Micro-seismic energy density cloud mapping method based on energy diffusion radius weighting

Abstract

The invention relates to a micro-seismic energy density cloud mapping method based on energy diffusion radius weighting. The micro-seismic energy density cloud mapping method comprises the following steps: collecting micro-seismic monitoring data; for the event E0 of which the energy value E is greater than or equal to a set energy threshold value Eth, obtaining an energy attenuation model through fitting; calculating an influence radius R according to an energy attenuation model and an energy threshold Eth; defining the influence radius R of the event E1 of which the energy value E is less than Eth as 0; dividing the working face space area into two-dimensional grids; for each event Ei, obtaining an energy density contribution value Eij of the grid in the influence radius R of the event Ei through an energy attenuation model; accumulating the energy density contribution values Eij of all events to the grid to obtain total energy density, and dividing the total energy density by the area to obtain an energy density value; and on the basis of the energy density values of all the grids, generating a continuous energy density cloud picture by adopting a spatial interpolation method. According to the method, the energy density cloud picture with clear physical significance and smooth transition can be generated.

Description

Technical Field

[0001] This invention belongs to the field of mine safety monitoring technology, specifically relating to a method for microseismic energy density cloud mapping based on energy diffusion radius weighting. Background Technology

[0002] In mining engineering, microseismic monitoring is a key technology for early warning of dynamic disasters such as rockbursts and rock bursts. Energy density cloud maps can visually display the spatial distribution of energy within the surrounding rock of the mining area, and are an important tool for analyzing high-stress concentration zones and potential risk areas.

[0003] Currently, the conventional method for generating energy density cloud maps is to divide the spatial area of ​​the mining face into grids with fixed side lengths (usually 20m to 30m to match the microseismic positioning error), count the total energy of microseismic events falling into each grid within a fixed time period, divide it by the grid area to obtain the energy density value at the grid center point, and finally generate a continuous cloud map through spatial interpolation.

[0004] However, traditional methods simplify all microseismic events, regardless of energy level, into a single "point source" located at the hypocenter. This simplification ignores the actual physical extent of the impact of high-energy microseismic events. In reality, a high-energy (e.g., 10⁻⁶) microseismic event... 4 Microseismic events (J) release stress waves that propagate a considerable distance within the rock mass, affecting the stability of the rock mass within a certain radius around the seismic source, not just the individual grid it resides in. Traditional methods cannot characterize this diffusion effect. To address this issue, there is an urgent need for a microseismic energy density cloud mapping method based on energy diffusion radius weighting. Summary of the Invention

[0005] To address the problems of the existing technologies, this invention provides a microseismic energy density cloud mapping method based on energy diffusion radius weighting. This method is simple to implement and has high generation accuracy. It can generate energy density cloud maps with clear physical meaning and smooth transitions. The energy density cloud maps generated by this method can clearly show the concentrated distribution areas and energy evolution patterns of microseismic events during the mining of steeply inclined coal seams. Through the distribution characteristics of the energy cloud maps, high-energy concentration areas can be identified intuitively and accurately, thereby providing an intuitive decision-making basis for rockburst risk assessment and better assisting in the formulation of rockburst prevention measures.

[0006] To achieve the above objectives, this invention provides a method for microseismic energy density cloud mapping based on energy diffusion radius weighting, comprising the following steps: Step 1: Acquisition and preprocessing of microseismic data; Collect microseismic monitoring data of the target mining face within a fixed time period T; Step 2: Determining the energy decay model and the radius of influence; For energy values ​​E greater than or equal to the set energy threshold E th For the microseismic event E0, based on the location of the epicenter of microseismic event E0 and the relative locations of each monitoring station, as well as the observation data from each monitoring station, an energy attenuation model and its parameters are obtained through a fitting algorithm; based on the energy attenuation model and a set energy threshold E0... th Calculate the radius of influence R of the microseismic event E0; for energy values ​​E less than E0... th For the microseismic event E1, the influence radius R of the microseismic event E1 is defined as 0; Step 3: Mesh generation and energy diffusion distribution; The target mining face spatial region is divided into a two-dimensional grid with side length a; for each microseismic event E i The microseismic event E was obtained through an energy decay model. i The energy density contribution value E of the grid within the radius R ij ; Accumulate all microseismic events E i Energy density contribution value E of the grid ij The total energy density of the grid is obtained. ; Step 4: Calculation of grid energy density; Total energy density of each grid Divide by the area of ​​the grid The energy density value of the grid is obtained. ; Step 5: Generation of energy density cloud map; Based on the energy density values ​​of all grids The values ​​are used to generate continuous energy density cloud maps using spatial interpolation methods.

[0007] As a preferred option, in step two, the energy decay model is obtained through the following process: S21: Construct a particle vibration velocity decay model based on formula (1); construct an energy decay model based on formula (2); (1); (2); In the formula, Distance from the epicenter The particle vibration velocity at a given point, expressed in m / s; The velocity of the particles at the earthquake source is expressed in m / s. For the overall attenuation coefficient, , The geometric diffusion index, The absorption coefficient of the medium; The propagation distance is expressed in meters (m). Distance from the epicenter The energy value at that location is expressed in J / m³. 2 ; This represents the energy value at the earthquake's source, expressed in J / m³. 2 ; The energy decay coefficient; S22: Based on the relationship that kinetic energy is proportional to the square of velocity, the energy decay coefficient is established according to formula (3). With the overall attenuation coefficient Relationship; (3); S23: Fit the peak velocity decay law of the particles recorded by the sensor according to formula (1); S24: Fitting using the least squares method and Data, solve for the overall attenuation coefficient ;according to Solve for the energy decay coefficient Combined with the energy value at the epicenter According to formula (4), the energy attenuation model of mine earthquake is obtained; (4); In the formula, The radius of influence of the microseismic event.

[0008] As a preferred option, in step two, an energy threshold E is set. th 10 4 J.

[0009] As a preferred option, in step two, a power-law function is used to fit the energy decay model.

[0010] As a preferred option, in step three, the side length a is taken to be 20-30m.

[0011] As a preferred option, in step four, the total energy density Obtained through the following process: For the center point G of each grid, the energy density contribution of each microseismic event to G is accumulated, and finally the total energy density of the grid is obtained by solving. .

[0012] As a preferred option, in step five, the spatial interpolation method is Kriging interpolation.

[0013] This invention provides a method for microseismic energy density cloud mapping based on energy diffusion radius weighting. First, continuous monitoring data within a fixed time period T ensures the temporal integrity of the data, providing a high-quality foundation for subsequent analysis. Next, by setting energy thresholds, the influence ranges of different energy events are distinguished, making the subsequent analysis more targeted. An energy attenuation model is fitted based on actual observation data, ensuring the objectivity and accuracy of the model. Based on this, the influence radius is obtained, and its quantified boundary provides a clear boundary basis for the subsequent energy allocation process, enabling a more accurate quantification of the impact of microseismic events on the surrounding rock mass within a certain range. Furthermore, dividing the working face space into a regular grid achieves discretization of the continuous space, facilitating quantitative calculations. Simultaneously, the energy attenuation model calculates the energy density contribution of each event to the grid, achieving precise spatial energy allocation and avoiding ambiguity in energy distribution. Finally, the grid energy density calculation process effectively eliminates the influence of grid size on the results, making the energy densities of different regions comparable. Finally, by using spatial interpolation to produce continuous cloud maps, the spatial distribution trend of energy density can be intuitively reflected, making it easier to identify high-energy focusing areas and assisting in rapid decision-making and early warning.

[0014] This invention innovatively introduces the concept of energy diffusion radius in the process of generating energy density cloud maps, and establishes an event-level energy power-law decay model based on station observation data. This model achieves a physically consistent diffusion distribution of the impact of high-energy events within their neighborhoods, realizing consistency between the energy density cloud map and the spatial propagation law of microseismic energy. Compared with traditional methods that simplify events to point sources and do not distinguish energy levels, this invention can reflect the characteristics of microseismic events with "high energy and wide impact" in the cloud map, significantly reducing the occurrence of energy omissions and local overfitting, and greatly improving the reliability of microseismic anomaly spatial identification and hazard area delineation.

[0015] This method is simple to implement and generates high-precision energy density cloud maps with clear physical meaning and smooth transitions. The energy density cloud maps generated by this method can clearly show the concentrated distribution areas and energy evolution patterns of microseismic events during the mining of steeply inclined coal seams. Through the distribution characteristics of the energy cloud maps, high-energy concentration areas can be identified intuitively and accurately, thus providing an intuitive basis for decision-making in rockburst risk assessment and better assisting in the formulation of rockburst prevention measures. Attached Figure Description

[0016] Figure 1 Flowchart of the present invention; Figure 2 This refers to the microseismic spatial positioning and station location information in this embodiment of the invention; Figure 3This is a schematic diagram of energy-distance power-law fitting for a single-event station in an embodiment of the present invention; Figure 4 This is the final cloud map formed in the embodiments of the present invention; Figure 5 This is a schematic diagram comparing the method in this invention with the point source method cloud map. Detailed Implementation

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

[0018] like Figures 1 to 2 As shown, this invention provides a method for microseismic energy density cloud mapping based on energy diffusion radius weighting, comprising the following steps: Step 1: Acquisition and preprocessing of microseismic data; Collect microseismic monitoring data of the target mining face within a fixed time period T; The microseismic monitoring data includes at least the source coordinates (X, Y), energy value E, and observation data from monitoring stations for each microseismic event. As a preferred option, after obtaining the microseismic monitoring data, microseismic events with excessively low signal-to-noise ratios or excessively large positioning errors need to be removed to ensure the quality of the data. Step 2: Determining the energy decay model and the radius of influence; For energy values ​​E greater than or equal to the set energy threshold E th For the microseismic event E0, based on the location of the epicenter of microseismic event E0 and the relative locations of each monitoring station, as well as the observation data from each monitoring station, an energy attenuation model and its parameters are obtained through a fitting algorithm; based on the energy attenuation model and a set energy threshold E0... th Calculate the radius of influence R of the microseismic event E0; for energy values ​​E less than E0... th For the microseismic event E1, the influence radius R of the microseismic event E1 is defined as 0, indicating that its influence can be ignored; Step 3: Mesh generation and energy diffusion distribution; The target mining face spatial region is divided into a two-dimensional grid with side length 'a'. Uniform gridding facilitates subsequent energy density calculation and spatial interpolation. For each microseismic event E... i The microseismic event E was obtained through an energy decay model. i The energy density contribution value E of the grid within the radius R ij ; Accumulate all microseismic events E i Energy density contribution value E of the grid ij The total energy density of the grid is obtained. ; In this way, the energy of microseismic events can be distributed into the space network according to the attenuation law, thereby providing input data for energy density calculation; Step 4: Calculation of grid energy density; Total energy density of each grid Divide by the area of ​​the grid The energy density value of the grid is obtained. In this way, spatial standardization comparison is achieved; Step 5: Generation of energy density cloud map; Based on the energy density values ​​of all grids The values ​​are used to generate continuous energy density cloud maps using spatial interpolation methods.

[0019] As a preferred option, in step two, the energy decay model is obtained through the following process: S21: Construct a particle vibration velocity decay model based on formula (1); construct an energy decay model based on formula (2); (1); (2); In the formula, Distance from the epicenter The particle vibration velocity at a given point, expressed in m / s; The velocity of the particles at the earthquake source is expressed in m / s. The overall attenuation coefficient includes geometric diffusion and medium absorption effects. , The geometric diffusion index, The absorption coefficient of the medium; The propagation distance is expressed in meters (m). Distance from the epicenter The energy value at that location; This represents the energy value at the epicenter. The energy decay coefficient; S22: Based on the relationship that kinetic energy is proportional to the square of velocity, the energy decay coefficient is established according to formula (3). With the overall attenuation coefficient Relationship; (3); S23: Fit the peak velocity decay law of the particles recorded by the sensor according to formula (1); S24: Fitting using the least squares method and Data, solve for the overall attenuation coefficient ;according to Solve for the energy decay coefficient Combined with the energy value at the epicenter According to formula (4), the energy attenuation model of mine earthquake is obtained; (4); In the formula, The radius of influence of the microseismic event.

[0020] As a preferred option, in step two, an energy threshold E is set. th 10 4 J.

[0021] As a preferred option, in step two, a power-law function is used to fit the energy decay model.

[0022] As a preferred option, in step three, the side length a is taken to be 20-30m.

[0023] As a preferred option, in step four, the total energy density Obtained through the following process: For the center point G of each grid, the energy density contribution of each microseismic event to G is accumulated, and finally the total energy density of the grid is obtained by solving. The energy density contribution value is calculated from the energy attenuation model of each microseismic event, the location information of the microseismic event, and the distance to the grid center point.

[0024] As a preferred option, in step five, the spatial interpolation method is Kriging interpolation.

[0025] Example: S1. 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 J has 8 events, 10 2 J~10 3 There are 35 events between J, 10 3 J~10 4 There are 16 events between J, 10 4 J~10 5 There is one event between J, which is greater than 10. 5 One event in J. Spatial distribution of microseismic events and seismic network layout as follows: Figure 3 As shown.

[0026] S2, Set the energy threshold E th = 10000J. Iterate through all events, and for each event with an energy value E ≥ 10000J, perform the following operations: S2-1: Extract all stations that recorded the event and their corresponding energy amplitude Aj (such as peak particle velocity) and epicentral distance dj.

[0027] S2-2: Using power-law functions For data points Perform least squares fitting to obtain the parameters. and ; S2-3: Read the energy values ​​of microseismic events from the microseismic monitoring system. ,according to and middle The relationship is obtained by substituting it into S2-2. Values ​​are obtained at different distances. Attenuated value ,in The lower limit of attenuation is 1×10 4 J, at this time the attenuation radius R is 300m.

[0028] S3. Divide the working area into a 10m × 10m grid. For the event with R = 300m calculated in the previous step, draw a circle with its epicenter at a radius of 300m. All grids covered by this circular area are then processed according to the fitted formula. Calculating the attenuated energy at different distances reveals that a grid 10m from the epicenter receives significantly more energy than a grid 40m from the epicenter. For an event with R=0, all its energy is accumulated in its unique grid cell.

[0029] S4. After all the covered grids have been traversed, each grid receives the sum of energy contributions from different events. .use Divide by grid area (100m²) 2 ), to obtain the energy density value of the grid. .

[0030] S5, All grids Using known points, Kriging interpolation is employed to interpolate the entire working surface area, generating a continuous and smooth energy density cloud map, such as... Figure 4 As shown.

[0031] This invention provides a method for microseismic energy density cloud mapping based on energy diffusion radius weighting. First, continuous monitoring data within a fixed time period T ensures the temporal integrity of the data, providing a high-quality foundation for subsequent analysis. Next, by setting energy thresholds, the influence ranges of different energy events are distinguished, making the subsequent analysis more targeted. An energy attenuation model is fitted based on actual observation data, ensuring the objectivity and accuracy of the model. Based on this, the influence radius is obtained, and its quantified boundary provides a clear boundary basis for the subsequent energy allocation process, enabling a more accurate quantification of the impact of microseismic events on the surrounding rock mass within a certain range. Furthermore, dividing the working face space into a regular grid achieves discretization of the continuous space, facilitating quantitative calculations. Simultaneously, the energy attenuation model calculates the energy density contribution of each event to the grid, achieving precise spatial energy allocation and avoiding ambiguity in energy distribution. Finally, the grid energy density calculation process effectively eliminates the influence of grid size on the results, making the energy densities of different regions comparable. Finally, by using spatial interpolation to produce continuous cloud maps, the spatial distribution trend of energy density can be intuitively reflected, making it easier to identify high-energy focusing areas and assisting in rapid decision-making and early warning.

[0032] This invention innovatively introduces the concept of energy diffusion radius in the process of generating energy density cloud maps, and establishes an event-level energy power-law decay model based on station observation data. This model achieves a physically consistent diffusion distribution of the impact of high-energy events within their neighborhoods, realizing consistency between the energy density cloud map and the spatial propagation law of microseismic energy. Compared with traditional methods that simplify events to point sources and do not distinguish energy levels, this invention can reflect the characteristics of microseismic events with "high energy and wide impact" in the cloud map, significantly reducing the occurrence of energy omissions and local overfitting, and greatly improving the reliability of microseismic anomaly spatial identification and hazard area delineation.

[0033] This method is simple to implement and generates high-precision energy density cloud maps with clear physical meaning and smooth transitions. For example... Figure 5 As shown, the energy density cloud map generated by this method can clearly show the concentrated distribution area and energy evolution law of microseismic events during the mining of steeply inclined coal seams. Through the distribution characteristics of the energy cloud map, high energy concentration areas can be identified intuitively and accurately, which can provide an intuitive decision-making basis for rockburst risk assessment and better assist in guiding the formulation of rockburst prevention measures.

Claims

1. A microseismic energy density clouding method based on energy diffusion radius weighting, characterized in that, The method comprises the following steps: Step one: microseismic data acquisition and preprocessing; Acquire microseismic monitoring data of the target mining working face in a fixed time period T; Step two: determination of energy attenuation model and influence radius; For microseismic events E0 with energy value E greater than or equal to a set energy threshold value E th , based on the focal position of the microseismic events E0 and the relative position of each monitoring station and the observation data of each monitoring station, an energy attenuation model and its parameters are obtained through a fitting algorithm; the influence radius R of the microseismic events E0 is calculated according to the energy attenuation model and the set energy threshold value E th ; for microseismic events E1 with energy value E less than E th , the influence radius R of the microseismic events E1 is defined as 0. Step three: grid division and energy diffusion distribution; Divide the target mining working face space region into a two-dimensional grid with side length a; for each microseismic event E i , get the energy density contribution value E i of the grid within the influence radius R of the microseismic event E ij ; accumulate the energy density contribution values E i of all microseismic events E ij to the grid to obtain the total energy density of the grid ; Step four: calculation of grid energy density; Total energy density of each grid Divide by the area of ​​the grid The energy density value of the grid is obtained. ; Step five: generation of energy density cloud chart; based on all grid energy density values values, a continuous energy density cloud is generated using a spatial interpolation method.

2. The microseismic energy density clouding method based on energy diffusion radius weighting according to claim 1, characterized in that, In step two, the energy attenuation model is obtained through the following process: S21: construct a particle vibration velocity attenuation model according to formula (1); construct an energy attenuation model according to formula (2); （1）； （2）； In the formula, Distance from the epicenter The particle vibration velocity at a given point, expressed in m / s; The velocity of the particles at the earthquake source is expressed in m / s. For the overall attenuation coefficient, , The geometric diffusion index, The medium absorption coefficient; The propagation distance is expressed in meters (m). Distance from the epicenter The energy value at that location; This represents the energy value at the epicenter. The energy decay coefficient; S22: Based on the relationship that kinetic energy is proportional to the square of velocity, the energy attenuation coefficient is established according to formula (3) and the relationship with the comprehensive attenuation coefficient ​ （3）； S23: fit the particle peak value velocity attenuation law recorded by the sensor according to formula (1); S24: fitting by least square method With Data, solve the comprehensive attenuation coefficient ; according to Solve the energy attenuation coefficient ; combined with the energy value at the source , according to formula (4) to get the mine earthquake energy attenuation model; （4）； In the formula, is the radius of influence of the microseismic event.

3. The microseismic energy density clouding method based on energy diffusion radius weighting according to claim 1, characterized in that, In step two, an energy threshold E is set th is 10 4 J.

4. The microseismic energy density clouding method based on energy diffusion radius weighting according to claim 1, characterized in that, In step two, the energy attenuation model is fitted by using a power law function.

5. The microseismic energy density clouding method based on energy diffusion radius weighting according to claim 5, characterized in that, In step three, the side length a is 20-30 m.

6. The microseismic energy density clouding method based on energy diffusion radius weighting according to claim 1, characterized in that, In step four, the total energy density was obtained by the process of For each grid center point G, the energy density contribution value of each microseismic event to G is accumulated, and finally the total energy density of the grid is solved .

7. The microseismic energy density clouding method based on energy diffusion radius weighting according to claim 6, characterized in that, In step five, the spatial interpolation method is Kriging interpolation.

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