Sensitivity evaluation method for micro-seismic monitoring system

Abstract

The invention relates to the technical field of sensitivity testing, in particular to a sensitivity evaluation method for a micro-seismic monitoring system. According to the sensitivity evaluation method provided by the invention, the station network monitoring capability under the current mine engineering structure condition and the micro-seismic monitoring system health degree can be actually reflected according to the micro-seismic event magnitude and the seismic source position within a period of time; according to the geometric parameter of the distance from the micro-seismic event to the sensor participating in positioning the micro-seismic event, grouping and refining to obtain the sensitivity of each interval with the maximum goodness-of-fit value; the sensitivity of any point is determined according to the distance from the point in the monitoring range to the fourth sensor closest to the point, and finally a station network sensitivity cloud picture can be obtained through an interpolation method. The method is used for evaluating the sensitivity of the micro-seismic monitoring system under the current mine engineering structure condition and the health degree of the micro-seismic monitoring system, and a basis is provided for current performance evaluation and subsequent optimization expansion of the system.

Description

Technical Field

[0001] The present invention relates to the field of sensitivity testing technology, and in particular to a method for evaluating the sensitivity of a microseismic monitoring system. Background Art

[0002] Microseismic monitoring technology has the characteristics of continuity and real-time performance, and has become an important means of monitoring ground pressure disasters in mines. Sensitivity is a key technical indicator for evaluating the performance of microseismic monitoring systems. It is measured by the minimum measurable magnitude and directly affects the system's ability to monitor weak vibration signals. The smaller the minimum measurable magnitude, the higher the sensitivity. In the process of designing the microseismic monitoring system network, the sensitivity cloud map of the network can be obtained through sensitivity analysis to evaluate the rationality and effectiveness of the current design network, and the sensor layout can be adjusted based on the calculation results to achieve the purpose of network optimization.

[0003] At present, the method for evaluating the sensitivity of microseismic monitoring systems is mainly to calculate the spatial layout of sensors, which is used in the design process before the construction of new microseismic monitoring systems. This method calculates the sensitivity cloud map of the designed network based on the input of sensor coordinates, P-wave velocity and error, S-wave velocity and error and other parameter information according to the network design. However, due to the complex geological conditions of mining engineering, the wave velocity parameters selected by this method are quite different from the actual engineering, and the analysis results are often limited to theoretical calculations.

[0004] For the microseismic monitoring system that has been built, with the changes in the mining engineering structure and the failure or loss of sensors, the sensitivity of the monitoring network will change greatly compared with the calculation results in the design stage. This patent proposes a method for evaluating the sensitivity of the microseismic monitoring system. For the microseismic monitoring system that has been built and used, the actual monitored microseismic events are used to evaluate the sensitivity of the microseismic monitoring system under the current mining engineering structure conditions and the health of the microseismic monitoring system, providing a basis for the current performance evaluation and subsequent optimization and expansion of the system.

[0005] (1) In the analysis process of designing the network before building a new microseismic monitoring system, parameters such as P-wave velocity and S-wave velocity are often selected based on experience or examples provided by the software. Due to the complex geological conditions of mining engineering, these parameters are quite different from the actual engineering. The analysis results are limited to theoretical calculations and cannot fully reflect the monitoring effect of the network.

[0006] (2) For the microseismic monitoring system that has been built, with the changes in the mine engineering structure and the failure or loss of sensors, the sensitivity of the monitoring network will change greatly compared with the calculation results in the design stage. If the sensitivity is calculated based on the spatial layout of the sensors, it can no longer reflect the actual situation of the current monitoring network.

[0007] For the built reservoir monitoring network, some scholars have proposed a method for evaluating the network's ability to record earthquakes based on magnitude and frequency. This method is based on seismological theory, mainly based on the magnitude-frequency relationship curve, and combined with the frequency-magnitude diagram to calculate the reservoir network's ability to record earthquakes. This method uses earthquake events recorded by the seismic network over a period of time to make NM and lgN-M diagrams of earthquakes. The distribution of the highest frequency magnitude can be obtained in the NM diagram. According to the Gutenberg GR relationship, the magnitude-frequency relationship straight line is fitted on the lgN-M diagram, and then the lowest magnitude point is obtained based on the coincidence position of the straight line and the observed data point, which is the lower limit of the monitoring capacity of the monitoring network, used to evaluate whether it meets the design requirements.

[0008] (1) This method uses the coincidence position of the magnitude-frequency relationship straight line and the observed data point to obtain only one value at the lowest magnitude point as the lower limit of the monitoring capability of the monitoring network, instead of evaluating the sensitivity of each position in the entire space of the monitoring network. As a result, it is impossible to obtain a sensitivity cloud map of the monitoring area.

[0009] (2) This method is mainly used in the monitoring of natural earthquakes in reservoirs. It does not consider the impact of small earthquakes on the monitoring targets. In the process of drawing the NM and lgN-M diagrams of earthquakes, earthquake events with a magnitude less than 0 are removed. Therefore, this method is not suitable for the sensitivity assessment of microseismic networks. Summary of the invention

[0010] The present invention discloses a microseismic monitoring system sensitivity evaluation method to solve any of the above and other potential problems in the prior art.

[0011] In order to solve the above technical problems, the technical solution of the present invention is: a method for evaluating the sensitivity of a microseismic monitoring system, the method specifically comprising the following steps:

[0012] The microseismic signals within a certain period of time t are obtained through the microseismic monitoring system, and the source locations and magnitudes of all microseismic events located by more than four microseismic sensors are determined;

[0013] Based on the source position of the microseismic event, calculate the distance Li from each microseismic event to all sensors involved in locating the microseismic event, and determine the distance L4 from the source position of each microseismic event in Li to the fourth sensor closest to the microseismic event;

[0014] According to the size of each microseismic event L4, the microseismic events are divided into a number of groups according to the interval;

[0015] According to the magnitude and cumulative number distribution of microseismic events in each group, a fitting straight line is obtained between the two, and different minimum magnitudes M are calculated. min The goodness of fit value R under the condition;

[0016] According to the maximum goodness of fit value in each group, the corresponding minimum magnitude M is selected min , determine the sensitivity M within each group of L4 S ;

[0017] According to the group of L4 corresponding to any point in the monitoring range, determine the sensitivity M of the point S , the sensitivity cloud map of the microseismic monitoring system is obtained by using the interpolation method.

[0018] Furthermore, the microseismic signal within the time period t should be within a range of no less than 30 days:

[0019] Furthermore, the source locations (x0, y0, z0) and magnitudes M of all microseismic events located by more than four microseismic sensors are determined, and the minimum number of sensors used to locate the events is set in the microseismic monitoring system.

[0020] Furthermore, the distance from each microseismic event to all sensors involved in locating the microseismic event (x i ,y i , z i ), the specific calculation method is:

[0021]

[0022] Furthermore, the fourth largest value among all Li of each microseismic event is determined as the distance L4 from the source position of the microseismic event to the fourth sensor closest to the microseismic event.

[0023] Further, according to the size of each microseismic event L4, the microseismic events are divided into several groups according to the interval. 4min As the base point, it is divided into several groups according to the spacing d {(L 4min , L4 min +a), (L 4min a,L 4min +2a)…}, where d is between 5m and 25m.

[0024] Furthermore, the magnitudes of the microseismic events M and M>M in each group are counted. i The distribution scatter plot of the cumulative number of microseismic events N is obtained by fitting the least square method according to the Gutenberg and Richard laws to obtain the distribution of the microseismic events with different minimum magnitudes M. min The fitting straight line of magnitude M and cumulative number N is as follows:

[0025] lgN=a+bM

[0026] Further, according to the scatter plot M>M in each group i The cumulative number of microseismic events Ai And the fitting straight line M>M i The cumulative number of microseismic events S i , calculate the minimum magnitude M min The goodness of fit value R of each fitting straight line is calculated as follows:

[0027]

[0028] Further, the minimum magnitude M with the maximum goodness of fit value R in each of the groups is selected. min , the M min The value is determined as the sensitivity M of the group S .

[0029] Furthermore, for any point within the monitoring range, the distance S from the point to the fourth sensor closest to it can be calculated, and the sensitivity M of any point within the monitoring range can be determined according to the L4 grouping interval where the distance S is located. S , the sensitivity cloud map of the microseismic monitoring system is obtained by using the interpolation method.

[0030] The beneficial effects of the present invention are as follows: due to the adoption of the above technical solution, the sensitivity evaluation method of the present invention can actually reflect the network monitoring capability under the current mining engineering structure conditions and the health level of the microseismic monitoring system according to the magnitude and source location of the microseismic event within a period of time;

[0031] According to the geometric parameter of the distance from the microseismic event to the sensor involved in locating the microseismic event, the sensitivity with the maximum goodness of fit value in each interval is obtained by grouping and refining;

[0032] The sensitivity of any point within the monitoring range is determined by the distance from the fourth sensor closest to it, and finally the sensitivity cloud map of the network can be obtained through interpolation in the software. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The present invention is a flowchart of a method for evaluating the sensitivity of a microseismic monitoring system.

[0034] Figure 2 Schematic diagram of the distribution of microseismic sensors and microseismic events in a mine according to an embodiment.

[0035] Figure 3 In the embodiment, different M min Schematic diagram of the fitting straight line when .

[0036] Figure 4 This is an example of a sensitivity cloud diagram obtained in the embodiment. DETAILED DESCRIPTION

[0037] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0038] like Figure 1 As shown, the present invention provides a method for evaluating the sensitivity of a microseismic monitoring system, and the method specifically comprises the following steps:

[0039] The microseismic signals within a certain period of time t are obtained through the microseismic monitoring system, and the source locations and magnitudes of all microseismic events located by more than four microseismic sensors are determined;

[0040] Based on the source position of the microseismic event, calculate the distance Li from each microseismic event to all sensors involved in locating the microseismic event, and determine the distance L4 from the source position of each microseismic event in Li to the fourth sensor closest to the microseismic event;

[0041] According to the size of each microseismic event L4, the microseismic events are divided into a number of groups according to the interval;

[0042] According to the magnitude and cumulative number distribution of microseismic events in each group, a fitting straight line is obtained between the two, and different minimum magnitudes M are calculated. min The goodness of fit value R under the condition;

[0043] According to the maximum goodness of fit value in each group, the corresponding minimum magnitude M is selected min , determine the sensitivity M within each group of L4 S ;

[0044] According to the group of L4 corresponding to any point in the monitoring range, determine the sensitivity M of the point S , the sensitivity cloud map of the microseismic monitoring system is obtained by using the interpolation method.

[0045] Furthermore, the range of the time period t should be no less than 30 days:

[0046] Furthermore, the source locations (x0, y0, z0) and magnitudes M of all microseismic events located by more than four microseismic sensors are determined, and the minimum number of sensors used to locate the events is set in the microseismic monitoring system.

[0047] Furthermore, the distance from each microseismic event to all sensors involved in locating the microseismic event (x i ,y i , z i ), the specific calculation method is:

[0048]

[0049] Furthermore, the fourth largest value among all Li of each microseismic event is determined as the distance L4 from the source position of the microseismic event to the fourth sensor closest to the microseismic event.

[0050] Further, according to the size of each microseismic event L4, the microseismic events are divided into several groups according to the interval. 4min As the base point, it is divided into several groups according to the spacing d {(L 4min , L4 min +a), (L 4min a,L 4min +2a)…}, where d is between 5m and 25m.

[0051] Furthermore, the magnitudes of the microseismic events M and M>M in each group are counted. i The distribution scatter plot of the cumulative number of microseismic events N is obtained by fitting the least square method according to the Gutenberg and Richard laws to obtain the distribution of the microseismic events with different minimum magnitudes M. min The fitting straight line of magnitude M and cumulative number N is as follows:

[0052] lgN=a+bM

[0053] Further, according to the scatter plot M>M in each group i The cumulative number of microseismic events A i And the fitting straight line M>M i The cumulative number of microseismic events S i , calculate the minimum magnitude M min The goodness of fit value R of each fitting straight line is calculated as follows:

[0054]

[0055] Further, the minimum magnitude M with the maximum goodness of fit value R in each of the groups is selected. min , the M min The value is determined as the sensitivity M of the group S .

[0056] Furthermore, for any point within the monitoring range, the distance S from the point to the fourth sensor closest to it can be calculated, and the sensitivity M of any point within the monitoring range can be determined according to the L4 grouping interval where the distance S is located. S , the sensitivity cloud map of the microseismic monitoring system is obtained by using the interpolation method.

[0057] Example:

[0058] like Figure 2As shown in the figure, a 16-channel microseismic monitoring system has been established at two levels in a mine. The red cones in the figure are sensor locations, and the spheres are microseismic events detected by the monitoring.

[0059] (1) The microseismic monitoring system was used to collect microseismic signals within a three-month period. A total of 910 microseismic events were located by more than four microseismic sensors, and the epicenter locations and magnitudes of all microseismic events were determined;

[0060] (2) The L4 of each microseismic event is calculated, where the minimum value of L4 is 32.5 m and the maximum value of L4 is 336.4 m. For the convenience of calculation, L min =30m.

[0061] (3) Divide L4 into 31 groups according to the interval a=10m, namely (30≤L4≤40, 40<L4≤50, 50<L4≤60, ..., 320<L4≤340).

[0062] (4) Plot the magnitude of microseismic events M and M>M in each group i The distribution scatter plot of the cumulative number of microseismic events N is obtained by fitting with the least square method to obtain the distribution of microseismic events with different minimum magnitudes M. min The fitting straight line between the magnitude M and the cumulative number N. Figure 3 For example, in the group of 60≤L4≤70, we get M min =-3.2 fitting straight line lgN = -2.21-1.25M, M min The fitting straight line lgN = -2.92-1.50M is -3.1.

[0063] (5) According to the scatter plot M>M in each group i The cumulative number of microseismic events A i And the fitting straight line M>M i The cumulative number of microseismic events S i , calculate the minimum magnitude M min The goodness of fit value R of each fitting line. As shown in Table 2, 60≤L4≤70 groups take different M min The goodness of fit value R of the fitting straight line is as follows: min When -3.1 is taken, the goodness of fit value R is the largest, so the sensitivity M of the group 60≤L4≤70 is S is -3.1.

[0064]

[0065] (6) Using the same method, we get the sensitivity M of 31 groups. min , see Table 2.

[0066] Table 2 Sensitivity M within each groupmin

[0067]

[0068] (7) According to the distance from any point in the monitoring range to the fourth sensor closest to it, the L4 of the point is obtained, and the sensitivity M of the point is determined according to Table 2 min , the sensitivity cloud map of the microseismic monitoring system is obtained by interpolation method, such as Figure 4 shown.

[0069] The present invention provides a method for determining the impact range of bottom pulling operations based on microseismic monitoring. The method can actually reflect the monitoring capability of the network under the current mining engineering structure conditions and the health of the microseismic monitoring system according to the magnitude and source location of the microseismic events within a period of time, and evaluate the sensitivity of the monitoring network more accurately.

[0070] According to the geometric parameter of the distance from the microseismic event to the sensor involved in locating the microseismic event, the sensitivity with the maximum goodness of fit value in each interval is obtained by grouping and refining;

[0071] According to the geometric relationship between the distance between the microseismic event and the sensor, the geometric relationship between the distance between any point in the space and the sensor is generalized to obtain the sensitivity of any point in the space.

[0072] (1) In order to solve the problem that the parameters selected for sensitivity calculation based on the spatial layout of sensors during the design phase are quite different from those of the actual project, the present invention proposes a sensitivity evaluation method based on real-time monitoring of microseismic event source parameters. The sensitivity of the monitoring network is evaluated based on the magnitude and source location of microseismic events over a period of time, reflecting the actual monitoring capability of the monitoring network.

[0073] (2) In order to solve the problem that the sensitivity evaluation based on the spatial layout of sensors is not applicable to changes in mining engineering structures and sensor failure or loss conditions, the present invention proposes a sensitivity evaluation method based on real-time monitoring of microseismic event source parameters to evaluate the sensitivity of the microseismic monitoring system under the current mining engineering structure conditions and the health of the microseismic monitoring system, thereby providing a basis for the current performance evaluation and subsequent optimization and expansion of the system.

[0074] (3) In order to solve the problem that only one value of the lowest magnitude point is obtained based on the coincidence position of the magnitude-frequency relationship line and the observed data point as the lower limit of the monitoring capability of the monitoring network, the present invention provides a sensitivity evaluation method based on the full spatial position of the monitoring network, and finally the sensitivity cloud map of the network can be obtained in the software through interpolation.

[0075] (4) The monitoring network capability assessment method for natural earthquakes in reservoirs does not take into account the problem of micro-earthquakes. The present invention provides a sensitivity assessment method based on the full magnitude range, which is suitable for the sensitivity assessment of micro-earthquake monitoring systems.

[0076] The above is a detailed introduction to a microseismic monitoring system sensitivity evaluation method provided by the embodiment of the present application. The description of the above embodiment is only used to help understand the method and its core idea of ​​the present application; at the same time, for those skilled in the art, 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.

[0077] For example, certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that hardware manufacturers may use different nouns to refer to the same component. This specification and claims do not use differences in names as a way to distinguish components, but use differences in the functions of components as the criteria for distinction. As mentioned throughout the specification and claims, "including" and "comprising" are open-ended terms, so they should be interpreted as "including / including but not limited to". "Approximately" means that within an acceptable error range, those skilled in the art can solve the technical problem within a certain error range and basically achieve the technical effect. The subsequent description of the specification is a preferred embodiment of the present application, but the description is for the purpose of illustrating the general principles of the present application, and is not used to limit the scope of the present application. The scope of protection of the present application shall be determined by the definition of the attached claims.

[0078] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a product or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such a product or system. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the product or system including the elements.

[0079] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0080] The above description shows and describes several preferred embodiments of the present application, but as mentioned above, it should be understood that the present application is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the application concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art do not deviate from the spirit and scope of the present application, and should be within the scope of protection of the claims attached to the present application.

Claims

1. A sensitivity evaluation method for a microseismic monitoring system, characterized in that: The sensitivity assessment method specifically comprises the following steps: The microseismic signals within a certain period of time t are obtained through the microseismic monitoring system, and the source locations and magnitudes of all microseismic events located by more than four microseismic sensors are determined; Based on the source position of the microseismic event, calculate the distance Li from each microseismic event to all sensors involved in locating the microseismic event, and determine the distance L4 from the source position of each microseismic event in Li to the fourth sensor closest to the microseismic event; According to the size of each microseismic event L4, the microseismic events are divided into a number of groups according to the interval; According to the magnitude and cumulative number distribution of microseismic events in each group, a fitting straight line is obtained between the two, and different minimum magnitudes M are calculated. min The goodness of fit value R under the condition; According to the maximum goodness of fit value in each group, the corresponding minimum magnitude M is selected min , determine the sensitivity M within each group of L4 S ; According to the group of L4 corresponding to any point in the monitoring range, the sensitivity M of the point is determined. S , the sensitivity cloud map of the microseismic monitoring system is obtained by using the interpolation method.

2. The sensitivity evaluation method according to claim 1, characterized in that: The range of the time period t should not be less than 30d.

3. The sensitivity evaluation method according to claim 1, characterized in that: Determine the source location (x0, y0, z0) and magnitude M of all microseismic events located by more than four microseismic sensors. The minimum number of sensors used to locate the event is set in the microseismic monitoring system.

4. The sensitivity evaluation method according to claim 1, characterized in that: Calculate the distance from each microseismic event to all sensors involved in locating the microseismic event (x i ,y i , z i ), the specific calculation method is:

5. The sensitivity evaluation method according to claim 1, characterized in that: The fourth largest value among all Li of each microseismic event is determined as the distance L4 from the source position of the microseismic event to the fourth sensor closest to the microseismic event.

6. The sensitivity evaluation method according to claim 1, characterized in that: According to the size of each microseismic event L4, the microseismic events are divided into a number of groups according to the interval; The minimum value L4 of all microseismic events 4min As the base point, it is divided into several groups according to the spacing d {(L 4min , L4 min +a), (L 4min a,L 4min +2a)…}, where d is between 5m and 25m.

7. The sensitivity evaluation method according to claim 1, characterized in that: Count the magnitudes of microseismic events M and M>M in each group i The distribution scatter plot of the cumulative number of microseismic events N is obtained by fitting the least square method according to the Gutenberg and Richard laws to obtain the distribution of the microseismic events with different minimum magnitudes M. min The fitting straight line of magnitude M and cumulative number N is as follows: lgN=a+bM.

8. The sensitivity evaluation method according to claim 1, characterized in that: According to the scatter plot M>M in each group i The cumulative number of microseismic events A i And the fitting straight line M>M i The cumulative number of microseismic events S i , calculate the minimum magnitude M min The goodness of fit value R of each fitting straight line is calculated as follows:

9. The sensitivity evaluation method according to claim 1, characterized in that: Select the minimum magnitude M with the maximum goodness-of-fit value R within each of the groups min , the M min The value is determined as the sensitivity M of the group S .

10. The sensitivity evaluation method according to claim 1, characterized in that: For any point in the monitoring range, the distance S from the point to the fourth sensor closest to it can be calculated. According to the L4 grouping interval where the distance S is located, the sensitivity M of any point in the monitoring range can be determined. S , the sensitivity cloud map of the microseismic monitoring system is obtained by using the interpolation method.

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