A water inrush hidden danger early warning method based on microseismic event distribution

Abstract

The application discloses a water inrush hidden danger early warning method based on microseismic event distribution, and steps include: obtaining microseismic events in the latest 3-5 days; using a clustering algorithm to divide the geographic position of the microseismic events, obtaining a plurality of event clustering families; calculating the average center position of each day for each event clustering family, judging whether the average center position of the microseismic events of each day has a downward trend; using a histogram statistical algorithm to statistically distribute the event depth of each event clustering family, judging whether the range from the coal seam to the aquifer is covered; when steps S3 and S4 are simultaneously satisfied, push the early warning information; the application can monitor the microseismic event distribution of the mine, understand the change process of the geological layer at the bottom of the mine, judge whether a water conducting channel is formed between the aquifer and the mine floor through an algorithm, and then identify a potential water inrush danger zone, provide a clear geographic position for grouting operation, and prevent further occurrence of water inrush accidents.

Description

Technical Field

[0001] This invention relates to the field of early warning of sudden water inrush hazards, and specifically to an early warning method for sudden water inrush hazards based on the distribution of microseismic events. Background Technology

[0002] With the rapid development of society and the economy, people's demand for mineral resources is increasing day by day, but this also brings with it various accidents during the mining process. For example, in the past, coal mine accidents were mainly collapses and gas outbursts, but with the upgrading of safety measures, these accidents have been effectively controlled. However, there are no effective preventive measures for water inrush accidents, and once they occur, they can cause significant loss of life and property.

[0003] Existing technologies generally determine the potential for water inrush in a mine based on the water level monitoring values ​​of each borehole and the mine's drainage volume, along with other factors. If a water inrush hazard exists, a water hazard warning message is generated and sent. One water hazard microseismic warning method, with publication number CN113565490A, includes: S1, real-time acquisition of environmental and vibration data of the mine floor; S2, obtaining a first probability of a potential water inrush hazard zone based on the environmental data, and a second probability based on the vibration data, and determining whether both the first and second probabilities are greater than a probability threshold: if yes, proceed to S3; if no, return to S1; S3, collect the borehole water level values ​​and mine drainage volume of each borehole; S4, predict whether a water hazard exists in the mine based on the borehole water level values ​​and drainage volume of each borehole: if yes, proceed to S5; if no, return to S3; S5, generate and send a water hazard warning message. When the possibility of water damage to the mine floor is relatively high, the borehole water level and drainage volume of each borehole are used to predict whether there is a potential water damage hazard in the mine, thus solving the technical problem of difficulty in identifying potential water inrush danger zones.

[0004] However, such methods require the deployment of numerous water level monitoring sensors and continuous monitoring of water level and drainage trends. Furthermore, by the time an abnormal water level is detected, a water inrush accident is already imminent, leaving no extra time for grouting to prevent it. In addition, the mechanisms of mine water hazards are complex, and existing technologies often overlook changes in geological structure, making it difficult to identify potential water inrush hazard areas. Summary of the Invention

[0005] The purpose of this invention is to provide a method for early warning of water inrush hazards based on the distribution of microseismic events. By monitoring the distribution of microseismic events in the mine, the method understands the changes in the geological layers at the bottom of the mine. An algorithm is used to determine whether a water-conducting channel has formed between the aquifer and the mine floor, thereby identifying potential water inrush hazard zones. This provides a clear geographical location for grouting operations and prevents further water inrush accidents. This method solves the technical problem of existing technologies' inability to effectively and promptly identify potential water inrush hazard zones.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for early warning of water inrush hazards based on microseismic event distribution includes the following steps:

[0008] S1: Obtain microseismic events from the last 3-5 days. The location information of the microseismic events includes three parameters (x, y, z). (x, y) are set in the geodetic coordinate system, and the z value represents the event elevation.

[0009] S2: Use clustering algorithms to divide the geographical locations of microseismic events, resulting in several event clusters;

[0010] S3: Calculate the average center position for each event cluster daily and determine whether the average center position of the micro-seismic events daily shows a downward trend;

[0011] S4: Use histogram statistical algorithm to perform distribution statistics on the event depth of each event cluster to determine whether it covers the range from coal seam to aquifer;

[0012] S5: When both steps S3 and S4 are met, a warning message is sent.

[0013] Step S2 includes the following: the clustering algorithm includes the DBSCAN clustering algorithm based on density clustering. The DBSCAN algorithm is used to cluster the planar coordinates (x, y) of microseismic events to obtain different cluster families in the planar geographical location.

[0014] The specific steps of the DBSCAN clustering algorithm are as follows:

[0015] S21: Randomly select a microseismic event location information p from the recent microseismic event location information;

[0016] S22: For the parameters neighborhood radius Eps and neighborhood number threshold MinPts, if the number of data points within Eps(P) with the point information P as the center and Eps as the radius exceeds the given minPts, then P is the core point. Find all data object points that can be reached from the density of p and form a cluster.

[0017] S23: If the selected point information p is an edge point, select another data object point;

[0018] S24: Repeat steps 2 and 3 until all points have been processed.

[0019] The Eps is used to control the radius of the family, and is set to 1 / 10 of the longest side of the mine; the MinPts is used to control the number of points within the family, and is set to 1 / 5 of the total number of microseismic events per day.

[0020] Step S3 includes:

[0021] S31: Divide the same microseismic event point clusters obtained in step S2 into groups by day, and calculate the average position of the microseismic event coordinates Z-axis for each day;

[0022] S32: Determine whether the average position of the Z-axis each day is less than that of the previous day. If so, it means that the center position has shifted downward. Count the number of days that meet this condition.

[0023] S33: If the ratio of the number of days that meet the downward shift condition to the total number of days is above 0.6, it can be determined that the micro-seismic events in the past few days have a downward shift trend.

[0024] Step S4 includes:

[0025] S41: Cluster the same microseismic event points into a histogram distribution statistics, using the depth location as the histogram interval division unit, and the near coal seam location and aquifer location as the distribution positions of the beginning and end of the histogram, and count the number of microseismic events that occurred in each sub-interval;

[0026] S42: Compare the number of microseismic events in each sub-interval with a pre-set fixed threshold;

[0027] S43: If the frequency of each interval is greater than the threshold, it means that a certain number of microseismic events have been detected in all rock strata from the aquifer to the coal seam.

[0028] The threshold in step S42 is set according to the geological activity of the mine, and is set to about 10 to 30. The number of histogram intervals is 4 to 8.

[0029] Step S5 includes:

[0030] S51: When the condition in step S3 is met, it indicates that the dense microseismic zone is continuously expanding into the deeper part, and at this time the risk of water inrush is gradually increasing.

[0031] S52: When the conditions in step S4 are met, it indicates that a continuous accumulation of microseismic dense zone has appeared between the coal seam and the aquifer, and the necessary conditions for water inrush have been met.

[0032] S53: When steps S3 and S4 are met simultaneously, it indicates a potential water inrush risk. An early warning message is sent and further preventative measures are taken.

[0033] The beneficial effects of the water inrush hazard early warning method based on microseismic event distribution provided by this invention are as follows: The method provided by this invention only requires microseismic monitoring data. Currently, most mines deploy microseismic monitoring systems, and the acquisition of raw data is relatively easy. This allows for understanding the changes in the geological layers at the bottom of the mine. The algorithm determines whether a water-conducting channel has formed between the aquifer and the mine floor, thereby identifying potential water inrush hazard zones. The conclusions obtained are more intuitive and reliable, playing an auxiliary role in expert decision-making and providing a clear geographical location for subsequent grouting operations, thus preventing further water inrush accidents. Attached Figure Description

[0034] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0035] Figure 1 This is a clustering result diagram of microseismic event points provided in an embodiment of the present invention. △, ☆, and ○ represent three different cluster families. The x and y axes correspond to the longitude and latitude of the geodetic coordinate system, and the z-axis represents altitude. In the diagram, 10—cluster family I; 20—cluster family II; 30—cluster family III; 40—geodetic longitude; 50—geodetic latitude; 60—altitude.

[0036] Figure 2 This is a histogram distribution chart of various rock layers for microseismic event points provided in this embodiment of the invention. The horizontal axis represents altitude, and the vertical axis represents the number of microseismic events in a single sub-interval.

[0037] Figure 3 This is a schematic diagram of a dense microseismic event zone penetrating a coal seam and an aquifer provided in an embodiment of the present invention. In the diagram, 10—cluster group I; 20—cluster group II; 40—geocentric longitude; 50—geocentric latitude; 60—altitude; 600—near coal seam surface; 70—aquifer surface. Detailed Implementation

[0038] The following will describe in more detail a method for early warning of water inrush hazards based on the distribution of microseismic events according to the present invention, with reference to schematic diagrams. The following embodiments will help those skilled in the art to further understand the function of the present invention, but do not limit the present invention in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0039] This invention infers the likelihood of a sudden water inrush based on the distribution of microseismic events over the past few days. Through long-term monitoring, some patterns of water inrush precursors related to microseismic events have been summarized:

[0040] 1. The spatial location of the dense microseismic event zone is fixed, and its plane does not change with the change of the mining line;

[0041] 2. As the mining line approaches the dense zone, the dense zone continues to expand into deeper areas;

[0042] 3. Dense zones extend into deep Ordovician limestone aquifers. Microseismic dense zones between coal seams and aquifers show continuous accumulation, indicating an increased risk of water inrush.

[0043] like Figures 1-3 As shown, based on the above patterns, this invention provides a method for early warning of water inrush hazards based on the distribution of microseismic events, comprising the following steps:

[0044] S1: Obtain microseismic events from the last 3-5 days. The location information of the microseismic events includes three parameters (x, y, z). (x, y) are set in the geodetic coordinate system, and the z value represents the event elevation.

[0045] With the continuous improvement of microseismic location technology, microseismic monitoring can now achieve spatiotemporal location of mine hazards. In recent years, it has been widely used in mine safety monitoring and has become a highly recognized means of monitoring and early warning of ground pressure disasters in the industry. The microseismic monitoring system analyzes the raw waveform data and inversely calculates parameters such as the source location, other event attributes, and rupture mechanisms.

[0046] This invention uses only the location information of microseismic events. The location information generally includes three parameters (x, y, z). (x, y) are usually set in a geodetic coordinate system, and the z value represents the event elevation.

[0047] Obtain microseismic events over the past 3-5 days. The time period can be adjusted based on the number of microseismic events per day. If the average number of microseismic events per day is less than 20, the time period can be set to 5 days, as a low number of microseismic events indicates a relatively stable geological structure, and the analysis period can be appropriately extended. Conversely, if the average number of microseismic events per day exceeds 80, it indicates an active geological structure, and the analysis period needs to be shortened to detect potential water inrush hazards in a timely manner.

[0048] S2: Use clustering algorithms to divide the geographical locations of microseismic events, resulting in several event clusters;

[0049] Microseismic events are often scattered throughout any location in a mine, but if there is a real risk of water inrush, microseismic events will usually cluster in a certain area on a plane. Clustering algorithms can be used to find microseismic events that are clustered together.

[0050] This invention employs the DBSCAN clustering algorithm based on density clustering. It defines a cluster as the largest set of density-connected points, enabling the division of regions with sufficiently high density into clusters and the discovery of clusters of arbitrary shapes in noisy spatial databases. The DBSCAN algorithm defines a cluster simply as the largest density-connected set of samples derived from density reachability relations. The algorithm is described below:

[0051] S21: Randomly select a microseismic event location information p from the recent microseismic event location information;

[0052] S22: For the parameters neighborhood radius Eps and neighborhood number threshold MinPts, if the number of data points within Eps(P) with the point information P as the center and Eps as the radius exceeds the given minPts, then P is the core point. Find all data object points that can be reached from the density of p and form a cluster.

[0053] Eps controls the radius of the cluster and can be adjusted according to the actual size of the mine. It is generally set to 1 / 10 of the longest side of the mine. MinPts controls the number of points in the cluster. Cluster points with fewer than MinPts will not form a cluster. It is generally set to 1 / 5 of the total number of microseismic events per day.

[0054] S23: If the selected data object point p is an edge point, select another data object point;

[0055] The DBSCAN algorithm was used to cluster the planar coordinates (x, y) of microseismic events, resulting in different cluster families at the planar geographical locations; the clustering results are illustrated in the diagram below. Figure 1 As shown.

[0056] S3: Calculate the average center position for each event cluster daily and determine whether the average center position of the micro-seismic events daily shows a downward trend;

[0057] S31: Divide the same microseismic event point clusters obtained in step S2 into groups by day, and calculate the average position of the microseismic event coordinates Z-axis for each day;

[0058] S32: Determine whether the average position of the Z-axis each day is less than that of the previous day. If so, it means that the center position has shifted downward. Count the number of days that meet this condition.

[0059] S33: If the ratio of the number of days that meet the downward shift condition to the total number of days is above 0.6, it can be determined that the micro-seismic events in the past few days have a downward shift trend.

[0060] S4: Use histogram statistical algorithm to perform distribution statistics on the event depth of each event cluster to determine whether it covers the range from coal seam to aquifer;

[0061] S41: Histogram distribution statistics are generated for clusters of the same microseismic event points. The depth location is used as the interval division unit, and the locations near the coal seam and aquifer are used as the start and end points of the histogram. The number of histogram intervals is set to 8. The microseismic event point cluster histogram is as follows: Figure 2 As shown, the elevation range of the near coal seam and aquifer is divided into 8 sub-ranges, and the number of microseismic events occurring in each sub-range is counted.

[0062] S42: Compare the number of microseismic events in each sub-interval with a pre-set fixed threshold. The threshold can be reasonably set according to the geological activity of the mine. This value is an empirical value and can generally be set to around 10 to 30.

[0063] S43: If the frequency of each interval is greater than the threshold, it indicates that a certain number of microseismic events have been detected in all rock strata from the aquifer to the coal seam; a schematic diagram of the dense microseismic event zone penetrating the coal seam and aquifer is shown below. Figure 3 As shown. Figure 3 In the diagram, the microseismic events of group I, represented by '○', extend through the aquifer and near the coal seam, while the microseismic events of group II, represented by '△', are concentrated only near the aquifer.

[0064] S5: When both steps S3 and S4 are met, a warning message is sent.

[0065] S51: When the condition in step S3 is met, it indicates that the dense microseismic zone is continuously expanding into the deeper part, and at this time the risk of water inrush is gradually increasing.

[0066] S52: When the conditions in step S4 are met, it indicates that a continuous accumulation of microseismic dense zone has appeared between the coal seam and the aquifer, and the necessary conditions for water inrush have been met.

[0067] S53: When steps S3 and S4 are met simultaneously, it indicates a potential water inrush risk. Relevant information can be sent to engineering personnel for their reference and to take further preventative measures.

[0068] The specific embodiments described above further illustrate the technical problems, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for early warning of water inrush hazards based on microseismic event distribution, characterized in that, Includes the following steps: S1: Obtain microseismic events from the last 3-5 days. The location information of the microseismic events includes three parameters (x, y, z). (x, y) are set in the geodetic coordinate system, and the z value represents the event elevation. S2: Use clustering algorithms to divide the geographical locations of microseismic events, resulting in several event clusters; S3: Calculate the average center position for each event cluster daily and determine whether the average center position of the micro-seismic events daily shows a downward trend; S31: Divide the same microseismic event point clusters obtained in step S2 into groups by day, and calculate the average position of the microseismic event coordinates Z-axis for each day; S32: Determine whether the average position of the Z-axis each day is less than that of the previous day. If so, it means that the center position has shifted downward. Count the number of days that meet this condition. S33: If the ratio of the number of days that meet the downward shift condition to the total number of days is above 0.6, it can be determined that the micro-seismic events in the past few days have a downward shift trend; S4: Use histogram statistical algorithm to perform distribution statistics on the event depth of each event cluster to determine whether it covers the range from coal seam to aquifer; S5: When both steps S3 and S4 are met, a warning message is sent.

2. The method for early warning of water inrush hazards based on microseismic event distribution according to claim 1, characterized in that, Step S2 includes the following: the clustering algorithm includes the DBSCAN clustering algorithm based on density clustering. The DBSCAN algorithm is used to cluster the planar coordinates (x, y) of microseismic events to obtain different cluster families in the planar geographical location.

3. The method for early warning of water inrush hazards based on microseismic event distribution according to claim 2, characterized in that, The specific steps of the DBSCAN clustering algorithm are as follows: S21: Randomly select a microseismic event location information p from the recent microseismic event location information; S22: For the parameters neighborhood radius Eps and neighborhood number threshold MinPts, if the number of data points within Eps(P) with the point information P as the center and Eps as the radius exceeds the given MinPts, then P is the core point. Find all data object points that can be reached from the density of p and form a cluster. S23: If the selected point information p is an edge point, select another data object point; S24: Repeat steps 2 and 3 until all points have been processed.

4. The method for early warning of water inrush hazards based on microseismic event distribution according to claim 3, characterized in that, The Eps is used to control the radius of the family, and is set to 1 / 10 of the longest side of the mine; the MinPts is used to control the number of points within the family, and is set to 1 / 5 of the total number of microseismic events per day.

5. The method for early warning of water inrush hazards based on microseismic event distribution according to claim 1, characterized in that, Step S4 includes: S41: Cluster the same microseismic event points into a histogram distribution statistics, using the depth location as the histogram interval division unit, and the near coal seam location and aquifer location as the distribution positions of the beginning and end of the histogram, and count the number of microseismic events that occurred in each sub-interval; S42: Compare the number of microseismic events in each sub-interval with a pre-set fixed threshold; S43: If the frequency of each interval is greater than the threshold, it means that a certain number of microseismic events have been detected in all rock strata from the aquifer to the coal seam.

6. The method for early warning of water inrush hazards based on microseismic event distribution according to claim 5, characterized in that, The threshold in step S42 is set according to the geological activity of the mine, and is set to 10-30, and the number of histogram intervals is 4-8.

7. A method for early warning of water inrush hazards based on microseismic event distribution according to claim 1, 5, or 6, characterized in that, Step S5 includes: S51: When the condition in step S3 is met, it indicates that the dense microseismic zone is continuously expanding into the deeper part, and at this time the risk of water inrush is gradually increasing. S52: When the conditions in step S4 are met, it indicates that a continuous accumulation of microseismic dense zone has appeared between the coal seam and the aquifer, and the necessary conditions for water inrush have been met. S53: When steps S3 and S4 are met simultaneously, it indicates a potential water inrush risk. An early warning message is sent and further preventative measures are taken.

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