Rock microstructure distribution characteristic analysis method and device and storage medium

Abstract

The invention provides a rock microstructure distribution characteristic analysis method and device and a storage medium, and the method comprises the steps: obtaining an image of a rock sample, and carrying out the preprocessing of the image; determining a matrix and a microstructure in the rock sample according to the preprocessed image, and assigning values to the matrix and the microstructure to obtain an information set containing assignment data; performing partition processing on the image to obtain a plurality of statistical regions; and according to the corresponding assignment information of the statistical region in the information set, calculating a variation coefficient of the microstructure so as to evaluate the heterogeneity of the microstructure in the rock sample. According to the rock microstructure distribution characteristic analysis method and device and the storage medium disclosed by the invention, the heterogeneity of the microstructure can be quantitatively described, and an important supplementary means is provided for characterization of the microstructure of rocks such as coal rocks and the like.

Description

Technical Field

[0001] The present invention relates to the field of rock engineering, and in particular to a method, a device and a storage medium for analyzing the distribution characteristics of rock microstructures. Background Art

[0002] In the process of studying the mesostructure of rocks, minerals and fracture structures have always been the focus of research. Research on minerals mainly focuses on the influence of mineral composition and its proportion on the mechanical properties of rocks, in order to reveal the mechanical changes of rocks under different mineral compositions. Research on fracture structures mainly focuses on the influence of parameters such as the shape, angle and position of fractures on the mechanical properties of rocks, aiming to gain a deep understanding of how fractures affect the integrity and mechanical response of rocks.

[0003] However, despite the remarkable achievements of these studies, the above parameters alone are still insufficient to fully describe the influence of minerals, fracture structures and other microstructures on the mechanical properties of rocks. As a complex non-continuous medium, the mechanical properties of rocks are not only affected by the mineral composition and proportion, but also by multiple factors such as the heterogeneity of mineral distribution, the complex distribution of fractures and the interaction between them. Summary of the invention

[0004] The present invention is based on the inventor's discovery and understanding of the following facts and problems:

[0005] In the study of the microstructure of coal and other rocks, the focus is mainly on two aspects: mineral distribution and fracture structure. Regarding mineral distribution, the research mainly focuses on how different mineral components and their proportions affect the mechanical properties of rocks. In the study of fracture structure, the significant effect of geometric parameters such as the shape, inclination and number of fractures on the mechanical properties of rocks is deeply explored. Although these studies have achieved certain results, they still focus on the independent analysis of parameters such as mineral type and proportion, fracture size and geometric morphology.

[0006] However, in the face of the complexity and randomness of the internal crack distribution of real coal and rock, how to effectively characterize this disordered crack field and deeply reveal its intrinsic connection with the mechanical properties of the rock has become an urgent problem to be solved. To this end, the present invention proposes a rock mesostructure distribution characteristic analysis method, which can quantitatively describe the heterogeneity of the mesostructure, and provide an important supplementary means for the characterization of the mesostructure of coal and other rocks.

[0007] In addition, the present invention also provides a rock microstructure distribution characteristic analysis device and a storage medium.

[0008] The rock microstructure distribution characteristics analysis method provided by the embodiment of the present invention comprises the following steps:

[0009] Acquire an image of a rock sample and preprocess the image;

[0010] Determine the matrix and the mesostructure in the rock sample according to the preprocessed image, assign values ​​to the matrix and the mesostructure, and obtain an information set containing the assigned data;

[0011] Performing partition processing on the image to obtain multiple statistical regions;

[0012] According to the assigned information corresponding to the statistical area in the information set, the coefficient of variation of the mesostructure is calculated to evaluate the heterogeneity of the mesostructure in the rock sample.

[0013] The rock mesostructure distribution characteristic analysis method provided in the embodiment of the present invention can calculate the characteristic parameters of the mineral and fracture parts in the mesostructure, which is beneficial to supplement the quantitative description and characterization system of the rock mesostructure, establish the complex relationship between the mesostructure distribution law and the mechanical properties of the rock, and study the influence of the mineral and fracture distribution on the physical and mechanical properties of the rock. It is not only crucial for the safe and efficient exploitation of fossil energy such as coal, oil and natural gas, but also provides theoretical and technical support for disaster warning and prevention in the fields of rock engineering and geological engineering.

[0014] In some embodiments, the step of determining the matrix and the mesostructure in the rock sample based on the preprocessed image, assigning a value to the mesostructure, and obtaining an information set containing the assigned data includes:

[0015] Identify the preprocessed images to determine the types of matrix and mesostructure within the rock sample;

[0016] According to the types of the matrix and the mesostructure, the matrix and the mesostructure in the image are assigned values ​​so that each pixel block in the image has phase-mapped assigned data;

[0017] An information set is constructed based on the position information of each pixel block in the image and the assignment data mapped to the pixel block.

[0018] In some embodiments, the step of identifying the pre-processed image to determine the type of matrix and mesostructure within the rock sample includes:

[0019] Identify the preprocessed image, obtain the gray value range corresponding to the matrix and the mesostructure, and construct a region search algorithm;

[0020] Merging pixel blocks in the image according to a region search algorithm to obtain a feature distribution region having a plurality of merged regions, wherein the merged regions correspond to a mesoscopic structure;

[0021] Based on the characteristic distribution map, the types of matrix and mesostructure in the rock sample are determined.

[0022] In some embodiments, the region search algorithm is configured as follows:

[0023] S01, select a pixel block in the image as the search starting point;

[0024] S02, scanning pixel blocks adjacent to the search starting point, merging pixel blocks that meet a preset expansion condition with the search starting point, and using the merged pixel blocks as new search starting points;

[0025] S03, repeating step S02 until there is no pixel block that meets the preset expansion condition, and the merged pixel blocks are a merged area;

[0026] S04, selecting a pixel block outside the merged area as a new search starting point;

[0027] S05, repeating steps S02 to S04 until the pixel blocks in the image are merged.

[0028] In some embodiments, the preset expansion condition is that the grayscale value corresponding to the search starting point and the grayscale value corresponding to the adjacent pixel block are within the grayscale value range corresponding to the same type of mesoscopic structure.

[0029] In some embodiments, the step of calculating the coefficient of variation of the mesostructure according to the assigned information corresponding to the statistical region in the information set includes:

[0030] Calculate the proportion and occurrence frequency of the mesostructure in the statistical area according to the assigned information corresponding to the statistical area in the information set;

[0031] According to the proportion and occurrence frequency of the mesostructure in the statistical area, the average value and standard deviation corresponding to the proportion and occurrence frequency are calculated;

[0032] The coefficient of variation of the mesostructure is calculated based on the mean value and the standard deviation, wherein the coefficient of variation is standard deviation / mean value.

[0033] In some embodiments, the mesostructure within the rock sample includes at least minerals and cracks, and the coefficient of variation includes a mineral distribution coefficient, a crack frequency coefficient, and a crack rate coefficient.

[0034] In some embodiments, the step of preprocessing the image includes at least grayscale equalization processing.

[0035] In addition, the rock mesostructure distribution characteristics analysis device provided by the present invention includes a storage module and a processing module, the storage module is used to store computer programs, and the processing module is used to execute the computer programs to implement the steps in the rock mesostructure distribution characteristics analysis method as described in the above embodiment.

[0036] The computer-readable storage medium provided by the present invention is used to store a computer program, and when the computer program is executed by a processing module, the steps in the method for analyzing the distribution characteristics of rock mesostructures as described above are implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic flow chart of a method for analyzing rock mesostructure distribution characteristics provided in an embodiment of the present invention.

[0038] Figure 2 It is a flow chart of the regional search algorithm in the rock mesostructure distribution characteristic analysis method provided in an embodiment of the present invention.

[0039] Figure 3 It is a schematic diagram of an image of a rock sample in the method for analyzing rock mesostructure distribution characteristics provided by an embodiment of the present invention.

[0040] Figure 4 It is a schematic table of information collection in the rock mesostructure distribution characteristics analysis method provided in an embodiment of the present invention.

[0041] Figure 5 It is a schematic diagram of searching in a three-dimensional image by a regional algorithm in a rock microstructure distribution characteristic analysis method provided in an embodiment of the present invention.

[0042] Figure 6 It is a schematic diagram of searching in a two-dimensional image by a regional algorithm in a rock microstructure distribution characteristic analysis method provided in an embodiment of the present invention.

[0043] Figure 7 It is a schematic diagram of several segmentation forms of sample partitioning in the rock microstructure distribution characteristic analysis method provided in an embodiment of the present invention.

[0044] Figure 8 It is a schematic diagram of an image of a rock sample in the method for analyzing rock mesostructure distribution characteristics provided by an embodiment of the present invention.

[0045] Fig. 9 It is a schematic diagram of slicing and numbering an image sequence in a rock mesostructure distribution characteristic analysis method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] Embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be understood as limiting the present invention.

[0047] like Figure 1 As shown, an embodiment of the present invention provides a method for analyzing rock mesostructure distribution characteristics, which includes the following steps:

[0048] S10, acquiring an image of the rock sample and preprocessing the image;

[0049] S20, determining the matrix and the mesostructure in the rock sample according to the preprocessed image, assigning values ​​to the matrix and the mesostructure, and obtaining an information set including the assigned data;

[0050] S30, performing partition processing on the image to obtain multiple statistical regions;

[0051] S40, calculating the coefficient of variation of the mesostructure according to the assigned information corresponding to the statistical area in the information set, so as to evaluate the heterogeneity of the mesostructure in the rock sample.

[0052] Specifically, the rock sample is first scanned to obtain a two-dimensional or three-dimensional image of the rock sample, so as to clearly reflect the mesostructural characteristics of the rock; the image is then preprocessed to remove noise interference and improve image quality, so that the pixel distribution of each gray level in the image is more uniform, thereby improving the image viewing effect and the accuracy of post-processing; then the preprocessed image is identified to obtain the matrix and mesostructure of the rock sample, and a value is assigned according to the type of the mesostructure to distinguish different types of mesostructures, and then an information set is constructed according to the position information of the pixel block in the image and the assignment information of the mesostructure corresponding to the pixel block;

[0053] The image is then partitioned to obtain multiple statistical regions, where the number and size of the statistical regions can be flexibly selected according to the size of the rock sample and the purpose of the study, such as equally spaced grid division or adaptive partitioning based on mesostructure, etc. Based on the above partitioning, the coefficient of variation of the mesostructure is calculated for the corresponding assignment information of each statistical region in the information set, thereby reflecting the degree of non-uniform distribution of the mesostructure inside the rock sample.

[0054] It should be noted that the coefficient of variation is an indicator to measure the degree of data dispersion, and its calculation formula is the ratio of the standard deviation to the mean deviation. In the analysis of rock mesostructure, the size of the coefficient of variation reflects the degree of non-uniformity of the spatial distribution of the mesostructure in the rock. The larger the coefficient of variation, the more uneven the distribution of the mesostructure; conversely, the more uniform it is.

[0055] In summary, the rock mesostructure distribution characteristic analysis method provided in the embodiment of the present invention can calculate the characteristic parameters of the mineral and fracture parts in the mesostructure, which is beneficial to supplement the quantitative description and characterization system of the rock mesostructure, establish the complex relationship between the mesostructure distribution law and the mechanical properties of the rock, and study the influence of the mineral and fracture distribution on the physical and mechanical properties of the rock. It is not only crucial for the safe and efficient exploitation of fossil energy such as coal, oil and natural gas, but also provides theoretical and technical support for disaster warning and prevention in the fields of rock engineering and geological engineering.

[0056] In this embodiment, the preprocessing of the image may at least include grayscale equalization processing to improve the presentation quality of the image and the processing accuracy of subsequent images. Specifically, the grayscale equalization processing includes the steps of:

[0057] Identify and read the original grayscale value of the pixel block on the image img hd , get the maximum grayscale value max on the image hd Grayscale minimum value min hd ;

[0058] The original gray value img hd 、Maximum grayscale value max hd Grayscale minimum value min hd Substitute the preset equalization formula to calculate the revised grayscale value img corresponding to the pixel block ’ hd ;

[0059] Based on the revised grayscale value img ’ hd The grayscale value of the pixel block is adjusted accordingly until the grayscale adjustment of all pixel blocks on the image is completed.

[0060] Among them, the revised gray value img ’ hd The calculation formula is as follows:

[0061]

[0062] In some embodiments, step S20 determines the matrix and the mesostructure in the rock sample according to the preprocessed image, assigns a value to the mesostructure, and obtains an information set containing the assigned data, including:

[0063] Identify the preprocessed images to determine the matrix and mesostructure within the rock sample;

[0064] According to the matrix and the mesostructure, the matrix and the mesostructure in the image are assigned values ​​so that each pixel block in the image has mapped assigned data;

[0065] An information set is constructed based on the position information of each pixel block in the image and the assignment data mapped to the pixel block.

[0066] Further, the step of identifying the preprocessed image to determine the matrix and mesostructure within the rock sample includes:

[0067] Identify the preprocessed image, obtain the gray value range corresponding to the matrix and the mesostructure, and construct a region search algorithm;

[0068] Merging pixel blocks in the image according to a region search algorithm to obtain a feature distribution map having a plurality of merged regions, wherein the merged regions correspond to a mesoscopic structure;

[0069] Based on the characteristic distribution map, the matrix and mesostructure in the rock sample are determined.

[0070] Furthermore, the area search algorithm is set as:

[0071] S01, selecting a pixel block in the image as a search starting point.

[0072] That is, in the preprocessed image, a pixel block is randomly or according to a specific rule, such as from the upper left corner or upper right corner of the image, as a search starting point, and the search starting point will be used as the initial area of ​​the search process.

[0073] S02, scanning pixel blocks adjacent to the search starting point, merging pixel blocks that meet a preset expansion condition with the search starting point, and using the merged pixel block as a new search starting point.

[0074] That is to say, starting from the current search starting point, scan the pixel blocks adjacent to it, which for a two-dimensional image may include directions such as up, down, left, right, and diagonal directions; for a three-dimensional image, it may include up, down, left, right, diagonal directions, and upper and lower pixel blocks.

[0075] For each adjacent pixel block, check whether it meets the preset expansion condition. If the adjacent pixel block meets the expansion condition, merge it with the merging area where the current search starting point is located, and use the merged area as the new search starting point.

[0076] Among them, the preset expansion condition is that the grayscale value corresponding to the search starting point and the grayscale value corresponding to the adjacent pixel block belong to the grayscale value range corresponding to the same type of mesostructure, ensuring that only those pixel blocks with similar grayscale values ​​and belonging to the same mesostructure type will be merged together.

[0077] S03, repeating step S02 until there is no pixel block that meets the preset expansion condition, and the merged pixel block is a merged area. That is, continue to execute step S02 with a new search starting point, and repeat until there is no adjacent pixel block that meets the preset expansion condition around the current merged area. At this point, a complete merged area has been formed.

[0078] S04, selecting a pixel block outside the merged area as a new search starting point. That is, after completing the search and merge of a merged area, a new starting point is selected from the pixel blocks that have not been merged.

[0079] S05, repeating steps S02 to S04 until the pixel blocks in the image are merged. In other words, repeating steps S02 to S04 until all pixel blocks in the image are merged into corresponding regions. At this point, the image will be divided into a plurality of merged regions, each corresponding to a matrix or a mesostructure.

[0080] Specifically, step S20 can be understood as: firstly, texture analysis and shape recognition are performed on the preprocessed image to distinguish the matrix and mesostructure in the image. The matrix usually refers to the continuously distributed, relatively large and relatively uniform part of the rock, while the mesostructure includes cracks, minerals and other heterogeneous bodies with specific shapes and properties.

[0081] Then, based on the grayscale of each pixel block in the preprocessed image, the grayscale value range corresponding to each type of mesostructure is identified; then, according to the preset regional search algorithm, each pixel block in the image is traversed, and the pixel blocks that meet the expansion conditions are merged into a merged area with a boundary, and each merged area corresponds to a specific matrix or mesostructure. After completing the above regional search, the pixel blocks in each merged area are assigned values, that is, the values ​​or labels representing different matrix and mesostructure types are assigned to the corresponding pixel blocks; finally, based on the position information of each pixel block in the image and its corresponding assignment information, an information set containing the assignment information is constructed.

[0082] It should be noted that, in some embodiments, in step S20, the pixel block may be assigned a value according to the grayscale value range corresponding to the mesostructure in which the grayscale value of the pixel block is located. For example, when the mesostructure in the image includes a crack, a first mineral, and a second mineral, when the grayscale value of the pixel block is in the grayscale value range corresponding to the first mineral, the pixel block is mapped to the numerical value or label corresponding to the first mineral; and then the regional search algorithm is run. In some other embodiments, the assignment process and the regional search algorithm may also be performed simultaneously, which will not be described one by one here.

[0083] exist Figure 3In the image of the rock sample shown, the rock sample includes at least matrix A, crack B, mineral C and mineral D. In the process of assigning values ​​to the mesostructure in the rock sample and constructing an information set, the pixel blocks whose grayscale values ​​are within the grayscale value range corresponding to matrix A are assigned a value of 0, the pixel blocks whose grayscale values ​​are within the grayscale value range corresponding to crack B are assigned a value of 1, the pixel blocks whose grayscale values ​​are within the grayscale value range corresponding to mineral C are assigned a value of 2, and the pixel blocks whose grayscale values ​​are within the grayscale value range corresponding to mineral D are assigned a value of 3. Then, according to the position of each pixel block, the following can be formed: Figure 4 The table shown is an information set containing assignment information. The position information of the pixel block includes the coordinates in the X direction of the image and the coordinates in the Y direction of the image. It should be noted that the X direction of the image is Figure 3 The left and right directions of the image shown, the Y direction on the image is Figure 3 Up-down direction in the image shown.

[0084] In addition, in some embodiments, Figure 4 In the table shown, the assigned data of the matrix and the mesostructure are their corresponding grayscale values.

[0085] In some embodiments, Figure 5 As shown, when the image can display a three-dimensional image of a three-dimensional structure, assuming that the coordinates of the search starting point in the area search algorithm are (x, y, z), the search starting point is located at Figure 5 The middle position of the image cube shown in the figure, when the grayscale value of the search starting point is 1 or the assigned value is 1, the coordinates of the pixel blocks adjacent to the search starting point are searched, and the adjacent pixel block coordinates are: (x-1, y-1, z-1), (x-1, y-1, z), (x-1, y-1, z+1), (x-1, y, z-1), (x-1, y, z), (x-1, y, z+1), (x-1, y+1, z-1), (x-1, y+1, z), (x-1, y+1, z+1), (x, y-1, z-1), If there is a pixel block with a gray value of 1 or a value information of 1 in the pixel blocks corresponding to the above 26 coordinates, the pixel block is merged with the search starting point to form a new search starting point, until the pixel blocks in the image are merged.

[0086] like Figure 6 As shown, when the image is a two-dimensional image showing the two-dimensional structure of the sample, assuming that the coordinates of the search starting point are (x, y), the search starting point is located at Figure 6 , when the grayscale value of the search starting point is 1 or the assigned value is 1, the coordinates of the pixels adjacent to it are searched as (x-1, y-1), (x, y-1), (x+1, y-1), (x-1, y), (x+1, y), (x-1, y+1), (x, y+1), (x+1, y+1). If there is a pixel block with a grayscale value of 1 or an assigned value of 1 in the pixel blocks corresponding to the above 8 coordinates, the pixel block is merged with the search starting point to form a new search starting point, until the pixel blocks in the image are merged.

[0087] In some embodiments, in step S30, the image is partitioned to obtain multiple statistical regions, and the division of the statistical regions can follow the following principles: the area / volume of each statistical region is approximately equal; when comparing different samples, the sample size and the number of statistical regions should be consistent. Figure 7 FIG. 1 is a schematic diagram of statistical region division of several two-dimensional and three-dimensional sample images in the rock microstructure distribution characteristic analysis method provided by an embodiment of the present invention, wherein Figure 7 The dotted line in the figure is the boundary between two adjacent statistical areas.

[0088] In addition, the number of statistical areas can be selected according to the size of the sample to avoid too many statistical areas, which may lead to serious cross-regional mesostructure problems and affect the frequency statistics of mesostructures in each statistical area; at the same time, it should also be avoided that the statistical number is too small, which may lead to small differences in the degree of discreteness of the mesostructure distribution in each statistical area, affecting the calculation of the coefficient of variation.

[0089] In some embodiments, in step S40, calculating the coefficient of variation of the mesostructure according to the value assignment information corresponding to the statistical region in the information set includes:

[0090] According to the assigned information corresponding to the statistical area in the information set, the proportion and occurrence frequency of the mesostructure in the statistical area are calculated.

[0091] That is, firstly, the position information of each pixel block in the area to be counted and the value assignment information corresponding to the pixel block are extracted from the information set, so as to calculate the proportion of each type of mesostructure in the statistical area and the frequency of occurrence of each type of mesostructure in the statistical area. The proportion can classify each pixel block, count the number of pixel blocks belonging to the same type of mesostructure, and then divide it by the total number of pixel blocks in the statistical area. The frequency of occurrence is the number of mesostructures of the same type in the statistical area.

[0092] According to the proportion and occurrence frequency of the mesostructure in the statistical area, the average value and standard deviation corresponding to the proportion and the occurrence frequency are calculated;

[0093] The coefficient of variation of the mesostructure is calculated based on the mean value and the standard deviation, wherein the coefficient of variation is standard deviation / mean value.

[0094] The calculation formula of the coefficient of variation is:

[0095]

[0096] Wherein, C represents the coefficient of variation, σ represents the standard deviation of the statistical data of coal rock minerals or cracks in different regions or unit regions, and μ represents the average value of the statistical data of coal rock minerals or cracks in different regions or unit regions. Of course, the coefficient of variation of the mesostructure is dimensionless, and in some embodiments, the coefficient of variation can also be expressed as a percentage.

[0097] In the rock mesostructure distribution characteristics analysis method provided by the present invention, the mesostructure in the rock sample includes at least minerals and cracks, and the coefficient of variation includes a mineral distribution coefficient, a crack frequency coefficient, and a crack rate coefficient. The calculation formula of the mineral distribution coefficient is:

[0098]

[0099] Where: C m represents the mineral distribution coefficient; n represents the mineral type; m represents the number of statistical areas in the image; is the average value of the proportion of the ith mineral in m statistical areas; Mj is the proportion of the ith mineral in the j statistical area.

[0100] Among them, the mineral distribution coefficient can indicate the degree of mineral non-uniformity in the rock sample, which can facilitate the comparison between different rocks. The larger the mineral distribution coefficient, the more uneven the mineral distribution in the sample, and the greater the impact on the physical and mechanical properties of the rock.

[0101] The calculation formula of the crack frequency coefficient is:

[0102]

[0103] Where: C P represents the crack frequency coefficient; m represents the number of statistical areas in the image; is the average frequency of cracks in m regions; P j is the frequency of crack occurrence in the jth region;

[0104] The calculation formula of the crack rate coefficient is:

[0105]

[0106] Where: C K represents the crack rate coefficient; m represents the number of statistical areas in the image; is the average value of the proportion of cracks in m statistical areas; K j is the proportion of cracks in the jth statistical area.

[0107] Specifically, the crack frequency coefficient and the crack rate coefficient can indicate the degree of non-uniformity of cracks in rock samples. Among them, when the crack frequency coefficient is small, it indicates that the cracks are distributed more evenly in the rock sample; correspondingly, when the crack frequency coefficient is large, the surface cracks are distributed unevenly in the rock sample. When the crack rate coefficient is small, it indicates that the cracks are less distributed in the sample; when the crack rate coefficient is large, it indicates that the cracks are more distributed in the sample. It should be noted that the above-mentioned mineral distribution coefficient, crack frequency coefficient and crack rate coefficient are all compared with the sample and the standard sample or at least two samples to evaluate the degree of non-uniformity of the microstructure in the rock sample.

[0108] In one embodiment of the present invention, the rock sample can be selected as coal rock, and the scanned image of the coal rock can be processed by partitioning to form 6 statistical areas; the crack frequencies in the 6 statistical areas are 3, 2, 3, 5, 4, and 4 respectively, and the crack rates are 2.1%, 1.2%, 1.6%, 3.2%, 2.5%, and 3.4% respectively; the average value of the crack frequency corresponding to the coal rock is 3.5; the standard deviation of the crack frequency is 2.35; the crack frequency coefficient corresponding to the coal rock is 2.35 / 3.5=0.67 or 67%. Correspondingly, the average value of the crack rate corresponding to the coal rock is 2.33%, and the standard deviation of the crack rate is 1.95%; the crack rate coefficient is 1.95 / 2.33=0.84 or 84%.

[0109] It should be noted that the value of the coefficient of variation has no unit. It provides a relative scale for the discreteness of the distribution of crack frequency and crack ratio, which is convenient for comparison between different rock samples. The larger the value of the coefficient of variation, the greater the discreteness of the crack distribution and the more dispersed the crack distribution.

[0110] In some embodiments, the method for analyzing the distribution characteristics of rock mesostructures obtains an image of a rock sample in step S10, and before preprocessing the image, also includes the steps of: processing the rock to be analyzed into a sample with a regular shape; placing the sample in a scanning system to scan and obtain an image with internal information of the sample.

[0111] The scanning system may be a CT scanning system, a scanning electron microscope or an electron microscope, etc. The shape of the sample may be a cylinder or a cube, thereby reducing scanning artifacts caused by irregular shapes and facilitating subsequent CT scanning and image analysis.

[0112] In this application, the scanning of coal rock in a CT scanning system is taken as an example to be specifically explained as follows: first, the coal rock to be analyzed is processed to form a cylindrical sample with a smooth surface; then, the sample is scanned with rays from multiple angles using CT scanning, and the ray intensity data at each angle is recorded, and then the ray intensity data is integrated to obtain an image containing the internal information of the sample.

[0113] Specifically, when the radiation source in the CT scanning system is activated, the radiation will penetrate the sample. Due to the different absorption capacities of different microstructures in the sample, some of the radiation is absorbed and the intensity of the radiation is attenuated. The greater the density of the microstructure, the stronger the absorption capacity of the radiation, and the brighter the brightness on the image, thus distinguishing different components in the coal rock. Figure 8 is an image formed after a scanning system scans a rock sample in one embodiment of the present invention, Figure 8 The inner arrow E indicates a crack in the sample, which appears approximately black in the image.

[0114] It should be noted that when the analysis area of ​​the rock sample is a cylinder or a cube, the image obtained by the scanning system after scanning is mostly a two-dimensional image sequence or a three-dimensional model. When the analysis area of ​​the rock sample is a sample surface or a thin sheet structure, the image obtained by the scanning system after scanning is mostly a two-dimensional image.

[0115] In some embodiments, when the scanning system obtains a two-dimensional image sequence or a three-dimensional model after scanning, it is necessary to perform slice numbering on the scanning result. The slice numbering mainly performs numbering according to the order of the scanning result in a specific direction to facilitate the analysis and processing of the microscopic structure.

[0116] In this embodiment, if Fig. 9 As shown in the figure, the scanning system scans and obtains a cylindrical image sequence of the rock sample, and the left and right direction of the rock sample is set as the X direction, the front and back direction is the Y direction, and the height direction is the Z direction. In the process of slicing and numbering the scanning results, the sample can be sliced ​​along the Z direction and numbered in order from top to bottom. Fig. 9 The closed dotted line in is the edge line between two image slices in the Z direction in the two-dimensional image sequence example.

[0117] Specifically, the top of the sample can be set as the starting position, then the first slice, that is, the slice closest to the top of the sample will be assigned a Z value of 1, the second slice will be assigned a Z value of 2, and so on, until the numbering of the entire image data is completed; then after the slice numbering is completed, each slice image is converted into a grayscale image, and then the image is pre-processed such as grayscale equalization for further image analysis.

[0118] Furthermore, the step of identifying the preprocessed image and determining the matrix and mesostructure in the rock sample further includes: selecting a portion of the image from the preprocessed image based on a preset selection condition; and then identifying the selected image to determine the type of mesostructure in the sample and the grayscale value range corresponding to the mesostructure in each type. The selection condition is set to include all mesostructures appearing in the sample in the image.

[0119] In addition, an embodiment of the present invention also provides a device for analyzing the distribution characteristics of rock mesostructures, which includes a storage module and a processing module. The storage module is used to store a computer program; the processing module is used to execute the computer program to implement the steps in the method for analyzing the distribution characteristics of rock mesostructures provided in the above embodiment.

[0120] Further, the storage module may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, at least one application required for a function, etc. In addition, the storage module may include a high-speed random access storage module, and may also include a non-volatile storage module, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage module, a flash memory device, or other volatile solid-state storage module.

[0121] The processing module can be a central processing unit (CPU), or other general processing modules, digital signal processing modules (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general processing module can be a microprocessing module or any conventional processing module. The processing module is the control center of the device, and uses various interfaces and lines to connect the various parts of the entire device.

[0122] It should be noted that the rock mesostructure distribution characteristic analysis device can execute the rock mesostructure distribution characteristic analysis method provided in the above embodiment. Therefore, the beneficial effects that can be achieved by the rock mesostructure distribution characteristic analysis device can refer to the beneficial effects corresponding to the rock mesostructure distribution characteristic analysis method provided above, which will not be repeated here.

[0123] In addition, an embodiment of the present invention further provides a computer-readable storage medium for storing a computer program, which, when executed by a processing module, implements the steps in the rock mesostructure distribution characteristics analysis method provided in the above embodiment.

[0124] In the description of the present invention, it is necessary to understand that the terms "upper", "lower", "front", "rear", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0125] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection, an electrical connection, or communication with each other; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0126] Various embodiments of the methods described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs, which may be executed and / or interpreted on a programmable system including at least one programmable processing module, which may be a dedicated or general programmable processing module, which may receive data and instructions from a storage module, at least one input device, and at least one output device, and transmit data and instructions to the storage module, the at least one input device, and the at least one output device.

[0127] The program code for implementing the method itself can be written in any combination of one or more programming languages. These program codes can be provided to a processing module or control unit of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processing module or control unit, causes the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0128] To provide interaction with a user, the methods described herein may be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0129] The methods described herein may be implemented in a computing system that includes a backend component (e.g., as a data server), or a computing system that includes a middleware component (e.g., an application server), or a computing system that includes a frontend component (e.g., a user computer with a graphical user interface or a grid browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication grid). Examples of communication grids include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain grid.

[0130] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication grid. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server for a distributed system, or a server combined with a blockchain.

[0131] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0132] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0133] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0134] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processing module, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or used in combination with these instruction execution systems, devices or apparatuses.

[0135] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a storage module and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0136] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0137] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0138] The storage medium mentioned above may be a read-only storage unit, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

[0139] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[0140] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for analyzing rock mesostructure distribution characteristics, characterized in that: The steps include: Acquire an image of a rock sample and preprocess the image; Determine the matrix and the mesostructure in the rock sample according to the preprocessed image, assign values ​​to the matrix and the mesostructure, and obtain an information set containing the assigned data; Performing partition processing on the image to obtain multiple statistical regions; According to the assigned information corresponding to the statistical area in the information set, the coefficient of variation of the mesostructure is calculated to evaluate the heterogeneity of the mesostructure in the rock sample.

2. The method for analyzing rock mesostructure distribution characteristics according to claim 1, characterized in that: The step of determining the matrix and the mesostructure in the rock sample according to the preprocessed image, assigning a value to the mesostructure, and obtaining an information set containing the assigned data includes: Identify the preprocessed images to determine the matrix and mesostructure within the rock sample; According to the matrix and the mesostructure, the matrix and the mesostructure in the image are assigned values ​​so that each pixel block in the image has phase-mapped assigned data; An information set is constructed based on the position information of each pixel block in the image and the assignment data mapped to the pixel block.

3. The method for analyzing rock mesostructure distribution characteristics according to claim 2, characterized in that: The steps of identifying the preprocessed image and determining the type of matrix and mesostructure within the rock sample include: Identify the preprocessed image, obtain the gray value range corresponding to the matrix and the mesostructure, and construct a region search algorithm; Merging pixel blocks in the image according to a region search algorithm to obtain a feature distribution map having a plurality of merged regions, wherein the merged regions correspond to a mesoscopic structure; Based on the characteristic distribution map, the types of matrix and mesostructure in the rock sample are determined.

4. The method for analyzing rock mesostructure distribution characteristics according to claim 3, characterized in that: The area search algorithm is set as: S01, select a pixel block in the image as the search starting point; S02, scanning pixel blocks adjacent to the search starting point, merging pixel blocks that meet a preset expansion condition with the search starting point, and using the merged pixel blocks as new search starting points; S03, repeating step S02 until there is no pixel block that meets the preset expansion condition, and the merged pixel blocks are a merged area; S04, selecting a pixel block outside the merged area as a new search starting point; S05, repeating steps S02 to S04 until the pixel blocks in the image are merged.

5. The method for analyzing rock mesostructure distribution characteristics according to claim 4, characterized in that: The preset expansion condition is that the grayscale value corresponding to the search starting point and the grayscale value corresponding to the adjacent pixel block belong to the same type of mesoscopic structure within the grayscale value range.

6. The method for analyzing rock mesostructure distribution characteristics according to claim 1, characterized in that: The step of calculating the coefficient of variation of the mesostructure according to the assigned information corresponding to the statistical area in the information set comprises: Calculate the proportion and occurrence frequency of the mesostructure in the statistical area according to the assigned information corresponding to the statistical area in the information set; According to the proportion and occurrence frequency of the mesostructure in the statistical area, the average value and standard deviation corresponding to the proportion and occurrence frequency are calculated; The coefficient of variation of the mesostructure is calculated based on the mean value and the standard deviation, wherein the coefficient of variation is standard deviation / mean value.

7. The method for analyzing rock mesostructure distribution characteristics according to claim 1, characterized in that: The microstructure in the rock sample includes at least minerals and cracks, and the coefficient of variation includes a mineral distribution coefficient, a crack frequency coefficient and a crack rate coefficient.

8. The method for analyzing rock mesostructure distribution characteristics according to claim 1, characterized in that: The step of preprocessing the image includes at least grayscale equalization processing.

9. A rock microstructure distribution characteristic analysis device, characterized in that: include: A storage module for storing a computer program; A processing module is used to execute the computer program to implement the steps in the rock mesostructure distribution characteristics analysis method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: Used to store a computer program, which, when executed by a processing module, implements the steps in the method for analyzing rock mesostructure distribution characteristics as described in any one of claims 1 to 8.

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