Method and system for classifying and identifying acoustic emission events in rock fracture accumulation area

Abstract

The invention discloses a rock fracture accumulation area acoustic emission event classification identification method and system, and relates to the technical field of acoustic emission, and the method comprises the steps: obtaining all acoustic emission events generated by rock fracture and the spatial distribution of the acoustic emission events; a cube covering method is adopted, the optimal size of a covering cube is determined firstly, and then all acoustic emission events are subjected to traversal covering through the covering cube with the optimal size; clustering all occupied cubes by adopting a percolation model correlation judgment method to obtain a plurality of groups; according to the number of cubes occupied in each group, identifying an optimal group and extracting acoustic emission events covered by the optimal group as main emission events; and carrying out traversal coverage on the main acoustic emission events step by step by utilizing cubes with different sizes, determining the spatial density of each main acoustic emission event, and carrying out secondary classification on all the main acoustic emission events according to the spatial density. According to the invention, accurate identification and secondary classification of main acoustic emission events in all acoustic emission events can be realized.

Description

Technical Field

[0001] The present invention relates to the field of acoustic emission technology, and in particular to a method and system for classifying and identifying acoustic emission events in rock fracture cluster areas. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] An acoustic emission event refers to a local material change that generates an acoustic emission signal. Acoustic emission event positioning is one of the important means of studying rock crack propagation. When multiple sensors are used, the location of the fracture inside the rock can be determined by calculating the time it takes for the elastic wave released by rock fracture to reach different sensors, and it is recorded as a positioning event. The acoustic emission events monitored by the acoustic emission test system are called all acoustic emission events, which include acoustic emission events generated by rock fracture and some divergent events. The spatial distribution of acoustic emission events is regional. The event distribution in the main damage area is more dense, and the farther away from the main damage area, the more divergent the event distribution. The acoustic emission events near the main rock fracture area (i.e., the rock fracture cluster area) play a major role in reflecting rock fracture and have a high degree of spatial aggregation. This part of the acoustic emission events can be called the main acoustic emission events. Accurately identifying and dividing the main acoustic emission events in all acoustic emission events is an important prerequisite for ensuring the subsequent accurate analysis of rock fracture.

[0004] However, the existing technology lacks the accurate distinction between the main acoustic emission events (acoustic emission events concentrated in the main damage area) and the divergent events in all acoustic emission events, which leads to certain errors in the analysis of rock fracture process through all acoustic emission events. At present, the distinction of acoustic emission events is mostly based on the spatial distribution of acoustic emission events. However, this method relies heavily on personnel experience and has poor distinction accuracy. For events between the two situations of aggregation and dispersion, there is often a problem of missing or misidentifying the main events, which will affect the accuracy of subsequent rock fracture analysis. Summary of the invention

[0005] In order to solve the shortcomings of the above-mentioned prior art, the present invention provides a method and system for classifying and identifying acoustic emission events in rock fracture cluster areas. With the help of the cube covering method, the optimal covering cube size is determined and all acoustic emission events are spatially traversed and covered. Then, the judgment method of the percolation model on the spatial correlation is used to distinguish the main acoustic emission events from the scattered events. In this way, the main acoustic emission events can be accurately identified among all acoustic emission events, thereby ensuring the accuracy of subsequent rock fracture analysis. At the same time, according to the spatial density of the identified main acoustic emission events, the main acoustic emission events are secondary classified to facilitate the subsequent in-depth analysis and processing of the main acoustic emission events.

[0006] In a first aspect, the present invention provides a method for classifying and identifying acoustic emission events in rock fracture cluster areas.

[0007] A method for classifying and identifying acoustic emission events in rock fracture cluster areas, comprising: Obtain all acoustic emission events generated by rock fracture and their spatial distribution; Based on the spatial distribution of all acoustic emission events, the cube covering method is adopted. All acoustic emission events are covered in turn using cubes of different sizes. The number of occupied cubes at different scales of the covering cube is counted and the relationship curve is fitted. The optimal size of the covering cube is determined according to the fitted relationship curve. All acoustic emission events are traversed and covered by using the optimal size covering cube, and the correlation judgment method of the percolation model is used to judge and cluster all occupied cubes under the current coverage to obtain several groups; According to the number of cubes occupied in each group, the optimal group is determined and the acoustic emission events covered by it are extracted to identify the main acoustic emission events; Cubes of different sizes are used to traverse and cover the main acoustic emission events step by step, the optimal group under each traversal is extracted and the main acoustic emission events in the group are assigned corresponding spatial density, and all the main acoustic emission events are secondary classified according to the spatial density.

[0008] In a second aspect, the present invention provides a system for classifying and identifying acoustic emission events in rock fracture cluster areas.

[0009] A rock fracture cluster area acoustic emission event classification and identification system, comprising: Event acquisition module, used to obtain all acoustic emission events generated by rock fracture and their spatial distribution; The optimal size determination module is used to traverse and cover all acoustic emission events in sequence using cubes of different sizes based on the spatial distribution of all acoustic emission events, count the number of occupied cubes at different scales of the covered cubes and perform relationship curve fitting, and determine the optimal size of the covered cube according to the fitted relationship curve; The event classification and recognition module is used to traverse and cover all acoustic emission events using the optimal size of the covering cube, and use the percolation model correlation judgment method to judge and cluster all the occupied cubes under the current coverage to obtain several groups; according to the number of occupied cubes in each group, the optimal group is determined and the acoustic emission events covered by it are extracted to identify the main acoustic emission events; The event secondary classification module is used to traverse and cover the main acoustic emission events step by step using cubes of different sizes, extract the optimal group under each traversal and assign the main acoustic emission events in the group with corresponding spatial density, and perform secondary classification on all the main acoustic emission events according to the spatial density.

[0010] In a third aspect, the present invention further provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing the above-mentioned method for classifying and identifying acoustic emission events in rock fracture cluster areas when executing the executable instructions stored in the memory.

[0011] In a fourth aspect, the present invention further provides a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-mentioned method for classifying and identifying acoustic emission events in rock fracture cluster areas.

[0012] In a fifth aspect, the present invention also provides a computer program product, which includes executable instructions stored in a computer-readable storage medium; wherein, when a processor of an electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the above-mentioned method for classifying and identifying acoustic emission events in rock fracture cluster areas is implemented.

[0013] One or more of the above technical solutions have the following beneficial effects: 1. The present invention provides a classification and identification method and system for acoustic emission events in rock fracture clustering areas. By means of a cube covering method, the optimal covering cube size is determined and all acoustic emission events are spatially traversed and covered. Then, the correlation judgment method of the percolation model on spatial correlation is used to distinguish between the main acoustic emission events and the scattered events. In this way, the main acoustic emission events (acoustic emission events concentrated in the main damage area) can be accurately identified from all acoustic emission events to ensure the accuracy of subsequent rock fracture analysis. At the same time, the main acoustic emission events are traversed and covered step by step (from small to large) by covering cubes of different sizes, and the percolation model correlation judgment method is used successively to extract the optimal group under each traversal and assign the corresponding spatial density of the main acoustic emission events in each optimal group. According to the characteristic of spatial density, the main acoustic emission events are re-separated and numbered to complete the secondary classification of the main acoustic emission events, so as to ensure the pertinence of the subsequent rock fracture analysis, so as to facilitate the subsequent in-depth analysis and processing of the main acoustic emission events.

[0014] 2. The present invention proposes a method for determining the optimal size of a covering cube in the application of an acoustic emission percolation model. In view of the problem that it is difficult to determine the covering size when using the cube covering method to cover the event space, the optimal size of the covering cube is calculated and determined by analyzing and fitting the correlation between the number of occupied cubes and the size of the covering cube at different sizes, thereby ensuring the effectiveness and accuracy of subsequent clustering with the help of the percolation model.

[0015] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0017] Figure 1 The present invention is a flowchart of a method for classifying and identifying acoustic emission events in rock fracture cluster areas according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] It should be noted that the following detailed descriptions are exemplary only, are intended to describe specific embodiments, are intended to provide further explanation of the present invention, and are not intended to limit exemplary embodiments according to the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those of ordinary skill in the art to which the present invention belongs. In addition, it should also be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0019] Explanation of terms All acoustic emission events: all acoustic emission events monitored by the acoustic emission test system during the rock failure process.

[0020] Major AE events: AE events concentrated in the main damage area.

[0021] Embodiment 1 This embodiment provides a method for classifying and identifying acoustic emission events in rock fracture cluster areas. Figure 1 As shown, the specific steps include: Step S1, obtaining all acoustic emission events generated by rock fracture and their spatial distribution. In this embodiment, an existing acoustic emission test system is used to monitor and obtain all acoustic emission events generated by rock fracture and their spatial distribution.

[0022] Further, considering that the percolation model is a mathematical theoretical model about the degree of interconnection in a random disordered system, the percolation model can provide a method for efficiently judging the spatial adjacent relationship between points according to the spatial distance. For this reason, the present embodiment uses the percolation model to judge and analyze the spatial correlation of the acoustic emission event, so as to distinguish the main acoustic emission events and scattered events in the event space; further, the use of the percolation model to analyze the acoustic emission event requires the use of the cube covering method, and the acoustic emission event is covered and identified by the cube. Among them, for the cube covering method, the selection of the covering cube size is one of the current difficulties. If the size of the covering cube used is too small, each acoustic emission event will be covered by an isolated cube, which will cause the occupied cubes to be discrete and non-adjacent to each other; on the contrary, if the size of the covering cube used is too large, all acoustic emission events will be covered in the same cube, which will not play a role in judgment and identification. That is, the selection of the covering cube size will affect the accuracy of the subsequent distinction of the main acoustic emission events.

[0023] To this end, this embodiment proposes a method for determining the optimal size of a covering cube in an acoustic emission percolation model application. By analyzing and fitting the correlation between the number of occupied cubes and the size of the covering cube under different sizes, the optimal size of the covering cube is calculated and determined, that is: Step S2: Based on the spatial distribution of all acoustic emission events, a cube covering method is used to traverse and cover all acoustic emission events in turn using cubes of different sizes, the number of occupied cubes at different scales of the covering cube is counted, and a relationship curve is fitted, and the optimal size of the covering cube is determined according to the fitted relationship curve.

[0024] Specifically, first, each cube is defined: all acoustic emission events are traversed and covered by the cubes, and each cube used to cover the acoustic emission events is called a covering cube, and the covering cube includes occupied cubes and empty cubes, among which the cube containing the acoustic emission events is called the occupied cube, and conversely, the cube not containing the acoustic emission events is called the empty cube.

[0025] Secondly, based on the spatial distribution of all acoustic emission events, the cube covering method is adopted, and cubes of different sizes are used to traverse and cover all acoustic emission events in turn, and the number and position of occupied cubes at different scales of the covered cubes are counted. According to the statistical results, the relationship between the number of occupied cubes and the change in the size of the covered cubes shows an S-shaped change trend. The demarcation point of the concavity and convexity of the change trend is related to the density of the acoustic emission events. In order to ensure that the final fitted curve can accurately reflect the S-shaped change trend, and then ensure the accuracy of the subsequent demarcation point determination, this embodiment adopts the Boltzmann function for curve fitting. The coordinates corresponding to the midpoint of the fitting curve can be directly obtained from the fitting parameters of the function, and the midpoint of the fitting curve can directly reflect the demarcation point of the concavity and convexity of the curve.

[0026] Therefore, in this embodiment, according to the statistical number of occupied cubes at different sizes of the covering cube, the relationship curve between the number of occupied cubes and the size of the covering cube is fitted by the Boltzmann function. The relationship curve between the number of occupied cubes and the size of the covering cube obtained by the fitting is: ; In the formula, , , , is the fitting parameter, represents the midpoint of the curve, represents the size of the covering cube, Indicates the number of occupied cubes.

[0027] Finally, based on the above fitting relationship curve, the size of the covering cube corresponding to the midpoint of the fitting relationship curve is taken as the optimal size of the covering cube.

[0028] Step S3: traverse and cover all acoustic emission events using the optimal size of the covering cube, and use the percolation model correlation judgment method to judge and cluster all occupied cubes under the current coverage to obtain several groups.

[0029] Specifically, all acoustic emission events are traversed and covered using a covering cube of optimal size, and the positions of all occupied cubes are identified and determined. On this basis, the occupied cubes are judged and clustered using a judgment method for correlation of a percolation model, that is, in the percolation model, two coplanar cubes are considered to be adjacent cubes. In this embodiment, a group of occupied cubes (i.e., adjacent cubes) that have a coplanar relationship between each other are clustered into one group, and occupied cubes in different groups do not have a coplanar relationship, so that several groups are obtained by clustering.

[0030] Step S4, according to the number of cubes occupied in each group, identify the optimal group and determine the optimal group space position, the optimal group covers the space is the rock fracture cluster area, the optimal group covers the acoustic emission events as the rock fracture cluster area acoustic emission events, the event is the main acoustic emission event.

[0031] Specifically, the number of cubes occupied by different groups is counted. The number of cubes occupied by a group reflects the spatial correlation of acoustic emission events. The number of cubes occupied by each group is used as the basis for judgment. According to this number, the group with the largest number of cubes has strong spatial correlation of acoustic emission events covered, which is the main acoustic emission event; the acoustic emission events covered by the remaining groups have weak spatial correlation, which is a scattered event. Based on the above judgment method, the group with the largest number of cubes is identified as the optimal group, and the acoustic emission events covered by the optimal group are extracted as the main acoustic emission events, that is, the acoustic emission events in the rock fracture cluster area.

[0032] Step S5: traverse and cover the main acoustic emission events step by step using cubes of different sizes, extract the optimal group under each traversal and assign the main acoustic emission events in the group a corresponding spatial density, and perform secondary classification on all the main acoustic emission events according to the spatial density.

[0033] Considering the characteristics of the main acoustic emission events, the main acoustic emission events are secondary classified to make the subsequent rock fracture analysis more targeted, including: First, for the main acoustic emission events identified by classification, cubes of different sizes are used to traverse and cover the main acoustic emission events step by step. In this process, the sizes of the cubes used are all smaller than the optimal size, and the sizes of the covering cubes are sorted from small to large, and the main acoustic emission events are traversed and covered in turn.

[0034] Secondly, the percolation model correlation judgment method is used to cluster the occupied cubes under each traversal coverage in turn to obtain several groups. Then, according to the number of occupied cubes in the group, the optimal group is identified, the main acoustic emission events covered by the optimal group are extracted and separated, and a sorting number corresponding to the current coverage cube size is assigned. The number corresponds to the spatial density of the main acoustic emission events. The smaller the number, the smaller the size of the coverage cube used, the closer the spatial adjacent relationship of the optimal group, and the higher the spatial density of the covered acoustic emission events. For the main acoustic emission events that have not been separated, the next level of coverage cube size is used for traversal coverage and number assignment.

[0035] Finally, by continuously looping the above-mentioned step-by-step traversal and covering process, the separation and numbering (i.e., serial number assignment) of all major acoustic emission events can be achieved, so that the secondary classification of major acoustic emission events can be completed according to the spatial density.

[0036] Embodiment 2 This embodiment provides a rock fracture cluster area acoustic emission event classification and identification system, including: Event acquisition module, used to obtain all acoustic emission events generated by rock fracture and their spatial distribution; The optimal size determination module is used to traverse and cover all acoustic emission events in sequence using cubes of different sizes based on the spatial distribution of all acoustic emission events, count the number of occupied cubes at different scales of the covered cubes and perform relationship curve fitting, and determine the optimal size of the covered cube according to the fitted relationship curve; The event classification and recognition module is used to traverse and cover all acoustic emission events using the optimal size of the covering cube, and use the percolation model correlation judgment method to judge and cluster all the occupied cubes under the current coverage to obtain several groups; according to the number of occupied cubes in each group, the optimal group is determined and the acoustic emission events covered by it are extracted to identify the main acoustic emission events; The event secondary classification module is used to traverse and cover the main acoustic emission events step by step using cubes of different sizes, extract the optimal group under each traversal and assign the main acoustic emission events in the group with corresponding spatial density, and perform secondary classification on all the main acoustic emission events according to the spatial density.

[0037] Embodiment 3 This embodiment provides an electronic device, including: a memory, used to store executable instructions; and a processor, used to implement the above method provided in this embodiment when executing the executable instructions stored in the memory.

[0038] Embodiment 4 This embodiment also provides a computer-readable storage medium storing executable instructions. When the executable instructions are executed by a processor, the processor will be caused to execute the above method provided in this embodiment.

[0039] Embodiment 5 This embodiment provides a computer program product, which includes an executable instruction, which is a computer instruction; the executable instruction is stored in a computer-readable storage medium. When a processor of an electronic device reads the executable instruction from the computer-readable storage medium and the processor executes the executable instruction, the electronic device executes the above method provided in this embodiment.

[0040] The steps involved in the above embodiments 2 to 5 correspond to the method embodiment 1. For the specific implementation, please refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0041] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0042] The above description is only a preferred embodiment of the present invention. Although the specific implementation mode of the present invention is described in conjunction with the accompanying drawings, it is not a limitation of the protection scope of the present invention. Those skilled in the art should understand that on the basis of the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without creative work are still within the protection scope of the present invention.

Claims

1. A method for classifying and identifying acoustic emission events in rock fracture cluster areas, characterized in that: include: Obtain all acoustic emission events generated by rock fracture and their spatial distribution; Based on the spatial distribution of all acoustic emission events, the cube covering method is adopted. All acoustic emission events are covered in turn using cubes of different sizes. The number of occupied cubes at different scales of the covering cube is counted and the relationship curve is fitted. The optimal size of the covering cube is determined according to the fitted relationship curve. All acoustic emission events are traversed and covered by using the optimal size covering cube, and the correlation judgment method of the percolation model is used to judge and cluster all occupied cubes under the current coverage to obtain several groups; According to the number of cubes occupied in each group, the optimal group is determined and the acoustic emission events covered by it are extracted to identify the main acoustic emission events; Cubes of different sizes are used to traverse and cover the main acoustic emission events step by step, the optimal group under each traversal is extracted and the main acoustic emission events in the group are assigned corresponding spatial density, and all the main acoustic emission events are secondary classified according to the spatial density.

2. A method for classifying and identifying acoustic emission events in rock fracture cluster areas according to claim 1, characterized in that: All acoustic emission events are traversed and covered by cubes. Each cube used to cover an acoustic emission event is called a covering cube. The covering cube includes occupied cubes and empty cubes. Among them, the cube containing an acoustic emission event is called an occupied cube, and conversely, the cube not containing an acoustic emission event is called an empty cube.

3. A method for classifying and identifying acoustic emission events in rock fracture cluster areas according to claim 1, characterized in that: The relationship curve between the number of occupied cubes and the size of covered cubes obtained by fitting the Boltzmann function is: ； In the formula, , , , is the fitting parameter, represents the midpoint of the curve, represents the size of the covering cube, Indicates the number of occupied cubes; The optimal size of the covering cube is determined according to the relationship curve: The size of the covering cube corresponding to the midpoint of the fitting relationship curve is the optimal size of the covering cube.

4. A method for classifying and identifying acoustic emission events in rock fracture cluster areas according to claim 1, characterized in that: All acoustic emission events are traversed and covered using the optimal size of the covering cube. The correlation judgment method of the infiltration model is used to judge and cluster all occupied cubes under the current coverage, and several groups are obtained, including: All acoustic emission events are traversed and covered using a covering cube of optimal size, and the positions of all occupied cubes are identified and determined; A group of occupied cubes with coplanar relationships between each other are clustered into one group. The occupied cubes in different groups do not have coplanar relationships, and several groups are obtained by clustering.

5. A method for classifying and identifying acoustic emission events in rock fracture cluster areas according to claim 1, characterized in that: According to the number of cubes occupied in each group, the optimal group is identified, and the acoustic emission events covered by the optimal group are extracted as the acoustic emission events in the rock fracture cluster area, including: The number of cubes occupied in each group is used as the basis for judgment. The group that occupies the largest number of cubes is identified as the optimal group, and the acoustic emission events covered by the optimal group are extracted as the main acoustic emission events, namely the acoustic emission events in the rock fracture cluster area.

6. A method for classifying and identifying acoustic emission events in rock fracture cluster areas according to claim 1, characterized in that: The secondary classification includes: For the main acoustic emission events identified by classification, cubes whose sizes are sorted from small to large are used to traverse and cover the main acoustic emission events in turn; wherein the sizes of the cubes used are all smaller than the optimal size; The percolation model correlation judgment method is used to cluster the occupied cubes under each traversal coverage in turn to obtain several groups. Then, according to the number of occupied cubes in the group, the optimal group is identified, the main acoustic emission events covered by the optimal group are extracted and separated, and the sorting sequence number corresponding to the current covering cube size is assigned. The sequence number reflects the spatial density of the main acoustic emission events; for the main acoustic emission events that have not been separated, the covering cube of the next level size is used for traversal coverage and numbering; By continuously looping through the coverage process step by step, all major acoustic emission events are separated and numbered, so that secondary classification of all major acoustic emission events is completed according to their spatial density.

7. A classification and identification system for acoustic emission events in rock fracture cluster areas, characterized in that: include: Event acquisition module, used to obtain all acoustic emission events generated by rock fracture and their spatial distribution; The optimal size determination module is used to traverse and cover all acoustic emission events in sequence using cubes of different sizes based on the spatial distribution of all acoustic emission events, count the number of occupied cubes at different scales of the covered cubes and perform relationship curve fitting, and determine the optimal size of the covered cube according to the fitted relationship curve; The event classification and recognition module is used to traverse and cover all acoustic emission events using the optimal size of the covering cube, and use the percolation model correlation judgment method to judge and cluster all the occupied cubes under the current coverage to obtain several groups; according to the number of occupied cubes in each group, the optimal group is determined and the acoustic emission events covered by it are extracted to identify the main acoustic emission events; The event secondary classification module is used to traverse and cover the main acoustic emission events step by step using cubes of different sizes, extract the optimal group under each traversal and assign the main acoustic emission events in the group with corresponding spatial density, and perform secondary classification on all the main acoustic emission events according to the spatial density.

8. An electronic device, characterized in that: include: A memory for storing executable instructions; The processor is used to implement the method for classifying and identifying acoustic emission events in rock fracture cluster areas as described in any one of claims 1 to 6 when executing the executable instructions stored in the memory.

9. A computer-readable storage medium, characterized in that: Executable instructions are stored, which are used to cause the processor to execute the executable instructions to implement the method for classifying and identifying acoustic emission events in rock fracture cluster areas as described in any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product includes executable instructions stored in a computer-readable storage medium; When the processor of the electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the method for classifying and identifying acoustic emission events in rock fracture cluster areas as described in any one of claims 1 to 6 is implemented.

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