Rockburst risk identification and early warning system based on microseismic monitoring data

Abstract

The present application relates to rock burst risk identification technical field, specifically disclose a rock burst risk identification early warning system based on microseismic monitoring data, including rock mass fracture energy monitoring module, rock mass magnitude parameter monitoring module, rock burst energy magnitude risk analysis module, rock mass microseismic state monitoring module, rock burst amplitude-frequency magnitude risk analysis module, rock burst risk comprehensive evaluation analysis module, rock burst type prediction analysis module and control terminal. Through multidimensional, multilevel comprehensive analysis, the rock burst risk is comprehensively evaluated, and the accuracy and reliability of risk identification are improved; based on the direction consistency parameter, cumulative energy value and energy release rate change rate of microseismic event, scatter diagram is drawn, which can effectively predict the rock burst type, provide basis for taking targeted prevention measures, integrate and process various data and analysis results, generate corresponding signals and determine the rock burst type, realize the intelligence and automation of the whole system, and help to prevent rock burst disaster in advance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of rockburst risk identification, and particularly relates to a rockburst risk identification and early warning system based on microseismic monitoring data. BACKGROUND

[0002] In the field of underground engineering, rockburst is a highly destructive disaster phenomenon, and its sudden occurrence often causes serious damage to engineering facilities, and even endangers the safety of construction personnel. The traditional rockburst monitoring method mainly relies on experience judgment and single parameter monitoring, such as only focusing on individual indicators such as energy release or magnitude. This method has obvious limitations. Since the occurrence of rockburst is a complex physical process, it is influenced by multiple factors, and a single parameter cannot comprehensively and accurately reflect the incubation and development state of rockburst, resulting in inaccurate rockburst risk assessment, untimely early warning, and difficulty in meeting 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 deficiencies. On the one hand, some systems are relatively simple in data processing, and fail to fully exploit the rich information contained in microseismic signals, such as only performing simple statistics on the energy or magnitude of microseismic events, while ignoring the internal relationship between 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 model, which cannot accurately quantify the rockburst risk level, and it is also difficult to issue effective early warning signals in a timely manner according to different risk levels, making it difficult for engineering personnel to make scientific and reasonable decisions when facing rockburst risks. SUMMARY

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

[0005] To achieve the above purpose, the present application realizes the following technical scheme: a rockburst risk identification and early warning system based on microseismic 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 microseismic state monitoring module, a rockburst amplitude-frequency-magnitude risk analysis module, a rockburst risk comprehensive evaluation analysis module, a rockburst type prediction analysis module, and a control terminal.

[0006] The rock mass fracture energy monitoring analysis module detects and collects the number of microseismic events and the energy released by each microseismic event in each monitoring period in the rockburst area corresponding to each distributed monitoring point based on the microseismic sensor, to obtain the energy rate value in each monitoring period in the rockburst area corresponding to each distributed monitoring point.

[0007] The vibration waveform and duration of each microseismic event in each monitoring period of the rock burst region corresponding to each distributed monitoring point are obtained by the rock mass magnitude parameter monitoring module based on the microseismic sensor.

[0008] The rock burst energy magnitude risk coefficient of each monitoring period of the rock burst region corresponding to each distributed monitoring point is obtained by the rock burst energy magnitude risk analysis module based on the energy rate value and the magnitude gravity value of each monitoring period of the rock burst region corresponding to each distributed monitoring point after standardization.

[0009] The rock burst amplitude-frequency risk coefficient ZF of each monitoring period of the rock burst region corresponding to each distributed monitoring point based on the microseismic signal amplitude is obtained by the rock mass microseismic state monitoring module based on the microseismic sensor, which continuously records the microseismic signal at a set high-frequency sampling rate, converts the physical changes caused by vibration into electrical signals and transmits them to the ground control terminal in real time, and comprehensively analyzes the amplitude and frequency of the collected microseismic signal.

[0010] Further, the energy released by each microseismic event in each monitoring period of the rock burst region corresponding to each distributed monitoring point is summed to obtain the energy rate value of each monitoring period of the rock burst region corresponding to each distributed monitoring point, and the energy rate value of each monitoring period of the rock burst region corresponding to each distributed monitoring point is standardized to obtain the standardized energy rate value of each monitoring period of the rock burst region corresponding to each distributed monitoring point, denoted as

[0011] Further, the vibration waveform and duration of each microseismic event are converted into magnitude values by the improved algorithm of Richter magnitude, and each magnitude interval is divided according to the magnitude data, taking each magnitude interval as a row and each monitoring period as a column to construct a magnitude-frequency distribution matrix.

[0012] Further, the center value of each magnitude interval is multiplied by the frequency corresponding to each magnitude interval, and the product results are summed to obtain the magnitude gravity value of each monitoring period of the rock burst region corresponding to each distributed monitoring point, and the magnitude gravity value of each monitoring period of the rock burst region corresponding to each distributed monitoring point is standardized to obtain the standardized magnitude gravity value of each monitoring period of the rock burst region corresponding to each distributed monitoring point, denoted as

[0013] Further, the rock burst energy magnitude risk coefficient of each monitoring period of the rock burst region corresponding to each distributed monitoring point is calculated as The specific calculation and analysis method is as follows:

[0014] According to the formula The rock burst energy magnitude risk coefficient of each monitoring period of the rock burst region corresponding to each distributed monitoring point is calculated as

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

[0016] The probability density curve P(A) of the amplitude peak value is drawn by using the kernel density estimation method on the collected original microseismic waveform data, the high amplitude threshold FH is set according to the engineering experience and rock characteristics, and the formula The microseismic anomaly detection coefficient SF of the rock burst area corresponding to each distributed monitoring point in each monitoring period is calculated based on the microseismic signal amplitude;

[0017] Based on historical data and experience combined with data statistical analysis, the early main frequency concentration interval [PF z1 , PF z2 ] and the late newly added low frequency interval [PF h1 , PF h2 ] are determined, the amplitude corresponding to each frequency in the early main frequency concentration interval and the late newly added low frequency interval is summed to obtain the early energy sum SE z and the late energy sum SE h , and the late energy sum is divided by the sum of the early energy sum and the late energy sum to obtain the main frequency change characteristic value, denoted as F1.

[0018] Further, according to historical experience combined with the common frequency bandwidth range of rock burst area, the reference frequency bandwidth is set, denoted as PF0, and the current frequency bandwidth is obtained from the collected microseismic signal, denoted as PF j , according to the formula The rock burst amplitude frequency risk coefficient ZF of the rock burst area corresponding to each distributed monitoring point in each monitoring period is calculated; wherein κ represents the set weight factor.

[0019] Further, through the rock burst amplitude frequency magnitude risk analysis module, the rock burst amplitude frequency magnitude risk coefficient FB of the rock burst area corresponding to each distributed monitoring point in each monitoring period is obtained based on the microseismic anomaly detection coefficient SF and the rock burst amplitude frequency risk coefficient ZF of the rock burst area corresponding to each distributed monitoring point in each monitoring period, and the specific analysis method is as follows:

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

[0021] Further, through the rock burst risk comprehensive evaluation analysis module, the rock burst comprehensive indication coefficient of the rock burst area corresponding to each distributed monitoring point in each monitoring period is obtained based on the rock burst volume risk coefficient and the rock burst amplitude frequency magnitude risk coefficient of the rock burst area corresponding to each distributed monitoring point in each monitoring period, and the specific comprehensive calculation analysis method is as follows:

[0022] The comprehensive rock burst indicator ZB of the rock burst area corresponding to each monitoring period in each distributed monitoring point is calculated according to the formula The natural constant e is represented.

[0023] Further, the rock burst type prediction analysis module is used to acquire and analyze the direction consistency parameter, the cumulative energy value and the energy release rate change rate of the microseismic event, and a rock burst type prediction scatter plot is drawn according to the above parameters, and the specific analysis mode is as follows:

[0024] The energy rate values of adjacent time points t1 and t2 are obtained and denoted as E(t1) and E(t2) respectively, the energy rate value of the time point t2 is subtracted from the energy rate value of the time point t1, and the time point t2 is subtracted from the time point t1, the difference value of the energy rate is divided by the difference value of the time point, and the energy release rate change rate kE is obtained; wherein the time points t2 and t1 are different time points, and the time point t2 is greater than the time point t1.

[0025] The time interval length of each monitoring period is obtained and denoted as Δt, and the formula The cumulative energy LN of the rock burst area corresponding to each distributed monitoring point is calculated.

[0026] The number of microseismic events in each monitoring period of the rock burst area corresponding to each distributed monitoring point is obtained and denoted as q, and the initial motion direction of each microseismic event is represented by a unit vector , wherein q=1, 2, …, w, and the formula The direction consistency parameter FY of the microseismic event of the rock burst area corresponding to each distributed monitoring point is calculated.

[0027] The beneficial effects of the present application are:

[0028] 1. By calculating the comprehensive energy rate value (reflecting energy accumulation and release rate) and the seismic intensity gravity value (reflecting the distribution characteristics of the seismic intensity), two key parameters, the one-sidedness caused by relying on a single parameter to evaluate the rock burst risk can be avoided, and the dynamic changes of the rock burst risk in space and time can be captured by considering each distributed monitoring point and each monitoring period, and the rock burst seismic risk coefficient of the rock burst area corresponding to each monitoring period in each distributed monitoring point obtained by comprehensive calculation can directly represent the rock burst risk degree of each monitoring point in each monitoring period, compared with the qualitative description, the quantitative risk coefficient can make the engineering personnel more clearly understand the risk size, thereby providing more persuasive data support for decision-making.

[0029] 2. Through multi-dimensional and multi-level comprehensive analysis, the rock burst risk is comprehensively evaluated, and the accuracy and reliability of risk identification are improved; the scatter diagram is drawn based on the direction consistency parameter, cumulative energy value and energy release rate change rate of microseismic events, the rock burst type can be effectively predicted, the basis for taking targeted prevention measures is provided, the integration and processing of various data and analysis results are used to generate corresponding signals and determine the rock burst type, the intelligence and automation of the whole system are realized, which is helpful to prevent rock burst disasters in advance, ensure the safety of underground engineering construction, personnel life and property safety, improve the engineering construction efficiency and quality, and reduce the huge loss and risk caused by rock burst accidents. BRIEF DESCRIPTION OF DRAWINGS

[0030] The application will be further described below in combination with the drawings.

[0031] Figure 1 The principle block diagram of the rock burst risk identification and early warning system based on microseismic monitoring data in the embodiment of the application is shown in the figure.

[0032] Figure 2 The strain type rock burst scatter diagram proposed in the embodiment of the application is shown in the figure.

[0033] Figure 3 The stress type rock burst scatter diagram proposed in the embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the application will be described clearly and completely below in combination with the drawings. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments of the application, all other embodiments obtained by those skilled in the art without creative labor also belong to the protection scope of the application.

[0035] As shown in the application and claims, unless the context clearly indicates otherwise, the words “one”, “a”, “an” and / or “the” do not specifically refer to the singular, but also include the plural. Generally, the terms “comprise” and “include” only indicate the inclusion of the steps and elements explicitly identified, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements.

[0036] Although the application makes various references to certain modules in the system according to the embodiments of the application, however, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0037] Flowcharts are used in the present disclosure to illustrate the operations performed by the systems according to embodiments of the present disclosure. It should be understood that the preceding or following operations are not necessarily performed in sequence. Instead, various steps can be processed in reverse order or simultaneously, as desired. Other operations can also be added to or removed from these processes, or one or more steps can be removed from these processes.

[0038] Hereinafter, example embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. It is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all embodiments of the present disclosure, and it should be understood that the present disclosure is not limited to the example embodiments described herein.

[0039] Embodiment 1:

[0040] Referring to Figure 1 As shown in the drawings, the rock burst risk identification and early warning system based on microseismic monitoring data comprises a rock mass fracture energy monitoring module, a rock mass magnitude parameter monitoring module, a rock burst energy magnitude risk analysis module, a rock mass microseismic state monitoring module, a rock burst amplitude-frequency magnitude risk analysis module, a rock burst risk comprehensive evaluation analysis module, a rock burst type prediction analysis module, and a control terminal.

[0041] The rock mass fracture energy monitoring analysis module detects and collects the number of microseismic events and the energy released by each microseismic event in each monitoring period in each distributed monitoring point corresponding to the rock burst area based on the microseismic sensor, sums up the energy released by each microseismic event in each monitoring period in each distributed monitoring point corresponding to the rock burst area, obtains the energy rate value of each monitoring period in each distributed monitoring point corresponding to the rock burst area, and standardizes the energy rate value of each monitoring period in each distributed monitoring point corresponding to 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, denoted as wherein i = 1, 2, …, n, i represents the number of each distributed monitoring point, n represents the total number of the number of each distributed monitoring point, j = 1, 2, …, m, j represents the number of each monitoring period, and m represents the total number of the number of each monitoring period.

[0042] Specifically, the energy rate value of each monitoring period in each distributed monitoring point corresponding to the rock burst area is standardized in the following manner: the mean value of the historical energy rate value of the rock burst area and the standard deviation of the historical energy rate value are obtained, the energy rate value of each monitoring period in each distributed monitoring point corresponding to the rock burst area is subtracted from the mean value of the historical energy rate value of the rock burst area, and the difference is calculated, and the difference calculation result is divided by 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. The energy rate value of each monitoring period in each distributed monitoring point corresponding to the rock burst area is converted into a standard normal distribution form with a mean value of 0 and a standard deviation of 1, facilitating subsequent comprehensive calculation.

[0043] The rock mass magnitude parameter monitoring module obtains the vibration waveform and duration of each microseismic event in each monitoring period in each distributed monitoring point in the rock burst area based on the microseismic sensor, converts the vibration waveform and duration of each microseismic event into a magnitude value through an improved algorithm of the Richter magnitude, divides each magnitude interval according to the magnitude data, takes each magnitude interval as a row and each monitoring period as a column, and constructs a magnitude-frequency distribution matrix.

[0044] The center value of each magnitude interval and the frequency corresponding to each magnitude interval are multiplied to obtain the magnitude gravity value of each monitoring period in each distributed monitoring point in the rock burst area, and the magnitude gravity value of each monitoring period in each distributed monitoring point in the rock burst area is standardized to obtain the standardized magnitude gravity value of each monitoring period in each distributed monitoring point in the rock burst area, denoted as The standardization processing mode of the magnitude gravity value of each monitoring period in each distributed monitoring point in the rock burst area is the same as the above-mentioned energy rate standardization processing mode.

[0045] The rock burst energy magnitude risk analysis module performs comprehensive calculation and analysis on the standardized energy rate and magnitude gravity value of each monitoring period in each distributed monitoring point in the rock burst area to obtain the rock burst quantity seismic risk coefficient of each monitoring period in each distributed monitoring point in the rock burst area By calculating the two key parameters of the comprehensive energy rate (reflecting the energy accumulation and release rate) and the magnitude gravity value (reflecting the magnitude distribution characteristics), the one-sidedness caused by relying on a single parameter to evaluate the rock burst risk can be avoided, and the dynamic changes of the rock burst risk in space and time can be captured by considering each distributed monitoring point and each monitoring period. The rock burst quantity seismic risk coefficient of each monitoring period in each distributed monitoring point in the rock burst area obtained by comprehensive calculation can directly represent the rock burst risk degree of each monitoring point in each monitoring period, and compared with the qualitative description, this quantitative risk coefficient can make the engineering personnel more clearly understand the risk size, thereby providing more persuasive data support for decision-making; the specific calculation and analysis method is as follows:

[0046] According to the formula The rock burst quantity seismic risk coefficient of each monitoring period in each distributed monitoring point in the rock burst area is calculated

[0047] The rock burst quantity seismic risk coefficient of each monitoring period in each distributed monitoring point in the rock burst area is calculated numerical analysis,

[0048] If the rock burst area corresponds to the rock burst seismic risk coefficient of a certain monitoring period in a certain distributed monitoring point , it indicates that the risk of rock burst in energy and magnitude is at a low level in the corresponding monitoring period of the distributed monitoring point, and a rock mass stable state signal is generated;

[0049] As the rock burst area corresponds to the rock burst seismic risk coefficient of a certain monitoring period in a certain distributed monitoring point, if , it indicates that the rock burst risk starts to rise in the corresponding monitoring period of the distributed monitoring point, enters the early warning interval, and generates a rock mass early warning state signal;

[0050] If , it indicates that the rock burst risk is high in the corresponding monitoring period of the distributed monitoring point, and the rock mass is close to the critical state of energy release, generating a rock mass rupture state signal;

[0051] If , it indicates that the rock burst is at the edge of high-risk outbreak or has occurred in the corresponding monitoring period of the distributed monitoring point, and the energy value reaches an extremely high level, the magnitude is high and the distribution is chaotic, generating a rock burst alarm state signal.

[0052] Embodiment 2:

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

[0054] The probability density curve P(A) of the amplitude peak value is drawn by using the kernel density estimation method on the collected original microseismic waveform data, the high amplitude threshold FH is set according to engineering experience and rock characteristics, and the microseismic anomaly detection coefficient SF of the rock burst area corresponding to each monitoring period in each distributed monitoring point based on the microseismic signal amplitude is calculated according to the formula

[0055] The early main frequency concentration interval [PF z1 , PF z2 ] and the late newly added low frequency interval [PF h1 , PF h2 ] are determined based on historical data and experience combined with data statistical analysis, the amplitude corresponding to each frequency in the early main frequency concentration interval and the late newly added low frequency interval is summed, and the early energy sum SE z and the late energy sum SE h ​The post-period energy sum is divided by the sum of the pre-period energy sum and the post-period energy sum to obtain a main frequency change characteristic value, denoted as F1; according to the formula The main frequency change characteristic value F1 is calculated.

[0056] According to historical experience and the common frequency bandwidth range of the rock burst area, a reference frequency bandwidth is set, denoted as PF0, and a current frequency bandwidth is obtained from the collected microseismic signals, denoted as PF j , according to the formula The rock burst amplitude-frequency risk coefficient ZF of the rock burst area corresponding to each monitoring period in each distributed monitoring point is calculated; wherein κ represents a set weight factor, by referring to existing rock burst detection projects of the same type or engineering data under similar geological conditions, and combining the influence degree of the main frequency change and the frequency bandwidth change on the rock burst risk, the numerical value is set, if the main frequency change has a relatively large influence on the rock burst risk in most similar projects, then β may take a relatively large value, such as 0.7-0.8; on the contrary, if the influence of the frequency bandwidth change is more important, then β may take 0.3-0.4.

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

[0058] According to the formula The rock burst amplitude-frequency risk coefficient FB of the rock burst area corresponding to each monitoring period in each distributed monitoring point is calculated;

[0059] Through the rock burst risk comprehensive evaluation analysis module, the rock burst comprehensive indication coefficient of the rock burst area corresponding to each monitoring period in each distributed monitoring point is obtained by comprehensive calculation and analysis based on the rock burst quantity earthquake risk coefficient and the rock burst amplitude-frequency magnitude risk coefficient of the rock burst area corresponding to each monitoring period in each distributed monitoring point, and the specific comprehensive calculation and analysis method is as follows:

[0060] According to the formula The rock burst comprehensive indication coefficient ZB of the rock burst area corresponding to each monitoring period in each distributed monitoring point is calculated, wherein e represents a natural constant.

[0061] The rock burst comprehensive indication coefficient ZB of the rock burst area corresponding to each monitoring period in each distributed monitoring point is analyzed,

[0062] If the rock burst region corresponds to a certain monitoring period of a certain distributed monitoring point, the rock burst comprehensive indicator ZB≤1, which indicates that the risk of rock burst in energy and magnitude is at a low level in the corresponding monitoring period of the distributed monitoring point, and a rock burst stable state signal is generated;

[0063] With the increase of the rock burst comprehensive indicator value of the rock burst region corresponding to a certain monitoring period of a certain distributed monitoring point, if 1<ZB≤1.3, it indicates that the risk of rock burst starts to rise in the corresponding monitoring period of the distributed monitoring point, enters the early warning interval, and a rock burst early warning state signal is generated.

[0064] If 1.3<ZB≤1.6, it indicates that the risk of rock burst is high in the corresponding monitoring period of the distributed monitoring point, 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 indicates that the rock burst is at the edge of high risk or has occurred in the corresponding monitoring period of the distributed monitoring point, the energy value reaches an extremely high level, the magnitude is high and the distribution is chaotic, and a rock burst alarm state signal is generated.

[0066] Embodiment 3:

[0067] Further, the rock burst type prediction analysis module is based on the direction consistency parameter, the cumulative energy value and the energy release rate change rate of the microseismic event to obtain and analyze, and the rock burst type prediction scatter plot is drawn combined with the above parameters, and the specific analysis method is as follows:

[0068] The energy rate values of adjacent time points t1 and t2 are obtained, which are denoted as E(t1) and E(t2) respectively, the energy rate value of time point t2 is subtracted from the energy rate value of time point t1, and the time point t2 is subtracted from the time point t1, the difference value of the energy rate value is divided by the difference value of the time point, and the energy release rate change rate is obtained, which is denoted as kE; wherein the time points t2 and t1 are different time points, and the time point t2 is greater than the time point t1.

[0069] The time interval length of each monitoring period is obtained, denoted as Δt, according to the formula The cumulative energy LN of the rock burst region corresponding to each distributed monitoring point is calculated;

[0070] The number of microseismic events in each monitoring period of the rock burst region corresponding to each distributed monitoring point is obtained, denoted as q, and the initial direction of each microseismic event is represented by a unit vector , wherein q=1, 2, …, w, according to the formula The direction consistency parameter FY of the microseismic event of the rock burst region corresponding to each distributed monitoring point is calculated;

[0071] As Figure 2As 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 rock burst is drawn by combining the energy release rate change rate of each distributed monitoring point in the rock burst area and the directional consistency parameter FY of the microseismic events of each distributed monitoring point in the rock burst 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 and there is both scatter point distribution in the strain type rockburst scatter diagram and 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 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 plot is compared with the strain type rockburst scatter plot and the stress type rockburst scatter plot to determine the rockburst type of each distributed monitoring point corresponding to the rockburst area.

[0075] In summary, in the present application, through the rock mass fracture energy monitoring module and the rock mass magnitude parameter monitoring module, the microseismic event energy and magnitude data of each distributed monitoring point at different monitoring periods can be accurately collected, providing detailed basis for subsequent analysis. The standardization processing of the energy rate value and the magnitude gravity value converts the data into a standard normal distribution form, facilitating comprehensive calculation and comparison, making the data of different monitoring points and periods comparable. The rock burst energy magnitude risk analysis module calculates the rock burst quantity magnitude risk coefficient by comprehensively considering the energy rate value and the magnitude gravity value, avoiding the one-sidedness of single parameter evaluation, and can capture the dynamic changes of rock burst risk in space and time. The quantitative risk coefficient is intuitive and easy to understand, providing strong data support for engineering personnel decision-making. Different risk coefficient intervals correspond to different rock mass state signals, which can timely and accurately warn the rock burst risk level. The rock mass microseismic state monitoring module records the microseismic signal at a high sampling rate and analyzes the rock burst amplitude frequency risk coefficient. Combined with historical data and experience, relevant parameters are determined to make the analysis more targeted and accurate. The rock burst amplitude frequency magnitude risk analysis module further comprehensively considers the microseismic anomaly detection coefficient and the rock burst amplitude frequency risk coefficient. The rock burst risk comprehensive evaluation analysis module further comprehensively considers the rock burst quantity magnitude risk coefficient and the rock burst amplitude frequency magnitude risk coefficient to obtain the rock burst comprehensive indication coefficient. Through multi-dimensional and multi-level comprehensive analysis, the rock burst risk is comprehensively evaluated, improving the accuracy and reliability of risk identification. Finally, the rock burst type prediction analysis module draws scatter plots based on the direction consistency parameter, cumulative energy value, and energy release rate change rate of microseismic events, which can effectively predict the rock burst type and provide a basis for taking targeted prevention measures. The control terminal integrates and processes various data and analysis results, generates corresponding signals, and determines the rock burst type, realizing the intelligence and automation of the entire system. This helps to prevent rock burst disasters in advance, ensures the safety of underground engineering construction, personnel life and property, improves engineering construction efficiency and quality, and reduces the huge losses and risks caused by rock burst accidents.

[0076] The above formulas are dimensionless numerical calculations. The formula is obtained by software simulation of a large amount of data to reflect the current situation. The size of the coefficient is a specific value obtained by quantifying each parameter. As long as the proportion relationship between the parameter and the quantized value is not affected, the size of the coefficient is acceptable.

[0077] Moreover, those skilled in the art will appreciate that the various aspects of the application can be illustrated and described by a number of exemplary formats or scenarios, including any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof. Accordingly, various aspects of the application can be embodied in hardware only, software only, or a combination of both hardware and software. The above hardware or software can be referred to as a "block," "module," "engine," "unit," "component," or "system." Furthermore, various aspects of the application can be embodied as a computer program product on one or more computer readable media, including 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 this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0079] The above description is that of current embodiments of the application, and is not to be construed as limiting the same. While numerous modifications to the exemplary embodiments can be contemplated, and it is to be understood that the examples described herein are presented by way of example only and that various changes in form and details can be made thereof without departing from the spirit, teaching and scope of the application. Accordingly, all such modifications are intended to be included within the scope of the application as defined in the claims. It is to be understood that the above description is intended to be illustrative, and not restrictive. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the application should, therefore, be determined not with reference to the above description, but should instead be determined with reference to the appended claims, along with their full scope of equivalents.

Claims

1. A 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 and analysis module, rock burst type prediction and 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 vibration waveform and duration of each microseismic event are converted into a magnitude value through the improved algorithm of the Richter magnitude. The magnitude intervals are divided according to the magnitude data. The magnitude intervals are used as rows and the monitoring periods are used as columns to construct a magnitude-frequency distribution matrix. The center value of each magnitude interval and the frequency corresponding to each magnitude interval are multiplied by the product, and then the product results are summed to obtain the magnitude centroid value of each monitoring period in each distributed monitoring point corresponding to the rock burst area. The magnitude centroid value of each monitoring period in each distributed monitoring point corresponding to the rock burst area is standardized to obtain the standardized magnitude centroid value of each monitoring period in each distributed monitoring point corresponding to the rock burst area, which is recorded as ; 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 the standardized rockburst area corresponding to each distributed monitoring point, and the rockburst magnitude risk coefficient of each monitoring period in the rockburst area corresponding to each distributed monitoring point 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 collected microseismic signals are comprehensively analyzed in terms of amplitude and frequency to obtain the rock burst amplitude-frequency risk coefficient for each distributed monitoring point in each monitoring period in the rock burst area based on the microseismic signal amplitude. ; The rockburst amplitude-frequency magnitude risk analysis module is based on the microseismic anomaly detection coefficient of each monitoring period in each distributed monitoring point corresponding to the rockburst area. and rockburst amplitude-frequency risk factor Comprehensive calculation and analysis are performed to obtain the rockburst amplitude-frequency magnitude risk coefficient for each monitoring period in each distributed monitoring point in the rockburst area. ; Through the rockburst risk comprehensive assessment and analysis module, a comprehensive calculation and analysis is performed based on the rockburst mass risk coefficient and the rockburst amplitude-frequency magnitude risk coefficient of each distributed monitoring point in each monitoring period in the rockburst area, and the rockburst comprehensive indication coefficient of each distributed monitoring point in each monitoring period in the rockburst area is obtained.

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 1 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 monitoring period in each distributed monitoring point in the rockburst area .

4. The rockburst risk identification and early warning system based on microseismic monitoring data according to claim 1 is characterized by: The rockburst amplitude-frequency risk coefficient of each monitoring period in each distributed monitoring point in the rockburst area based on the microseismic signal amplitude , the specific analysis method is as follows: The probability density curve of the peak amplitude is drawn using the kernel density estimation method for the collected original microseismic waveform data. , according to engineering experience and rock characteristics, the high amplitude threshold is set as FH, according to the formula Calculate the microseismic anomaly detection coefficient of each monitoring period in each distributed monitoring point in the rockburst area based on the microseismic signal amplitude ; Determine the main frequency concentration interval in the early stage based on historical data and experience combined with data statistical analysis And the low-frequency range added later , sum the amplitudes corresponding to each frequency in the early main frequency concentration interval and the late newly added low frequency interval to obtain the early energy sum value And later energy and value , divide the late energy sum value by the sum of the early energy sum value and the late energy sum value to obtain the main frequency change characteristic value, 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 reference frequency bandwidth is set based on historical experience and the common frequency bandwidth range in rock burst areas, denoted as , and at the same time obtain the current frequency bandwidth from the collected microseismic signal, recorded as , according to the formula Calculate the rockburst amplitude-frequency risk coefficient for each monitoring period in each distributed monitoring point in the rockburst area ;in Expressed as the set weight factor.

6. The rockburst risk identification and early warning system based on microseismic monitoring data according to claim 5 is characterized by: Rockburst amplitude-frequency magnitude risk coefficient for each monitoring period at each distributed monitoring point in the rockburst area , the specific analysis method is as follows: According to the formula Calculate the rockburst amplitude-frequency risk coefficient for each monitoring period in each distributed monitoring point in the rockburst area .

7. The rockburst risk identification and early warning system based on microseismic monitoring data according to claim 6 is characterized by: The rockburst comprehensive indicator coefficient for each monitoring period at each distributed monitoring point in the rockburst area is calculated and analyzed in the following way: According to the formula Calculate the rockburst comprehensive indication coefficient of each monitoring period in each distributed monitoring point in the rockburst area , where e is a natural constant.

8. 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 obtains and analyzes the directional consistency parameters, cumulative energy values, and energy release rate change rates of microseismic events, and draws a rockburst type prediction scatter plot based on the above parameters. The specific analysis method is as follows: Get the energy rate values ​​at adjacent time points t1 and t2, which are recorded as and By calculating the difference between the energy rate value at time point t2 and the energy rate value at time point t1, and dividing the difference between time point t2 and time point t1, the energy release rate change rate is obtained by dividing the difference between the energy rate values ​​by the difference between the time points, which is recorded as ; 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 , according to the formula Calculate the cumulative energy of each distributed monitoring point in the rockburst area ; Obtain the number of microseismic events in each monitoring period at each distributed monitoring point in the rockburst area, recorded as q, and use the unit vector Indicates that, where q=1,2,…,w, according to the formula Calculate the directional consistency parameters of microseismic events at each distributed monitoring point in the rockburst area .

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