Method for regional generation of gentle energy density nephogram based on micro-seismic energy

Abstract

A method for regional generation of a gentle energy density cloud picture based on micro-seismic energy comprises the following steps: dividing a research region into four quadrants based on energy space distribution; the events with the energy higher than the high-energy micro-seismic threshold value serve as high-energy micro-seismic events, and the number of the high-energy micro-seismic events in each quadrant is counted; grid scales are set in a differentiated mode; for the quadrant without the high-energy micro-seismic event, the energy density is calculated according to the ratio of the sum of the total energy of all the high-energy micro-seismic events falling into the grid to the area of the grid; for a quadrant with a high-energy micro-seismic event; determining a grid neighborhood by taking the grid where the high-energy micro-seismic event is located as the center and combining the diffusion radius, and calculating the average energy density according to the ratio of the sum of the total energy of all grids in the grid neighborhood to the sum of the areas of all the grids; if the current grid energy density is lower than the average energy density, updating to the average value, otherwise, keeping the original value; and generating a gentle energy density nephogram. According to the method, the energy density cloud picture with natural, continuous and smooth energy density field transition can be obtained.

Description

Technical Field

[0001] This invention belongs to the field of coal mine dynamic disaster monitoring and early warning technology, specifically involving a method for generating smooth energy density cloud maps based on microseismic energy regionalization. Background Technology

[0002] With the application of artificial intelligence in rockburst monitoring, using images as input data to build neural network models has become a mature and effective solution. In coal mining, microseismic monitoring systems can record the energy release process of underground rock masses in real time. Traditional energy density cloud map generation methods typically employ fixed grid division (grid side length 20m–30m), summing the energy within each grid and interpolating. However, due to the large spatial differences between high-energy and low-energy events, energy values ​​between adjacent grids can differ by several orders of magnitude, leading to abrupt energy changes in the generated cloud maps, which does not conform to the diffusion law of microseismic energy in the medium.

[0003] Furthermore, traditional fixed-grid methods neglect the differences in energy event distribution density across different regions. In practical applications, high-energy regions often require finer spatial resolution to characterize energy concentration features, while low-energy regions can use larger grids to reduce computational load. Therefore, there is an urgent need for an adaptive contour map generation method that can simultaneously consider differences in energy spatial distribution and energy diffusion characteristics. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method for generating smooth energy density cloud maps based on microseismic energy regionalization. This method is simple to implement and has low implementation cost. Using this method, energy density cloud maps with natural transitions and continuous smoothness of energy density fields can be obtained, which helps to more accurately reveal the laws of energy accumulation and evolution in mining areas and can be better used in the training and visualization analysis of microseismic early warning models.

[0005] To achieve the above objectives, the present invention provides a method for generating smooth energy density cloud maps based on microseismic energy regionalization, comprising the following steps: Step 1: Based on the spatial distribution of microseismic event energy, the study area is divided into four quadrants; Step 2: Count the number of high-energy microseismic events in each quadrant; set a high-energy microseismic threshold, and define microseismic events with energy higher than or equal to the high-energy microseismic threshold as high-energy microseismic events, and count the number of high-energy microseismic events in each quadrant; Step 3: Determine the quadrant grid resolution based on the distribution location of high-energy microseismic events; set the grid edge length of the region containing high-energy microseismic events as 'a'. g The grid side length of the region without high-energy microseismic events is set to a. l , where a g <al ; Step 4: Calculation of energy density over a fixed time period; S41: For quadrants without high-energy microseismic events, based on the grid location... The sum of the total energies of all high-energy microseismic events and the grid Area ratio calculation grid Energy density ; S42: For quadrants containing high-energy microseismic events; based on the grid where the high-energy microseismic event W is located. Centered on the diffusion radius Determine the grid The neighborhood, and according to the grid The sum of the total energy of all grids in the neighborhood and the grid The average energy density is calculated by the ratio of the sum of the areas of all grid cells within the neighborhood. If the current grid energy density Below average energy density Then update to average energy density. Otherwise, retain the original value; Step 6: Generation of smooth energy density cloud map; Spatial interpolation is performed on the energy density data to generate a high-resolution smooth energy density cloud map.

[0006] Furthermore, in order to accurately reflect the influence range of microseismic events of different energy levels, in step four, S42, the diffusion radius is set according to the energy level of high-energy microseismic events. The process is as follows: Let 'a' be the initial diffusion radius of the high-energy microseismic threshold. For a high-energy microseismic event, the diffusion radius increases by one energy level relative to the high-energy microseismic threshold. It increases in increments of 1.5a to 2a.

[0007] As a preferred embodiment, in step four, S41, the mesh is calculated according to formula (1). Energy density ; (1); In the formula, To fall into the grid The sum of the total energy of all microseismic events , To fall into the grid Energy values ​​of microseismic events; For grid The area.

[0008] Furthermore, in order to accurately obtain the average energy density, in step S42 of step four, the grid is calculated according to formula (2). Average energy density of the neighborhood grid ; (2).

[0009] As a preferred embodiment, in step four, S42, the updated current grid energy density is obtained according to formula (3). ; (3).

[0010] As a preferred option, in step two, the high-energy microseismic threshold is 10. 4 J.

[0011] Furthermore, to ensure the accuracy of the energy density cloud map, in step two, the threshold for high-energy microseismic events is determined based on expert experience and historical microseismic events.

[0012] As a preferred option, in step three, a g The value is 5m~10m, a l The value is between 20m and 30m.

[0013] As a preferred option, spatial interpolation is performed using the Kriging interpolation method in step six.

[0014] Furthermore, in order to balance the influence weights of each direction and ensure the rationality of quadrant division, the process of dividing the study area into four quadrants in step one is as follows: S11: Acquire microseismic monitoring data through a microseismic monitoring system, and determine the leading influence distance D of microseismic events within a fixed time period based on the microseismic monitoring data. c The direction of the goaf affects the distance D. f Lateral influence distance D between the working face and the two roadways b ; S12: Using micro-seismic events to influence distance D in advance c The direction of the goaf affects the distance D. f Lateral influence distance D between the working face and the two roadways b The geometric center point is used as the origin of the coordinate system; simultaneously, coordinate axes are drawn along the direction and dip of the working surface, and the study area is divided into four quadrants S in a counterclockwise direction. i i This approach more accurately reflects the symmetry of microseismic energy propagation, avoiding positioning errors caused by distance dominance due to a single factor influencing the process. Simultaneously, it aligns quadrant division with the direction of mining activities, facilitating accurate analysis of the spatial correlation between microseismic activity and mining-induced stress.

[0015] To address the problem of abrupt contour line changes in traditional microseismic energy density cloud maps due to coarse grid division and energy abrupt changes, this invention provides an adaptive cloud map generation method that simultaneously considers differences in spatial energy distribution and energy diffusion characteristics. First, based on the spatial distribution of energy, the study area is divided into four quadrants, facilitating the analysis of microseismic activity characteristics in different quadrants. Simultaneously, by transforming continuous spatial influence distances into discrete quadrants, data complexity is reduced, facilitating rapid identification of key areas. Next, high-energy microseismic events in each quadrant are statistically analyzed, filtering out interference from low-energy events and allowing direct focus on high-energy microseismic events more likely to reflect rock mass instability or stress concentration. Furthermore, the quadrant statistical process quickly locates which quadrant contains the hazardous area, aiding in rapid response to risk events. Moreover, by differentiating the spatial differences between high-energy and low-energy events, the grid scale is set differently, with shorter grid sides for high-energy events, effectively ensuring improved spatial resolution in high-energy areas. This ensures that the generated energy density cloud map does not exhibit abrupt energy changes and more accurately displays the diffusion patterns of microseismic energy in the medium. Then, during the energy density calculation, direct calculation was used for quadrants without high-energy microseismic events. For quadrants with high-energy microseismic events, the influence range was defined by the diffusion radius corresponding to the energy level of the high-energy microseismic event. The average energy density of this influence range was then used as a benchmark value. Grid nodes within the influence range that were below the benchmark value were corrected, while nodes that were above the benchmark value retained their original values. This smooths out abnormally low-density areas caused by local rock mass heterogeneity or monitoring errors, making the corrected results more consistent with actual geological conditions and more accurately reflecting the actual propagation range of microseismic energy in the rock mass. At the same time, retaining the original values ​​of nodes above the benchmark value ensures that the original data of high-energy events is not diluted, avoiding the obscuring of key risk points. Thus, the correction process ensures that the subsequently generated energy density cloud map can more clearly identify high-risk areas and areas requiring reinforcement. Finally, interpolation is used to fill in the blank areas of the original grid data, further improving the resolution and ensuring that the final generated energy density cloud map can more clearly display areas of concentrated energy anomalies, avoiding the omission of high-energy points due to coarse grids.

[0016] This invention introduces an adaptive grid partitioning and energy density calculation averaging mechanism, enabling the generated energy density cloud map to effectively eliminate non-physical abrupt boundary changes and form a continuous and smooth energy transition representation. This more realistically reflects the actual impact range of high-energy microseismic events in the rock mass, significantly improving the interpretative value and reliability of the cloud map in mine dynamic disaster early warning. It solves the problems of traditional methods that use fixed grids for statistics and interpolation, which are prone to energy step changes and boundary distortions due to uneven distribution of microseismic energy, making it difficult to accurately represent the true impact domain of large-energy events.

[0017] This method is simple to implement and has low implementation costs. It can obtain energy density cloud maps with natural and smooth transitions in the energy density field, which helps to reveal the energy accumulation and evolution laws in the mining area more accurately and can be better used for microseismic early warning model training and visualization analysis. Attached Figure Description

[0018] Figure 1 This is a flowchart of the present invention; Figure 2 This is a schematic diagram of the research area in an embodiment of the present invention; Figure 3 This is a schematic diagram of the spatial distribution of microseismic events within the study area in this embodiment of the invention; Figure 4 This is a schematic diagram of the regional grid division in an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the average value assignment of the high-energy-level diffusion range in an embodiment of the present invention; Figure 6 This is a smooth energy density cloud map generated in the embodiments of the present invention; Figure 7 This is a comparison diagram of the smooth energy density cloud map generated in the embodiments of the present invention and the cloud map generated by the traditional method. Detailed Implementation

[0019] The invention will now be further described with reference to the accompanying drawings.

[0020] like Figures 1 to 5 As shown, this invention provides a method for generating smooth energy density cloud maps based on microseismic energy regionalization, comprising the following steps: Step 1: Based on the spatial distribution of microseismic event energy, the study area is divided into four quadrants; To balance the influence weights of each direction and ensure the rationality of quadrant division, the process of dividing the study area into four quadrants is as follows: S11: Acquire microseismic monitoring data through a microseismic monitoring system, and determine the leading influence distance D of microseismic events within a fixed time period based on the microseismic monitoring data. c The direction of the goaf affects the distance D. f Lateral influence distance D between the working face and the two roadways b ; S12: Using micro-seismic events to influence distance D in advance c The direction of the goaf affects the distance D. f Lateral influence distance D between the working face and the two roadways b The geometric center point is used as the origin of the coordinate system; simultaneously, coordinate axes are drawn along the direction and dip of the working surface, and the study area is divided into four quadrants S in a counterclockwise direction. i i .

[0021] This approach more accurately reflects the symmetry of microseismic energy propagation, avoiding positioning errors caused by distance dominance due to a single factor. Simultaneously, it aligns quadrant division with the direction of mining activities, facilitating accurate analysis of the spatial correlation between microseismic activity and mining-induced stress.

[0022] Step 2: Count the number of high-energy microseismic events in each quadrant; set a high-energy microseismic threshold, and define microseismic events with energy higher than or equal to the high-energy microseismic threshold as high-energy microseismic events, and count the number of high-energy microseismic events in each quadrant; To ensure the accuracy of the energy density cloud map, a high-energy microseismic threshold is determined based on expert experience and historical microseismic events. More preferably, the high-energy microseismic threshold is 10. 4 J; Step 3: Determine the quadrant grid resolution based on the distribution location of high-energy microseismic events; set the grid edge length of the region containing high-energy microseismic events as 'a'. g The grid side length of the region without high-energy microseismic events is set to a. l , where a g <a l ; As a preferred option, a g The value is 5m~10m, a l The value is between 20m and 30m, a g and a l It is determined based on the planar positioning error of the coal mine microseismic monitoring system.

[0023] Step 4: Calculation of energy density over a fixed time period; S41: For quadrants without high-energy microseismic events, based on the grid location... The sum of the total energies of all high-energy microseismic events and the grid Area ratio calculation grid Energy density ; As a preferred method, the mesh is calculated according to formula (1). Energy density ; (1); In the formula, To fall into the grid The sum of the total energy of all microseismic events , To fall into the grid Energy values ​​of microseismic events; For grid The area.

[0024] S42: For quadrants containing high-energy microseismic events; based on the grid where the high-energy microseismic event W is located. Centered on the diffusion radius Determine the grid The neighborhood, and according to the grid The sum of the total energy of all grids in the neighborhood and the grid The average energy density is calculated by the ratio of the sum of the areas of all grid cells within the neighborhood. If the current grid energy density Below average energy density Then update to average energy density. Otherwise, retain the original value; correct outliers in local energy density using neighborhood information to make the energy distribution more consistent with actual geological conditions; To accurately reflect the influence range of microseismic events of different energy levels, the diffusion radius is set according to the energy level of high-energy microseismic events. The process is as follows: Let 'a' be the initial diffusion radius of the high-energy microseismic threshold. For a high-energy microseismic event, the diffusion radius increases by one energy level relative to the high-energy microseismic threshold. It increases in increments of 1.5a to 2a; To facilitate the calculation of the average energy density, the energy level of the high-energy microseismic event W can also be used. Directly determine the diffusion radius ,when , ;when , ;when , .

[0025] In order to accurately obtain the average energy density, the grid is calculated according to formula (2). Average energy density of the neighborhood grid ; (2).

[0026] As a preferred option, the updated current grid energy density is obtained according to formula (3). ; (3).

[0027] Step Six: Generation of a smooth energy density cloud map; Spatial interpolation is performed on the energy density data to generate a high-resolution (1 to 2 m) smooth energy density cloud map. Kriging interpolation is preferred for spatial interpolation.

[0028] Example: Step 1: Taking a coal mine as an example, microseismic monitoring data for 7 days, from November 23, 2024 to November 30, 2024, were selected. The selected study area is as follows: Figure 2 As shown, a total of 61 microseismic events were recorded, of which less than 10 were observed. 2 J8, 10 2 J~10 3 35 items between J, 10 3 J~10 4 There are 16 items between J, 10 4 J~10 5 One of J is between J, and the number is greater than 10. 5 J1. Spatial distribution of microseismic events as follows: Figure 3 As shown.

[0029] Step 2: During this time period, there were two high-energy microseismic events, one in the first quadrant and the other in the second quadrant. According to the grid division standard, the grid side length for the first and second quadrants is 10m, and the grid side length for the third and fourth quadrants is 30m. The grid division is as follows: Figure 4 .

[0030] Step 3: Calculate the energy density and average value of the first and second quadrant grids and their surrounding nine cells. Set the diffusion radius according to the energy level; here, the diffusion radius is set to 30m. Use the average energy density within this range as the benchmark value. Grid nodes with energy densities lower than this benchmark are corrected, while nodes with energy densities higher than the benchmark retain their original values. An example calculation is shown below. Figure 5 As shown.

[0031] Step 4: Perform spatial interpolation on the corrected energy density results, with a step size of 1m, to generate an energy density contour map as shown below. Figure 6 As shown in the figure, a comparison between the smooth energy density cloud map generated in this embodiment of the invention and the cloud map generated by the traditional method is shown in the figure. Figure 7 As shown.

[0032] Case studies demonstrate that this method can effectively overcome the "cliff-like" abrupt changes in high and low energy ranges in traditional cloud maps. The generated energy density field has a natural and smooth transition, which better matches the physical mechanism of mine seismic wave propagation and provides more reliable technical support for accurately assessing mining stress fields and risk zones.

[0033] To address the problem of abrupt contour line changes in traditional microseismic energy density cloud maps due to coarse grid division and energy abrupt changes, this invention provides an adaptive cloud map generation method that simultaneously considers differences in spatial energy distribution and energy diffusion characteristics. First, based on the spatial distribution of energy, the study area is divided into four quadrants, facilitating the analysis of microseismic activity characteristics in different quadrants. Simultaneously, by transforming continuous spatial influence distances into discrete quadrants, data complexity is reduced, facilitating rapid identification of key areas. Next, high-energy microseismic events in each quadrant are statistically analyzed, filtering out interference from low-energy events and allowing direct focus on high-energy microseismic events more likely to reflect rock mass instability or stress concentration. Furthermore, the quadrant statistical process quickly locates which quadrant contains the hazardous area, aiding in rapid response to risk events. Moreover, by differentiating the spatial differences between high-energy and low-energy events, the grid scale is set differently, with shorter grid sides for high-energy events, effectively ensuring improved spatial resolution in high-energy areas. This ensures that the generated energy density cloud map does not exhibit abrupt energy changes and more accurately displays the diffusion patterns of microseismic energy in the medium. Then, during the energy density calculation, direct calculation was used for quadrants without high-energy microseismic events. For quadrants with high-energy microseismic events, the influence range was defined by the diffusion radius corresponding to the energy level of the high-energy microseismic event. The average energy density of this influence range was then used as a benchmark value. Grid nodes within the influence range that were below the benchmark value were corrected, while nodes that were above the benchmark value retained their original values. This smooths out abnormally low-density areas caused by local rock mass heterogeneity or monitoring errors, making the corrected results more consistent with actual geological conditions and more accurately reflecting the actual propagation range of microseismic energy in the rock mass. At the same time, retaining the original values ​​of nodes above the benchmark value ensures that the original data of high-energy events is not diluted, avoiding the obscuring of key risk points. Thus, the correction process ensures that the subsequently generated energy density cloud map can more clearly identify high-risk areas and areas requiring reinforcement. Finally, interpolation is used to fill in the blank areas of the original grid data, further improving the resolution and ensuring that the final generated energy density cloud map can more clearly display areas of concentrated energy anomalies, avoiding the omission of high-energy points due to coarse grids.

[0034] This invention introduces an adaptive grid partitioning and energy density calculation averaging mechanism, enabling the generated energy density cloud map to effectively eliminate non-physical abrupt boundary changes and form a continuous and smooth energy transition representation. This more realistically reflects the actual impact range of high-energy microseismic events in the rock mass, significantly improving the interpretative value and reliability of the cloud map in mine dynamic disaster early warning. It solves the problems of traditional methods that use fixed grids for statistics and interpolation, which are prone to energy step changes and boundary distortions due to uneven distribution of microseismic energy, making it difficult to accurately represent the true impact domain of large-energy events.

[0035] This method is simple to implement and has low implementation costs. It can obtain energy density cloud maps with natural and smooth transitions in the energy density field, which helps to reveal the energy accumulation and evolution laws in the mining area more accurately and can be better used for microseismic early warning model training and visualization analysis.

Claims

1. A method for generating a smooth energy density cloud based on microseismic energy by sub-region, characterized in that, The method comprises the following steps: Step one: divide the study area into four quadrants based on the spatial distribution of microseismic event energy; Step two: count the number of high-energy microseismic events in each quadrant; Set a high-energy microseismic threshold, and count the number of high-energy microseismic events in each quadrant; Step three: determine the quadrant grid resolution according to the distribution position of high-energy microseismic events; The grid side length of the region with high-energy microseismic events is set as a g The grid side length of the region without high-energy microseismic events is set as a l Wherein, a g <a l ; Step four: calculate the energy density in a fixed time period; S41: For the quadrant without high-energy microseismic events, the energy density of the grid is calculated according to the ratio of the sum of the total energy of all high-energy microseismic events falling into the grid to the area of the grid . S42: For the quadrant with high-energy microseismic events, the energy density of the grid is calculated according to the ratio of the sum of the total energy of all high-energy microseismic events falling into the grid to the area of the grid . S43: The energy density of the grid is calculated according to the ratio of the sum of the S42: for the quadrant where high-energy microseismic events exist; take the grid where the high-energy microseismic event W is located as the center, combine the diffusion radius Determine the grid neighborhood, and calculate the average energy density according to the ratio of the sum of the energy of all grids in the grid neighborhood and the sum of the areas of all grids in the grid neighborhood ; if the current grid energy density is lower than the average energy density , update it to the average energy density , otherwise keep the original value ; Step six: generate a smooth energy density cloud chart; perform spatial interpolation on the energy density data to generate a high-resolution smooth energy density cloud chart.

2. The method for generating a flat energy density cloud map based on microseismic energy in sub-regions according to claim 1, characterized in that, In S42 of step four, the diffusion radius is set according to the energy level of the high-energy microseismic event , as follows: The initial diffusion radius of setting high-energy microseismic threshold is a, for high-energy microseismic event, relative to high-energy microseismic threshold, each increase of an energy level, the corresponding diffusion radius of high-energy microseismic event is increased by 1.5a-2a. The amplitude is increased by 1.5a-2a.

3. The method for generating a flat energy density cloud map based on microseismic energy in sub-regions according to claim 1, characterized in that, In step S41 of step four, the mesh is calculated according to formula (1). Energy density ; （1）； wherein is the area of the grid is the sum of the total energy of all microseismic events, , is the area of the grid is the energy value of a microseismic event; is the area of the grid .

4. The method for generating a flat energy density cloud map based on microseismic energy in sub-regions according to claim 1, characterized in that, In step four, S42, the grid is calculated according to equation (2) Average energy density of the neighborhood grid ; （2）。 5. The method for generating a flat energy density cloud map based on microseismic energy in sub-regions according to claim 1, characterized in that, In step four, S42, the updated current grid energy density is obtained according to equation (3) ; （3）。 6. The method for generating a flat energy density cloud map based on microseismic energy in sub-regions according to claim 1, characterized in that, In step two, the high energy microseismic threshold was 10 4 J.

7. The method of claim 1, wherein, In step two, the high-energy microseismic threshold is determined based on expert experience combined with historical microseismic events.

8. The method for generating a flat energy density cloud map based on microseismic energy in sub-regions according to claim 1, characterized in that, In step three, a g The value is 5m~10m, a l The value is 20m~30m.

9. The method for generating a flat energy density cloud map by sub-regions based on microseismic energy according to claim 1 or 2, characterized in that, In step six, the Kriging interpolation method is used for spatial interpolation.

10. The method for generating a flat energy density cloud map based on microseismic energy in sub-regions according to claim 1, characterized in that, In step one, the process of dividing the study area into four quadrants is as follows: S11: Obtain microseismic monitoring data through the microseismic monitoring system, and determine the microseismic advanced influence distance D in a fixed time period based on the microseismic monitoring data c , the goaf direction influence distance D f , and the working face two-roadway lateral influence distance D b ; S12: the microseismic advance influence distance D c , the goaf direction influence distance D f , and the working face two roadway lateral influence distance D b ; the geometric center point is taken as the coordinate origin; at the same time, the coordinate axes are drawn along the working face strike and dip, and the research area is divided into four quadrants S i , i .

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