Event detection frequency weighting-based micro-seismic event positioning confidence ellipsoid construction method

Abstract

The invention discloses a micro-seismic event positioning confidence ellipsoid construction method based on event detection frequency weighting, and the method comprises the steps: operating a micro-seismic monitoring system for a long time, generating a record containing an event number and a marking amount for reflecting the availability of a station for each micro-seismic event, and constructing a historical micro-seismic event directory; performing event screening to obtain a micro-seismic event set; counting the number of micro-seismic events which are successfully recorded by the stations in the station network and participate in positioning; constructing a detection frequency index reflecting the actual monitoring capability of the station; constructing a weight matrix of all stations in the station network by taking the detection frequency index as the weight of the stations; solving an event positioning result; constructing a residual vector and a sensitivity matrix; estimating a parameter covariance matrix by adopting a weighted method; 3 * 3 sub-matrixes corresponding to the position parameters are extracted from the covariance matrix, and characteristic decomposition is carried out; and solving the half-axis length of the confidence ellipsoid under a given confidence level. According to the method, the confidence ellipsoid with higher precision can be constructed, and the precision and reliability of micro-seismic monitoring can be improved.

Description

Technical Field

[0001] This invention belongs to the field of coal mine microseismic monitoring and network evaluation technology, specifically involving a method for constructing a microseismic event location information ellipsoid based on event detection frequency weighting. Background Technology

[0002] In underground mines and deep engineering projects, microseismic monitoring systems use multiple stations deployed in roadways to collect microseismic waveform data over long periods and locate events based on the arrival times of P-waves at each station. To evaluate the reliability of the location results, it is usually necessary to account for the uncertainty of the event location, such as the covariance matrix and confidence ellipsoid. Existing work does not adequately consider the long-term monitoring performance of stations (e.g., how many events can actually be detected over a period of time, and the missed detection rate). In actual mine operations, factors such as station operating status, environmental noise, and hardware failures can cause some stations to record almost no events or record far fewer events than other stations for extended periods. These stations are often still treated equally when locating events, resulting in a mismatch between their "long-term actual monitoring capabilities" and the covariance estimation and confidence ellipsoid construction. Therefore, there is an urgent need to propose a method for constructing a microseismic event location ellipsoid based on event detection frequency weighting. This method uses the "number of events actually recorded by the station within a certain period" as a statistical indicator of long-term monitoring capability and data integrity. By constructing station weights through normalization, the method can be used only in the calculation of the covariance matrix and the confidence ellipsoid. This allows the confidence ellipsoid to better reflect long-term monitoring facts without changing the existing location process, thus providing an accurate basis for engineering decisions. Summary of the Invention

[0003] To address the problems existing in the prior art, this invention provides a method for constructing a microseismic event location confidence ellipsoid based on event detection frequency weighting. This method is simple to implement and has low implementation cost. It can construct a more accurate confidence ellipsoid, thereby improving the accuracy and reliability of microseismic monitoring. It is particularly suitable for scenarios that require long-term stable monitoring.

[0004] To achieve the above objectives, the present invention provides a method for constructing a microseismic event location information ellipsoid based on event detection frequency weighting, comprising the following steps; Step 1: Construct a catalog of historical microseismic events based on historical microseismic data; deploy monitoring equipment in the target monitoring area. Several microseismic monitoring stations form a network, continuously acquiring microseismic signals through long-term operation of the microseismic monitoring system for a set duration; and using microseismic event localization algorithms to determine time windows. The microseismic events within the area are located, and a record containing an event number is generated for each microseismic event. and reaction station availability markers The records were used to construct a catalog of historical microseismic events; Step 2: Extraction of the target microseismic event set; setting weight calibration time windows according to engineering requirements. The event filtering criteria are defined, and then the event set is obtained by filtering from the historical microseismic event catalog. ; Step 3: Count the number of events involved in the localization at each station; based on the microseismic event set. Statistical station network Number of microseismic events successfully recorded and involved in localization ; Step 4: Construct station detection frequency indices; based on the total number of microseismic events successfully recorded and involved in localization within the station network. Construct reaction station Detection frequency index of actual monitoring capability ; Step 5: Construct a weight matrix for the station network based on historical records; use detection frequency indicators. As a station The weights are used to construct a weight matrix for all stations in the station network. ; Step Six: Perform microseismic location analysis on the target event; for newly occurring microseismic events, use unweighted least squares method or existing engineering location algorithms to solve for the event location results. ; Step 7: Construct a confidence ellipsoid based on the microseismic location results and weight matrix; S71: Based on the location results At this point, construct the residual vector. and sensitivity matrix S72: Based on the weight matrix The weighted method is used to estimate the parameter covariance matrix. S73: From the covariance matrix Extract the 3×3 submatrix corresponding to the position parameters. Then, perform eigenvalue decomposition to obtain the eigenvalue matrix. and eigenvector matrix S74: At a given confidence level Next, the length of the semi-major axis of the confidence ellipsoid is calculated. Length of the middle half axis and short half-axis length .

[0005] Furthermore, in order to achieve rapid filtering of available data through the amount of markings, thereby improving positioning efficiency and reliability, in step one, the station is obtained according to formula (1). The number of tags ; (1).

[0006] Furthermore, to ensure the integrity and reliability of the event set through multiple filtering mechanisms, in step two, the event filtering conditions include energy filtering conditions and spatial filtering conditions; the energy filtering condition is the event energy. ,in, The minimum complete energy level is defined; the spatial selection criterion is that the geographical range of the event occurs within the defined target area. In the event filtering process, time windows are first defined by weight. Extract data within a specified time frame and then remove it. The events are identified, and records falling within the target area are retained. Finally, an event set is obtained based on all eligible microseismic events. .

[0007] Furthermore, in order to effectively quantify long-term monitoring activity and data integrity, in step three, the number of events is obtained according to formula (2). ; (2); In the formula, .

[0008] Furthermore, in order to quantify the actual monitoring capability of a station by the proportion of station events, and thus optimize the weight allocation of the station network, in step four, the detection frequency index is obtained according to formula (3). In step five, the weight matrix is ​​constructed according to formula (4). ; (3); (4); In the formula, ,and , indicating the station The percentage of events involved in location services; .

[0009] Furthermore, in order to facilitate high-precision and high-efficiency positioning operations, in step six, the event positioning results are solved. The process is as follows: For newly occurring microseismic events, collect P-wave arrival time observation datasets from each station. Under given velocity model conditions, the theoretical calculation time is... ,in, For the event model parameter vector, , () represents the event space coordinates. The earthquake occurred at a specific time; the event location was determined using unweighted least squares method or existing engineering location algorithms. ,in, .

[0010] Furthermore, in order to effectively quantify the station's sensitivity to positioning parameters and provide a direct basis for improving accuracy, in step S71 of step seven, a residual vector is constructed according to formula (5). The station is obtained according to formula (6). Sensitivity vector ; (5); In the formula, ; (6); In the formula, For the theoretical time to the earthquake source Partial derivatives of coordinates; For the theoretical time to the earthquake source Partial derivatives of coordinates; This is the partial derivative of the theory with respect to the source coordinates at that time. The partial derivative of the theory with respect to the moment of earthquake occurrence.

[0011] Furthermore, in order to quantify the reliability of the positioning parameters, and at the same time, to quickly and accurately obtain the key parameters for constructing the confidence ellipsoid, in step S72 of step seven, the parameter covariance matrix is ​​obtained according to formula (7). In step seven, S73, a 3×3 submatrix is ​​obtained according to formula (8). ; (7).

[0012] In the formula, , The number of model parameters; (8); In the formula, For the eigenvalue matrix, This is the eigenvector matrix.

[0013] Furthermore, in order to transform the positioning parameters into an intuitive geometric form to provide a quantitative basis for subsequent engineering decisions, in step seven, the length of the major axis of the confidence ellipsoid is obtained according to formulas (9), (10), and (11), respectively. Length of the central axis and the length of the minor half-axis ; (9); (10); (11); In the formula, The threshold for a chi-square distribution with 3 degrees of freedom.

[0014] Furthermore, in order to dynamically adapt to changes caused by station movement and ensure the continuous accuracy and reliability of positioning results, in step five, when a station in the station network moves, the weight calibration time window is periodically updated using a sliding method. And for each new time window Repeat steps one through five to generate a new weight matrix. .

[0015] This invention proposes a method for constructing a microseismic event location confidence ellipsoid based on determining station weights from historical microseismic event monitoring records. First, a complete initial event library is constructed through long-term operation of the microseismic monitoring system and its location algorithm. Simultaneously, the generation of markers not only provides clear input for subsequent location and statistical processes, enabling rapid and accurate filtering of available data, but also avoids invalid stations slowing down subsequent location processes. Furthermore, it effectively reduces interference from noise and outliers, thus providing a reliable data foundation for subsequent analysis. Second, by setting time windows and filtering conditions, low-quality events can be eliminated through multiple filtering methods, focusing only on key events. This ensures the stability and reliability of each event in the event set, effectively reducing subsequent computational load and significantly improving the accuracy of subsequent statistical processes, providing a high-quality event set for the accurate construction of the confidence ellipsoid. Finally, by statistically analyzing the number of microseismic events successfully recorded and participated in location by each station, the contribution of each station is quantified, providing a direct basis for optimizing the station network. Subsequently, a detection frequency index reflecting the actual monitoring capability of a station is constructed based on the total number of successfully recorded microseismic events that participated in the localization process. This index objectively reflects the station's actual monitoring capability through the proportion of events. Furthermore, the detection frequency index is calculated using a normalized method, facilitating direct conversion of subsequent weights. Moreover, using the detection frequency index as the station weights achieves an effective conversion from station contribution to weight. Higher-frequency stations have higher weights, while lower-frequency stations have lower weights, ensuring that the constructed weight matrix accurately reflects the availability and reliability of each station, significantly improving localization accuracy and computational efficiency. Then, unweighted least squares is used for conventional microseismic localization, which requires less computation, is suitable for real-time processing, and meets the timeliness requirements of microseismic monitoring. Finally, the weight matrix, which accurately reflects the availability and reliability of each station, is applied to the calculation of the covariance matrix and confidence ellipsoid, but not to the solution and update process of the event localization results. This ensures consistency between the description of localization uncertainty and the actual capability of the monitoring system. Meanwhile, by incorporating station data integrity and long-term reliability information into the error ellipsoid calculation, the amplification effect of stations with long-term "few records and easy missed detections" on covariance and confidence ellipsoids is effectively suppressed. This yields a more accurate characterization of positioning uncertainty that better reflects the actual monitoring capabilities of the system, and effectively improves the construction accuracy of the confidence ellipsoid, providing a more accurate basis for subsequent engineering decisions. In mine ground pressure monitoring, this scheme can visually reflect the uncertainty of the seismic source location through the semi-axis length of the confidence ellipsoid, thus enabling a more realistic reflection of the actual monitoring capabilities through the confidence ellipsoid.

[0016] This method is simple to implement and has low implementation costs. It can construct a more accurate confidence ellipsoid, which improves the accuracy and reliability of microseismic monitoring. It is particularly suitable for scenarios that require long-term stable monitoring. Attached Figure Description

[0017] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0018] The invention will now be further described with reference to the accompanying drawings.

[0019] like Figure 1 As shown, the present invention provides a method for constructing a microseismic event location information ellipsoid based on event detection frequency weighting, including the following steps; Step 1: Construct a catalog of historical microseismic events based on historical microseismic data; Deployment in the target monitoring area Several microseismic monitoring stations form a network, continuously acquiring microseismic signals through long-term operation of the microseismic monitoring system for a set duration; and using microseismic event localization algorithms to determine time windows. The microseismic events within the area are located, and a record containing an event number is generated for each microseismic event. and reaction station availability markers The records include the location of the earthquake source and the energy of the microseismic events. A historical microseismic event catalog is constructed based on the records of all microseismic events. Step 2: Extraction of the target microseismic event set; Set the weight calibration time window according to project requirements. ( To clearly define the time period for analysis, and to define event filtering criteria, a set of microseismic events is obtained by filtering events from the historical microseismic event catalog. This process can effectively reduce subsequent computation by focusing on key events through time windows and filtering conditions. Step 3: Count the number of events involved in location tracking at each station; Based on microseismic event set Statistical station network Number of microseismic events successfully recorded and involved in localization ; Step 4: Construct station detection frequency indicators; Based on the total number of microseismic events successfully recorded and used for localization in the station network Construct reaction station Detection frequency index of actual monitoring capability ; Step 5: Construct the weight matrix of the station network based on historical records; Based on detection frequency index As a station The weights are used to construct a weight matrix for all stations in the station network. Weight matrix It reflects the reliability and availability of each station in the station network. Among them, high-frequency stations have higher weights, which is beneficial to the accurate construction of subsequent confidence ellipsoids. Step Six: Perform microseismic localization of the target event; For newly occurring microseismic events, the location results can be obtained using unweighted least squares method or existing engineering location algorithms. ; Step 7: Construct a confidence ellipsoid based on the microseismic location results and weight matrix; S71: In the positioning results At this point, construct the residual vector. and sensitivity matrix S72: Based on the weight matrix Without changing Under the premise of this, the weighted method is used to estimate the parameter covariance matrix. S73: From the covariance matrix Extract the 3×3 submatrix corresponding to the position parameters. Then, perform eigenvalue decomposition to obtain the eigenvalue matrix. and eigenvector matrix S74: At a given confidence level Next, the length of the semi-major axis of the confidence ellipsoid is calculated. Length of the middle half axis and short half-axis length .

[0020] To enable rapid filtering of available data through marker quantities, thereby improving positioning efficiency and reliability, the arrival time difference of seismic waves is analyzed to determine the occurrence time of microseismic events, and the arrival time of P-waves at each station is automatically picked up to generate marker quantities. Specifically, in step one, the station is obtained according to formula (1). The number of tags ; (1).

[0021] To ensure the integrity and reliability of the event set through multiple filtering mechanisms, in step two, the event filtering conditions include energy filtering conditions and spatial filtering conditions; the energy filtering condition is the event energy. ,in, As the minimum complete energy level, it is a preferred option that can be used in the energy screening process to reduce the event energy. Using logarithmic energy form At the same time, make the smallest complete energy level Also in logarithmic energy form Directly eliminate those below the energy lower limit The system can directly filter out extremely low-energy events that are below the actual detection capability of the microseismic monitoring system or that are recorded unstablely, thereby improving the stability of the station event data statistics; the spatial filtering condition is that the geographical range of the event is located within the limited target area. In the event filtering process, time windows are first defined by weight. Extract data within a specified time frame and then remove it. The events are identified, and records falling within the target area are retained. Finally, an event set is obtained based on all eligible microseismic events. .

[0022] In order to effectively quantify the long-term monitoring activity and data integrity, in step three, the number of events is obtained according to formula (2). Number of events Used to characterize the long-term monitoring activity and data integrity level of the stations; (2); In the formula, .

[0023] In order to quantify the actual monitoring capability of a station by the proportion of station events, and thus optimize the weight allocation of the station network, in step four, the detection frequency index is obtained according to formula (3). In step five, the weight matrix is ​​constructed according to formula (4). ; (3); (4); In the formula, ,and , indicating the station The percentage of events involved in location services; .

[0024] To facilitate high-precision and high-efficiency positioning operations, step six involves solving for the event positioning results. The process is as follows: For newly occurring microseismic events, collect P-wave arrival time observation datasets from each station. , For the station Observed arrival time of the P-wave; calculate the theoretical arrival time under given velocity model conditions. ,in, For the event model parameter vector, , () represents the event space coordinates. The earthquake occurred at a specific time; the event location was determined using unweighted least squares method or existing engineering location algorithms. ,in, .

[0025] In order to effectively quantify the sensitivity of the station to positioning parameters and provide a direct basis for improving the accuracy, in step S71 of step seven, the residual vector is constructed according to formula (5). The station is obtained according to formula (6). Sensitivity vector Sensitivity vector It can effectively reflect the station's sensitivity to positioning parameters; (5); In the formula, , which is the difference between the observed and theoretical arrival times of the P-wave; (6); In the formula, For the theoretical time to the earthquake source Partial derivatives of coordinates; For the theoretical time to the earthquake source Partial derivatives of coordinates; This is the partial derivative of the theory with respect to the source coordinates at that time. The partial derivative of the theory with respect to the moment of earthquake occurrence.

[0026] To quantify the reliability of the positioning parameters, and to quickly and accurately obtain the key parameters for constructing the confidence ellipsoid, in step S72 of step seven, the parameter covariance matrix is ​​obtained according to formula (7). In step seven, S73, a 3×3 submatrix is ​​obtained according to formula (8). ; (7).

[0027] In the formula, , The number of model parameters is, as a preferred embodiment, set to 4, where, Simply participating in the calculation of the covariance matrix does not directly affect the positioning results. Solving for; (8); In the formula, For the eigenvalue matrix, This is the eigenvector matrix.

[0028] In order to transform the positioning parameters into an intuitive geometric form and provide a quantitative basis for subsequent engineering decisions, in step seven, the length of the major axis of the confidence ellipsoid is obtained according to formulas (9), (10), and (11), respectively. Length of the central axis and the length of the minor half-axis ; (9); (10); (11); In the formula, The threshold for a chi-square distribution with 3 degrees of freedom.

[0029] Among them, the station weights are determined solely by the matrix. It participates in the calculation of the covariance matrix and confidence ellipsoid, but not in the event localization results. The process of solving and updating.

[0030] To dynamically adapt to changes caused by station movement and ensure the continuous accuracy and reliability of positioning results, in step five, when a station in the station network moves, the weight calibration time window is periodically updated using a sliding method. And for each new time window Repeat steps one through five to generate a new weight matrix. This is to effectively reflect the impact of changes in the monitoring status of stations over time.

[0031] This invention proposes a method for constructing a microseismic event location confidence ellipsoid based on determining station weights from historical microseismic event monitoring records. First, a complete initial event library is constructed through long-term operation of the microseismic monitoring system and its location algorithm. Simultaneously, the generation of markers not only provides clear input for subsequent location and statistical processes, enabling rapid and accurate filtering of available data, but also avoids invalid stations slowing down subsequent location processes. Furthermore, it effectively reduces interference from noise and outliers, thus providing a reliable data foundation for subsequent analysis. Second, by setting time windows and filtering conditions, low-quality events can be eliminated through multiple filtering methods, focusing only on key events. This ensures the stability and reliability of each event in the event set, effectively reducing subsequent computational load and significantly improving the accuracy of subsequent statistical processes, providing a high-quality event set for the accurate construction of the confidence ellipsoid. Finally, by statistically analyzing the number of microseismic events successfully recorded and participated in location by each station, the contribution of each station is quantified, providing a direct basis for optimizing the station network. Subsequently, a detection frequency index reflecting the actual monitoring capability of a station is constructed based on the total number of successfully recorded microseismic events that participated in the localization process. This index objectively reflects the station's actual monitoring capability through the proportion of events. Furthermore, the detection frequency index is calculated using a normalized method, facilitating direct conversion of subsequent weights. Moreover, using the detection frequency index as the station weights achieves an effective conversion from station contribution to weight. Higher-frequency stations have higher weights, while lower-frequency stations have lower weights, ensuring that the constructed weight matrix accurately reflects the availability and reliability of each station, significantly improving localization accuracy and computational efficiency. Then, unweighted least squares is used for conventional microseismic localization, which requires less computation, is suitable for real-time processing, and meets the timeliness requirements of microseismic monitoring. Finally, the weight matrix, which accurately reflects the availability and reliability of each station, is applied to the calculation of the covariance matrix and confidence ellipsoid, but not to the solution and update process of the event localization results. This ensures consistency between the description of localization uncertainty and the actual capability of the monitoring system. Meanwhile, by incorporating station data integrity and long-term reliability information into the error ellipsoid calculation, the amplification effect of stations with long-term "few records and easy missed detections" on covariance and confidence ellipsoids is effectively suppressed. This yields a more accurate characterization of positioning uncertainty that better reflects the actual monitoring capabilities of the system, and effectively improves the construction accuracy of the confidence ellipsoid, providing a more accurate basis for subsequent engineering decisions. In mine ground pressure monitoring, this scheme can visually reflect the uncertainty of the seismic source location through the semi-axis length of the confidence ellipsoid, thus enabling a more realistic reflection of the actual monitoring capabilities through the confidence ellipsoid.

[0032] This method is simple to implement and has low implementation costs. It can construct a more accurate confidence ellipsoid, which improves the accuracy and reliability of microseismic monitoring. It is particularly suitable for scenarios that require long-term stable monitoring.

Claims

1. A method for constructing a microseismic event location information ellipsoid based on event detection frequency weighting, characterized in that, Includes the following steps; Step 1: Construct a catalog of historical microseismic events based on historical microseismic data; deploy monitoring equipment in the target monitoring area. Several microseismic monitoring stations form a network, continuously acquiring microseismic signals through long-term operation of the microseismic monitoring system for a set duration; and using microseismic event localization algorithms to determine time windows. The microseismic events within the area are located, and a record containing an event number is generated for each microseismic event. and reaction station availability markers The records were used to construct a catalog of historical microseismic events; Step 2: Extraction of the target microseismic event set; setting weight calibration time windows according to engineering requirements. The event filtering criteria are defined, and then the event set is obtained by filtering from the historical microseismic event catalog. ; Step 3: Count the number of events involved in the localization at each station; based on the microseismic event set. Statistical station network Number of microseismic events successfully recorded and involved in localization ; Step 4: Construct station detection frequency indices; based on the total number of microseismic events successfully recorded and involved in localization within the station network. Construct reaction station Detection frequency index of actual monitoring capability ; Step 5: Construct a weight matrix for the station network based on historical records; use detection frequency indicators. As a station The weights are used to construct a weight matrix for all stations in the station network. ; Step Six: Perform microseismic location analysis on the target event; for newly occurring microseismic events, use unweighted least squares method or existing engineering location algorithms to solve for the event location results. ; Step 7: Construct a confidence ellipsoid based on the microseismic location results and weight matrix; S71: In the positioning results At this point, construct the residual vector. and sensitivity matrix ; S72: Based on weight matrix The weighted method is used to estimate the parameter covariance matrix. S73: From the covariance matrix Extract the 3×3 submatrix corresponding to the position parameters. Then, perform eigenvalue decomposition to obtain the eigenvalue matrix. and eigenvector matrix S74: At a given confidence level Next, the length of the semi-major axis of the confidence ellipsoid is calculated. Length of the middle half axis and the length of the short half-axis .

2. The method for constructing a microseismic event location information ellipsoid based on event detection frequency weighting according to claim 1, characterized in that, In step one, the station is obtained according to formula (1). The number of tags ; （1）。 3. The method for constructing a microseismic event location information ellipsoid based on event detection frequency weighting according to claim 1, characterized in that, In step two, the event filtering criteria include energy filtering criteria and spatial filtering criteria; the energy filtering criteria are the event energy. ,in, The minimum complete energy level is defined; the spatial selection criterion is that the geographical range of the event occurs within the defined target area. In the event filtering process, time windows are first defined by weight. Extract data within a specified time frame, then remove data from that frame. The events are identified, and records falling within the target area are retained. Finally, an event set is obtained based on all eligible microseismic events. .

4. The method for constructing a microseismic event location information ellipsoid based on event detection frequency weighting according to claim 1, characterized in that, In step three, the number of events is obtained according to formula (2). ; （2）； In the formula, .

5. The method for constructing a microseismic event location information ellipsoid based on event detection frequency weighting according to claim 1, characterized in that, In step four, the detection frequency index is obtained according to formula (3). ; In step five, the weight matrix is ​​constructed according to formula (4). ; （3）； （4）； In the formula, ,and , indicating the station The percentage of events involved in location services; .

6. The method for constructing a microseismic event location information ellipsoid based on event detection frequency weighting according to claim 1, characterized in that, In step six, the event localization result is obtained. The process is as follows: For newly occurring microseismic events, collect P-wave arrival time observation datasets from each station. Under given velocity model conditions, the theoretical calculation time is... ,in, For the event model parameter vector, , () represents the event space coordinates. The earthquake occurred at a specific time; the event location was determined using unweighted least squares method or existing engineering location algorithms. ,in, .

7. The method for constructing a microseismic event location information ellipsoid based on event detection frequency weighting according to claim 1, characterized in that, In step S71 of step seven, the residual vector is constructed according to formula (5). The station is obtained according to formula (6). Sensitivity vector ; （5）； In the formula, ; （6）； In the formula, For the theoretical time to the earthquake source Partial derivatives of coordinates; For the theoretical time to the earthquake source Partial derivatives of coordinates; This is the partial derivative of the theory with respect to the source coordinates at that time. The partial derivative of the theory with respect to the moment of earthquake occurrence.

8. The method for constructing a microseismic event location information ellipsoid based on event detection frequency weighting as described in claim 1, characterized in that, In step S72 of step seven, the parameter covariance matrix is ​​obtained according to formula (7). In step seven, S73, a 3×3 submatrix is ​​obtained according to formula (8). ; （7） ； In the formula, , The number of model parameters; （8）； In the formula, For the eigenvalue matrix, This is the eigenvector matrix.

9. The method for constructing a microseismic event location information ellipsoid based on event detection frequency weighting as described in claim 1, characterized in that, In step seven, the length of the major axis of the confidence ellipsoid is obtained according to formulas (9), (10), and (11), respectively. Length of the central axis and the length of the minor half-axis ; （9）； （10）； （11）； In the formula, The threshold for a chi-square distribution with 3 degrees of freedom.

10. The method for constructing a microseismic event location information ellipsoid based on event detection frequency weighting according to claim 1, characterized in that, In step five, when a station in the station network moves, the weight calibration time window is periodically updated using a sliding method. And for each new time window Repeat steps one through five to generate a new weight matrix. .

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