each distributed monitoring point in the rockburst area is summed and calculated to obtain the energy rate value of each monitoring period in each distributed monitoring point in the rockburst area. The energy rate value of each monitoring period in each distributed monitoring point in the rockburst area is standardized to obtain the standardized energy rate value of each monitoring period in each distributed monitoring point in the rockburst area, which is recorded as

[0011] Furthermore, the vibration waveform and duration of each microseismic event are converted into a magnitude value through an improved algorithm of the Richter magnitude. The magnitude ranges are divided according to the magnitude data, and the magnitude-frequency distribution matrix is ​​constructed with each magnitude range as a row and each monitoring period as a column.

[0012] Furthermore, by multiplying the center value of each magnitude interval and the frequency corresponding to each magnitude interval, and then summing the product results, the magnitude centroid value of each monitoring period in each distributed monitoring point corresponding to the rockburst area is obtained, and the magnitude centroid value of each monitoring period in each distributed monitoring point corresponding to the rockburst area is standardized to obtain the standardized magnitude centroid value of each monitoring period in each distributed monitoring point corresponding to the rockburst area, which is recorded as

[0013] Furthermore, the rockburst risk coefficient of each distributed monitoring point in each monitoring period in the rockburst area is calculated. The specific calculation and analysis methods are as follows:

[0014] According to the formula Calculate the rockburst risk coefficient of each distributed monitoring point in each monitoring period in the rockburst area

[0015] Furthermore, based on the rockburst amplitude-frequency risk coefficient ZF of each monitoring period in each distributed monitoring point in the rockburst area corresponding to the microseismic signal amplitude, the specific analysis method is as follows:

[0016] The kernel density estimation method is used to draw the probability density curve P(A) of the peak amplitude of the collected original microseismic waveform data. The high amplitude threshold is set as FH according to engineering experience and rock characteristics. According to the formula Calculate the microseismic anomaly detection coefficient SF of each monitoring period in each distributed monitoring point in the rockburst area based on the microseismic signal amplitude;

[0017] Based on historical data and experience combined with data statistical analysis, the previous main frequency concentration interval [PF z1 , PF z2 ] and the later added low frequency range [PF h1 , PF h2 ], sum the amplitudes corresponding to each frequency in the main frequency concentration interval in the early stage and the low frequency interval added later, and obtain the early energy sum value SE z And later energy and value SE h , divide the later energy sum value by the sum of the previous energy sum value and the later energy sum value to obtain the main frequency change characteristic value, recorded as F 1 .

[0018] Furthermore, based on historical experience and the common frequency bandwidth range in rockburst areas, a reference frequency bandwidth is set, denoted as PF 0 , and at the same time obtain the current frequency bandwidth from the collected microseismic signal, recorded as PF j , according to the formula The rockburst amplitude-frequency risk coefficient ZF of each monitoring period in each distributed monitoring point in the rockburst area is calculated; where κ represents the set weight factor.

[0019] Furthermore, the rockburst amplitude-frequency magnitude risk analysis module performs comprehensive calculation and analysis based on the microseismic anomaly detection coefficient SF and the rockburst amplitude-frequency risk coefficient ZF of each monitoring period in each distributed monitoring point corresponding to the rockburst area, and obtains the rockburst amplitude-frequency magnitude risk coefficient FB of each monitoring period in each distributed monitoring point corresponding to the rockburst area. The specific analysis method is as follows:

[0020] According to the formula The rockburst amplitude-frequency risk coefficient FB of each distributed monitoring point in the rockburst area corresponding to each monitoring period is calculated.

[0021] Furthermore, the rockburst risk comprehensive assessment and analysis module performs comprehensive calculation and analysis based on the rockburst volume risk coefficient and the rockburst amplitude-frequency magnitude risk coefficient of each monitoring period in each distributed monitoring point corresponding to the rockburst area, and obtains the rockburst comprehensive indication coefficient of each monitoring period in each distributed monitoring point corresponding to the rockburst area. The specific comprehensive calculation and analysis method is as follows:

[0022] According to the formula The rockburst comprehensive indication coefficient ZB of each distributed monitoring point in the rockburst area corresponding to each monitoring period is calculated, where e is represented by a natural constant.

[0023] Furthermore, the rockburst type prediction and analysis module is used to obtain and analyze the directional consistency parameters, cumulative energy values, and energy release rate change rates of microseismic events, and a rockburst type prediction scatter plot is drawn in combination with the above parameters. The specific analysis method is as follows:

[0024] The energy rate values ​​at adjacent time points t1 and t2 are obtained, denoted as E(t1) and E(t2) respectively, and the energy rate value at time point t2 is calculated by subtracting the energy rate value at time point t1, and the time point t2 is calculated by subtracting the energy rate value from the time point t1, and the energy rate value difference is divided by the time point difference to obtain the energy release rate change rate, denoted as kE; wherein time points t2 and t1 are different moments, and time point t2>time point t1.

[0025] Get the time interval length of each monitoring period, recorded as Δt, according to the formula Calculate the cumulative energy LN of each distributed monitoring point corresponding to the rockburst area;

[0026] The number of microseismic events in each monitoring period at each distributed monitoring point in the rockburst area is obtained, recorded as q, and the initial motion direction of each microseismic event is expressed by a unit vector Indicates, where q = 1, 2, ..., w, according to the formula The directional consistency parameter FY of microseismic events corresponding to each distributed monitoring point in the rockburst area is calculated.

[0027] Beneficial effects of the present invention:

[0028] 1. The calculation of two key parameters, the comprehensive energy rate value (reflecting the energy accumulation and release rate) and the magnitude centroid value (reflecting the magnitude distribution characteristics), can avoid the one-sidedness caused by relying on a single parameter to evaluate the rockburst risk. At the same time, considering each distributed monitoring point and each monitoring period, it can capture the dynamic changes of rockburst risk in space and time. The rockburst quantitative seismic risk coefficient of each monitoring period in the rockburst area corresponding to each distributed monitoring point obtained by comprehensive calculation can intuitively represent the degree of rockburst risk of each monitoring point in each monitoring period. Compared with qualitative description, this quantitative risk coefficient can allow engineers to understand the size of the risk more clearly, thereby providing more convincing data support for decision-making.

[0029] 2. Through multi-dimensional and multi-level comprehensive analysis, the rockburst risk is comprehensively assessed, and the accuracy and reliability of risk identification are improved; based on the directional consistency parameters, cumulative energy values, and energy release rate change rates of microseismic events, scatter plots are drawn to effectively predict the type of rockburst, providing a basis for taking targeted prevention and control measures. By integrating and processing various types of data and analysis results, corresponding signals are generated and the type of rockburst is determined, realizing the intelligence and automation of the entire system, which is helpful to prevent rockburst disasters in advance, ensure the safety of underground engineering construction and the safety of life and property of personnel, improve the efficiency and quality of engineering construction, and reduce the huge losses and risks caused by rockburst accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The present invention will be further described below in conjunction with the accompanying drawings.

[0031] Figure 1 This is a principle block diagram of a rockburst risk identification and early warning system based on microseismic monitoring data according to an embodiment of the present invention;

[0032] Figure 2 It is a scatter diagram of strain-type rockburst proposed in an embodiment of the present invention.

[0033] Figure 3 It is a scatter diagram of stress-type rock burst proposed in an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work also fall within the scope of protection of the present invention.

[0035] As shown in the present invention and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular, but also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list, and the method or device may also include other steps or elements.

[0036] Although the present invention has made various references to certain modules in the system according to an embodiment of the present invention, any number of different modules can be used and run on a user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0037] The present invention uses a flow chart to illustrate the operations performed by the system according to an embodiment of the present invention. It should be understood that the preceding or following operations are not necessarily performed precisely in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0038] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0039] Embodiment 1:

[0040] See also Figure 1 As shown, the rockburst risk identification and early warning system based on microseismic monitoring data includes: a rock fracture energy monitoring module, a rock magnitude parameter monitoring module, a rockburst energy magnitude risk analysis module, a rock microseismic state monitoring module, a rockburst amplitude-frequency magnitude risk analysis module, a rockburst risk comprehensive assessment and analysis module, a rockburst type prediction and analysis module and a control terminal.

[0041] The rock mass fracture energy monitoring and analysis module detects and collects the number of microseismic events in each monitoring period in each distributed monitoring point corresponding to the rock burst area and the energy released by each microseismic event based on the microseismic sensor. The energy released by each microseismic event in each monitoring period in each distributed monitoring point corresponding to the rock burst area is summed and calculated to obtain the energy rate value of each monitoring period in each distributed monitoring point corresponding to the rock burst area. The energy rate value of each monitoring period in each distributed monitoring point corresponding to the rock burst area is standardized to obtain the standardized energy rate value of each monitoring period in each distributed monitoring point corresponding to the rock burst area, which is recorded as Wherein, i=1,2,...,n, i represents the number of each distributed monitoring point, n represents the total number of distributed monitoring point numbers, j=1,2,...,m, j represents the number of each monitoring period, m represents the total number of each monitoring period number.

[0042] Specifically, the standardization processing method for the energy rate value of each monitoring period in each distributed monitoring point corresponding to the rock burst area is as follows: obtain the mean of the historical energy rate values ​​of the rock burst area, and the standard deviation of the historical energy rate values, calculate the difference between the energy rate value of each monitoring period in each distributed monitoring point corresponding to the rock burst area and the mean of the historical energy rate value of the rock burst area, and then compare the difference calculation result with the standard deviation of the historical energy rate value of the rock burst area to obtain the standardized energy rate value of each monitoring period in each distributed monitoring point corresponding to the rock burst area, and convert the energy rate value of each monitoring period in each distributed monitoring point corresponding to the rock burst area into a standard normal distribution form with a mean of 0 and a standard deviation of 1, so as to facilitate subsequent comprehensive calculations.

[0043] Through the rock mass magnitude parameter monitoring module, the vibration waveform and duration of each microseismic event in each monitoring period in each distributed monitoring point corresponding to the rockburst area are obtained based on microseismic sensors. The vibration waveform and duration of each microseismic event are converted into a magnitude value through the improved algorithm of the Richter magnitude. According to the magnitude data, each magnitude interval is divided, and the magnitude-frequency distribution matrix is ​​constructed with each magnitude interval as a row and each monitoring period as a column. Among them, when dividing the magnitude data into each magnitude interval, it can be divided into 0.2 increments, specifically 0-0.2, 0.2-0.4, etc.

[0044] By multiplying the center value of each magnitude interval and the frequency corresponding to each magnitude interval, and then summing the product results, the magnitude centroid value of each monitoring period in each distributed monitoring point corresponding to the rockburst area is obtained. The magnitude centroid value of each monitoring period in each distributed monitoring point corresponding to the rockburst area is standardized to obtain the standardized magnitude centroid value of each monitoring period in each distributed monitoring point corresponding to the rockburst area, which is recorded as Among them, the standardization processing method for the centroid value of the magnitude in each monitoring period in each distributed monitoring point corresponding to the rock burst area adopts the same energy rate value standardization processing method as mentioned above.

[0045] The rockburst energy magnitude risk analysis module is used to perform comprehensive calculation and analysis based on the energy rate value and magnitude centroid value of each monitoring period in each distributed monitoring point corresponding to the standardized rockburst area, and the rockburst magnitude risk coefficient of each monitoring period in each distributed monitoring point corresponding to the rockburst area is obtained. By calculating the two key parameters of comprehensive energy rate value (reflecting the energy accumulation and release rate) and magnitude gravity center value (reflecting the magnitude distribution characteristics), we can avoid the one-sidedness brought by relying on a single parameter to evaluate rockburst risk. At the same time, considering each distributed monitoring point and each monitoring period, we can capture the dynamic changes of rockburst risk in space and time. The rockburst risk coefficient of each monitoring period in each distributed monitoring point in the rockburst area obtained by comprehensive calculation can intuitively represent the degree of rockburst risk of each monitoring point in each monitoring period. Compared with qualitative description, this quantitative risk coefficient can allow engineers to understand the size of the risk more clearly, thereby providing more convincing data support for decision-making; the specific calculation and analysis method is as follows:

[0046] According to the formula Calculate the rockburst risk coefficient of each distributed monitoring point in each monitoring period in the rockburst area

[0047] The rockburst risk coefficient of each distributed monitoring point in each monitoring period in the rockburst area is calculated. Perform numerical analysis,

[0048] If the rockburst area corresponds to the rockburst risk coefficient of a certain monitoring period in a distributed monitoring point This means that during the monitoring period corresponding to the distributed monitoring point, the risk of rock burst in terms of energy and magnitude is at a low level, generating a rock mass stable state signal;

[0049] As the rockburst risk coefficient value of a distributed monitoring point in a rockburst area during a certain monitoring period increases, This means that during the monitoring period corresponding to the distributed monitoring point, the risk of rock burst begins to rise and enters the warning interval, generating a rock mass warning state signal;

[0050] like This means that during the monitoring period corresponding to the distributed monitoring point, the risk of rock burst is high, the rock mass is close to the critical state of energy release, and a rock mass fracture state signal is generated;

[0051] like This means that during the monitoring period corresponding to the distributed monitoring point, the rockburst is on the verge of a high-risk outbreak or has already occurred, the energy rate value reaches an extremely high level, the magnitude is high and the distribution is chaotic, and a rockburst alarm status signal is generated.

[0052] Embodiment 2:

[0053] Furthermore, the rock mass microseismic state monitoring module continuously records microseismic signals at a set high-frequency sampling rate based on microseismic sensors, converts the physical changes caused by vibration into electrical signals and transmits them to the ground control terminal in real time, performs a comprehensive analysis of the amplitude and frequency of the collected microseismic signals, and obtains the rock burst amplitude-frequency risk coefficient ZF for each monitoring period in each distributed monitoring point in the rock burst area based on the amplitude of the microseismic signal. The specific analysis method is as follows:

[0054] The kernel density estimation method is used to draw the probability density curve P(A) of the peak amplitude of the collected original microseismic waveform data. The high amplitude threshold is set as FH according to engineering experience and rock characteristics. According to the formula Calculate the microseismic anomaly detection coefficient SF of each monitoring period in each distributed monitoring point in the rockburst area based on the microseismic signal amplitude;

[0055] Based on historical data and experience combined with data statistical analysis, the previous main frequency concentration interval [PF z1 , PF z2 ] and the later added low frequency range [PF h1 , PF h2 ], sum the amplitudes corresponding to each frequency in the main frequency concentration interval in the early stage and the low frequency interval added later, and obtain the early energy sum value SE z And later energy and value SE h , divide the later energy sum value by the sum of the previous energy sum value and the later energy sum value to obtain the main frequency change characteristic value, recorded as F 1 According to the formula Calculate the main frequency change characteristic value F 1 .

[0056] The reference frequency bandwidth is set based on historical experience and the common frequency bandwidth range in rock burst areas, denoted as PF 0 , and at the same time obtain the current frequency bandwidth from the collected microseismic signal, recorded as PF j , according to the formula The rockburst amplitude-frequency risk coefficient ZF of each monitoring period in each distributed monitoring point in the rockburst area is calculated; where κ is represented by the set weight factor. By consulting the existing engineering data of the same type of rockburst detection projects or similar geological conditions, the numerical value is set in combination with the influence of the main frequency change and the frequency bandwidth change on the rockburst risk. If most similar projects believe that the main frequency change has a relatively large impact on the rockburst risk, then β may take a relatively large value, such as 0.7-0.8; conversely, if the impact of the frequency bandwidth change is considered to be more important, β may take a relatively large value of 0.3-0.4.

[0057] The rockburst amplitude-frequency magnitude risk analysis module is used to perform comprehensive calculation and analysis based on the microseismic anomaly detection coefficient SF and the rockburst amplitude-frequency risk coefficient ZF of each monitoring period in each distributed monitoring point in the rockburst area, and the rockburst amplitude-frequency magnitude risk coefficient FB of each monitoring period in each distributed monitoring point in the rockburst area is obtained. The specific analysis method is as follows:

[0058] According to the formula Calculate the rockburst amplitude-frequency risk factor FB for each monitoring period in each distributed monitoring point in the rockburst area;

[0059] The rockburst risk comprehensive assessment and analysis module is used to perform comprehensive calculation and analysis based on the rockburst volume risk coefficient and the rockburst amplitude-frequency magnitude risk coefficient of each distributed monitoring point in each monitoring period in the rockburst area, and obtain the rockburst comprehensive indication coefficient of each distributed monitoring point in each monitoring period in the rockburst area. The specific comprehensive calculation and analysis method is as follows:

[0060] According to the formula The rockburst comprehensive indication coefficient ZB of each distributed monitoring point in the rockburst area corresponding to each monitoring period is calculated, where e is represented by a natural constant.

[0061] By numerically analyzing the rockburst comprehensive indication coefficient ZB of each distributed monitoring point in the rockburst area at each monitoring period,

[0062] If the rockburst comprehensive indication coefficient ZB≤1 in a certain monitoring period of a distributed monitoring point corresponding to the rockburst area, it means that the risk of rockburst in terms of energy and magnitude is at a low level in the distributed monitoring point corresponding to the monitoring period, and a rockburst stable state signal is generated;

[0063] As the value of the rockburst comprehensive indication coefficient of a distributed monitoring point in a rockburst area corresponding to a certain monitoring period increases, if 1<ZB≤1.3, it means that during the distributed monitoring point corresponding to the monitoring period, the rockburst risk begins to rise and enters the warning interval, and a rockburst warning state signal is generated;

[0064] If 1.3<ZB≤1.6, it means that during the monitoring period corresponding to the distributed monitoring point, the rock burst risk is high and the rock mass is close to the critical state of energy release, and a rock burst risk state signal is generated;

[0065] If ZB>1.6, it means that during the monitoring period corresponding to the distributed monitoring point, the rockburst is on the verge of a high-risk outbreak or has already occurred, the energy rate value reaches an extremely high level, the magnitude is high and the distribution is chaotic, and a rockburst alarm status signal is generated.

[0066] Embodiment 3:

[0067] Furthermore, the rockburst type prediction and analysis module is used to obtain and analyze the directional consistency parameters, cumulative energy values, and energy release rate change rates of microseismic events, and a rockburst type prediction scatter plot is drawn in combination with the above parameters. The specific analysis method is as follows:

[0068] The energy rate values ​​at adjacent time points t1 and t2 are obtained, denoted as E(t1) and E(t2) respectively, and the energy rate value at time point t2 is calculated by subtracting the energy rate value at time point t1, and the time point t2 is calculated by subtracting the energy rate value from the time point t1, and the energy rate value difference is divided by the time point difference to obtain the energy release rate change rate, denoted as kE; wherein time points t2 and t1 are different moments, and time point t2>time point t1.

[0069] Get the time interval length of each monitoring period, recorded as Δt, according to the formula Calculate the cumulative energy LN of each distributed monitoring point corresponding to the rockburst area;

[0070] The number of microseismic events in each monitoring period at each distributed monitoring point in the rockburst area is obtained, recorded as q, and the initial motion direction of each microseismic event is expressed by a unit vector Indicates, where q = 1, 2, ..., w, according to the formula Calculate the directional consistency parameter FY of the microseismic events at each distributed monitoring point in the rockburst area;

[0071] like Figure 2 As shown in the figure, the scatter plot of strain-type rockburst is drawn by combining the cumulative energy LN of each distributed monitoring point corresponding to the rockburst area and the directional consistency parameter FY of the microseismic events of each distributed monitoring point corresponding to the rockburst area. Figure 2 If the distribution of the scattered points is similar, the rockburst type corresponding to each distributed monitoring point in the rockburst area is determined to be strain-type rockburst;

[0072] like Figure 3 As shown in the figure, the scatter plot of stress-type rockburst is drawn by combining the energy release rate change rate of each distributed monitoring point corresponding to the rockburst area and the directional consistency parameter FY of the microseismic events of each distributed monitoring point corresponding to the rockburst area. Figure 3 If the distribution of the scattered points is similar, the rockburst type corresponding to each distributed monitoring point in the rockburst area is determined to be stress-type rockburst;

[0073] In addition, when the rockburst type prediction scatter diagram is drawn, which contains both the scatter point distribution in the strain type rockburst scatter diagram and the scatter point distribution in the stress type rockburst scatter diagram, it is determined that the rockburst type corresponding to each distributed monitoring point in the rockburst area is a mixed rockburst.

[0074] Through the control terminal, the rockburst quantitative seismic risk coefficient and the rockburst comprehensive indication coefficient of each monitoring period in each distributed monitoring point corresponding to the rockburst area are numerically analyzed to generate the corresponding rockburst signal. At the same time, the rockburst type prediction scatter diagram is drawn and compared with the strain type rockburst scatter diagram and the stress type rockburst scatter diagram to determine the rockburst type of each distributed monitoring point corresponding to the rockburst area.

[0075] In summary, the present invention uses the rock fracture energy monitoring module and the rock magnitude parameter monitoring module to accurately collect the energy and magnitude data of microseismic events at each distributed monitoring point in different monitoring periods, providing a detailed basis for subsequent analysis. Among them, the standardized processing of the energy rate value and the magnitude centroid value converts the data into a standard normal distribution form, which is convenient for comprehensive calculation and comparison, and makes the data of different monitoring points and time periods comparable; the rock burst energy magnitude risk analysis module calculates the rock burst risk coefficient by combining the energy rate value and the magnitude centroid value, avoiding the one-sidedness of single parameter evaluation, and can capture the dynamic changes of rock burst risk in time and space. The quantified risk coefficient is intuitive and easy to understand, providing strong data support for engineering personnel's decision-making. Different risk coefficient intervals correspond to different rock state signals, which can timely and accurately warn of rock bursts. Risk level; The rock mass microseismic state monitoring module records microseismic signals at a high-frequency sampling rate and analyzes them to obtain the rockburst amplitude-frequency risk coefficient. The relevant parameters are determined by combining historical data and experience to make the analysis more targeted and accurate. The rockburst amplitude-frequency magnitude risk analysis module further integrates the microseismic anomaly detection coefficient and the rockburst amplitude-frequency risk coefficient. The rockburst risk comprehensive assessment and analysis module integrates the rockburst volume risk coefficient and the rockburst amplitude-frequency magnitude risk coefficient to obtain the rockburst comprehensive indication coefficient. Through multi-dimensional and multi-level comprehensive analysis, the rockburst risk is comprehensively assessed, and the accuracy and reliability of risk identification are improved. Finally, the rockburst type prediction and analysis module draws a scatter plot based on the directional consistency parameters, cumulative energy values, and energy release rate change rates of microseismic events, which can effectively predict the rockburst type and provide a basis for taking targeted prevention and control measures. The control terminal integrates and processes various types of data and analysis results, generates corresponding signals, and determines the rockburst type, realizing the intelligence and automation of the entire system, which helps to prevent rockburst disasters in advance, ensure the safety of underground engineering construction and the safety of life and property of personnel, improve the efficiency and quality of engineering construction, and reduce the huge losses and risks caused by rockburst accidents.

[0076] The above formulas are all dimensionless and numerical calculations. The formula is a formula that is obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The size of the coefficient is to quantify each parameter to obtain a specific value. Regarding the size of the coefficient, it is acceptable as long as it does not affect the proportional relationship between the parameter and the quantified value.

[0077] In addition, those skilled in the art will appreciate that various aspects of the present invention may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present invention may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present invention may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0078] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention belongs. It should also be understood that terms such as those defined in common dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology and should not be interpreted in an idealized or extremely formal sense, unless explicitly defined as such herein.

[0079] The above is an explanation of the present invention and should not be considered as a limitation thereof. Although several exemplary embodiments of the present invention have been described, it will be readily appreciated by those skilled in the art that many modifications may be made to the exemplary embodiments without departing from the novel teachings and advantages of the present invention. Therefore, all such modifications are intended to be included within the scope of the present invention as defined in the claims. It should be understood that the above is an explanation of the present invention and should not be considered as being limited to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The present invention is defined by the claims and their equivalents.

Claims

1. Rockburst risk identification and early warning system based on microseismic monitoring data, characterized by: It includes rock fracture energy monitoring module, rock magnitude parameter monitoring module, rock burst energy magnitude risk analysis module, rock microseismic state monitoring module, rock burst amplitude frequency magnitude risk analysis module, rock burst risk comprehensive assessment analysis module, rock burst type prediction analysis module and control terminal; The rock mass fracture energy monitoring and analysis module detects and collects the number of microseismic events in each monitoring period at each distributed monitoring point in the rockburst area and the energy released by each microseismic event based on microseismic sensors, and obtains the energy rate value of each monitoring period at each distributed monitoring point in the rockburst area; The rock mass magnitude parameter monitoring module is used to obtain the vibration waveform and duration of each microseismic event in each monitoring period at each distributed monitoring point corresponding to the rockburst area based on the microseismic sensor; The rockburst energy magnitude risk analysis module is used to perform comprehensive calculation and analysis based on the energy rate value and magnitude centroid value of each monitoring period in each distributed monitoring point corresponding to the standardized rockburst area, and the rockburst magnitude risk coefficient of each monitoring period in each distributed monitoring point corresponding to the rockburst area is obtained. The rock mass microseismic state monitoring module continuously records microseismic signals at a set high-frequency sampling rate based on microseismic sensors, converts the physical changes caused by vibration into electrical signals and transmits them to the ground control terminal in real time. The amplitude and frequency of the collected microseismic signals are comprehensively analyzed to obtain the rockburst amplitude-frequency risk coefficient ZF for each monitoring period in each distributed monitoring point in the rockburst area based on the amplitude of the microseismic signal.

2. The rockburst risk identification and early warning system based on microseismic monitoring data according to claim 1 is characterized by: The energy released by each microseismic event in each monitoring period of each distributed monitoring point in the rockburst area is summed up and calculated to obtain the energy rate value of each monitoring period in each distributed monitoring point in the rockburst area. The energy rate value of each monitoring period in each distributed monitoring point in the rockburst area is standardized to obtain the standardized energy rate value of each monitoring period in each distributed monitoring point in the rockburst area, which is recorded as 3. The rockburst risk identification and early warning system based on microseismic monitoring data according to claim 2 is characterized by: The vibration waveform and duration of each microseismic event are converted into a magnitude value through an improved algorithm of the Richter magnitude. The magnitude ranges are divided according to the magnitude data, and the magnitude-frequency distribution matrix is ​​constructed with each magnitude range as a row and each monitoring period as a column.

4. The rockburst risk identification and early warning system based on microseismic monitoring data according to claim 3 is characterized by: By multiplying the center value of each magnitude interval and the frequency corresponding to each magnitude interval, and then summing the product results, the magnitude centroid value of each monitoring period in each distributed monitoring point corresponding to the rockburst area is obtained. The magnitude centroid value of each monitoring period in each distributed monitoring point corresponding to the rockburst area is standardized to obtain the standardized magnitude centroid value of each monitoring period in each distributed monitoring point corresponding to the rockburst area, which is recorded as 5. The rockburst risk identification and early warning system based on microseismic monitoring data according to claim 4 is characterized by: The rockburst risk coefficient of each distributed monitoring point in each monitoring period in the rockburst area is The specific calculation and analysis methods are as follows: According to the formula Calculate the rockburst risk coefficient of each distributed monitoring point in each monitoring period in the rockburst area 6. The rockburst risk identification and early warning system based on microseismic monitoring data according to claim 1 is characterized by: Based on the rockburst amplitude-frequency risk coefficient ZF of each monitoring period in each distributed monitoring point in the rockburst area corresponding to the microseismic signal amplitude, the specific analysis method is as follows: The kernel density estimation method is used to draw the probability density curve P(A) of the peak amplitude of the collected original microseismic waveform data. The high amplitude threshold is set as FH according to engineering experience and rock characteristics. According to the formula Calculate the microseismic anomaly detection coefficient SF of each monitoring period in each distributed monitoring point in the rockburst area based on the microseismic signal amplitude; Based on historical data and experience combined with data statistical analysis, the previous main frequency concentration interval [PF z1 , PF z2 ] and the later added low frequency range [PF h1 , PF h2 ], sum the amplitudes corresponding to each frequency in the main frequency concentration interval in the early stage and the low frequency interval added later, and obtain the early energy sum value SE z And later energy and value SE h , divide the later energy sum value by the sum of the previous energy sum value and the later energy sum value to obtain the main frequency change characteristic value, which is recorded as F1.

7. The rockburst risk identification and early warning system based on microseismic monitoring data according to claim 6 is characterized by: The reference frequency bandwidth is set based on historical experience and the common frequency bandwidth range in rockburst areas, denoted as PF0. At the same time, the current frequency bandwidth is obtained from the collected microseismic signal, denoted as PF j , according to the formula The rockburst amplitude-frequency risk coefficient ZF of each monitoring period in each distributed monitoring point in the rockburst area is calculated; where κ represents the set weight factor.

8. The rockburst risk identification and early warning system based on microseismic monitoring data according to claim 7 is characterized by: The rockburst amplitude-frequency magnitude risk analysis module is used to perform comprehensive calculation and analysis based on the microseismic anomaly detection coefficient SF and the rockburst amplitude-frequency risk coefficient ZF of each monitoring period in each distributed monitoring point in the rockburst area, and the rockburst amplitude-frequency magnitude risk coefficient FB of each monitoring period in each distributed monitoring point in the rockburst area is obtained. The specific analysis method is as follows: According to the formula The rockburst amplitude-frequency risk coefficient FB of each distributed monitoring point in the rockburst area corresponding to each monitoring period is calculated.

9. The rockburst risk identification and early warning system based on microseismic monitoring data according to claim 5 or 8, characterized in that: The rockburst risk comprehensive assessment and analysis module is used to perform comprehensive calculation and analysis based on the rockburst volume risk coefficient and the rockburst amplitude-frequency-magnitude risk coefficient of each distributed monitoring point in each monitoring period in the rockburst area, and obtain the rockburst comprehensive indication coefficient of each distributed monitoring point in each monitoring period in the rockburst area. The specific comprehensive calculation and analysis method is as follows: According to the formula The rockburst comprehensive indication coefficient ZB of each distributed monitoring point in the rockburst area corresponding to each monitoring period is calculated, where e is represented by a natural constant.

10. The rockburst risk identification and early warning system based on microseismic monitoring data according to claim 1 is characterized by: The rockburst type prediction and analysis module is used to obtain and analyze the directional consistency parameters, cumulative energy values, and energy release rate change rates of microseismic events. A rockburst type prediction scatter plot is drawn based on the above parameters. The specific analysis method is as follows: The energy rate values ​​at adjacent time points t1 and t2 are obtained, denoted as E(t1) and E(t2) respectively, and the energy rate value at time point t2 is calculated by subtracting the energy rate value at time point t1, and the time point t2 is calculated by subtracting the energy rate value from the time point t1, and the energy rate value difference is divided by the time point difference to obtain the energy release rate change rate, denoted as kE; wherein time points t2 and t1 are different moments, and time point t2>time point t1. Get the time interval length of each monitoring period, recorded as Δt, according to the formula Calculate the cumulative energy LN of each distributed monitoring point corresponding to the rockburst area; The number of microseismic events in each monitoring period at each distributed monitoring point in the rockburst area is obtained, recorded as q, and the initial motion direction of each microseismic event is expressed by a unit vector Indicates, where q = 1, 2, ..., w, according to the formula The directional consistency parameter FY of microseismic events corresponding to each distributed monitoring point in the rockburst area is calculated.

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