A method for sensitivity evaluation of microseismic monitoring systems

Abstract

This invention relates to the field of sensitivity testing technology, and more particularly to a method for evaluating the sensitivity of a microseismic monitoring system. This method, based on the magnitude and source location of microseismic events over a period of time, can realistically reflect the network monitoring capability under current mine engineering structural conditions and the health status of the microseismic monitoring system. It uses the geometric parameter of the distance from the microseismic event to the sensor involved in locating the event to group and refine the sensitivity of each interval to obtain the maximum goodness of fit. The sensitivity of any point within the monitoring range is determined by the distance to the fourth nearest sensor. Finally, a network sensitivity cloud map can be obtained through interpolation. This method is used to evaluate the sensitivity of the microseismic monitoring system under current mine engineering structural conditions and the health status of the microseismic monitoring system, providing a basis for the current performance evaluation and subsequent optimization and expansion of the system.

Description

Technical Field

[0001] This 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 Technology

[0002] Microseismic monitoring technology, characterized by its continuity and real-time nature, has become an important means of monitoring ground pressure hazards in mines. Sensitivity is a key technical indicator for evaluating the performance of a microseismic monitoring system. Measured by the minimum measurable magnitude, it directly affects the system's ability to monitor weak vibration signals. The smaller the minimum measurable magnitude, the higher the sensitivity. In the design of a microseismic monitoring system network, sensitivity analysis can be used to obtain a sensitivity contour map of the network, thereby assessing the rationality and effectiveness of the current network design. Based on the calculation results, adjustments can be made to the sensor deployment to achieve network optimization.

[0003] Currently, the main method for sensitivity assessment of microseismic monitoring systems is calculation based on the spatial deployment of sensors, used in the design process before the construction of new microseismic monitoring systems. This method calculates the sensitivity contour map of the designed network based on parameters such as sensor coordinates, P-wave velocity and error, and S-wave velocity and error. However, due to the complex geological conditions in mining engineering, the wave velocity parameters selected by this method often differ significantly from those in actual engineering projects, and the analysis results are often limited to theoretical calculations.

[0004] For existing microseismic monitoring systems, the sensitivity of the monitoring network can vary significantly from the initial design calculations due to changes in the mine's structural integrity and sensor malfunctions or absences. This patent proposes a sensitivity evaluation method for microseismic monitoring systems. For existing systems, it utilizes actual monitored microseismic events to assess the system's sensitivity under current mine structural conditions and its overall health status, providing a basis for current performance evaluation and future optimization.

[0005] (1) In the process of analyzing the design network before the construction of the 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 far from the actual engineering, and the analysis results are limited to theoretical calculations and cannot fully reflect the monitoring effect of the network.

[0006] (2) For existing microseismic monitoring systems, the sensitivity of the monitoring network will change significantly compared to the calculation results during the design phase due to changes in the mine engineering structure and sensor failures or missing sensors. If the sensitivity is calculated based on the spatial layout of the sensors, it will no longer reflect the actual situation of the current monitoring network.

[0007] For existing reservoir monitoring networks, some scholars have proposed a method for assessing the network's earthquake recording capacity based on magnitude and frequency. This method, based on seismological theory, primarily uses the magnitude-frequency relationship curve, combined with a frequency-magnitude diagram, to calculate the reservoir network's earthquake recording capacity. The method utilizes earthquake events recorded by the network over a period of time to create NM and lgN-M diagrams. The NM diagram reveals the distribution of the most frequent magnitudes. Using the Gutenberg-Gräger relation, a linear relationship between magnitude and frequency is obtained by fitting the lgN-M diagram. The lowest magnitude point is then identified by the location where this line coincides with the observed data points; this is the lower limit of the monitoring network's capacity, used to assess whether it meets design requirements.

[0008] (1) Based on the position of the linear relationship between magnitude and frequency and the location of the observation data points, this method only obtains the value of the lowest magnitude point as the lower limit of the monitoring capability of the monitoring network, instead of evaluating the sensitivity of each location in the entire space of the monitoring network, and cannot obtain the 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 magnitude less than 0 are removed. Therefore, this method is not suitable for the sensitivity assessment of microseismic networks. Summary of the Invention

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

[0011] To solve the above-mentioned technical problems, the technical solution of the present invention is: a method for sensitivity evaluation of a microseismic monitoring system, the method specifically including the following steps:

[0012] The microseismic monitoring system acquires microseismic signals within a certain time period t and determines the source location and magnitude of all microseismic events located by four or more microseismic sensors.

[0013] Based on the source location 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 location of each microseismic event in Li to the fourth nearest sensor involved in locating the microseismic event.

[0014] Based on the magnitude of each microseismic event L4, the microseismic events are divided into several groups according to the interval;

[0015] Based on the distribution of magnitude and cumulative number of microseismic events within each group, a fitted straight line is obtained between the two, and different minimum magnitudes M are calculated. min Goodness-of-fit value R under the given conditions;

[0016] The minimum magnitude M is selected based on the maximum goodness-of-fit value within each group. min Determine the sensitivity M within each group of L4. S ;

[0017] Determine the sensitivity M of any point within the monitoring range based on the group of L4 it belongs to. S The sensitivity cloud map of the microseismic monitoring system was obtained by interpolation.

[0018] Furthermore, the microseismic signals within the time period t should have a range of not less than 30 days.

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

[0020] Furthermore, the calculation links each microseismic event to all sensors involved in locating that microseismic event (x... i y i , z i The distance Li is calculated as follows:

[0021]

[0022] Furthermore, the fourth largest value among all Li values ​​for each microseismic event is determined as the distance L4 from the source location of the microseismic event to the fourth nearest sensor involved in locating the microseismic event.

[0023] Furthermore, based on the magnitude of each microseismic event L4, the microseismic events are divided into several groups according to intervals. The minimum value L of all microseismic events L4 is then used as the grouping factor. 4min Using the base point, divide it into several groups according to the spacing d {(L 4min L4 min +a), (L 4min a, L 4min +2a)…}, where d takes values ​​from 5m to 25m.

[0024] Furthermore, the magnitudes M and M>M of microseismic events within each group are statistically analyzed. i The cumulative number N of microseismic events is distributed by scatter plot. Based on Gutenberg and Richard's laws, the least squares method is used to fit the data to obtain the minimum magnitudes M. min The fitted straight line between the magnitude M and the cumulative number N is given by the following formula:

[0025] lgN=a+bM

[0026] Furthermore, based on the scatter plot M > M within each group... i The cumulative number of microseismic events Ai And the fitted 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 fitted straight line is calculated as follows:

[0027]

[0028] Furthermore, the smallest magnitude M with the maximum goodness-of-fit value R within each group is selected. min , the M min The value is determined as the sensitivity M of this group. S .

[0029] Furthermore, for any point within the monitoring range, the distance S from that point to the fourth nearest sensor can be calculated. Based on the L4 grouping interval where the distance S falls, the sensitivity M of any point within the monitoring range can be determined. S The sensitivity cloud map of the microseismic monitoring system was obtained by interpolation.

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

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

[0032] The sensitivity of a point is determined by the distance from any point within the monitoring range to the fourth nearest sensor. Finally, the sensitivity cloud map of the network can be obtained in the software through interpolation. Attached Figure Description

[0033] Figure 1 This is a flowchart of a sensitivity evaluation method for a microseismic monitoring system according to the present invention.

[0034] Figure 2 This is a schematic diagram of a microseismic sensor and the distribution of microseismic events in a mine, as shown in the example.

[0035] Figure 3 To illustrate, different M values ​​are used in the examples. min A schematic diagram of the fitted line when the time is right.

[0036] Figure 4 This is an example of a sensitivity cloud map obtained as part of an implementation plan. Detailed Implementation

[0037] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

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

[0039] The microseismic monitoring system acquires microseismic signals within a certain time period t and determines the source location and magnitude of all microseismic events located by four or more microseismic sensors.

[0040] Based on the source location 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 location of each microseismic event in Li to the fourth nearest sensor involved in locating the microseismic event.

[0041] Based on the magnitude of each microseismic event L4, the microseismic events are divided into several groups according to the interval;

[0042] Based on the distribution of magnitude and cumulative number of microseismic events within each group, a fitted straight line is obtained between the two, and different minimum magnitudes M are calculated. min Goodness-of-fit value R under the given conditions;

[0043] The minimum magnitude M is selected based on the maximum goodness-of-fit value within each group. min Determine the sensitivity M within each group of L4. S ;

[0044] Determine the sensitivity M of any point within the monitoring range based on the group of L4 it belongs to. S The sensitivity cloud map of the microseismic monitoring system was obtained by interpolation.

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

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

[0047] Furthermore, the calculation links each microseismic event to all sensors involved in locating that microseismic event (x... i y i , z i The distance Li is calculated as follows:

[0048]

[0049] Furthermore, the fourth largest value among all Li values ​​for each microseismic event is determined as the distance L4 from the source location of the microseismic event to the fourth nearest sensor involved in locating the microseismic event.

[0050] Furthermore, based on the magnitude of each microseismic event L4, the microseismic events are divided into several groups according to intervals. The minimum value L of all microseismic events L4 is then used as the grouping factor. 4min Using the base point, divide it into several groups according to the spacing d {(L 4min L4 min +a), (L 4min a, L 4min +2a)…}, where d takes values ​​from 5m to 25m.

[0051] Furthermore, the magnitudes M and M>M of microseismic events within each group are statistically analyzed. i The cumulative number N of microseismic events is distributed by scatter plot. Based on Gutenberg and Richard's laws, the least squares method is used to fit the data to obtain the minimum magnitudes M. min The fitted straight line between the magnitude M and the cumulative number N is given by the following formula:

[0052] lgN=a+bM

[0053] Furthermore, based on the scatter plot M > M within each group... i The cumulative number of microseismic events A i And the fitted 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 fitted straight line is calculated as follows:

[0054]

[0055] Furthermore, the smallest magnitude M with the maximum goodness-of-fit value R within each group is selected. min , the M min The value is determined as the sensitivity M of this group. S .

[0056] Furthermore, for any point within the monitoring range, the distance S from that point to the fourth nearest sensor can be calculated. Based on the L4 grouping interval where the distance S falls, the sensitivity M of any point within the monitoring range can be determined. S The sensitivity cloud map of the microseismic monitoring system was obtained by interpolation.

[0057] Example:

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

[0059] (1) Microseismic signals were collected over a three-month period using a microseismic monitoring system. A total of 910 microseismic events were located by four or more microseismic sensors. The source location and magnitude of all microseismic events were determined.

[0060] (2) The L4 value for each microseismic event was calculated. The minimum value of L4 was 32.5 m, and the maximum value of L4 was 336.4 m. For ease of calculation, L4 was taken as 32.5 m. 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 magnitudes M and M>M of microseismic events within each group. i The cumulative number N of microseismic events is distributed by scatter plot. The least squares method is used to fit the data to obtain the minimum magnitude M. min The fitted straight line between the magnitude M and the cumulative number N. For example... Figure 3 For example, M is obtained in the grouping of 60≤L4≤70 respectively. min The fitted line for -3.2 is lgN = -2.21 - 1.25M, where M is the linear value. min The fitted line is lgN = -2.92 - 1.50M = -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 fitted line M > M i The cumulative number of microseismic events S i Calculate the minimum magnitude M min The goodness-of-fit values ​​R for each fitted straight line. As shown in Table 2, different M values ​​are taken for the grouping of 60≤L4≤70. min The goodness-of-fit value R of the fitted straight line at time M indicates that when M min When the value is -3.1, the goodness-of-fit value R is the largest, therefore the sensitivity M of the 60≤L4≤70 group is the highest. S It is -3.1.

[0064]

[0065] (6) Using the same method, the sensitivity M of the 31 groups was obtained. min See Table 2.

[0066] Table 2 Sensitivity M within each groupmin

[0067]

[0068] (7) Based on the distance from any point within the monitoring range to the fourth sensor closest to it, obtain the L4 of that point, and determine the sensitivity M of that point according to Table 2. min The sensitivity contour map of the microseismic monitoring system was obtained using an interpolation method, such as... Figure 4 As shown.

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

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

[0071] Based on the geometric relationship between the distance between a microseismic event and a sensor, this relationship can be extended to the geometric relationship between the distance between any point in space and a sensor, thereby obtaining the sensitivity of any point in space.

[0072] (1) In view of the problem that the parameters selected for sensitivity calculation based on the spatial layout of sensors in the design stage are significantly different from those in actual engineering, this invention proposes a sensitivity evaluation method based on real-time monitoring of microseismic event source parameters. Based on the magnitude and source location of microseismic events over a period of time, the actual monitoring capability of the monitoring network is reflected, and the sensitivity of the monitoring network is evaluated.

[0073] (2) To address the problem that the sensitivity assessment based on the spatial deployment of sensors is not applicable to changes in the mining engineering structure or the failure or absence of sensors, this invention proposes a sensitivity assessment method based on real-time monitoring of the source parameters of microseismic events. This method is used to assess the sensitivity of the microseismic monitoring system under the current mining engineering structure conditions and the health status of the microseismic monitoring system, providing a basis for the current performance assessment and subsequent optimization and expansion of the system.

[0074] (3) To address the problem that only the lowest magnitude point is obtained as the lower limit of the monitoring capability of the monitoring network based on the position of the line of magnitude-frequency relationship and the location of the observation data points, this invention provides a method for sensitivity assessment based on the full spatial location of the monitoring network. Finally, the network sensitivity cloud map can be obtained in the software through interpolation.

[0075] (4) In view of the problem that the monitoring network capability assessment method for reservoir natural earthquakes does not consider micro-earthquakes, the present invention provides a sensitivity assessment method based on the full magnitude range, which is applicable to the sensitivity assessment of micro-earthquake monitoring systems.

[0076] The above provides a detailed description of a sensitivity evaluation method for a microseismic monitoring system provided in the embodiments of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas; furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

[0077] Certain terms are used in the specification and claims to refer to specific components. Those skilled in the art will understand that hardware manufacturers may use different names to refer to the same component. This specification and claims do not distinguish components based on differences in name, but rather on differences in function. The terms "comprising" and "including" used throughout the specification and claims are open-ended and should be interpreted as "comprising / including but not limited to". "Approximately" means that within an acceptable margin of error, those skilled in the art can solve the technical problem and substantially achieve the technical effect within a certain margin of error. The following descriptions in the specification are preferred embodiments for carrying out this application; however, these descriptions are for the purpose of illustrating the general principles of this application and are not intended to limit the scope of this application. The scope of protection of this application shall be determined by the appended claims.

[0078] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a product or system comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a product or system. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the product or system that includes said element.

[0079] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0080] The foregoing description illustrates and describes several preferred embodiments of this application. However, as previously stated, it should be understood that this application is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the application concept described herein through the foregoing teachings or techniques or knowledge in related fields. Any modifications and variations made by those skilled in the art that do not depart from the spirit and scope of this application should be within the protection scope of the appended claims.

Claims

1. A sensitivity evaluation method for a microseismic monitoring system, characterized in that, The sensitivity assessment method specifically includes the following steps: The microseismic monitoring system acquires microseismic signals within a certain time period t and determines the source location and magnitude of all microseismic events located by four or more microseismic sensors. Based on the source location 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 location of each microseismic event in Li to the fourth nearest sensor involved in locating the microseismic event. Based on the magnitude of each microseismic event L4, the microseismic events are divided into several groups according to the interval; Based on the distribution of magnitude and cumulative number of microseismic events within each group, a fitted straight line is obtained between the two, and different minimum magnitudes M are calculated. min Goodness-of-fit value R under the given conditions; The minimum magnitude M is selected based on the maximum goodness-of-fit value within each group. min Determine the sensitivity M within each group of L4. S ; Determine the sensitivity M of any point within the monitoring range based on the group of L4 it belongs to. S The sensitivity cloud map of the microseismic monitoring system was obtained by interpolation.

2. The sensitivity evaluation method according to claim 1, characterized in that, The time period t should be no less than 30 days.

3. The sensitivity assessment method according to claim 1, characterized in that, The source locations (x0, y0, z0) and magnitude M of all microseismic events located by four or more microseismic sensors are determined. The minimum number of sensors used to locate the events is set by the microseismic monitoring system.

4. The sensitivity assessment method according to claim 1, characterized in that, Calculate each microseismic event to all sensors involved in locating that microseismic event (x i y i , z i The distance Li is calculated as follows: 。 5. The sensitivity assessment method according to claim 1, characterized in that, The fourth largest value among all Li values ​​for each microseismic event is determined as the distance L4 from the source location of that microseismic event to the fourth nearest sensor involved in locating that microseismic event.

6. The sensitivity assessment method according to claim 1, characterized in that, Based on the magnitude of each microseismic event L4, the microseismic events are divided into several groups according to the interval; The minimum value L4 of all microseismic events 4min Using the base point as the reference point, divide it into several groups {(L)} according to the spacing d. 4min L 4min +a), (L 4min +a, L 4min +2a)…},where d takes values ​​from 5m to 25m.

7. The sensitivity assessment method according to claim 1, characterized in that, Statistically analyze the magnitudes M and M>M of microseismic events within each group. i The cumulative number N of microseismic events is distributed by scatter plot. Based on Gutenberg and Richard's laws, the least squares method is used to fit the data to obtain the minimum magnitudes M. min The fitted straight line between the magnitude M and the cumulative number N is given by the following formula: 。 8. The sensitivity assessment method according to claim 1, characterized in that, According to the scatter plot M>M within each group i The cumulative number of microseismic events A i And the fitted 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 fitted straight line is calculated as follows: 。 9. The sensitivity assessment method according to claim 1, characterized in that, Select the smallest magnitude M with the maximum goodness-of-fit value R within each group. min , the M min The value is determined as the sensitivity M of this group. S .

10. The sensitivity evaluation method according to claim 1, characterized in that, For any point within the monitoring range, the distance S from that point to the fourth nearest sensor can be calculated. Based on the L4 grouping interval where the distance S falls, the sensitivity M of any point within the monitoring range can be determined. S The sensitivity cloud map of the microseismic monitoring system was obtained by interpolation.

Images