A rock burst dangerous area division method and system based on mine shock information

Abstract

The application provides a rock burst dangerous area division method and system based on mine earthquake information, and relates to the underground excavation engineering and coal rock dynamic disaster early warning technical field. The method comprises the following steps: collecting mine earthquake monitoring data in a target area of a mine and preprocessing, then dividing the target area into a space grid and determining the corresponding mine earthquake set of each grid, calculating the rock burst precursor early warning indexes corresponding to the mine earthquake set in each grid and normalizing, using a Gaussian space smoothing model to draw a spatial distribution nephogram of each early warning index, taking the standard deviation of the nephogram as the weight of image fusion, combining a weighted average image fusion method to obtain a rock burst dangerous spatial distribution nephogram, and combining a mine bottom map to determine a local rock burst dangerous area. The application realizes efficient division of the rock burst dangerous space area of the mine by using the mine earthquake information, can provide a scheme for accurate early warning of the underground rock burst danger, and effectively reduces the prevention and control cost.

Description

Technical Field

[0001] This invention relates to the fields of underground excavation engineering and early warning technology for coal and rock dynamic disasters, and in particular to a method and system for delineating rockburst hazard zones based on mine seismic information. Background Technology

[0002] Rockbursts are a typical dynamic disaster faced during underground coal mining operations. Characterized by their suddenness and intensity, they pose a significant threat to the lives and property of miners and other personnel. Furthermore, with the near depletion of shallow mineral resources and the increasing expansion of coal mining into deeper parts of the earth, the mechanisms of rockbursts are becoming increasingly complex due to the combined influence of factors such as geostress and geological conditions, posing a serious challenge to safe coal mine production.

[0003] In recent years, microseismic monitoring systems have been considered one of the most effective monitoring methods for preventing rockbursts due to their ability to monitor coal and rock mass fractures over large areas of mines in real time. However, due to factors such as system positioning errors and network layout, the accuracy of early warning still has considerable room for improvement. To ensure the safety of underground workers and property, it is often necessary to implement various rockburst prevention measures on a large scale, the effectiveness of which is difficult to verify, resulting in the waste of some resources and an increase in mining costs. Therefore, it is necessary to further mine seismic monitoring data to obtain spatial distribution information of mine rockburst hazards and propose a method for delineating rockburst hazard zones. Summary of the Invention

[0004] This invention proposes a method for delineating rockburst hazard zones based on mine seismic information, which enables efficient delineation of underground rockburst hazard zones and reduces rockburst prevention costs. It also solves the problems of existing technologies, such as the difficulty in real-time delineation of local rockburst hazard zones in mines, which leads to resource waste and increased mining costs due to the need for large-scale implementation of various rockburst prevention measures.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0006] This invention provides a method for delineating rockburst hazard zones based on mine seismic information. This method is applicable to electronic devices and includes the following steps:

[0007] S1. Collect seismic monitoring data within the target area of ​​the mine and perform preprocessing;

[0008] S2. Divide the target area into spatial grids and determine the set of mine seismic events corresponding to each grid by combining the pre-processed mine seismic monitoring data.

[0009] S3. Calculate the early warning indicators of the shock precursors corresponding to the set of mine tremors in each grid, and normalize the early warning indicators.

[0010] S4. Use the Gaussian spatial smoothing model to draw the spatial distribution cloud map of each early warning indicator;

[0011] S5. Calculate the standard deviation of the spatial distribution cloud map of each early warning indicator, and use the standard deviation as the weight for image fusion.

[0012] S6. Based on weights, obtain the spatial distribution cloud map of impact hazard using the weighted average image fusion method;

[0013] S7. Based on the pre-collected mine bottom map, determine and delineate local impact hazard zones.

[0014] Preferably, in S1, the collection of seismic monitoring data within the target area of ​​the mine includes:

[0015] Acquire real-time microseismic monitoring data collected by the mine microseismic monitoring system. Each data point includes the three-dimensional coordinates (X, Y, Z) of the seismic event, the occurrence time (T), and the source energy (E).

[0016] The target area is the region within 200m before and after the downhole working face.

[0017] Preferably, in S1, the preprocessing of the mine seismic monitoring data includes:

[0018] The raw microseismic monitoring data were screened to identify seismic events with three-dimensional coordinates located within the target area and energy E greater than 100 J.

[0019] Preferably, in S2, the target area is divided into spatial grids to determine the set of seismic events corresponding to each grid, including:

[0020] The grid size of the target area is determined based on the positioning error of the mine microseismic monitoring system;

[0021] All seismic events located within a grid constitute the set of seismic events corresponding to that grid.

[0022] Preferably, in S3, the shock precursor warning index corresponding to the set of mine tremors within each grid is calculated and normalized, including:

[0023] Obtain the shock precursor warning indicators corresponding to the set of mine seismic events in each grid. The shock precursor warning indicators include: total fault area, A(b) value, total daily frequency, average frequency, and microseismic activity.

[0024] The impact precursor warning index is transformed into a constant between 0 and 1 after dimensionless conversion according to the following formula (1):

[0025] (1)

[0026] in, The normalized warning indicator value, These are early warning indicator values; This represents the minimum value of the early warning indicator throughout the entire cycle. This represents the maximum value of the early warning indicator over the entire cycle.

[0027] Preferably, in S4, a Gaussian space smoothing model is used to draw a spatial distribution cloud map of each early warning indicator, including:

[0028] Construct a Gaussian smooth model according to the following formula (2):

[0029] (2)

[0030] in, For the first i Gaussian space smoothing values ​​corresponding to each grid cell. For the first i The first grid edge j Normalized warning indicator values ​​in each grid; For the first i The grid and the first j The distance between grid cells; c For the relevant distance, i 3 around the grid c Calculated from the data within the inner grid .

[0031] Preferably, in S5, the standard deviation of the spatial distribution cloud map of each early warning indicator is calculated, and the standard deviation is used as the weight for image fusion, including:

[0032] Extract all pixel values ​​from the RGB three channels of the image, and denote them as X. R X G X B The standard deviation of the cloud map is calculated according to the following formula (3):

[0033] (3),

[0034] in, The standard deviation of the cloud plot is represented by... E( X R )、E( X G )、E( X B ) It expresses expectation.

[0035] Preferably, in S6, a weighted average image fusion method is used to obtain a spatial distribution cloud map of impact hazard based on weights, including:

[0036] Extract the grayscale values ​​of each early warning indicator cloud map, denoted as Y, and calculate the grayscale value F of the spatial distribution cloud map of the impact hazard according to the following formula (4):

[0037] (4),

[0038] in, n This indicates the total number of warning indicator cloud maps. This indicates the weights corresponding to the early warning indicator cloud map.

[0039] A system for delineating rockburst hazard zones based on seismic information, the system being used in the aforementioned method for delineating rockburst hazard zones based on seismic information, the system comprising:

[0040] The data acquisition block is used to collect and preprocess seismic monitoring data within the target area of ​​the mine.

[0041] The grid division module is used to divide the target area into spatial grids and determine the set of mine seismic events corresponding to each grid by combining the pre-processed mine seismic monitoring data.

[0042] The early warning calculation module is used to calculate the early warning indicators of the shock precursors corresponding to the set of mine tremors in each grid and to normalize the early warning indicators.

[0043] The early warning cloud map drawing module is used to draw spatial distribution cloud maps of various early warning indicators using a Gaussian spatial smoothing model;

[0044] The standard deviation calculation module is used to calculate the standard deviation of the spatial distribution cloud map of each early warning indicator, and use the standard deviation as the weight for image fusion.

[0045] The impact hazard cloud map drawing module is used to obtain the spatial distribution cloud map of impact hazard based on weights and using a weighted average image fusion method.

[0046] The shock hazard classification module is used to determine and classify local shock hazard zones by combining pre-collected mine bottom maps.

[0047] Preferably, the data acquisition block is further used to acquire real-time microseismic monitoring data collected by the mine microseismic monitoring system. Each data entry includes the three-dimensional coordinates X, Y, and Z of the seismic event, the occurrence time T, and the source energy E.

[0048] The target area is the region within 200m before and after the downhole working face.

[0049] On the one hand, an electronic device is provided, which includes a processor and a memory, wherein the memory stores at least one instruction, which is loaded and executed by the processor to implement the above-mentioned method for delineating rockburst hazard zones based on seismic information.

[0050] On the one hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement the above-mentioned method for delineating rockburst hazard zones based on seismic information.

[0051] The above technical solution has at least the following advantages compared with the existing technology:

[0052] The above-mentioned solution, the present invention provides a method for delineating rockburst hazard zones based on mine seismic information: 1) It can use mine seismic monitoring information to delineate rockburst hazard zones, assisting staff in efficiently identifying local hazard zones;

[0053] 2) It can use modular programming in computer languages ​​to automatically collect microseismic monitoring data and draw real-time cloud maps of the spatial distribution of impact hazards, greatly shortening the time required to identify impact hazard zones;

[0054] 3) Assist in identifying localized hazardous areas caused by rockbursts, guide mines to take targeted pressure relief and mitigation measures, and reduce the cost of rockburst prevention and control. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 The flowchart of a method for delineating rockburst hazard zones based on mine seismic information provided by the present invention is shown.

[0057] Figure 2 Spatial distribution cloud map of the total fault area early warning index provided in this embodiment of the invention;

[0058] Figure 3 Provided for embodiments of the present invention A(b) Spatial distribution cloud map of early warning indicators;

[0059] Figure 4 Spatial distribution cloud map of the daily total frequency warning index provided in this embodiment of the invention;

[0060] Figure 5 Spatial distribution cloud map of the average frequency warning index provided in this embodiment of the invention;

[0061] Figure 6 Spatial distribution cloud map of microseismic activity early warning indicators provided in this embodiment of the invention;

[0062] Figure 7This is a spatial distribution cloud map of impact hazards after image fusion, provided in an embodiment of the present invention.

[0063] Figure 8 This is a schematic diagram of a rockburst hazard zone delineation system based on mine seismic information provided by the present invention.

[0064] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0066] This invention addresses the problem in existing technologies that make it difficult to delineate localized rockburst hazard zones in mines in real time, leading to resource waste and increased mining costs due to the need for large-scale rockburst prevention measures. It proposes a rockburst hazard zone delineation scheme based on mine seismic information. This invention utilizes modular programming in computer languages ​​to automatically collect microseismic monitoring data and generate real-time spatial distribution cloud maps of rockburst hazards, significantly reducing the time required for rockburst hazard zone identification.

[0067] Figure 1 This is a flowchart of a method for delineating rockburst hazard zones based on seismic information according to the present invention. This method can be implemented by electronic equipment. The method is used in a system for delineating rockburst hazard zones based on seismic information, and the method includes:

[0068] S101. Collect seismic monitoring data within the target area of ​​the mine and perform preprocessing.

[0069] In one feasible implementation, seismic monitoring data is collected within the target area of ​​the mine, including:

[0070] Acquire microseismic monitoring data collected by mine microseismic monitoring systems such as ARAMIS M / E and SOS. Each data point includes the three-dimensional coordinates (X, Y, Z) of the seismic event, the occurrence time (T), and the source energy (E).

[0071] The target area is the area within 200m before and after the downhole working face.

[0072] In one feasible implementation, the mine seismic monitoring data is preprocessed, including:

[0073] The raw microseismic monitoring data were screened to identify seismic events with three-dimensional coordinates located within the target area and energy E greater than 100 J.

[0074] In one feasible implementation, the collection of seismic monitoring data within the target area of ​​the mine includes: the target area is a key area of ​​concern underground that may have a significant impact on normal mining production, generally within 200m before and after the working face; the seismic monitoring data consists of microseismic monitoring data collected by the mine's ARAMIS M / E, SOS, and other microseismic monitoring systems, with each data point containing the three-dimensional coordinates (X, Y, Z), occurrence time (T), and source energy (E / J) of the seismic event. The preprocessing of the seismic monitoring data specifically involves: filtering the raw microseismic monitoring data to identify seismic events whose coordinates are located within the target area and whose energy is greater than 100J.

[0075] S102. Divide the target area into spatial grids and determine the set of seismic events corresponding to each grid by combining the preprocessed seismic monitoring data.

[0076] In one feasible implementation, the target area is divided into spatial grids, and the set of seismic events corresponding to each grid is determined, including:

[0077] The grid size of the target area is determined based on the positioning error of the mine microseismic monitoring system, generally within the range of 10 to 50 meters, but should be determined in conjunction with the actual geological conditions of the mine.

[0078] All seismic events located within a grid constitute the set of seismic events corresponding to that grid.

[0079] S103. Calculate the early warning index of the shock precursor corresponding to the set of mine tremors in each grid, and normalize the early warning index.

[0080] In one feasible implementation, the shock precursor early warning index corresponding to the set of mine tremors within each grid is calculated and normalized, including:

[0081] Obtain the shock precursor warning indicators corresponding to the set of mine seismic events in each grid. The shock precursor warning indicators include: total fault area, A(b) value, total daily frequency, average frequency, and microseismic activity.

[0082] The impact precursor warning index is transformed into a constant between 0 and 1 after dimensionless conversion according to the following formula (1):

[0083] (1)

[0084] in, The normalized warning indicator value, These are early warning indicator values; This represents the minimum value of the early warning indicator throughout the entire cycle. This represents the maximum value of the early warning indicator over the entire cycle.

[0085] In one feasible implementation, the total fault area is calculated using the following formula:

[0086] ,

[0087] in, k 0 This represents the lower limit of the energy level for micro-vibrations within the time window. k For each microseismic event, N(k) The energy level within the time window is k The number of microseismic events;

[0088] A(b) The value is calculated by the following formula:

[0089] ,

[0090] in, N This represents the total number of micro-earthquakes. M i Energy level of microseismic events;

[0091] The total daily frequency is calculated using the following formula:

[0092] ,

[0093] in, n This indicates the total frequency of microseismic events within the previous 24 hours;

[0094] The average total frequency is calculated using the following formula:

[0095] ,

[0096] in, T Indicates the length of the time window. F (t) express t The frequency of micro-vibrations at any given moment;

[0097] The value of microseismic activity is calculated by the following formula:

[0098] ,

[0099] in, N This represents the total number of micro-earthquakes. E i For microseismic events, M This represents the maximum energy level of a microseismic event.

[0100] S104. Use a Gaussian spatial smoothing model to draw spatial distribution cloud maps of each early warning indicator;

[0101] In one feasible implementation, a Gaussian space smoothing model is used to plot the spatial distribution cloud map of each early warning indicator, including:

[0102] Construct a Gaussian smooth model according to the following formula (2):

[0103] (2)

[0104] in, For the first i Gaussian space smoothing values ​​corresponding to each grid cell. For the first i The first grid edge j Normalized warning indicator values ​​in each grid; For the first i The grid and the first j The distance between grid cells; c For the relevant distance, i 3 around the grid c Calculated from the data within the inner grid .

[0105] S105. Calculate the standard deviation of the spatial distribution cloud map of each early warning indicator, and use the standard deviation as the weight for image fusion.

[0106] In one feasible implementation, the standard deviation of the spatial distribution cloud map of each early warning indicator is calculated, and the standard deviation is used as the weight for image fusion, including:

[0107] Extract all pixel values ​​from the RGB three channels of the image, and denote them as X. R X G X B The standard deviation of the cloud map is calculated according to the following formula (3):

[0108] (3),

[0109] in, The standard deviation of the cloud plot is represented by... E( X R )、E( X G )、E( X B ) It expresses expectation.

[0110] S106. Based on weights, a weighted average image fusion method is used to obtain a spatial distribution cloud map of impact hazard.

[0111] In one feasible implementation, a weighted average image fusion method is used to obtain a spatial distribution cloud map of impact hazard based on weights, including:

[0112] Extract the grayscale values ​​of each channel of the cloud map of each early warning indicator, and denote them as Y. Calculate the grayscale values ​​F of each channel of the spatial distribution cloud map of the impact hazard according to the following formula (4):

[0113] (4),

[0114] in, n This indicates the total number of warning indicator cloud maps. This indicates the weights corresponding to the early warning indicator cloud map.

[0115] S107. Based on the pre-collected mine bottom map, determine and delineate local impact hazard zones.

[0116] In one feasible implementation, based on the mine floor map, the dark areas (i.e., high grayscale areas) in the cloud map have a higher impact risk than other areas, and pressure relief measures such as drilling and blasting should be applied to these local areas. The mine floor map is a diagram of the mine's roadway layout.

[0117] In one feasible implementation, this embodiment of the invention takes a working face of a coal mine as an example, takes a 200m range before and after the working face as the target area, collects the original monitoring data of microseismic events occurring in the target area, and removes microseismic events with energy less than 100J.

[0118] The target area was divided into grids of 10m × 10m, and the microseismic event set to which each grid belonged was determined. For each microseismic set, the corresponding total fault area, A(b) value, daily total frequency, average frequency, and microseismic activity warning index value were calculated. After normalization, the spatial distribution cloud map of each index was plotted using a Gaussian spatial smoothing model. With c=3, the contourf command from the matplotlib third-party library in Python was used to plot the corresponding cloud map. The results are as follows. Figure 2 The image shown is a spatial distribution cloud map of the fault total area early warning index in this embodiment; as shown... Figure 3 The image shown is a spatial distribution cloud map of the A(b) value early warning indicator in this embodiment; as shown... Figure 4 The image shown is a spatial distribution cloud map of the daily total frequency warning index in this embodiment; as shown... Figure 5 The image shown is a spatial distribution cloud map of the average frequency warning index in this embodiment; as shown... Figure 6 The image shows a spatial distribution cloud map of the microseismic activity warning indicators in this embodiment. It can be seen that, due to differences in the calculation principles of each indicator, the corresponding high-impact hazard areas are not entirely consistent.

[0119] We used the `Image.open` command from the Pillow library in Python to read the cloud images of various warning indicators, combined with the `array` command from the NumPy library to assemble the data from the cloud images into an array, and then used the `np.std` command to calculate the standard deviation of each cloud image. The results are as follows:

[0120] 1. Total fault area: 54.985

[0121] 2. Value of A(b): 50.713

[0122] 3. Total daily frequency: 40.813

[0123] 4. Average frequency: 52.837

[0124] 5. Microseismic activity: 65.661

[0125] The weights of each cloud map are:

[0126] 1. Total fault area: 0.2075

[0127] 2. Value of A(b): 0.1914

[0128] 3. Total daily frequency: 0.1540

[0129] 4. Average frequency: 0.1994

[0130] 5. Microseismic activity: 0.2478

[0131] Next, the Image.blend command from the Pillow third-party library was used for image fusion to obtain a spatial distribution cloud map of impact hazard. The result is as follows: Figure 7 As shown. It can be seen that, Figure 7 The localized impact hazard zone is mainly concentrated about 50m behind the working face, and is concentrated on the roof side. The impact hazard level on the middle rock pillar side is relatively low. Therefore, relevant personnel in the mine should implement localized pressure relief measures such as deep and shallow hole combined pressure relief and hydraulic fracturing in this area.

[0132] In this embodiment of the invention, the method for delineating rockburst hazard zones based on mine seismic information first collects and preprocesses mine seismic monitoring data within the target area of ​​the mine. Then, the target area is divided into spatial grids, and the seismic set corresponding to each grid is determined. The precursor warning indicators for each seismic set within the grid are calculated and normalized. A Gaussian spatial smoothing model is used to draw a spatial distribution cloud map of each warning indicator. The standard deviation of the cloud map is then used as the weight for image fusion, and a weighted average image fusion method is combined to obtain a spatial distribution cloud map of rockburst hazard. Combined with the mine floor map, local rockburst hazard zones are determined, and corresponding prevention and control measures are taken. This method can dynamically delineate rockburst hazard zones based on real-time microseismic monitoring data, assisting workers in efficiently identifying local hazard zones. Furthermore, it can utilize modular programming in computer languages ​​to automatically collect microseismic monitoring data and draw a spatial distribution cloud map of rockburst hazard in real time, greatly shortening the time required for rockburst hazard zone identification. It also assists in identifying local rockburst hazard zones, guiding mines to take targeted pressure relief and mitigation measures, and reducing the cost of rockburst prevention and control.

[0133] like Figure 8 As shown, this embodiment of the invention provides a rockburst hazard zone delineation system 200 based on mine seismic information, which can be implemented by electronic devices. Figure 8 The diagram shown illustrates a rockburst hazard zone delineation system 200 based on mine seismic information. This system 200 includes:

[0134] Data acquisition block 210 is used to collect seismic monitoring data within the target area of ​​the mine and perform preprocessing;

[0135] The grid division module 220 is used to divide the target area into spatial grids and determine the set of mine seismic events corresponding to each grid by combining the pre-processed mine seismic monitoring data.

[0136] The early warning calculation module 230 is used to calculate the early warning indicators of the shock precursors corresponding to the set of mine tremors in each grid and to normalize the early warning indicators.

[0137] The early warning cloud map drawing module 240 is used to draw spatial distribution cloud maps of various early warning indicators using a Gaussian space smoothing model.

[0138] The standard deviation calculation module 250 is used to calculate the standard deviation of the spatial distribution cloud map of each early warning indicator, and uses the standard deviation as the weight for image fusion.

[0139] The impact hazard cloud map drawing module 260 is used to obtain the spatial distribution cloud map of impact hazard based on weights and using a weighted average image fusion method.

[0140] The impact hazard classification module 270 is used to determine and classify local impact hazard zones by combining the pre-collected mine bottom map.

[0141] Preferably, the data acquisition block 210 is further used to acquire real-time microseismic monitoring data collected by the mine microseismic monitoring system. Each data entry includes the three-dimensional coordinates X, Y, and Z of the mine earthquake, the occurrence time T, and the source energy E.

[0142] The target area is the region within 200m before and after the downhole working face.

[0143] Preferably, the data acquisition block 210 is further used to screen the raw microseismic monitoring data to identify seismic events whose three-dimensional coordinates are located within the target area and whose energy E is greater than 100J.

[0144] Preferably, the grid division module 220 is further used to determine the grid size of the target area based on the positioning error of the mine microseismic monitoring system;

[0145] All seismic events located within a grid constitute the set of seismic events corresponding to that grid.

[0146] Preferably, the early warning calculation module 230 is further used to obtain the early warning indicators of the precursors of the mine earthquakes corresponding to each grid, wherein the early warning indicators of the precursors of the earthquakes include: total fault area, A(b) value, total daily frequency, average frequency and microseismic activity.

[0147] The impact precursor warning index is transformed into a constant between 0 and 1 after dimensionless conversion according to the following formula (1):

[0148] (1)

[0149] in, The normalized warning indicator value, These are early warning indicator values; This represents the minimum value of the early warning indicator throughout the entire cycle. This represents the maximum value of the early warning indicator over the entire cycle.

[0150] Preferably, the early warning cloud map drawing module 240 is further used to construct a Gaussian space smooth model according to the following formula (2):

[0151] (2)

[0152] in, For the first i Gaussian space smoothing values ​​corresponding to each grid cell. For the first i The first grid edge j Normalized warning indicator values ​​in each grid; For the first i The grid and the first j The distance between grid cells; cFor the relevant distance, i 3 around the grid c Calculated from the data within the inner grid .

[0153] Preferably, the standard deviation calculation module 250 is further used to extract all pixel values ​​of the RGB three channels of the image, denoted as X. R X G X B The standard deviation of the cloud map is calculated according to the following formula (3):

[0154] (3),

[0155] in, The standard deviation of the cloud plot is represented by... E( X R )、E( X G )、E( X B ) It expresses expectation.

[0156] Preferably, the impact hazard cloud map drawing module 260 is further used to extract the grayscale values ​​of each early warning indicator cloud map, denoted as Y, and to calculate the grayscale value F of the impact hazard spatial distribution cloud map according to the following formula (4):

[0157] (4),

[0158] in, n This indicates the total number of warning indicator cloud maps. This indicates the weights corresponding to the early warning indicator cloud map.

[0159] This invention provides a method for delineating rockburst hazard zones based on mine seismic information. Addressing existing technological shortcomings, this method firstly utilizes mine seismic monitoring information to delineate rockburst hazard zones, assisting workers in efficiently identifying localized hazard areas. Secondly, it employs modular programming in computer languages ​​to automatically collect microseismic monitoring data and generate real-time spatial distribution maps of rockburst hazards. Finally, it assists in identifying localized rockburst hazard zones, guiding mines to implement targeted pressure relief and mitigation measures, thereby reducing the cost of rockburst prevention and control.

[0160] Figure 9This is a schematic diagram of the structure of an electronic device 300 provided in an embodiment of the present invention. The electronic device 300 can vary considerably due to differences in configuration or performance. It may include one or more central processing units (CPUs) 301 and one or more memories 302. The memory 302 stores at least one instruction, which is loaded and executed by the processor 301 to implement the steps of the following method for delineating rockburst hazard zones based on seismic information:

[0161] S1. Collect seismic monitoring data within the target area of ​​the mine and perform preprocessing;

[0162] S2. Divide the target area into spatial grids and determine the set of mine seismic events corresponding to each grid by combining the pre-processed mine seismic monitoring data.

[0163] S3. Calculate the early warning indicators of the shock precursors corresponding to the set of mine tremors in each grid, and normalize the early warning indicators.

[0164] S4. Use the Gaussian spatial smoothing model to draw the spatial distribution cloud map of each early warning indicator;

[0165] S5. Calculate the standard deviation of the spatial distribution cloud map of each early warning indicator, and use the standard deviation as the weight for image fusion.

[0166] S6. Based on weights, obtain the spatial distribution cloud map of impact hazard using the weighted average image fusion method;

[0167] S7. Based on the pre-collected mine bottom map, determine and delineate local impact hazard zones.

[0168] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including instructions that can be executed by a processor in a terminal to complete the aforementioned method for delineating rockburst hazard zones based on seismic information. For example, the computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, or optical data storage device.

[0169] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

Claims

1. A method for delineating rockburst hazard zones based on mine seismic information, characterized in that, The method steps include: S1. Collect seismic monitoring data within the target area of ​​the mine and perform preprocessing; The seismic monitoring data collected by S1 within the target area of ​​the mine includes: Acquire real-time microseismic monitoring data collected by the mine microseismic monitoring system. Each data point includes the three-dimensional coordinates X, Y, and Z of the seismic event, the occurrence time T, and the source energy E. The target area is the area within 200m before and after the downhole working face; The preprocessing of the mine seismic monitoring data in step S1 includes: The raw microseismic monitoring data were screened to identify mine seismic events whose three-dimensional coordinates were located within the target area and whose energy E was greater than 100J. S2. Divide the target area into spatial grids, and determine the set of mine seismic events corresponding to each grid by combining the pre-processed mine seismic monitoring data. Specifically, S2 involves dividing the target area into spatial grids and determining the set of seismic events corresponding to each grid, including: The grid size of the target area is determined based on the positioning error of the mine microseismic monitoring system. Among them, all the seismic events located within the grid constitute the seismic set corresponding to the grid; S3. Calculate the early warning index of the shock precursor corresponding to the set of mine tremors in each grid, and normalize the early warning index. Specifically, S3 involves calculating and normalizing the precursory warning indicators corresponding to the set of mine tremors within each grid, including: Obtain the shock precursor warning indicators corresponding to the set of mine seismic events in each grid, wherein the shock precursor warning indicators include: total fault area, A(b) value, total daily frequency, average frequency and microseismic activity. The impact precursor warning index is transformed into a constant between 0 and 1 after dimensionless processing according to the following formula (1): （1） in, The normalized warning indicator value, These are early warning indicator values; This represents the minimum value of the early warning indicator throughout the entire cycle. This represents the maximum value of the early warning indicator throughout the entire cycle; S4. Use the Gaussian spatial smoothing model to draw the spatial distribution cloud map of each early warning indicator; Specifically, S4, which uses a Gaussian space smoothing model to draw spatial distribution cloud maps of each early warning indicator, includes: Construct a Gaussian smooth model according to the following formula (2): （2） in, For the first i Gaussian space smoothing values ​​corresponding to each grid cell. For the first i The first grid edge j Normalized warning indicator values ​​in each grid; For the first i The grid and the first j The distance between grid cells; c For the relevant distance, i 3 around the grid c Calculated from the data within the inner grid ; S5. Calculate the standard deviation of the spatial distribution cloud map of each early warning indicator, and use the standard deviation as the weight for image fusion; Specifically, S5 calculates the standard deviation of the spatial distribution cloud map of each early warning indicator, and uses the standard deviation as the weight for image fusion, including: Extract all pixel values ​​from the RGB three channels of the image, and denote them as X. R X G and X B The standard deviation of the cloud map is calculated according to the following formula (3): （3）， in, The standard deviation of the cloud plot is represented by... E( X R )、E( X G ) and E( X B ) Expressing expectations; S6. Based on the weights, obtain the spatial distribution cloud map of impact hazard using the weighted average image fusion method; Specifically, S6, based on the weights, uses a weighted average image fusion method to obtain a spatial distribution cloud map of impact hazard, including: Extract the grayscale values ​​of each early warning indicator cloud map, denoted as Y, and calculate the grayscale value F of the spatial distribution cloud map of the impact hazard according to the following formula (4): （4）， in, n This indicates the total number of warning indicator cloud maps. This indicates the weights corresponding to the early warning indicator cloud map; S7. Based on the pre-collected mine bottom map, determine and delineate local impact hazard zones.

2. A system for classifying rockburst hazard zones based on seismic information, wherein the system is used to implement the method described in claim 1, characterized in that, The system includes: The data acquisition block is used to collect and preprocess seismic monitoring data within the target area of ​​the mine. The data acquisition block is further used to acquire real-time microseismic monitoring data collected by the mine microseismic monitoring system. Each data entry includes the three-dimensional coordinates X, Y and Z of the mine earthquake, the occurrence time T and the source energy E. The target area is the area within 200m before and after the downhole working face; The grid division module is used to divide the target area into spatial grids and determine the set of mine seismic events corresponding to each grid by combining the pre-processed mine seismic monitoring data. The early warning calculation module is used to calculate the early warning indicators of the shock precursors corresponding to the set of mine tremors in each grid, and to normalize the early warning indicators. The early warning cloud map drawing module is used to draw spatial distribution cloud maps of various early warning indicators using a Gaussian spatial smoothing model; The standard deviation calculation module is used to calculate the standard deviation of the spatial distribution cloud map of each early warning indicator, and use the standard deviation as the weight for image fusion. The impact hazard cloud map drawing module is used to obtain an impact hazard spatial distribution cloud map based on the weights using a weighted average image fusion method. The shock hazard classification module is used to determine and classify local shock hazard zones by combining pre-collected mine bottom maps.

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