Robust positioning method for micro-seismic source

Abstract

The invention relates to a micro-seismic source robust positioning method, which comprises the following steps of: obtaining an optimization equation, sequentially substituting a plurality of parts of monitoring data into the optimization equation, obtaining a plurality of micro-seismic source approximate solutions, obtaining an initial clustering center and a plurality of clustering centers, obtaining a mahalanobis distance of each clustering center, obtaining an abnormal value threshold value, and finally obtaining a micro-seismic source robust positioning result. Screening to obtain a plurality of effective clustering centers, calculating an aggregation factor, and obtaining a final clustering positioning result, namely a three-dimensional position coordinate of the micro-seismic source; the optimization equation is obtained based on an arrival time difference model, and the used data is the arrival time difference of the first arrival waves, so that the method does not depend on the pickup precision of the arrival time data of the first arrival waves; the clustering center is obtained through calculation instead of a random numerical value in the prior art, and representativeness and cluster density are dense enough during clustering, so that the positioning precision is high. Therefore, the design does not depend on the pickup precision of the arrival time data of the first arrival waves, and the positioning precision of the micro-seismic source is high.

Description

TECHNICAL FIELD

[0001] The application relates to a seismic source positioning method and belongs to the technical field of microseismic monitoring. BACKGROUND

[0002] Microseismic monitoring technology is a core means for mine safety monitoring, geological engineering stability analysis and oil and gas reservoir evaluation. Through collection and analysis of elastic wave signals generated by rock mass rupture, the stress state inside the rock mass can be inverted, the rupture position can be located, and the stability level of the rock mass can be evaluated. In this technical system, the accuracy of seismic source positioning directly determines the application effect of microseismic monitoring technology. Accurate seismic source position information can not only reveal the spatial distribution law of rock mass rupture, but also provide key decision basis for disaster prevention and early warning.

[0003] A mine microseismic source positioning method and device and a microseismic event inversion method are disclosed in Chinese Patent No. 202510661850.4, filed on May 22, 2025, which belong to the technical field of mine microseismic monitoring. The method comprises: collecting microseismic signal data in a mine in real time and performing flow reading; pre-processing the collected microseismic signal data; preliminarily screening the microseismic waveform window based on the initial signal-to-noise ratio, the end signal-to-noise ratio and the main frequency characteristics, and combining the feature screening algorithm; picking up the initial time and the end time; and positioning the microseismic source by combining the least square method and the Geiger algorithm. Although this design can complete the positioning of the microseismic source based on the initial arrival time data, it still has the following defects:

[0004] In the design, although the feature screening algorithm is used to screen the initial arrival time data to exclude data interference caused by interference sources such as blasting, there are still uncertainties in the wave velocity model caused by the heterogeneity of the rock mass, and the interference of the initial arrival time data caused by the abnormal wave propagation path in the complex geological conditions. These interferences will result in low picking accuracy of the initial arrival time data, thereby resulting in low positioning accuracy of the microseismic source.

[0005] The information disclosed in this background section is intended only to increase an understanding of the general context of the present application, and it should not be taken as an acknowledgement or any form of suggestion that this information forms prior art that is already widely known in the art before the filing date of the present application. SUMMARY

[0006] The purpose of the present application is to overcome the defects and problems of the prior art, such as the dependence on the picking accuracy of the initial arrival time data and the low positioning accuracy of the microseismic source, and to provide a microseismic source robust positioning method with high positioning accuracy of the microseismic source without dependence on the picking accuracy of the initial arrival time data.

[0007] To achieve the above purpose, the technical solution of the present application is:

[0008] A microseismic source robust positioning method, the method comprising the following steps:

[0009] First step: first, get the travel time difference mathematical model according to the specified formula, then get the optimization model according to the travel time difference mathematical model, and then construct the four four combination four element quadratic nonlinear optimization equation according to the optimization model;

[0010] Second step: first, get multiple monitoring data according to multiple microseismic network measuring points, each monitoring data including P wave first wave arrival time data and three-dimensional position coordinates of the microseismic network measuring point, then sequentially bring the multiple monitoring data into the four four combination four element quadratic nonlinear optimization equation, and then calculate the multiple microseismic source approximate solutions through the genetic algorithm;

[0011] Third step: first, calculate the multiple microseismic source approximate solutions according to the specified screening algorithm, and then get the initial clustering center;

[0012] Fourth step: first, bring the initial clustering center into the clustering algorithm, and then get a plurality of clustering centers;

[0013] Fifth step: first, construct the clustering center matrix according to the plurality of clustering centers, then get the reference center vector according to the clustering center matrix, then get the difference value matrix according to the reference center vector and the clustering center matrix, then get the median absolute deviation according to the reference center vector and the clustering center matrix, then get the scale difference value matrix according to the median absolute deviation and the difference value matrix, and then get the Mahalanobis distance of each clustering center according to the scale difference value matrix;

[0014] Sixth step: first, get the median absolute deviation of the Mahalanobis distance according to the Mahalanobis distance, and then get the outlier threshold according to the Mahalanobis distance and the median absolute deviation of the Mahalanobis distance;

[0015] Seventh step: first, screen the Mahalanobis distance of each clustering center according to the outlier threshold, and then get a plurality of effective clustering centers;

[0016] Eighth step: first, calculate the aggregation factor of the plurality of clustering centers, and then calculate the final clustering positioning result, i.e. the three-dimensional position coordinates of the microseismic source, according to the aggregation factor and the clustering center.

[0017] In the fifth step, the clustering center matrix is constructed according to the vector coordinates of the plurality of clustering centers;

[0018] The reference center vector is obtained according to the clustering center matrix, which means that the median is calculated according to the clustering center matrix, and the obtained median is composed of the reference center vector;

[0019] The difference matrix obtained according to the reference center vector and the cluster center matrix refers to subtracting the reference center vector from the cluster center matrix to obtain the difference matrix;

[0020] The median absolute deviation obtained according to the reference center vector and the cluster center matrix refers to subtracting the reference center vector from the vector coordinates of the cluster centers in the cluster center matrix to obtain absolute deviations, then obtaining the median of the absolute deviations, then multiplying the median of the absolute deviations by a deviation estimation constant to obtain the median absolute deviation;

[0021] The scale difference matrix obtained according to the median absolute deviation and the difference matrix refers to point dividing the median absolute deviation by the difference matrix to obtain the scale difference matrix;

[0022] The Mahalanobis distance of each cluster center obtained according to the scale difference matrix refers to sequentially performing matrix element squaring, row summation and row square root on the scale difference matrix to obtain the Mahalanobis distance.

[0023] In the fifth step, the vector coordinates of the cluster centers refer to:

[0024] ;

[0025] In the formula, is the vector coordinate of the i-th cluster center, is the vector coordinate of the j-th cluster center, is the vector coordinate of the k-th cluster center, is the vector coordinate of the l-th cluster center, is the vector coordinate of the m-th cluster center, is the vector coordinate of the n-th cluster center, is the vector coordinate of the o-th cluster center, is the vector coordinate of the p-th cluster center, is the vector coordinate of the q-th cluster center, is the vector coordinate of the r-th cluster center, is the vector coordinate of the s-th cluster center, is the total number of cluster centers;

[0026] The cluster center matrix constructed according to the vector coordinates of the cluster centers refers to:

[0027] ;

[0028] In the formula, is the cluster center matrix;

[0029] The reference center vector composed of the medians of the cluster center matrix refers to calculating the medians of the cluster center matrix by column, and then obtaining the reference center vector composed of the medians;

[0030] ;

[0031] In the formula, is the reference center vector, Cluster center matrix of The median of the coordinates, Cluster center matrix of The median of the coordinates, Cluster center matrix of The median of the coordinates;

[0032] The step of subtracting the reference center vector from the cluster center matrix to obtain the difference matrix refers to:

[0033] ;

[0034] In the formula, It is a difference matrix;

[0035] The absolute deviation is obtained by subtracting the reference center vector from the multiple vector coordinates of the cluster centers in the cluster center matrix. The median of the absolute deviation is then obtained based on the absolute deviation. Finally, the median of the absolute deviation is multiplied by the deviation estimation constant to obtain the median. The absolute deviation refers to:

[0036] ;

[0037] In the formula, This represents the absolute deviation of the median. This is the deviation estimation constant;

[0038] The method of using the difference matrix to divide the absolute deviation of the median to obtain the scaling difference matrix refers to:

[0039] ;

[0040] In the formula, This is the scale difference matrix;

[0041] The process of squaring the matrix elements, summing the rows, and taking the square root of the rows to obtain the Mahalanobis distance refers to:

[0042] ;

[0043] In the formula, This is the Mahalanobis distance.

[0044] In the sixth step, the median absolute deviation of the Mahalanobis distance obtained based on the Mahalanobis distance refers to:

[0045] ;

[0046] In the formula, This represents the median absolute deviation of the Mahalanobis distance. To calculate the median absolute deviation of the Mahalanobis distance;

[0047] The outlier threshold obtained based on Mahalanobis distance and the median absolute deviation of Mahalanobis distance refers to:

[0048] ;

[0049] In the formula, The threshold for outliers. The median of the Mahalanobis distance. For threshold coefficient, This is the deviation estimation constant.

[0050] In the seventh step, the step of filtering each cluster center based on the Mahalanobis distance of the outlier threshold to obtain several effective cluster centers means: comparing the Mahalanobis distance of all cluster centers with the outlier threshold in turn, retaining the cluster centers whose Mahalanobis distance is less than the outlier threshold, and then obtaining several effective cluster centers.

[0051] In the eighth step, calculating the clustering factor of several cluster centers refers to:

[0052] ;

[0053] In the formula, As an aggregation factor, For the first Cluster centers, for The number of approximate source solutions of the cluster group The total number of cluster centers;

[0054] The final clustering location result calculated based on the clustering factor and cluster center refers to:

[0055] ;

[0056] In the formula, This represents the final clustering and localization result.

[0057] In the third step, the specified screening algorithm refers to the tribal competition and member cooperation algorithm.

[0058] In the fourth step, the clustering algorithm refers to the standard KMeans algorithm.

[0059] In the first step, the specified formula refers to the seismic wave propagation theory formula, which is:

[0060] ;

[0061] In the formula, For the first The absolute spatial distance from each microseismic network measuring point to the microseismic source. For the first Coordinates of the microseismic network monitoring points The coordinates of the microseismic source are given. , This represents the total number of microseismic network monitoring points that received valid seismic wave signals. For seismic wave velocity, For the first Data on the arrival time of the first P-wave recorded by a microseismic network monitoring point;

[0062] ;

[0063] In the formula, For the first The microseismic network monitoring points and the first The difference in arrival time of the first P-wave recorded by a microseismic network monitoring point. For the first The arrival time of the first P-wave recorded by a microseismic network. For the first The location of the microseismic network monitoring points from the microseismic source and the first The difference in spatial distance between each microseismic network measuring point and the location of the microseismic source. For the first The absolute value of the spatial distance from each microseismic network measuring point to the microseismic source. .

[0064] In the first step, the time difference mathematical model refers to:

[0065] ;

[0066] In the formula, To obtain a mathematical model of time difference;

[0067] The optimization model refers to:

[0068] ;

[0069] In the formula, For the optimization model, for The number of non-repeating combinations of any two microseismic network points in a given microseismic network. for The corresponding time difference data, for The difference in corresponding spatial distances.

[0070] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0071] 1. A robust microseismic source localization method according to the present invention includes the following steps: Step 1: First, obtain an optimal model based on the time difference mathematical model, and then construct a four-variable quadratic nonlinear optimal equation based on the optimal model; Step 2: First, obtain multiple sets of monitoring data, and then sequentially substitute the multiple sets of monitoring data into the aforementioned optimal equation to obtain multiple approximate solutions for microseismic sources; Step 3: First, calculate the multiple approximate solutions for microseismic sources according to a specified screening algorithm to obtain initial cluster centers; Step 4: First, substitute the initial cluster centers into the clustering algorithm to obtain several clusters. The fifth step involves constructing a cluster center matrix based on several cluster centers, then obtaining the difference matrix, median absolute deviation, scale difference matrix, and Mahalanobis distance for each cluster center; the sixth step involves obtaining an outlier threshold based on the Mahalanobis distance; the seventh step involves filtering the Mahalanobis distance of each cluster center based on the outlier threshold, then obtaining several effective cluster centers; the eighth step involves calculating the clustering factor for several cluster centers, then calculating the final clustering location result based on the clustering factor and the cluster centers, i.e., the three-dimensional location coordinates of the microseismic source. The advantages of this invention also include:

[0072] Firstly, this invention can obtain the three-dimensional position coordinates of the microseismic source;

[0073] Secondly, this invention calculates the microseismic source based on the arrival time difference mathematical model. The arrival time difference mathematical model uses the arrival time difference of the first arrival wave from multiple monitoring data, rather than the arrival time of the first arrival wave. Therefore, it does not depend on the picking accuracy of the arrival time data of the first arrival wave. Thus, it can effectively deal with the location anomalies caused by the picking error of the first arrival wave of the seismic wave, the local inaccuracy of the velocity model, or the mixing of non-tectonic events (such as blasting and collapse), and has excellent anti-interference ability.

[0074] Thirdly, traditional clustering algorithms use random numbers as cluster centers, while the cluster centers of this invention are calculated based on monitoring data, resulting in higher positioning accuracy. Furthermore, subsequent screening based on outlier thresholds removes abnormal cluster centers, preventing the positioning results from being "skewed" and making the final determined epicenter closer to the true location, thus improving the robustness of the positioning process. Finally, by assigning weights to the cluster centers through clustering factors, the final clustering positioning result is obtained, further improving the positioning accuracy.

[0075] Fourthly, the positioning error of this invention is approximately 28-35 meters, which is far smaller than the error of several kilometers or even tens of kilometers in the prior art;

[0076] Therefore, this invention does not rely on the accuracy of the first arrival wave data and has high accuracy in locating the microseismic source.

[0077] 2. In the robust microseismic source localization method of this invention, in the fifth step, a cluster center matrix is ​​first constructed based on the vector coordinates of several cluster centers. Then, the median is calculated column-wise, a reference center vector is obtained, followed by the difference matrix and the absolute deviation of the median, then the scale difference matrix, and finally the Mahalanobis distance. This facilitates subsequent screening of cluster centers. In application, the median collapse point is 50%, meaning that more than 50% of the cluster centers need to be outliers for the median to be considered anomaly. The absolute deviation of the median is calculated based on the median and also has a high tolerance for outliers. Even when there are as many as 50% outliers in the cluster center data, this method can still provide a reasonable localization estimate, thus greatly improving the reliability of this method under complex geological conditions or noisy observation environments. At the same time, the absolute deviation of the median standardizes the coordinates of the cluster centers. In cases where different coordinate axes may have different degrees of dispersion, this avoids the influence of a dimension with a large degree of dispersion on the subsequent calculation of the Mahalanobis distance, thereby achieving a fairer and more accurate outlier measurement in multidimensional space. Therefore, this invention has strong reliability.

[0078] 3. In the robust microseismic source localization method of the present invention, in the sixth step, the median absolute deviation of the Mahalanobis distance is obtained based on the Mahalanobis distance, and then an outlier threshold is obtained based on the median and the median absolute deviation of the Mahalanobis distance. Then, cluster centers are screened based on the outlier threshold. In application, since the median and median deviation in the fifth step have high tolerance for outliers, the Mahalanobis distance and the outlier threshold themselves are not affected by a few abnormal cluster centers, thus making the outlier threshold accurate. Furthermore, the outlier threshold is dynamically calculated, thus enabling accurate and robust identification of abnormal cluster centers. Therefore, the anomaly identification effect of the present invention is good.

[0079] 4. In the robust microseismic source localization method of this invention, in step eight, the clustering factor is first obtained, and then the final clustering localization result is calculated based on the clustering factor and the cluster centers. In application, since the location of the cluster centers and the number of their associated clusters reflect the spatial clustering density and hit probability of the source approximation solution, simply calculating the arithmetic mean of the effective cluster centers cannot accurately express all the distribution information of the effective cluster centers. Therefore, by assigning weights to the cluster centers through the clustering factor, the final clustering localization result is obtained based on the clustering density and hit probability, further improving the localization accuracy. Therefore, this invention has a better effect on the distribution processing of cluster centers.

[0080] 5. In the robust microseismic source localization method of this invention, the specified screening algorithm in the three steps refers to the tribal competition and member cooperation algorithm, and the clustering algorithm in the fourth step refers to the standard KMeans algorithm. In application, the tribal competition and member cooperation algorithm is first used to calculate a large number of approximate source solutions. Then, the globally optimal individual obtained is used as the initial cluster center. The initial cluster center is then input into the standard KMeans algorithm to obtain several cluster centers. In existing technologies, the initial cluster center is a spatially random location, which leads to unreliable clustering results. That is, the representativeness and cluster density of the final cluster centers are insufficient, resulting in overly dispersed clusters, failing to achieve the true purpose of clustering. Furthermore, when the data dimension is high, the results obtained by the standard KMeans algorithm become highly sensitive to initial conditions, minor data perturbations, or parameter settings, leading to inconsistent and unreliable clustering results. The tribal competition and member cooperation algorithm, however, possesses significant global optimization characteristics and excellent ability to avoid local optima. Therefore, the representativeness of the initial cluster centers and the cluster density are sufficiently dense, significantly improving clustering accuracy and quality. This gives the standard KMeans algorithm strong robustness and stability during clustering, enhancing its ability to handle complex data structures. Compared to directly calculating a large number of approximate solutions for seismic sources, using the standard KMeans algorithm requires less computation, effectively improving computational efficiency. Therefore, the clustering calculation effect of this invention is better.

[0081] 6. In the robust microseismic source location method of this invention, in the first step, a time difference mathematical model is obtained based on the seismic wave propagation theory formula. Then, an optimal model is obtained based on the time difference data model. Next, a four-variable quadratic nonlinear optimal equation is constructed based on the optimal model. In application, the time difference mathematical model does not rely on the accuracy of the first arrival wave arrival time data, but rather uses the relative time difference of the wave arrival at different microseismic network measurement points for location. The optimal model is obtained through the time difference model, and its minimum value is optimized. The closer it is to zero, the closer the independent variable (the three-dimensional coordinates of the source approximation solution) is to the true microseismic source coordinates. That is, the subsequent multiple microseismic source approximation solutions are close to the true microseismic source coordinates, thus improving the accuracy of subsequent microseismic source coordinate calculations. Therefore, this invention has high calculation accuracy. Attached Figure Description

[0082] Figure 1 This is a schematic diagram illustrating the application of the present invention.

[0083] Figure 2 This is a flowchart of the present invention. Detailed Implementation

[0084] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0085] Please seeFigure 1 — Figure 2 A robust method for locating microseismic sources, the method comprising the following steps:

[0086] Step 1: First, obtain the time difference mathematical model based on the specified formula, then obtain the optimization model based on the time difference mathematical model, and finally construct a four-variable quadratic nonlinear optimization equation based on the optimization model.

[0087] The second step is to obtain multiple sets of monitoring data based on multiple microseismic network measuring points. Each set of monitoring data includes the arrival time data of the first P wave and the three-dimensional position coordinates of the microseismic network measuring points. Then, the multiple sets of monitoring data are successively substituted into the four-variable quadratic nonlinear optimization equation with four-four combinations. Finally, multiple approximate solutions of the microseismic source are obtained by calculating through a genetic algorithm.

[0088] Step 3: First, calculate the approximate solutions of multiple microseismic sources according to the specified screening algorithm, and then obtain the initial cluster centers;

[0089] Step 4: First, input the initial cluster centers into the clustering algorithm to obtain several cluster centers;

[0090] Step 5: First, construct a cluster center matrix based on several cluster centers. Then, obtain the reference center vector based on the cluster center matrix. Next, obtain the difference matrix based on the reference center vector and the cluster center matrix. Then, obtain the median absolute deviation based on the reference center vector and the cluster center matrix. Next, obtain the scale difference matrix based on the median absolute deviation and the difference matrix. Finally, obtain the Mahalanobis distance of each cluster center based on the scale difference matrix.

[0091] Step 6: First, obtain the median absolute deviation of the Mahalanobis distance based on the Mahalanobis distance, and then obtain the outlier threshold based on the Mahalanobis distance and the median absolute deviation of the Mahalanobis distance.

[0092] Step 7: First, filter the Mahalanobis distance of each cluster center based on the outlier threshold, and then obtain a number of effective cluster centers;

[0093] Step 8: First, calculate the clustering factor of several cluster centers, and then calculate the final clustering location result based on the clustering factor and cluster centers, that is, the three-dimensional location coordinates of the microseismic source.

[0094] In the fifth step, constructing a cluster center matrix based on several cluster centers means: constructing a cluster center matrix based on the vector coordinates of several cluster centers;

[0095] The process of obtaining the reference center vector based on the cluster center matrix refers to: first calculating the median of the cluster center matrix column by column, and then using the obtained medians to form the reference center vector;

[0096] The method of obtaining the difference matrix based on the reference center vector and the cluster center matrix means: subtracting the reference center vector from the cluster center matrix to obtain the difference matrix.

[0097] The method of obtaining the absolute deviation of the median based on the reference center vector and the cluster center matrix refers to: first, subtracting the reference center vector from the multiple vector coordinates of the cluster centers in the cluster center matrix to obtain the absolute deviation; then, obtaining the median of the absolute deviation based on the absolute deviation; and finally, multiplying the median of the absolute deviation by the deviation estimation constant to obtain the absolute deviation of the median.

[0098] The statement that the scale difference matrix is ​​obtained based on the absolute deviation of the median and the difference matrix means that the scale difference matrix is ​​obtained by dividing the absolute deviation of the median by the difference matrix.

[0099] The method of obtaining the Mahalanobis distance of each cluster center based on the scale difference matrix refers to: first, squaring the matrix elements, summing the rows, and taking the square root of the rows in sequence, and then obtaining the Mahalanobis distance.

[0100] In the fifth step, the vector coordinates of the cluster centers refer to:

[0101] ;

[0102] In the formula, For the first The vector coordinates of the cluster centers For the first Cluster centers coordinate, For the first Cluster centers coordinate, For the first Cluster centers coordinate, The total number of cluster centers;

[0103] The construction of a cluster center matrix based on the vector coordinates of several cluster centers refers to:

[0104] ;

[0105] In the formula, This is the cluster center matrix;

[0106] The step of calculating the median of the cluster center matrix column by column, and then using the obtained medians to form the reference center vector, refers to:

[0107] ;

[0108] In the formula, As the reference center vector, Cluster center matrix of The median of the coordinates, Cluster center matrix of The median of the coordinates, Cluster center matrix of The median of the coordinates;

[0109] The step of subtracting the reference center vector from the cluster center matrix to obtain the difference matrix refers to:

[0110] ;

[0111] In the formula, It is a difference matrix;

[0112] The absolute deviation is obtained by subtracting the reference center vector from the multiple vector coordinates of the cluster centers in the cluster center matrix. The median of the absolute deviation is then obtained based on the absolute deviation. Finally, the median of the absolute deviation is multiplied by the deviation estimation constant to obtain the median. The absolute deviation refers to:

[0113] ;

[0114] In the formula, This represents the absolute deviation of the median. This is the deviation estimation constant;

[0115] The method of using the difference matrix to divide the absolute deviation of the median to obtain the scaling difference matrix refers to:

[0116] ;

[0117] In the formula, This is the scale difference matrix;

[0118] The process of squaring the matrix elements, summing the rows, and taking the square root of the rows to obtain the Mahalanobis distance refers to:

[0119] ;

[0120] In the formula, This is the Mahalanobis distance.

[0121] In the sixth step, the median absolute deviation of the Mahalanobis distance obtained based on the Mahalanobis distance refers to:

[0122] ;

[0123] In the formula, This represents the median absolute deviation of the Mahalanobis distance. To calculate the median absolute deviation of the Mahalanobis distance;

[0124] The outlier threshold obtained based on Mahalanobis distance and the median absolute deviation of Mahalanobis distance refers to:

[0125] ;

[0126] In the formula, The threshold for outliers. The median of the Mahalanobis distance. For threshold coefficient, This is the deviation estimation constant.

[0127] In the seventh step, the step of filtering each cluster center based on the Mahalanobis distance of the outlier threshold to obtain several effective cluster centers means: comparing the Mahalanobis distance of all cluster centers with the outlier threshold in turn, retaining the cluster centers whose Mahalanobis distance is less than the outlier threshold, and then obtaining several effective cluster centers.

[0128] In the eighth step, calculating the clustering factor of several cluster centers refers to:

[0129] ;

[0130] In the formula, As an aggregation factor, For the first Cluster centers, for The number of approximate source solutions of the cluster group The total number of cluster centers;

[0131] The final clustering location result calculated based on the clustering factor and cluster center refers to:

[0132] ;

[0133] In the formula, This represents the final clustering and localization result.

[0134] In the third step, the specified screening algorithm refers to the tribal competition and member cooperation algorithm.

[0135] In the fourth step, the clustering algorithm refers to the standard KMeans algorithm.

[0136] In the first step, the specified formula refers to the seismic wave propagation theory formula, which is:

[0137] ;

[0138] In the formula, For the first The absolute spatial distance from each microseismic network measuring point to the microseismic source. For the first Coordinates of the microseismic network monitoring points The coordinates of the microseismic source are given. , This represents the total number of microseismic network monitoring points that received valid seismic wave signals. For seismic wave velocity, For the first Data on the arrival time of the first P-wave recorded by a microseismic network monitoring point;

[0139] ;

[0140] In the formula, For the first The microseismic network monitoring points and the first The difference in arrival time of the first P-wave recorded by a microseismic network monitoring point. For the first The arrival time of the first P-wave recorded by a microseismic network. For the first The location of the microseismic network monitoring points from the microseismic source and the first The difference in spatial distance between each microseismic network measuring point and the location of the microseismic source. For the first The absolute value of the spatial distance from each microseismic network measuring point to the microseismic source. .

[0141] In the first step, the time difference mathematical model refers to:

[0142] ;

[0143] In the formula, To obtain a mathematical model of time difference;

[0144] The optimization model refers to:

[0145] ;

[0146] In the formula, For the optimization model, for The number of non-repeating combinations of any two microseismic network points in a given microseismic network. for The corresponding time difference data, for The difference in corresponding spatial distances.

[0147] The following are supplementary descriptions of the present invention:

[0148] The microseismic monitoring technology described in this invention refers to a geophysical detection technology that locates the seismic source and analyzes the stability of the rock mass by capturing the weak seismic waves released when underground rock masses fracture.

[0149] Example 1:

[0150] Please see Figure 1 — Figure 2 A robust method for locating microseismic sources, the method comprising the following steps:

[0151] Step 1: First, obtain the time difference mathematical model based on the specified formula, then obtain the optimization model based on the time difference mathematical model, and finally construct a four-variable quadratic nonlinear optimization equation based on the optimization model.

[0152] The second step is to obtain multiple sets of monitoring data based on multiple microseismic network measuring points. Each set of monitoring data includes the arrival time data of the first P wave and the three-dimensional position coordinates of the microseismic network measuring points. Then, the multiple sets of monitoring data are successively substituted into the four-variable quadratic nonlinear optimization equation with four-four combinations. Finally, multiple approximate solutions of the microseismic source are obtained by calculating through a genetic algorithm.

[0153] Step 3: First, calculate the approximate solutions of multiple microseismic sources according to the specified screening algorithm, and then obtain the initial cluster centers;

[0154] Step 4: First, input the initial cluster centers into the clustering algorithm to obtain several cluster centers;

[0155] Step 5: First, construct a cluster center matrix based on several cluster centers. Then, obtain the reference center vector based on the cluster center matrix. Next, obtain the difference matrix based on the reference center vector and the cluster center matrix. Then, obtain the median absolute deviation based on the reference center vector and the cluster center matrix. Next, obtain the scale difference matrix based on the median absolute deviation and the difference matrix. Finally, obtain the Mahalanobis distance of each cluster center based on the scale difference matrix.

[0156] Step 6: First, obtain the median absolute deviation of the Mahalanobis distance based on the Mahalanobis distance, and then obtain the outlier threshold based on the Mahalanobis distance and the median absolute deviation of the Mahalanobis distance.

[0157] Step 7: First, filter the Mahalanobis distance of each cluster center based on the outlier threshold, and then obtain a number of effective cluster centers;

[0158] Step 8: First, calculate the clustering factor of several cluster centers, and then calculate the final clustering location result based on the clustering factor and cluster centers, that is, the three-dimensional location coordinates of the microseismic source.

[0159] Example 2:

[0160] The basic content is the same as in Example 1, except that:

[0161] Please see Figure 1 — Figure 2 In the fifth step, constructing a cluster center matrix based on several cluster centers means: constructing a cluster center matrix based on the vector coordinates of several cluster centers; obtaining a reference center vector based on the cluster center matrix means: first calculating the median of the cluster center matrix column by column, and then using the obtained medians to form the reference center vector; obtaining a difference matrix based on the reference center vector and the cluster center matrix means: subtracting the reference center vector from the cluster center matrix to obtain the difference matrix; obtaining the absolute deviation of the median based on the reference center vector and the cluster center matrix means: first using the cluster center matrix... The absolute deviation is obtained by subtracting the reference center vector from the multiple vector coordinates of the cluster centers. The median of the absolute deviation is then obtained based on the absolute deviation. Finally, the median absolute deviation is multiplied by the deviation estimation constant to obtain the median absolute deviation. The process of obtaining the scaling difference matrix based on the median absolute deviation and the difference matrix involves dividing the median absolute deviation by the difference matrix. The process of obtaining the Mahalanobis distance for each cluster center based on the scaling difference matrix involves squaring the matrix elements, summing the rows, and taking the square root of the rows in sequence to obtain the Mahalanobis distance. In the fifth step, the vector coordinates of the cluster centers refer to:

[0162] ;

[0163] In the formula, For the first The vector coordinates of the cluster centers For the first Cluster centers coordinate, For the first Cluster centers coordinate, For the first Cluster centers coordinate, The total number of cluster centers;

[0164] The construction of a cluster center matrix based on the vector coordinates of several cluster centers refers to:

[0165] ;

[0166] In the formula, This is the cluster center matrix;

[0167] The step of calculating the median of the cluster center matrix column by column, and then using the obtained medians to form the reference center vector, refers to:

[0168] ;

[0169] In the formula, As the reference center vector, Cluster center matrix of The median of the coordinates, Cluster center matrix of The median of the coordinates, Cluster center matrix of The median of the coordinates;

[0170] The step of subtracting the reference center vector from the cluster center matrix to obtain the difference matrix refers to:

[0171] ;

[0172] In the formula, It is a difference matrix;

[0173] The absolute deviation is obtained by subtracting the reference center vector from the multiple vector coordinates of the cluster centers in the cluster center matrix. The median of the absolute deviation is then obtained based on the absolute deviation. Finally, the median of the absolute deviation is multiplied by the deviation estimation constant to obtain the median. The absolute deviation refers to:

[0174] ;

[0175] In the formula, This represents the absolute deviation of the median. This is the deviation estimation constant;

[0176] The method of using the difference matrix to divide the absolute deviation of the median to obtain the scaling difference matrix refers to:

[0177] ;

[0178] In the formula, This is the scale difference matrix;

[0179] The process of squaring the matrix elements, summing the rows, and taking the square root of the rows to obtain the Mahalanobis distance refers to:

[0180] ;

[0181] In the formula, This is the Mahalanobis distance.

[0182] Preferably, the The value is 1.4826.

[0183] When applying this method, first use the vector coordinates of several cluster centers. Constructing a cluster center matrix Then the cluster center matrix Calculate the three medians column by column, and then use the three medians to form a reference center vector. Then use the cluster center matrix Subtract the reference center vector Then the difference matrix is ​​obtained. Then, the vector coordinates of the cluster centers Subtract the reference center vector sequentially to obtain the absolute deviation Then multiply the absolute deviation by the deviation estimation constant. Obtain the absolute deviation of the median Then use the difference matrix absolute deviation of the median Dot division yields the scale difference matrix. Then, for the scale difference matrix The matrix elements are squared, summed row-wise, and then squared row-wise in sequence to obtain the Mahalanobis distance. In order to obtain the outlier threshold later Make preparations; The value 1.4826 is used to calculate the absolute deviation of the median. This is converted into a consistent estimate of the standard deviation of the normal distribution.

[0184] Example 3:

[0185] The basic content is the same as in Example 1, except that:

[0186] Please see Figure 1 — Figure 2 In the sixth step, the absolute deviation of the median of the Mahalanobis distance obtained based on the Mahalanobis distance refers to:

[0187] ;

[0188] In the formula, This represents the median absolute deviation of the Mahalanobis distance. To calculate the median absolute deviation of the Mahalanobis distance;

[0189] The outlier threshold obtained based on Mahalanobis distance and the median absolute deviation of Mahalanobis distance refers to:

[0190] ;

[0191] In the formula, The threshold for outliers. The median of the Mahalanobis distance. For threshold coefficient, This is the deviation estimation constant.

[0192] In the seventh step, the step of filtering each cluster center based on the Mahalanobis distance of the outlier threshold to obtain several effective cluster centers means: comparing the Mahalanobis distance of all cluster centers with the outlier threshold in turn, retaining the cluster centers whose Mahalanobis distance is less than the outlier threshold, and then obtaining several effective cluster centers.

[0193] Preferably, the The value is 1.4826.

[0194] Preferably, the The value is 3.

[0195] When applying it, first refer to the multiple Mahalanobis distances in step five. Obtain the median of the Mahalanobis distance absolute deviation of the median of Mahalanobis distance Then take the median of the Mahalanobis distance. absolute deviation of the median of Mahalanobis distance Threshold coefficient Deviation estimation constant Multiply to obtain the outlier threshold Then calculate the Mahalanobis distance of the cluster centers. Outlier threshold Compare the results, and then retain the Mahalanobis distance. Less than the outlier threshold The cluster centers are obtained, that is, several effective cluster centers are obtained; The value is 3, corresponding to the 99.7% confidence interval; The value 1.4826 is used to calculate the absolute deviation of the median. This is converted into a consistent estimate of the standard deviation of the normal distribution.

[0196] Example 4:

[0197] The basic content is the same as in Example 1, except that:

[0198] Please see Figure 1 — Figure 2 In the eighth step, calculating the clustering factor of several cluster centers refers to:

[0199] ;

[0200] In the formula, As an aggregation factor, For the first Cluster centers, for The number of approximate source solutions of the cluster group The total number of cluster centers;

[0201] The final clustering location result calculated based on the clustering factor and cluster center refers to:

[0202] ;

[0203] In the formula, This represents the final clustering and localization result.

[0204] When applying this method, first calculate the cluster centers. Aggregation factor That is, based on each cluster center The number of source approximate solutions of the cluster to which it belongs, divided by the number of cluster centers. The number of source approximate solutions for all clusters, then through the clustering factor. Cluster centers Weighting is performed to accurately represent the distribution information of effective cluster centers, and then the final clustering location result, namely the three-dimensional location coordinates of the microseismic source, is obtained.

[0205] Example 5:

[0206] The basic content is the same as in Example 1, except that:

[0207] Please see Figure 1 — Figure 2 In the third step, the specified screening algorithm refers to the tribal competition and member cooperation algorithm. In the fourth step, the clustering algorithm refers to the standard KMeans algorithm.

[0208] In application, multiple approximate solutions for microseismic sources are first calculated using a tribal competition and member cooperation algorithm. The process is as follows: after loading the dataset and initializing the parameters, the population is initialized first, then the fitness is initialized, then the initial fitness is calculated, then the fitness of each population is recorded, and the team optimum and global optimum are updated. Then, an iterative loop is performed. After the iteration ends, the global optimum individual is used as the initial cluster center. Then, the initial cluster centers are used in the standard KMeans algorithm. The process is as follows: first, ensure that the data is numerical, then select the number of clusters k, and use the elbow method, silhouette coefficient, or other methods to determine the optimal value of k. Then, the initial cluster centers are used as the initial centers, and each source approximate solution is assigned to the nearest cluster center. Then, the cluster center of each cluster (i.e., the mean of all points in the cluster) is recalculated. The assignment and calculation steps are repeated until convergence. Then, several cluster centers and cluster labels, the sum of squared distances from each point to its cluster center, etc. are output.

[0209] Example 6:

[0210] The basic content is the same as in Example 1, except that:

[0211] Please see Figure 1 — Figure 2 In the first step, the specified formula refers to the seismic wave propagation theory formula, which is:

[0212] ;

[0213] In the formula, For the first The absolute spatial distance from each microseismic network measuring point to the microseismic source. For the first Coordinates of the microseismic network monitoring points The coordinates of the microseismic source are given. , This represents the total number of microseismic network monitoring points that received valid seismic wave signals. For seismic wave velocity, For the first Data on the arrival time of the first P-wave recorded by a microseismic network monitoring point;

[0214] ;

[0215] In the formula, For the first The microseismic network monitoring points and the first The difference in arrival time of the first P-wave recorded by a microseismic network monitoring point. For the first The arrival time of the first P-wave recorded by a microseismic network. For the first The location of the microseismic network monitoring points from the microseismic source and the first The difference in spatial distance between each microseismic network measuring point and the location of the microseismic source. For the first The absolute value of the spatial distance from each microseismic network measuring point to the microseismic source. .

[0216] In the first step, the time difference mathematical model refers to:

[0217] ;

[0218] In the formula, To obtain a mathematical model of time difference;

[0219] The optimization model refers to:

[0220] ;

[0221] In the formula, For the optimization model, for The number of non-repeating combinations of any two microseismic network points in a given microseismic network. for The corresponding time difference data, for The difference in corresponding spatial distances.

[0222] In application, the relationship between the absolute value of the spatial distance between the microseismic network measuring points and the microseismic source and the velocity and time of the first P-wave is first obtained based on the seismic wave propagation theory formula. Then, based on the aforementioned relationship, the relationship between the spatial distance between two microseismic network measuring points and the velocity and time of the first P-wave is obtained, thus deriving the time difference mathematical model. To the mathematical model of time difference The theoretical value is 0, but after inputting monitoring data, its actual value is not 0; further, it is compared with the time difference mathematical model. Minimum optimization yields the optimal model. Then optimize the model The objective function (fitness function) used in genetic algorithms for optimization is the optimization model. The closer the minimum value is to 0, the closer the approximate solution of the microseismic source is to the actual source coordinates.

[0223] The above description is only a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. Any equivalent modifications or changes made by those skilled in the art based on the content disclosed in the present invention should be included within the scope of protection set forth in the claims.

Claims

1. A method of robust positioning of a microseismic source, characterized in that: The method comprises the following steps: The first step is to obtain a time difference mathematical model according to a specified formula, to obtain an optimization model according to the time difference mathematical model, and to construct a four-by-four combined four-element quadratic nonlinear optimization equation according to the optimization model; The second step is to obtain multiple monitoring data according to multiple microseismic network measuring points, each of which comprises P-wave first arrival time data and three-dimensional position coordinates of the microseismic network measuring points, to sequentially bring the multiple monitoring data into the four-by-four combined four-element quadratic nonlinear optimization equation, and to obtain multiple microseismic source approximate solutions through genetic algorithm calculation; The third step is to calculate the multiple microseismic source approximate solutions according to a specified screening algorithm, and to obtain an initial clustering center; The fourth step is to bring the initial clustering center into a clustering algorithm, and to obtain a plurality of clustering centers; The fifth step is to construct a clustering center matrix according to the plurality of clustering centers, to obtain a reference center vector according to the clustering center matrix, to obtain a difference matrix according to the reference center vector and the clustering center matrix, to obtain a median absolute deviation according to the reference center vector and the clustering center matrix, to obtain a scale difference matrix according to the median absolute deviation and the difference matrix, and to obtain a Mahalanobis distance of each clustering center according to the scale difference matrix; The sixth step is to obtain a median absolute deviation of the Mahalanobis distance according to the Mahalanobis distance, and to obtain an outlier threshold according to the Mahalanobis distance and the median absolute deviation of the Mahalanobis distance; The seventh step is to screen the Mahalanobis distance of each clustering center according to the outlier threshold, and to obtain a plurality of effective clustering centers; The eighth step is to calculate a clustering factor of the plurality of clustering centers, and to calculate a final clustering positioning result, i.e., three-dimensional position coordinates of a microseismic source, according to the clustering factor and the clustering center.

2. The method of claim 1, wherein: In the fifth step, the construction of the clustering center matrix according to the vector coordinates of the plurality of clustering centers refers to constructing the clustering center matrix according to the vector coordinates of the plurality of clustering centers; The reference center vector obtained according to the clustering center matrix refers to calculating the median of the clustering center matrix by column, and then composing the reference center vector from the obtained median; The difference matrix obtained according to the reference center vector and the clustering center matrix refers to subtracting the reference center vector from the clustering center matrix to obtain the difference matrix; The median absolute deviation obtained according to the reference center vector and the clustering center matrix refers to sequentially subtracting the vector coordinates of the clustering centers in the clustering center matrix from the reference center vector to obtain absolute deviations, obtaining an absolute deviation median according to the absolute deviations, multiplying the absolute deviation median by a deviation estimation constant to obtain the median absolute deviation; The scale difference matrix obtained according to the median absolute deviation and the difference matrix refers to point dividing the median absolute deviation by the difference matrix to obtain the scale difference matrix; The Mahalanobis distance of each clustering center obtained according to the scale difference matrix refers to sequentially performing matrix element squaring, row summation, and row square root on the scale difference matrix to obtain the Mahalanobis distance.

3. The method of claim 2, wherein: In the fifth step, the vector coordinates of the clustering center refer to: ； wherein, is the vector coordinate of the th cluster center, is the vector coordinate of the th cluster center, is the vector coordinate of the th cluster center, is the vector coordinate of the th cluster center, is the vector coordinate of the th cluster center, is the vector coordinate of the is the total number of cluster centers. The construction of the clustering center matrix according to the vector coordinates of the plurality of clustering centers refers to: ； In the formula, is the clustering center matrix; The step of calculating the median of the clustering center matrix by column, and then forming the reference center vector by the obtained median is: ； wherein is the reference center vector, is the cluster center matrix is the median of the coordinates of is the cluster center matrix is the median of the coordinates of is the cluster center matrix is the median of the coordinates of The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: ； In the formula, is the difference matrix; The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: ； wherein is the median absolute deviation, is the bias estimation constant; The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: ； In the formula, is the scale difference matrix; The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: ； wherein is the Mahalanobis distance.

4. The method of claim 1, wherein: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: ； wherein is the median absolute deviation of the Mahalanobis distances, is the median absolute deviation of the Mahalanobis distances; The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: ； wherein is an outlier threshold value, is a median of Mahalanobis distances, is a threshold coefficient, is a bias estimation constant.

5. The method of claim 4, wherein: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is:

6. The method of claim 1, wherein: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: ； wherein is the aggregation factor, is the th cluster center, is the number of source approximations of the cluster group, is the total number of cluster centers; The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: ； In the formula, is the final clustering positioning result.

7. The method of claim 1, wherein: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is:

8. The method of claim 7, wherein: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is:

9. The method of claim 1, wherein: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: ； In the formula, For the first The absolute spatial distance from each microseismic network measuring point to the microseismic source. For the first Coordinates of the microseismic network monitoring points The coordinates of the microseismic source are given. , This represents the total number of microseismic network monitoring points that received valid seismic wave signals. For seismic wave velocity, For the first Data on the arrival time of the first P-wave recorded by a microseismic network monitoring point; ； In the formula, is the P-wave first arrival time recorded by the first microseismic network measuring point, is the P-wave first arrival time recorded by the second microseismic network measuring point, is the difference between the P-wave first arrival times recorded by the first microseismic network measuring point and the second microseismic network measuring point, is the P-wave first arrival time data recorded by the first microseismic network measuring point, is the P-wave first arrival time data recorded by the second microseismic network measuring point, is the difference between the P-wave first arrival time data recorded by the first microseismic network measuring point and the second microseismic network measuring point, is the spatial distance from the first microseismic network measuring point to the microseismic source position, is the spatial distance from the second microseismic network measuring point to the microseismic source position, is the difference between the spatial distances from the first microseismic network measuring point and the second microseismic network measuring point to the microseismic source position, is the absolute value of the spatial distance from the first microseismic network measuring point to the microseismic source, .

10. The method of claim 9, wherein: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: ； In the formula, is a time difference mathematical model; The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is: The step of subtracting the reference center vector from the clustering center matrix to obtain a difference matrix is ； In the formula, is the optimal model, is The number of non-repeated combinations of any two microseismic network measuring points in the microseismic network, is The corresponding arrival time difference data, is The corresponding difference in spatial distance.

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