Support effect evaluation method and device based on micro-seismic index and processing equipment

Abstract

The invention provides a supporting effect evaluation method and device based on a microseismic index and processing equipment, which are used for constructing a set of novel processing architecture for evaluating the supporting effect on the basis of the microseismic index, so that the supporting effects of different deeply-buried hard rock projects can be effectively and accurately evaluated, and the working efficiency of the deep-buried hard rock projects is improved. And powerful data support is provided for engineering work.

Description

Technical Field

[0001] The present application relates to the field of pyrotechnics prevention and control, and specifically to a support effect evaluation method, device and processing equipment based on microseismic indicators. Background Art

[0002] With the continuous expansion of underground space activities, the burial depth of various underground projects continues to increase, facing more complex geological conditions and geostress environment, and rock burst disasters are becoming more frequent and severe, with strong uncertainty, suddenness and destructiveness, directly affecting the safety risks of construction personnel, equipment use, construction progress and project construction costs, and restricting the construction level of deep underground projects. Therefore, in high-risk areas for rock burst, reasonable and appropriate prevention and control measures should be taken in time to avoid the hazards that may be caused by rock bursts.

[0003] Deep buried hard rock projects have a high risk of rock burst disasters. If support and control measures are implemented in high-risk sections during the excavation process, and the control effects after implementation are evaluated, the support measures can be adjusted and optimized in time to achieve the best control effect, the hazards of rock burst can be greatly avoided or reduced. Among them, initial support is an important measure for rock burst prevention and control, and it is particularly important to evaluate the control effect of rock burst after its implementation.

[0004] The inventors of the present application have discovered that the existing support effect evaluation method currently established mainly analyzes the changing trends of common microseismic parameters before and after support, but this method has low applicability and effectiveness. First, the geological environment and construction methods of the surrounding rock bodies of different deep-buried hard rock projects are different, and the sensitivity of various microseismic indicators in different projects is different. A suitable support effect evaluation set has not been constructed for different projects; second, an effective support control effect evaluation standard has not been established, and a reasonable and effective division of the advantages and disadvantages of the initial support control effect has not been made. Summary of the invention

[0005] The present application provides a support effect evaluation method, device and processing equipment based on microseismic indicators, which is used to construct a novel processing architecture for evaluating support effects based on microseismic indicators. In this way, the support effects of different deep-buried hard rock projects can be effectively and accurately evaluated, providing strong data support for the project work.

[0006] In a first aspect, the present application provides a support effect evaluation method based on microseismic indicators, the method comprising:

[0007] For deep-buried hard rock projects, the microseismic time series information before and after the implementation of initial support measures is obtained, and a microseismic information database of the entire process of rockburst incubation including support types is constructed based on the microseismic time series information. Then, engineering cases of similar projects are classified according to the project type. Among them, the microseismic information database of the entire process of rockburst incubation involves various microseismic indicators;

[0008] Real-time collection of microseismic time series information in the early warning area of ​​deep-buried hard rock projects, and real-time update of the microseismic information database of the entire rockburst incubation process as the project excavation progresses;

[0009] According to the temporal evolution of various microseismic indicators in the process of rockburst incubation, the change types of various microseismic indicators are identified;

[0010] The actual rockburst occurrence level after the support measures are implemented is collected and compared with the early warning rockburst level to obtain the comparison result;

[0011] After adaptive normalization processing of the indicators collected from the microseismic information database of the whole process of rockburst incubation according to the change types of various microseismic indicators, the time series change slope values ​​of various microseismic indicators are calculated to identify the main controlling factors reflecting the support effect. Combined with the comparison results, a multi-indicator support effect evaluation method based on fuzzy mathematical comprehensive judgment is constructed to determine the control effect of support measures.

[0012] In a second aspect, the present application provides a support effect evaluation device based on microseismic indicators, the device comprising:

[0013] The acquisition unit is used to obtain the microseismic time series information before and after the implementation of the initial support measures for deep-buried hard rock projects, and to construct a microseismic information database of the whole process of rockburst incubation including the support type based on the microseismic time series information, and then classify the engineering cases of similar projects according to the engineering type. Among them, the microseismic information database of the whole process of rockburst incubation involves various microseismic indicators;

[0014] The updating unit is used to collect the microseismic time series information in the early warning area of ​​the deep-buried hard rock project in real time, and update the microseismic information database of the whole process of rock burst incubation in real time according to the excavation situation of the project;

[0015] The discrimination unit is used to discriminate the change types of various microseismic indicators according to the temporal evolution process of various microseismic indicators in the process of rock burst incubation;

[0016] The comparison unit is used to collect the actual rockburst occurrence level after the support measures are implemented, and compare it with the early warning rockburst level to obtain the comparison result;

[0017] The evaluation unit is used to adapt and normalize the indicators collected from the microseismic information database of the whole process of rockburst incubation according to the change types of various microseismic indicators, and then calculate the time series change slope values ​​of various microseismic indicators to identify the main controlling factors reflecting the support effect. Combined with the comparison results, a multi-indicator support effect evaluation method based on fuzzy mathematical comprehensive judgment is constructed to determine the control effect of support measures.

[0018] In a third aspect, the present application provides a processing device, including a processor and a memory, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the method provided in the first aspect of the present application or any possible implementation method of the first aspect of the present application is executed.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium, which stores multiple instructions, and the instructions are suitable for a processor to load to execute the method provided by the first aspect of the present application or any possible implementation of the first aspect of the present application.

[0020] From the above content, it can be concluded that the present application has the following beneficial effects:

[0021] Aiming at the goal of supporting effect evaluation, this application constructs a novel processing framework for evaluating supporting effect based on microseismic indicators. In this way, the supporting effect of different deep-buried hard rock projects can be effectively and accurately evaluated, providing strong data support for engineering work. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A schematic diagram of a flow chart of a support effect evaluation method based on microseismic indicators in this application;

[0024] Figure 2 A structural schematic diagram of a support effect evaluation device based on microseismic indicators of this application;

[0025] Figure 3 A schematic diagram of the structure of the processing equipment of this application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0027] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The naming or numbering of steps in this application does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The process steps that have been named or numbered can change the execution order according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved.

[0028] The division of modules in this application is a logical division. There may be other division methods when it is implemented in actual applications. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection between modules can be electrical or other similar forms, which are not limited in this application. In addition, the modules or submodules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed in multiple circuit modules, and some or all of the modules may be selected according to actual needs to achieve the purpose of the present application.

[0029] Before introducing the support effect evaluation method based on microseismic indicators provided by this application, the background content involved in this application is first introduced.

[0030] The support effect evaluation method, device and computer-readable storage medium based on microseismic indicators provided in this application can be applied to processing equipment to construct a novel processing architecture for evaluating support effects based on microseismic indicators. In this way, the support effects of different deep-buried hard rock projects can be effectively and accurately evaluated, providing strong data support for the project work.

[0031] The support effect evaluation method based on microseismic indicators mentioned in this application can be executed by a support effect evaluation device based on microseismic indicators, or a server, physical host or user equipment (UE) and other different types of processing equipment integrated with the support effect evaluation device based on microseismic indicators. Among them, the support effect evaluation device based on microseismic indicators can be implemented in hardware or software, and the UE can be a terminal device such as a smart phone, a tablet computer, a laptop computer, a desktop computer or a personal digital assistant (PDA), and the processing device can be set in a device cluster.

[0032] It can be understood that the processing equipment that executes the support effect evaluation method based on microseismic indicators provided in the present application, or the processing equipment equipped with the corresponding application service of the support effect evaluation method based on microseismic indicators provided in the present application, can be either a ready-made project equipment or a device that provides data reference for the project site in a remote service manner. Considering that the present application scheme is usually data processing based on ready-made data, the equipment type and equipment deployment form of the processing equipment are relatively flexible and can be flexibly configured according to actual needs.

[0033] Of course, if the processing equipment also involves the specific collection and processing of microseismic time series information and other information, it means that the processing equipment needs to be configured with corresponding software and hardware collection conditions in actual application.

[0034] Next, we will introduce the support effect evaluation method based on microseismic indicators provided by this application.

[0035] First, see Figure 1 , Figure 1 A schematic flow chart of the support effect evaluation method based on microseismic indicators of the present application is shown. The support effect evaluation method based on microseismic indicators provided by the present application may specifically include the following steps S101 to S105:

[0036] Step S101, for deep-buried hard rock projects, obtain microseismic time series information before and after the implementation of initial support measures, and construct a microseismic information database of the whole process of rockburst incubation including support types based on the microseismic time series information, and then classify engineering cases of similar projects according to the engineering type, wherein the microseismic information database of the whole process of rockburst incubation involves various microseismic indicators;

[0037] It can be understood that initial support is an important measure for rock burst prevention and control, and it is particularly important to evaluate the control effect of rock burst after its implementation. Therefore, for the support effect evaluation target based on a series of microseismic indicators, this application is similar to the prior art and needs to obtain microseismic time series information before and after the implementation of initial support measures. These microseismic time series information contain the contents of various microseismic indicators and can be understood as relatively initial information.

[0038] Next, the microseismic time series information can be structured to build a microseismic information database of the entire rockburst incubation process including support types. The information recorded in the database can be configured based on a series of identified / sorted microseismic indicators, and then engineering cases of similar projects can be classified according to the engineering type, thus forming a refined database and laying the foundation for subsequent convenient data processing.

[0039] As an exemplary embodiment, in the present application, the various types of microseismic indicators involved may specifically include the number of microseismic events, microseismic release energy, cumulative apparent volume, moment magnitude, apparent stress, stress drop, microseismic activity, microseismic activity scale, equivalent energy level parameters, average distance square and missing magnitude (missing earthquakes) and other specific indicators.

[0040] It can be understood that these indicators themselves are existing indicators in the engineering field. Considering that the focus of this application is that this application can build the support effect evaluation framework involved in this application around these specific indicators, this application does not provide a specific explanation of these indicators.

[0041] Step S102, real-time collection of microseismic time series information in the early warning area of ​​the deep-buried hard rock project, and real-time update of the microseismic information database of the whole process of rock burst incubation according to the excavation situation of the project;

[0042] After the initial support is applied, the database can continue to be updated. In this process, the focus can be on the pre-selected and / or real-time adjusted warning area, and the microseismic time series information in the warning area can be collected (the information type is consistent with the microseismic time series information obtained before and after the initial support measures are implemented), and the database can be updated in real time under the condition of following the engineering excavation situation.

[0043] Step S103, according to the time series evolution process of various microseismic indicators in the process of rock burst incubation, the change type of various microseismic indicators is determined;

[0044] It can be understood that when evaluating the support effect in the future, the change types of various microseismic indicators can be identified based on the various microseismic indicators recorded in the current database and according to the temporal evolution process of various microseismic indicators in the process of rock burst incubation. Microseismic indicators of different change types can obviously make different types of contributions to the subsequent evaluation of the support effect, that is, different treatments will be involved, which will help to form a more refined support effect evaluation effect.

[0045] Specifically, the change types involved in this application can be configured in a binary manner. As an exemplary embodiment, the change types of various microseismic indicators are judged according to the temporal evolution process of various microseismic indicators in the process of rock burst incubation, which may specifically include:

[0046] According to the temporal evolution of various microseismic indicators in the process of rockburst incubation, the indicators with positive correlation changes are classified as positive abnormal indicators, and the indicators with negative correlation changes are classified as negative abnormal indicators.

[0047] It is understandable that when the present application is actually applied, for different deep-buried hard rock projects, the types of changes (such as positive abnormal indicators and negative abnormal indicators) are not fixed and will change accordingly with the specific conditions of the project.

[0048] As an example, the indicators such as the number of microseismic events, microseismic release energy, cumulative apparent volume, moment magnitude, stress drop, apparent stress, equivalent energy level parameters, etc., which are positively correlated with time during the rockburst incubation process, can be classified as positive abnormal indicators; the indicators such as the average squared distance and missing magnitude, which are negatively correlated with time during the rockburst incubation process, can be classified as negative abnormal indicators.

[0049] Step S104, collecting the actual rockburst occurrence level after the support measures are implemented, and comparing it with the early warning rockburst level to obtain a comparison result;

[0050] Similar to the above identification of the types of changes in microseismic indicators in current deep-buried hard rock projects, for actual rockbursts after support measures are taken, it is necessary to collect / determine the actual rockburst occurrence level and compare it with the pre-set / real-time adjusted warning rockburst level. The comparison results also provide a basis for subsequent processing, so as to form a more refined support effect evaluation effect.

[0051] Step S105, after performing adaptive normalization processing on the indicators collected from the microseismic information database of the entire process of rockburst incubation according to the change types of various microseismic indicators, the time series change slope values ​​of various microseismic indicators are calculated to identify the main controlling factors reflecting the support effect, and then a multi-indicator support effect evaluation method based on fuzzy mathematical comprehensive judgment is constructed in combination with the comparison results to determine the control effect of the support measures.

[0052] After completing the preliminary configuration work on the microseismic information database of the entire rockburst incubation process, the identification of the change types of various microseismic indicators, and the comparison of the actual rockburst level with the warning rockburst level, the specific support effect evaluation and processing involved here can be carried out.

[0053] Specifically, the support effect evaluation process involved here involves several major aspects. It is necessary to first carry out adaptive normalization processing on various microseismic indicators currently recorded in the database according to the respective change types identified previously. The normalization process is easy to understand. The purpose is to reduce the correlation between features and improve the generalization ability of subsequent data processing. After the normalization process, the time series change slope (slope change value) can be calculated for these microseismic indicators. The purpose is to identify the indicators with higher sensitivity (greater impact) to the support effect in the original microseismic indicators based on the time series change slope. These indicators can be referred to as main controlling factors or sensitive factors. Next, we can focus on the determined main controlling factors, combine the comparison results of the actual rock burst level and the warning rock burst level, introduce fuzzy mathematical comprehensive evaluation, and construct a multi-indicator support effect evaluation method based on fuzzy mathematical comprehensive evaluation, so as to determine the control effect of the support measures of the current deep buried hard rock engineering (i.e., support effect).

[0054] For the normalization processing, combined with the aforementioned discrimination processing results of the change types of the positive abnormality index and the negative abnormality index, the present application provides a set of specific implementation schemes. Specifically, as an exemplary embodiment, the positive abnormality index can be normalized according to the following formula:

[0055] Q i (t)=(Q i -Q imin ) / (Q imax -Q imin ),

[0056] Negative anomaly indicators can be normalized as follows:

[0057] Q i (t)=(Q imax -Q i ) / (Q imax -Q imin ),

[0058] Among them, Q i is the indicator sequence value, Q imax is the maximum value of the indicator sequence, Q imin is the minimum value of the indicator sequence, Q i (t) is the normalized indicator sequence value.

[0059] As an example, taking the number of microseismic events in a support case as an example, the warning time is June 25, and the data from June 20 to June 28 can be selected. The number of daily microseismic events is 1, 0, 1, 4, 40, 52, 23, 29, 20, among which Q imax is 52, Q imin is 0, the number of microseismic events is the positive anomaly indicator, and substituting it into the normalization formula of the positive anomaly indicator above, the normalized numbers of microseismic events are 0.019, 0, 0.019, 0.077, 0.769, 1, 0.442, 0.558, and 0.385 respectively.

[0060] In addition, for the calculation and processing of the time series variation slope value, the present application also provides a more specific implementation scheme. Specifically, as an exemplary embodiment, the time series variation slope value k of various microseismic indicators can be calculated according to the following formula:

[0061] k=Q i (t1)-Q i (t0),

[0062] Among them, Q i (t1) is the index sequence value on the day after the support measures are implemented, Q i (t0) is the normalized indicator sequence value one day before the implementation of the support measures. The two indicator sequence values ​​substituted for each indicator are processed according to the numerical type adapted by the indicator.

[0063] Each indicator is adapted to a corresponding numerical type. Therefore, based on the same time series change slope value calculation formula above, there are slight differences in the sources of obtaining the specific indicator sequence values ​​​​substituted into the formula.

[0064] As an example, microseismic indicators such as the number of microseismic events and cumulative apparent volume use cumulative values ​​based on one day, microseismic indicators such as apparent stress and stress drop use maximum values ​​based on one day, and microseismic indicators such as microseismic activity and equivalent energy level parameters use calculated values ​​based on one day.

[0065] As an example, taking 6 support cases collected in a certain project as an example, for the calculated time series change slope value k, the number of microseismic events is -0.238, the logarithm of microseismic release energy is -0.501, the microseismic apparent volume is -0.366, the apparent stress is -0.477, the moment magnitude is -0.572, the microseismic activity is -0.413, the microseismic activity scale is -0.395, the missing earthquake is -0.425, the stress drop is -0.446, and the equivalent energy level parameter is -0.501.

[0066] In addition, for the process of identifying the main controlling factors based on the time series change slope value, as an exemplary embodiment, the time series change slope values ​​of various microseismic indicators are calculated to identify the main controlling factors reflecting the support effect, which may include:

[0067] When k<-0.5, the current indicator is identified as the main control factor, and the evaluation main control factor set U is established based on various main control factors.

[0068] It can be seen that the discrimination threshold of the specific configuration of "-0.5" is used as the basis here to screen out the main controlling factors from the original, normalized microseismic indicators, and the various main controlling factors obtained can be constructed into a set, namely, the evaluation main controlling factor set U, for subsequent use.

[0069] As an example, the average slope values ​​k of microseismic release energy, equivalent energy level parameters and moment magnitude are all lower than -0.5, indicating that these three indicators are more sensitive to the support effect. Therefore, the three can be used as key factors for establishing the support effect evaluation method under the current engineering background, and U = {lgE, EEM, M} is obtained.

[0070] Next, the time series variation slope values ​​of various microseismic indicators are calculated to identify the main controlling factors reflecting the support effect, and then a multi-index support effect evaluation method based on fuzzy mathematical comprehensive judgment is constructed in combination with the comparison results to determine the control effect of the support measures. In this process, the present application also provides a set of specific implementation schemes. As an exemplary embodiment, there may be:

[0071] (1) Based on the comparison results, a support effect evaluation set V is established, where V = {v1, v2} = {good support effect, poor support effect}. When the actual rockburst level is reduced by more than or equal to 2 levels compared to the warning rockburst level or no rockburst occurs, it indicates that the support effect is good and the expected goal has been achieved; when the actual rockburst level is reduced by no more than 1 level compared to the warning rockburst level, it indicates that the support effect is poor.

[0072] (2) By analyzing the numerical conditions of various main control factors after support, a single-index qualitative evaluation method is constructed between the support effect evaluation set V and the evaluation main control factor set U (various main control factors). Based on this, the support effect trapezoidal membership function of various main control factors is established. The numerical values ​​of various main control factors on the day of support in the evaluation case are substituted into the support effect trapezoidal membership function to determine the fuzzy matrix R:

[0073]

[0074] Among them, r i1 represents the value of various main control factors in the trapezoidal membership function of the support effect on the day after support when the support effect is good, r i2 Represents the values ​​of various main control factors in the trapezoidal membership function of the support effect on the day after support when the support effect is poor;

[0075] As an example, still taking the three main controlling factors of microseismic release energy, equivalent energy level parameters and moment magnitude mentioned above as an example, this application analyzes the six collected support cases to establish the relationship between the support effect evaluation set V and each key factor as shown in Table 1 below, and establishes a trapezoidal membership function based on this.

[0076] Table 1

[0077]

[0078] The logarithm of microseismic energy release on the day after support is 1.23, the equivalent energy level parameter is 0.02, and the moment magnitude is -1.31. Substituting the values ​​of the main control factors on the day of support into the membership function, the fuzzy matrix R is obtained as follows:

[0079]

[0080] (3) The support effect is predicted using a single-index qualitative evaluation method, and the F value of each main control factor is calculated according to the following formula:

[0081]

[0082] Among them, TP is the number of cases where both the support effect predicted by the single-indicator qualitative evaluation method and the actual support effect are good, FN is the number of cases where the support effect predicted by the single-indicator qualitative evaluation method is poor and the actual support effect is good, and FP is the number of cases where the support effect predicted by the single-indicator qualitative evaluation method is good and the actual support effect is poor.

[0083] (4) The weights are allocated as follows to determine the weight set A of each type of main control factor, A = {a1, a2, ..., a i}:

[0084]

[0085] in, is the average F value of the ith main control factor, and ΣF is the sum of the F values ​​of various main control factors;

[0086] (5) Obtain the fuzzy subset B as follows:

[0087] B=A*R,

[0088] Among them, the value of each component vector represents the probability distribution of each support effect, and the support effect evaluation level with the largest probability is regarded as the control effect of the support measure.

[0089] Continuing with the previous example, after calculation, the index weights of the logarithm of microseismic release energy, equivalent energy level parameter and moment magnitude are 0.36, 0.36 and 0.28 respectively. Therefore, we can get the weight set A = {0.36, 0.36, 0.28}, and then we can get the fuzzy subset B = {0.64, 0.36}. At this time, it can be judged that the support measures in this case are better.

[0090] At this point, for the above-mentioned solution contents of this application, in general, for the support effect evaluation goal, this application constructs a novel processing framework for evaluating support effects based on microseismic indicators. In this way, the support effects of different deep-buried hard rock projects can be effectively and accurately evaluated, providing strong data support for the project work.

[0091] In terms of details, there are:

[0092] (1) The slope value method is used to quantitatively identify the main controlling factors that affect the support effect of deep-buried hard rock engineering. It is possible to construct a suitable support effect evaluation set for different projects, making the support effect evaluation method constructed in this application more suitable for specific engineering practices.

[0093] (2) By continuously enriching the support examples in various engineering backgrounds in the database and calculating the indicator weights based on the single indicator qualitative evaluation method and the F-value calculation method, the weight distribution of each main control factor can be continuously optimized in actual situations, thereby accurately updating the support effect according to the real-time situation of the project.

[0094] (3) By comparing the changes in the warning rockburst level before the implementation of the support measures and the actual rockburst level after the implementation, the corresponding support control effect evaluation criteria are established. The output results can directly judge the rationality of the support measures.

[0095] The above is an introduction to the support effect evaluation method based on microseismic indicators provided in this application. In order to facilitate better implementation of the support effect evaluation method based on microseismic indicators provided in this application, this application also provides a support effect evaluation device based on microseismic indicators from the perspective of functional modules.

[0096] See also Figure 2 , Figure 2 Schematic diagram of a structure of a support effect evaluation device based on microseismic indicators in this application. In this application, the support effect evaluation device 200 based on microseismic indicators may specifically include the following structure:

[0097] The acquisition unit 201 is used to acquire the microseismic time series information before and after the implementation of the initial support measures for the deep-buried hard rock project, and to construct a microseismic information database of the whole process of rockburst incubation including the support type based on the microseismic time series information, and then classify the engineering cases of similar projects according to the engineering type, wherein the microseismic information database of the whole process of rockburst incubation involves various microseismic indicators;

[0098] An updating unit 202 is used to collect microseismic time series information in the early warning area of ​​a deep-buried hard rock project in real time, and to update a microseismic information database of the entire process of rockburst incubation in real time according to the excavation of the project;

[0099] The identification unit 203 is used to identify the change type of various microseismic indicators according to the time series evolution process of various microseismic indicators in the process of rock burst incubation;

[0100] The comparison unit 204 is used to collect the actual rock burst occurrence level after the support measures are implemented, and compare it with the warning rock burst level to obtain a comparison result;

[0101] Evaluation unit 205 is used to perform adaptive normalization processing on the indicators collected from the microseismic information database of the whole process of rockburst incubation according to the change types of various microseismic indicators, and then calculate the time series change slope values ​​of various microseismic indicators to identify the main controlling factors reflecting the support effect, and then combine the comparison results to construct a multi-indicator support effect evaluation method based on fuzzy mathematical comprehensive judgment to determine the control effect of the support measures.

[0102] In an exemplary embodiment, various types of microseismic indicators specifically include the number of microseismic events, microseismic release energy, cumulative apparent volume, moment magnitude, apparent stress, stress drop, microseismic activity, microseismic activity scale, equivalent energy level parameters, average distance square and missing magnitude.

[0103] In another exemplary embodiment, the determination unit 203 is specifically configured to:

[0104] According to the temporal evolution of various microseismic indicators in the process of rockburst incubation, the indicators with positive correlation changes are classified as positive abnormal indicators, and the indicators with negative correlation changes are classified as negative abnormal indicators.

[0105] In another exemplary embodiment, the positive anomaly indicator is normalized according to the following formula:

[0106] Q i (t)=(Q i -Q imin ) / (Q imax -Q imin ),

[0107] Negative abnormal indicators are normalized according to the following formula:

[0108] Q i (t)=(Q imax -Q i ) / (Q imax -Q imin ),

[0109] Among them, Q i is the indicator sequence value, Q imax is the maximum value of the indicator sequence, Q imin is the minimum value of the indicator sequence, Q i (t) is the normalized indicator sequence value.

[0110] In another exemplary embodiment, the time series variation slope value k of each type of microseismic index is calculated according to the following formula:

[0111] k=Q i (t1)-Q i (t0),

[0112] Among them, Q i (t1) is the normalized index sequence value on the day after the support measures are implemented, Q i (t0) is the normalized indicator sequence value one day before the implementation of the support measures. The two indicator sequence values ​​substituted for each indicator are processed according to the numerical type adapted by the indicator.

[0113] In another exemplary embodiment, the time series variation slope values ​​of various microseismic indicators are calculated to identify the main controlling factors reflecting the support effect, including:

[0114] When k<-0.5, the current indicator is identified as the main control factor, and the evaluation main control factor set U is established based on various main control factors.

[0115] In another exemplary embodiment, the time series variation slope values ​​of various microseismic indicators are calculated to identify the main control factors reflecting the support effect, and then a multi-index support effect evaluation method based on fuzzy mathematical comprehensive judgment is constructed in combination with the comparison results to determine the control effect of the support measures.

[0116] Based on the comparison results, a support effect evaluation set V is established, where V = {v1, v2} = {good support effect, poor support effect}. When the actual rockburst level is reduced by more than or equal to 2 levels compared to the warning rockburst level or no rockburst occurs, it indicates that the support effect is good and the expected goal has been achieved; when the actual rockburst level is reduced by no more than 1 level compared to the warning rockburst level, it indicates that the support effect is poor.

[0117] By analyzing the numerical conditions of various main control factors after support, and constructing a single-index qualitative evaluation method between the support effect evaluation set V and the evaluation main control factor set U, the support effect trapezoidal membership function of various main control factors is established. The numerical values ​​of various main control factors on the day of support in the evaluation case are substituted into the support effect trapezoidal membership function to determine the fuzzy matrix R:

[0118]

[0119] Among them, r i1 represents the value of various main control factors in the trapezoidal membership function of the support effect on the day after support when the support effect is good, r i2 Represents the values ​​of various main control factors in the trapezoidal membership function of the support effect on the day after support when the support effect is poor;

[0120] The support effect is predicted using a single-index qualitative evaluation method, and the F value of each main control factor is calculated according to the following formula:

[0121]

[0122] Among them, TP is the number of cases where both the support effect predicted by the single-indicator qualitative evaluation method and the actual support effect are good, FN is the number of cases where the support effect predicted by the single-indicator qualitative evaluation method is poor and the actual support effect is good, and FP is the number of cases where the support effect predicted by the single-indicator qualitative evaluation method is good and the actual support effect is poor.

[0123] The weights are allocated as follows to determine the weight set A of various main control factors, A = {a1, a2, ..., a i}:

[0124]

[0125] in, is the average F value of the ith main control factor, and ΣF is the sum of the F values ​​of various main control factors;

[0126] The fuzzy subset B is obtained as follows:

[0127] B=A*R,

[0128] Among them, the value of each component vector represents the probability distribution of each support effect, and the support effect evaluation level with the largest probability is regarded as the control effect of the support measure.

[0129] This application also provides a processing device from the perspective of hardware structure, see Figure 3 , Figure 3 301, a memory 302, and an input / output device 303. The processor 301 is used to execute the computer program stored in the memory 302 to implement the following Figure 1 The steps of the support effect evaluation method based on microseismic indicators in the corresponding embodiment; or, the processor 301 is used to execute the computer program stored in the memory 302 to implement the following Figure 2 Corresponding to the functions of each unit in the embodiment, the memory 302 is used to store the processor 301 executing the above Figure 1 The computer program required for the support effect evaluation method based on microseismic indicators in the corresponding embodiment.

[0130] Exemplarily, the computer program may be divided into one or more modules / units, one or more modules / units are stored in the memory 302, and executed by the processor 301 to complete the present application. One or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0131] The processing device may include, but is not limited to, a processor 301, a memory 302, and an input / output device 303. Those skilled in the art will appreciate that the illustration is merely an example of a processing device and does not constitute a limitation on the processing device, and may include more or fewer components than shown in the illustration, or a combination of certain components, or different components. For example, the processing device may also include a network access device, a bus, etc., and the processor 301, the memory 302, the input / output device 303, etc. are connected via a bus.

[0132] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the processing device, and uses various interfaces and lines to connect various parts of the entire device.

[0133] The memory 302 can be used to store computer programs and / or modules. The processor 301 implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 302 and calling the data stored in the memory 302. The memory 302 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the processing device, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0134] When the processor 301 is used to execute the computer program stored in the memory 302, the following functions can be implemented:

[0135] For deep-buried hard rock projects, the microseismic time series information before and after the implementation of initial support measures is obtained, and a microseismic information database of the entire process of rockburst incubation including support types is constructed based on the microseismic time series information, and then engineering cases of similar projects are classified according to the engineering type, wherein the microseismic information database of the entire process of rockburst incubation involves various microseismic indicators;

[0136] Collecting microseismic time series information in the early warning area of ​​the deep-buried hard rock project in real time, and updating the microseismic information database of the whole process of rock burst incubation in real time according to the excavation situation of the project;

[0137] According to the time series evolution process of the various microseismic indicators in the process of rockburst incubation, the change types of the various microseismic indicators are determined;

[0138] The actual rockburst occurrence level after the support measures are implemented is collected and compared with the early warning rockburst level to obtain the comparison result;

[0139] After adaptive normalization processing is performed on the indicators collected from the microseismic information database of the entire process of rockburst incubation according to the change types of the various microseismic indicators, the time series change slope values ​​of the various microseismic indicators are calculated to identify the main controlling factors reflecting the support effect, and then a multi-indicator support effect evaluation method based on fuzzy mathematical comprehensive judgment is constructed in combination with the comparison results to determine the control effect of the support measures.

[0140] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the support effect evaluation device based on microseismic indicators, the processing equipment and the corresponding units described above can refer to the following. Figure 1 The description of the support effect evaluation method based on microseismic indicators in the corresponding embodiment will not be repeated here.

[0141] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0142] To this end, the present application provides a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the present application as follows: Figure 1 The steps of the support effect evaluation method based on microseismic indicators in the corresponding embodiment, the specific operation can be referred to as follows Figure 1 The description of the support effect evaluation method based on microseismic indicators in the corresponding embodiment will not be repeated here.

[0143] The computer-readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0144] Due to the instructions stored in the computer-readable storage medium, the present application can be executed. Figure 1 The steps of the support effect evaluation method based on microseismic indicators in the corresponding embodiment, therefore, the present application can be implemented as follows Figure 1 The beneficial effects that can be achieved by the support effect evaluation method based on microseismic indicators in the corresponding embodiment are detailed in the previous description and will not be repeated here.

[0145] The support effect evaluation method, device, processing equipment and computer-readable storage medium based on microseismic indicators provided by the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A support effect evaluation method based on microseismic indicators, characterized in that: The method comprises: For deep-buried hard rock projects, the microseismic time series information before and after the implementation of initial support measures is obtained, and a microseismic information database of the entire process of rockburst incubation including support types is constructed based on the microseismic time series information, and then engineering cases of similar projects are classified according to the engineering type, wherein the microseismic information database of the entire process of rockburst incubation involves various microseismic indicators; Collecting microseismic time series information in the early warning area of ​​the deep-buried hard rock project in real time, and updating the microseismic information database of the whole process of rock burst incubation in real time according to the excavation situation of the project; According to the time series evolution process of the various microseismic indicators in the process of rockburst incubation, the change types of the various microseismic indicators are determined; The actual rockburst occurrence level after the support measures are implemented is collected and compared with the early warning rockburst level to obtain the comparison result; After adaptive normalization processing is performed on the indicators collected from the microseismic information database of the entire process of rockburst incubation according to the change types of the various microseismic indicators, the time series change slope values ​​of the various microseismic indicators are calculated to identify the main controlling factors reflecting the support effect, and then a multi-indicator support effect evaluation method based on fuzzy mathematical comprehensive judgment is constructed in combination with the comparison results to determine the control effect of the support measures.

2. The method according to claim 1, characterized in that: The various types of microseismic indicators specifically include the number of microseismic events, microseismic release energy, cumulative apparent volume, moment magnitude, apparent stress, stress drop, microseismic activity, microseismic activity scale, equivalent energy level parameters, average distance square and missing magnitude.

3. The method according to claim 1, characterized in that According to the temporal evolution of the various microseismic indicators in the process of rock burst incubation, the change types of the various microseismic indicators are judged as follows: According to the temporal evolution process of the various microseismic indicators in the process of rock burst incubation, the indicators showing positive correlation changes are classified as positive abnormal indicators, and the indicators showing negative correlation changes are classified as negative abnormal indicators.

4. The method according to claim 3, characterized in that: The positive anomaly index is normalized according to the following formula: Q i (t)=(Q i -Q imin ) / (Q imax -Q imin ), Negative abnormal indicators are normalized according to the following formula: Q i (t)=(Q imax -Q i ) / (Q imax -Q imin ), Among them, Q i is the indicator sequence value, Q imax is the maximum value of the indicator sequence, Q imin is the minimum value of the indicator sequence, Q i (t) is the normalized indicator sequence value.

5. The method according to claim 1, characterized in that: The time series variation slope value k of each type of microseismic index is calculated as follows: k=Q i (t1)-Q i (t0), Among them, Q i (t1) is the normalized index sequence value on the day after the support measures are implemented, Q i (t0) is the normalized indicator sequence value one day before the implementation of the support measures. The two indicator sequence values ​​substituted for each indicator are processed according to the numerical type adapted by the indicator.

6. The method according to claim 5, characterized in that The process of calculating the time series variation slope values ​​of various microseismic indicators to identify the main controlling factors reflecting the support effect includes: When k<-0.5, the current indicator is identified as the main control factor, and an evaluation main control factor set U is established based on each type of the main control factors.

7. The method according to claim 6, characterized in that The calculation of the time series change slope values ​​of the various microseismic indicators is used to identify the main control factors reflecting the support effect, and then the multi-index support effect evaluation method based on fuzzy mathematical comprehensive judgment is constructed in combination with the comparison results to determine the control effect of the support measures. In this process, there are: Based on the comparison results, a support effect evaluation set V is established, where V = {v1, v2} = {good support effect, poor support effect}, wherein when the actual rockburst level is reduced by more than or equal to 2 levels compared to the warning rockburst level or no rockburst occurs, it indicates that the support effect is good and the expected goal has been achieved; when the actual rockburst level is reduced by no more than 1 level compared to the warning rockburst level, it indicates that the support effect is poor; By analyzing the numerical conditions of various main control factors after support, and constructing a single-index qualitative evaluation method between the support effect evaluation set V and the evaluation main control factor set U, the support effect trapezoidal membership function of various main control factors is established accordingly. The numerical values ​​of various main control factors on the day of support of the evaluation case are substituted into the support effect trapezoidal membership function to determine the fuzzy matrix R: Among them, r i1 represents the value of each of the main control factors in the trapezoidal membership function of the support effect on the day after support when the support effect is good, r i2 Represents the values ​​of various main control factors in the trapezoidal membership function of the support effect on the day after support when the support effect is poor; The support effect is predicted using a single-index qualitative evaluation method, and the F value of each of the main control factors is calculated according to the following formula: Among them, TP is the number of cases where both the support effect predicted by the single-indicator qualitative evaluation method and the actual support effect are good, FN is the number of cases where the support effect predicted by the single-indicator qualitative evaluation method is poor and the actual support effect is good, and FP is the number of cases where the support effect predicted by the single-indicator qualitative evaluation method is good and the actual support effect is poor. The weights are allocated as follows to determine the weight set A of each type of the main control factors, A = {a1, a2, ..., a i }: in, is the average value of the F value of the main control factor mentioned in item i, and ΣF is the sum of the F values ​​of each type of main control factor mentioned; The fuzzy subset B is obtained as follows: B=A*R, Among them, the value of each component vector represents the probability distribution of each support effect, and the support effect evaluation level with the largest probability is regarded as the control effect of the support measure.

8. A support effect evaluation device based on microseismic indicators, characterized in that: The device comprises: An acquisition unit is used to acquire microseismic time series information before and after the implementation of initial support measures for deep-buried hard rock projects, and to construct a microseismic information database of the entire process of rockburst incubation including support types based on the microseismic time series information, and then classify engineering cases of similar projects according to engineering types, wherein the microseismic information database of the entire process of rockburst incubation involves various microseismic indicators; An updating unit, used for collecting microseismic time series information in the early warning area of ​​the deep-buried hard rock project in real time, and updating the microseismic information database of the whole process of rockburst incubation in real time according to the excavation situation of the project; A discriminating unit, used to discriminate the change type of the various microseismic indicators according to the temporal evolution process of the various microseismic indicators in the process of rockburst incubation; The comparison unit is used to collect the actual rockburst occurrence level after the support measures are implemented, and compare it with the early warning rockburst level to obtain the comparison result; The evaluation unit is used to perform adaptive normalization processing on the indicators collected from the microseismic information database of the whole process of rockburst incubation according to the change types of the various microseismic indicators, and then calculate the time series change slope values ​​of the various microseismic indicators to identify the main controlling factors reflecting the support effect, and then combine the comparison results to construct a multi-indicator support effect evaluation method based on fuzzy mathematical comprehensive judgment to determine the control effect of the support measures.

9. A processing device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and the processor executes the method according to any one of claims 1 to 7 when calling the computer program in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the method according to any one of claims 1 to 7.

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