Microseismic event plane positioning confidence ellipse calculation method based on station weighting

Abstract

The invention discloses a station weighting-based micro-seismic event plane positioning confidence ellipse calculation method, which comprises the following steps of: screening P-wave arrival time data simultaneously received by a plurality of stations, and calculating an initial seismic source position coordinate and an arrival time residual error of each station; the arrival time equation is linearized, and a design matrix and an initial weight matrix are constructed; constructing a normal equation based on the design matrix and the station weight matrix, solving a parameter correction amount, and updating a seismic source position coordinate and a seismic moment; calculating a covariance matrix and extracting a sub-matrix, performing eigenvalue decomposition to obtain eigenvalues and eigenvectors, and calculating an ellipse area under a preset confidence level; respectively resetting the weight of each station to zero, recalculating the area of the confidence ellipse, constructing an influence index of the station on the area of the ellipse, and adjusting the weight of the station according to the influence to obtain an updated weight matrix; and judging a convergence state based on the relative change rate of the confidence ellipse area, and outputting a final seismic source plane position and confidence ellipse parameters during convergence. According to the invention, rapid and accurate positioning operation of a micro-seismic monitoring scene can be realized.

Description

Technical Field

[0001] This invention belongs to the field of intelligent microseismic monitoring and positioning technology, specifically relating to a method for calculating the ellipse of the plane location signal of microseismic events based on station weighting. Background Technology

[0002] In early warning of dynamic disasters in coal mine rock masses, monitoring of hydraulic fracturing in oil and gas reservoirs, and stability analysis of underground caverns, microseismic monitoring technology uses sensor arrays deployed in the rock mass to collect P-wave and S-wave signals generated by rock mass fracturing, and determines the spatial coordinates and time of origin of the earthquake source based on their arrival time difference.

[0003] Currently, least squares iterative algorithms based on the Gauss-Newton method are the core means of seismic source location. After the location calculation is completed, the covariance matrix of the source parameters is usually solved based on the observed time residuals and the spatial geometric distribution of the stations, and then a plane confidence ellipse at a specific confidence level is drawn to quantify the uncertainty of the location results. However, this traditional method has inherent limitations: First, it assigns equal trust to all stations, without considering anomalous data introduced by local geological anomalies, sensor performance drift, or picking deviations in the arrival of seismic phases; second, such anomalous data not only leads to an increase in the location residuals, but also significantly distorts and amplifies the area and shape of the confidence ellipse due to the coupling effect of the station's geometric configuration, resulting in a serious misjudgment of the location accuracy assessment. Existing technologies lack effective intrinsic mechanisms to identify and suppress the adverse effects of problematic stations on the error ellipse. To effectively solve this problem, there is an urgent need to provide a station-weighted method for calculating the plane location confidence ellipse for microseismic events. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method for calculating the confidence ellipse of microseismic events based on station weighting. This method has strong adaptability, high positioning accuracy and efficiency. It can suppress the amplification effect of abnormal stations or stations with unfavorable geometric layout on the confidence ellipse, enabling rapid and accurate positioning operations in microseismic monitoring scenarios. It can significantly improve the reliability of microseismic positioning results and its engineering application value.

[0005] To achieve the above objectives, the present invention provides a method for calculating the ellipse of the plane location information of microseismic events based on station weighting, comprising the following steps; Step 1: Data acquisition and initial location; statistical analysis of microseismic events recorded by the microseismic monitoring system in the target area, and screening of P-wave arrival time data received by multiple stations simultaneously; based on a given wave velocity model, the initial source location coordinates and initial earthquake time are calculated using the geometric relationship of arrival time data from multiple stations, and the arrival time residuals of each station are obtained. Step 2: Construct the design matrix and initial weight matrix for each station; linearize the arrival equations at the initial source location, combine the linearized equations of all stations, construct the design matrix including partial derivatives, and construct the initial weight matrix for each station based on the arrival residuals and picking uncertainties of each station. Step 3: Weighted Least Squares Location and Covariance Matrix Calculation; Based on the design matrix and station weight matrix, construct the normal equation of the weighted least squares method, solve for the parameter correction, and use the parameter correction to update the source location coordinates and the time of origin; At the same time, calculate the covariance matrix of the parameter estimation. Step 4: Preliminary calculation of the area of ​​the plane confidence ellipse; extract the 2×2 submatrix corresponding to the source plane coordinates from the covariance matrix, and obtain two eigenvalues ​​and the corresponding eigenvectors through eigenvalue decomposition, and then calculate the area of ​​the ellipse under the preset confidence level; Step 5: Adaptive adjustment of station weights based on the area of ​​the confidence ellipse; while keeping the design matrix unchanged, reset the weights of each station to zero, repeat steps 3 and 4, calculate the area of ​​the confidence ellipse excluding stations with zero weights, construct an impact index of stations with zero weights on the ellipse area, and adjust the weights of stations with zero weights according to the impact index to obtain the updated weight matrix. Step 6: Determine the convergence state of the iteration based on the relative change rate of the confidence ellipse area; repeat steps 3 to 4, and determine the convergence state based on the relative change rate of the confidence ellipse area. When the convergence state is reached, proceed directly to step 7; when the convergence state is not reached, repeat step 5. Step 7: Output the location results; output the final source plane location and confidence ellipse parameters.

[0006] As a preferred option, in step one, the initial source location is calculated based on the time-observation equation, as shown in formula (1); (1); In the formula, For the first When the P-wave arrives at each station The moment the earthquake struck; The preset average P-wave propagation velocity; These are the source coordinates of the microseismic event in a three-dimensional coordinate system. For the first Spatial coordinates of each station; For the first Arrival error term for each station.

[0007] As a preferred option, the process of constructing the design matrix containing partial derivatives in step two is as follows: S21: At the initial hypocenter location and the initial earthquake time At that point, according to formula (2), the equation at that time is expanded by first-order Taylor to obtain the linearized equation; (2); In the formula, To the initial focal location The theoretical calculations obtained at that time will be available; For the first Residual error upon arrival at the platform ; This is the correction amount for spatial coordinates. , , ; This is the correction amount for the moment of the earthquake. ; S22: Solve for the coordinates of the P-wave travel time relative to the source position using formulas (3), (4), and (5), respectively. partial derivatives , , ; (3); (4); (5); In the formula, The first The partial derivative coefficients of each station with respect to spatial coordinates upon arrival; From the initial focal location to the th The European-style distance between each station; S23: Construct a design matrix containing partial derivatives according to formula (6) ; (6); In the formula, ; , The number of stations participating in the positioning; ; As a preferred option, in step two, an initial weight matrix is ​​constructed according to formula (7). ; (7); In the formula, For the first The initial weight of each station, , For the first Standard deviation of P-wave pickup at each station A coefficient used to balance the effects of picking error and residual.

[0008] As a preferred option, in step three, the process of updating the source location coordinates and the time of origin using parameter corrections is as follows: S31: Construct the normal equations according to formula (8) and solve for the parameter corrections. ; (8); In the formula, This is the station weight matrix for the current iteration; It is a 4×4 normal matrix; It is a 4×1 constant term vector.

[0009] S32: Utilizing parameter correction amounts Correcting the initial hypocenter location and the initial earthquake time The coordinates of the earthquake source were obtained. and the moment the event occurred .

[0010] As a preferred option, in step three, the covariance matrix of the parameter estimation is constructed according to formula (9). ; (9); In the formula, For the mean squared estimate of the weighted residuals, , The number of unknown parameters.

[0011] As a preferred option, the process of calculating the area of ​​the ellipse under the preset information level in step four is as follows: S41: Obtain the source plane coordinates according to formula (10) The corresponding covariance submatrix ; (10); In the formula, The variance of the x-coordinate in the plane. The variance of the y-coordinate in the plane. Let x and y coordinates be the covariance. S42: According to formula (11), the covariance submatrix Eigenvalue decomposition yields two eigenvalues. and eigenvalues eigenvectors eigenvalues eigenvectors ;in, ; (11); In the formula, ; S43: Preset signal level is The major axis radius of the plane confidence ellipse is calculated according to formulas (12) and (13) respectively. minor axis radius Meanwhile, the elliptic azimuth of the plane confidence ellipse is calculated according to formula (14). ; (12); (13); (14); In the formula, For a degree of freedom of 2 and a confidence level of The critical value of the chi-square distribution; S44: Obtain the area of ​​the ellipse according to formula (15) ; (15); To effectively remove the adverse effects of outlier data and ensure positioning accuracy, the process of obtaining the updated weight matrix in step five is as follows: S51: Construct the first according to formula (16) The impact of individual reset zero-station on the elliptic area index ; (16); In the formula, The area of ​​the confidence ellipse that includes all stations; To remove the first Area of ​​the confidence ellipse after each station; S52: When Explanation of the first The individual stations have no impact on positioning accuracy, maintaining the [number] [unit / station / location] [accuracy / maintaining ... The weight of each station remains unchanged; when This indicates that the first one was removed. The area of ​​the confidence ellipse after each station decreases, reducing the first... The weight of each station, when This indicates that the first one was removed. The area of ​​the confidence ellipse increases after each station, increasing the number of stations. The weights of each station are determined, and the updated weight matrix is ​​obtained.

[0012] In order to efficiently and accurately adjust the weights of each station, in step S52 of step five, when or At that time, the first number is obtained according to formula (17). Updates on individual TV stations ; (17); In the formula, For the first The initial weight of each station; The weighting adjustment coefficient satisfies , This is the lower bound of the weights, used to avoid geometric singularities caused by the weights dropping to zero.

[0013] To ensure positioning accuracy, in step six, the convergence state is determined by comparing the relative change rate of the confidence ellipse area with the preset convergence threshold. When the formula (18) is satisfied, it is determined that the iterative convergence state has been reached. (18); In the formula, For the first The area of ​​the confidence ellipse obtained in the next iteration. For the first The area of ​​the confidence ellipse obtained in the next iteration; The preset convergence threshold is set to 1% to 5%.

[0014] This invention provides a method for calculating the planar confidence ellipse for microseismic events based on adaptive adjustment of station weights. First, cross-validation using multi-station data allows for rapid preliminary identification of the seismic source range using geometric relationships, providing reliable initial data values ​​for subsequent precise positioning, avoiding blind searches, and significantly improving computational efficiency. Next, linearization transforms the complex nonlinear problem into a solvable system of linear equations. The design of the matrix and the allocation of initial weights lay the foundation for subsequent mathematical optimization, ensuring the algorithm's stability and convergence. Furthermore, the weighted least squares method, by assigning different weights to different observation data, effectively suppresses the influence of outliers, improving positioning accuracy and robustness. The calculated covariance matrix provides a basis for subsequent error analysis. Finally, eigenvalue decomposition transforms the high-dimensional covariance matrix into an intuitive planar confidence ellipse, whose area and direction directly reflect the uncertainty of the seismic source location and the influence of station distribution, providing quantitative indicators for subsequent station optimization. Next, the relative impact of removing or changing the weights of each station on the current confidence ellipse area is quantitatively calculated, and a station influence factor is constructed as an indicator of the station's influence on the confidence ellipse area, thus achieving a scientific and objective quantification of the station's influence. By simulating the working condition of a single missing station, the ellipse area is recalculated; that is, after "removing" a station from the weight matrix, the positioning and covariance matrices are re-solved to obtain the confidence ellipse area. The amplification or reduction effect of each station on the ellipse area is evaluated, effectively quantifying the contribution of each station to positioning accuracy. Based on the influence index, the weights of unsuitable stations are adaptively reduced, while the weights of suitable stations are increased, achieving dynamic adjustment of station weights and optimizing the allocation of station resources. Through the adaptive weight optimization process, accurate and robust estimation of positioning result uncertainty is achieved, significantly improving positioning efficiency. Finally, by monitoring the rate of change of the confidence ellipse area to form a station weight iterative update mechanism, the influence of abnormal stations was effectively suppressed, providing a clear convergence criterion for the algorithm and avoiding unnecessary calculations. Finally, the positioning results and confidence ellipse were recalculated under the convergence weight, ensuring that the algorithm terminated in time after reaching the predetermined accuracy.

[0015] This method is highly adaptive, accurate, and efficient in positioning. It organically combines geometric positioning, mathematical optimization, and dynamic adjustment, which can suppress the amplification effect of abnormal stations or stations with unfavorable geometric layout on the confidence ellipse. Under the premise of ensuring no systematic shift in the source position, it significantly reduces the area of ​​the confidence ellipse and obtains a more realistic range of planar position error. It can realize rapid and accurate positioning operations in microseismic monitoring scenarios, and significantly improve the reliability and engineering application value of microseismic positioning results. Attached Figure Description

[0016] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] like Figure 1 As shown, the present invention provides a method for calculating the ellipse of the plane location information of microseismic events based on station weighting, including the following steps; Step 1: Acquisition of observation data and initial positioning; The microseismic events recorded by the microseismic monitoring system in the target area are statistically analyzed, and P-wave arrival time data received by multiple stations simultaneously are filtered. Based on a given wave velocity model, the initial source location coordinates and initial time of origin are calculated using the geometric relationship of arrival time data from multiple stations, and the arrival time residuals of each station are obtained. Step 2: Construct the design matrix and the initial weight matrix for each station; The arrival equations are linearized at the initial source location. The linearized equations of all stations are combined to construct a design matrix containing partial derivatives. The initial weight matrix of each station is constructed based on the arrival residuals and picking uncertainties of each station. Step 3: Weighted least squares localization and covariance matrix calculation; Based on the design matrix and the station weight matrix, a weighted least squares normal equation is constructed to solve for the parameter corrections for the source location and the time of earthquake. The source location coordinates and the time of earthquake are then updated using the parameter corrections. Simultaneously, the covariance matrix of the parameter estimation is calculated. Step 4: Preliminary calculation of the area of ​​the plane confidence ellipse; Extract the 2×2 submatrix corresponding to the source plane coordinates from the covariance matrix, and obtain two eigenvalues ​​and the corresponding eigenvectors through eigenvalue decomposition. Then calculate the area of ​​the ellipse under the preset information level. Step 5: Adaptive adjustment of station weights based on the area of ​​the confidence ellipse; While keeping the design matrix unchanged, reset the weights of each station to zero, repeat steps three and four, calculate the area of ​​the confidence ellipse excluding stations with zero weights, construct an influence index of stations with zero weights on the ellipse area, and adjust the weights of stations with zero weights according to the influence index to obtain the updated weight matrix. Step 6: Determine the convergence status of the iteration based on the relative rate of change of the confidence ellipse area; Repeat steps three and four, and determine the convergence state based on the relative change rate of the confidence ellipse area. When the convergence state is reached, directly execute step seven. When the convergence state is not reached, repeat step five. Step 7: Output the location results; Output the final source plane location and confidence ellipse parameters.

[0019] As a preferred option, in step one, the initial source location is calculated based on the time-observation equation, as shown in formula (1); (1); In the formula, For the first When the P-wave arrives at each station The moment the earthquake struck; The preset average P-wave propagation velocity; These are the source coordinates of the microseismic event in a three-dimensional coordinate system. For the first The spatial coordinates of each station in the same coordinate system; For the first Arrival error term for each station.

[0020] As a preferred option, the process of constructing the design matrix containing partial derivatives in step two is as follows: S21: At the initial hypocenter location and the initial earthquake time At that point, according to formula (2), the equation at that time is expanded by first-order Taylor to obtain the linearized equation; (2); In the formula, To the initial focal location The theoretical calculations obtained at that time will be available; For the first Residual error upon arrival at the platform ; This is the correction amount for spatial coordinates. , , ; This is the correction amount for the moment of the earthquake. ; S22: Solve for the coordinates of the P-wave travel time relative to the source position using formulas (3), (4), and (5), respectively. partial derivatives , , ; (3); (4); (5); In the formula, The first The partial derivative coefficients of each station with respect to spatial coordinates upon arrival; From the initial focal location to the th The European-style distance between each station; S23: Construct a design matrix containing partial derivatives according to formula (6) Design Matrix Each row corresponds to one station, and each column corresponds to... ; (6); In the formula, ; , The number of stations participating in the positioning; ; As a preferred option, in step two, an initial weight matrix is ​​constructed according to formula (7). ; (7); In the formula, For the first The initial weight of each station, , For the first Standard deviation of P-wave pickup at each station A coefficient used to balance the effects of picking error and residual.

[0021] As a preferred option, in step three, the process of updating the source location coordinates and the time of origin using parameter corrections is as follows: S31: Construct the normal equations according to formula (8) and solve for the parameter corrections. ; (8); In the formula, This is the station weight matrix for the current iteration; It is a 4×4 normal matrix; It is a 4×1 constant term vector.

[0022] S32: Utilizing parameter correction amounts Correcting the initial hypocenter location and the initial earthquake time The coordinates of the earthquake source were obtained. and the moment the event occurred .

[0023] As a preferred option, in step three, the covariance matrix of the parameter estimation is constructed according to formula (9). ; It is a 4×4 parameter covariance matrix, where the diagonal elements are the variances of each parameter and the off-diagonal elements are the covariances between parameters. (9); In the formula, For the mean squared estimate of the weighted residuals, , Number of unknown parameters, in the process of locating .

[0024] As a preferred option, the process of calculating the area of ​​the ellipse under the preset information level in step four is as follows: S41: Obtain the source plane coordinates according to formula (10) The corresponding covariance submatrix ; (10); In the formula, The variance of the x-coordinate in the plane. The variance of the y-coordinate in the plane. Let x and y coordinates be the covariance. S42: According to formula (11), the covariance submatrix Eigenvalue decomposition yields two eigenvalues. and eigenvalues eigenvectors eigenvalues eigenvectors ;in, ; (11); In the formula, ; S43: Preset signal level is The major axis radius of the plane confidence ellipse is calculated according to formulas (12) and (13) respectively. minor axis radius Meanwhile, the elliptic azimuth of the plane confidence ellipse is calculated according to formula (14). ; (12); (13); (14); In the formula, For a degree of freedom of 2 and a confidence level of The critical value of the chi-square distribution; S44: Obtain the area of ​​the ellipse according to formula (15) ; (15); To effectively remove the adverse effects of outlier data and ensure positioning accuracy, the process of obtaining the updated weight matrix in step five is as follows: S51: Construct the first according to formula (16) The impact of individual reset zero-station on the elliptic area index ; (16); In the formula, The area of ​​the confidence ellipse that includes all stations; To remove the first Area of ​​the confidence ellipse after each station; S52: When Explanation of the first The individual stations have no impact on positioning accuracy, maintaining the [number] [unit / station / location] [accuracy / maintaining ... The weight of each station remains unchanged; when This indicates that the first one was removed. The area of ​​the confidence ellipse after each station decreases, reducing the first... The weight of each station, when This indicates that the first one was removed. The area of ​​the confidence ellipse increases after each station, increasing the number of stations. The weights of each station are determined, and the updated weight matrix is ​​obtained. At the same time, it is ensured that the sum of the weights of all stations is 1.

[0025] In order to efficiently and accurately adjust the weights of each station, in step S52 of step five, when or At that time, the first number is obtained according to formula (17). Updates on individual TV stations ; (17); In the formula, For the first The initial weight of each station; The weighting adjustment coefficient satisfies , This is the lower bound of the weights, used to avoid geometric singularities caused by the weights dropping to zero.

[0026] To ensure positioning accuracy, in step six, the convergence state is determined by comparing the relative change rate of the confidence ellipse area with the preset convergence threshold. When the formula (18) is satisfied, it is determined that the iterative convergence state has been reached. (18); In the formula, For the first The area of ​​the confidence ellipse obtained in the next iteration. For the first The area of ​​the confidence ellipse obtained in the next iteration; The preset convergence threshold is set to 1% to 5%.

[0027] This invention provides a method for calculating the planar confidence ellipse for microseismic events based on adaptive adjustment of station weights. First, cross-validation using multi-station data allows for rapid preliminary identification of the seismic source range using geometric relationships, providing reliable initial data values ​​for subsequent precise positioning, avoiding blind searches, and significantly improving computational efficiency. Next, linearization transforms the complex nonlinear problem into a solvable system of linear equations. The design of the matrix and the allocation of initial weights lay the foundation for subsequent mathematical optimization, ensuring the algorithm's stability and convergence. Furthermore, the weighted least squares method, by assigning different weights to different observation data, effectively suppresses the influence of outliers, improving positioning accuracy and robustness. The calculated covariance matrix provides a basis for subsequent error analysis. Finally, eigenvalue decomposition transforms the high-dimensional covariance matrix into an intuitive planar confidence ellipse, whose area and direction directly reflect the uncertainty of the seismic source location and the influence of station distribution, providing quantitative indicators for subsequent station optimization. Next, the relative impact of removing or changing the weights of each station on the current confidence ellipse area is quantitatively calculated, and a station influence factor is constructed as an indicator of the station's influence on the confidence ellipse area, thus achieving a scientific and objective quantification of the station's influence. By simulating the working condition of a single missing station, the ellipse area is recalculated; that is, after "removing" a station from the weight matrix, the positioning and covariance matrices are re-solved to obtain the confidence ellipse area. The amplification or reduction effect of each station on the ellipse area is evaluated, effectively quantifying the contribution of each station to positioning accuracy. Based on the influence index, the weights of unsuitable stations are adaptively reduced, while the weights of suitable stations are increased, achieving dynamic adjustment of station weights and optimizing the allocation of station resources. Through the adaptive weight optimization process, accurate and robust estimation of positioning result uncertainty is achieved, significantly improving positioning efficiency. Finally, by monitoring the rate of change of the confidence ellipse area to form a station weight iterative update mechanism, the influence of abnormal stations was effectively suppressed, providing a clear convergence criterion for the algorithm and avoiding unnecessary calculations. Finally, the positioning results and confidence ellipse were recalculated under the convergence weight, ensuring that the algorithm terminated in time after reaching the predetermined accuracy.

[0028] This method is highly adaptive, accurate, and efficient in positioning. It organically combines geometric positioning, mathematical optimization, and dynamic adjustment, which can suppress the amplification effect of abnormal stations or stations with unfavorable geometric layout on the confidence ellipse. Under the premise of ensuring no systematic shift in the source position, it significantly reduces the area of ​​the confidence ellipse and obtains a more realistic range of planar position error. It can realize rapid and accurate positioning operations in microseismic monitoring scenarios, and significantly improve the reliability and engineering application value of microseismic positioning results.

Claims

1. A method for calculating the ellipse of a plane location signal for microseismic events based on station weighting, characterized in that, Includes the following steps; Step 1: Acquisition of observation data and initial positioning; The microseismic events recorded by the microseismic monitoring system in the target area are statistically analyzed, and P-wave arrival time data received by multiple stations simultaneously are filtered. Based on a given wave velocity model, the initial source location coordinates and initial time of origin are calculated using the geometric relationship of arrival time data from multiple stations, and the arrival time residuals of each station are obtained. Step 2: Construct the design matrix and initial weight matrix for each station; linearize the arrival equations at the initial source location, combine the linearized equations of all stations, construct the design matrix including partial derivatives, and construct the initial weight matrix for each station based on the arrival residuals and picking uncertainties of each station. Step 3: Weighted Least Squares Location and Covariance Matrix Calculation; Based on the design matrix and station weight matrix, construct the normal equation of the weighted least squares method, solve for the parameter correction, and use the parameter correction to update the source location coordinates and the time of origin; At the same time, calculate the covariance matrix of the parameter estimation. Step 4: Preliminary calculation of the area of ​​the plane confidence ellipse; extract the 2×2 submatrix corresponding to the source plane coordinates from the covariance matrix, and obtain two eigenvalues ​​and the corresponding eigenvectors through eigenvalue decomposition, and then calculate the area of ​​the ellipse under the preset confidence level; Step 5: Adaptive adjustment of station weights based on the area of ​​the confidence ellipse; while keeping the design matrix unchanged, reset the weights of each station to zero, repeat steps 3 and 4, calculate the area of ​​the confidence ellipse excluding stations with zero weights, construct an impact index of stations with zero weights on the ellipse area, and adjust the weights of stations with zero weights according to the impact index to obtain the updated weight matrix. Step 6: Determine the convergence status of the iteration based on the relative rate of change of the confidence ellipse area; Repeat steps three and four, and determine the convergence state based on the relative change rate of the confidence ellipse area. When the convergence state is reached, directly execute step seven. When the convergence state is not reached, repeat step five. Step 7: Output the location results; output the final source plane location and confidence ellipse parameters.

2. The method for calculating the ellipse of a microseismic event plane location signal based on station weighting according to claim 1, characterized in that, In step one, the initial source location is calculated according to the time observation equation, as shown in formula (1); （1）； In the formula, For the first When the P-wave arrives at each station The moment the earthquake struck; The preset average P-wave propagation velocity; These are the source coordinates of the microseismic event in a three-dimensional coordinate system. For the first Spatial coordinates of each station; For the first Arrival error term for each station.

3. The method for calculating the ellipse of a microseismic event plane location signal based on station weighting as described in claim 1 or 2, characterized in that, In step two, the process of constructing the design matrix containing partial derivatives is as follows: S21: At the initial hypocenter location and the initial earthquake time At that point, according to formula (2), the equation at that time is expanded by first-order Taylor to obtain the linearized equation; （2）； In the formula, To the initial focal location The theoretical calculations obtained at that time will be available; For the first Residual error upon arrival at the platform ; This is the correction amount for spatial coordinates. , , ; This is the correction amount for the moment of the earthquake. ; S22: Solve for the coordinates of the P-wave travel time relative to the source position using formulas (3), (4), and (5), respectively. partial derivatives , , ; （3）； （4）； （5）； In the formula, The first The partial derivative coefficients of each station with respect to spatial coordinates upon arrival; From the initial focal location to the th The European-style distance between each station; S23: Construct a design matrix containing partial derivatives according to formula (6) ; （6）； In the formula, ; , The number of stations participating in the positioning; .

4. The method for calculating the ellipse of a microseismic event plane location signal based on station weighting according to claim 3, characterized in that, In step two, the initial weight matrix is ​​constructed according to formula (7). ; （7）； In the formula, For the first The initial weight of each station, , For the first Standard deviation of P-wave pickup at each station A coefficient used to balance the effects of picking error and residual.

5. The method for calculating the ellipse of a microseismic event plane location signal based on station weighting according to claim 4, characterized in that, In step three, the process of updating the source location coordinates and the time of origin using parameter corrections is as follows: S31: Construct the normal equations according to formula (8) and solve for the parameter corrections. ; （8）； In the formula, This is the station weight matrix for the current iteration; It is a 4×4 normal matrix; It is a 4×1 constant term vector; S32: Utilizing parameter correction amounts Correcting the initial hypocenter location and the initial earthquake time The coordinates of the earthquake source were obtained. and the moment the event occurred .

6. The method for calculating the ellipse of a microseismic event plane location signal based on station weighting according to claim 4, characterized in that, In step three, the covariance matrix of the parameter estimation is constructed according to formula (9). ; （9）； In the formula, For the mean squared estimate of the weighted residuals, , The number of unknown parameters.

7. The method for calculating the ellipse of a microseismic event plane location signal based on station weighting according to claim 4, characterized in that, In step four, the process of calculating the area of ​​the ellipse under the preset information level is as follows: S41: Obtain the source plane coordinates according to formula (10) The corresponding covariance submatrix ; （10）； In the formula, The variance of the x-coordinate in the plane. The variance of the y-coordinate in the plane. Let x and y coordinates be the covariance. S42: According to formula (11), the covariance submatrix Eigenvalue decomposition yields two eigenvalues. and eigenvalues eigenvectors eigenvalues eigenvectors ;in, ; （11）； In the formula, ; S43: Preset signal level is The major axis radius of the plane confidence ellipse is calculated according to formulas (12) and (13) respectively. minor axis radius Meanwhile, the elliptic azimuth of the plane confidence ellipse is calculated according to formula (14). ; （12）； （13）； （14）； In the formula, For a degree of freedom of 2 and a confidence level of The critical value of the chi-square distribution; S44: Obtain the area of ​​the ellipse according to formula (15) ; （15）。 8. The method for calculating the ellipse of a microseismic event plane location signal based on station weighting as described in claim 7, characterized in that, In step five, the process of obtaining the updated weight matrix is ​​as follows: S51: Construct the first according to formula (16) The impact of individual reset zero-station on the elliptic area index ; （16）； In the formula, The area of ​​the confidence ellipse that includes all stations; To remove the first Area of ​​the confidence ellipse after each station; S52: When Explanation of the first The individual stations have no impact on positioning accuracy, maintaining the [number] [unit / station / location] [accuracy / maintaining ... The weight of each station remains unchanged; when This indicates that the first one was removed. The area of ​​the confidence ellipse after each station decreases, reducing the first... The weight of each station, when This indicates that the first one was removed. The area of ​​the confidence ellipse increases after each station, increasing the number of stations. The weights of each station are determined, and the updated weight matrix is ​​obtained.

9. The method for calculating the ellipse of a microseismic event plane location signal based on station weighting as described in claim 8, characterized in that, In step five, S52, when or At that time, the first number is obtained according to formula (17). Updates on individual TV stations ; （17）； In the formula, For the first The initial weight of each station; The weighting adjustment coefficient satisfies , This is the lower bound of the weights, used to avoid geometric singularities caused by the weights dropping to zero.

10. The method for calculating the ellipse of a microseismic event plane location signal based on station weighting as described in claim 9, characterized in that, In step six, the convergence state is determined by comparing the relative change rate of the confidence ellipse area with the preset convergence threshold. When the formula (18) is satisfied, it is determined that the iterative convergence state has been reached. （18）； In the formula, For the first The area of ​​the confidence ellipse obtained in the next iteration. For the first The area of ​​the confidence ellipse obtained in the next iteration; The preset convergence threshold is set to 1% to 5%.

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