A microscopic image analysis method for fine-grained sedimentary rocks based on ImageJ

Abstract

The present invention discloses a microscopic image analysis method for fine-grained sedimentary rocks based on ImageJ, which includes taking pictures of shale thin sections containing siltstone under a polarized light microscope and saving the pictures; decomposing and converting the microscopic images into 8-bit images according to the three primary colors of red, yellow, and blue, respectively adjusting the color thresholds that can reflect the siltstone minerals and organic matter, and converting the 8-bit images into binary images; constructing a two-dimensional coordinate system for the binary images, statistically analyzing the target areas of the siltstone minerals and organic matter in the binary images, and determining the image area ratio occupied by the target areas and the distribution trend of the target areas; the present invention can automatically statistically identify the proportion of coarse-grained minerals and organic matter content in the microscopic images of fine-grained rocks such as shale and siltstone, and can obtain the mineral particle size distribution, average particle size data, image area ratio of the rocks, and the mineral distribution trend of the rocks.

Description

Technical Field

[0001] The present invention relates to the technical field of sedimentary rock image analysis, and particularly to a microscopic image analysis method for fine-grained sedimentary rocks based on ImageJ. Background Art

[0002] During the geological process, rock masses have undergone deformation and damage, and are geological bodies composed of certain rock components, with certain structures and occurring in certain geological environments. According to their origin, rocks are divided into igneous rocks, sedimentary rocks, and metamorphic rocks. The origin of rocks determines the type of connection and the primary structure or occurrence of rock masses.

[0003] Under the action of flowing water, groundwater, gravity, waves, glaciers, wind and sand, etc., the weathering of rocks causes changes in the mineral composition, structure, and texture of rocks, thereby changing the physical and mechanical properties of rock blocks, resulting in the simultaneous presence of a large number of mesoscopic defects such as microcracks and micropores, as well as mineral inclusions inside the rocks, making the rocks exhibit significant characteristics of discontinuity, inhomogeneity, and anisotropy. Therefore, the strength of rock blocks decreases, the anti-deformation performance weakens, the porosity increases, and the permeability enhances.

[0004] Scholars at home and abroad generally believe that the phenomena of rock damage and failure are multi-scale couplings from the mesoscopic to the macroscopic. Fundamentally speaking, the macroscopic mechanical behavior of rocks is determined by the evolution and development of their internal mesoscopic structures under the action of external forces, such as the initiation and propagation of microcracks, the growth of micropores, and the damage of mineral inclusions (including interface damage).

[0005] Currently, most microscopic image analyses of sedimentary rocks can only obtain data on mineral grain size distribution and average grain size, and cannot further determine the distribution trend of mineral grain sizes, so the weathering direction of rock masses cannot be determined. Summary of the Invention

[0006] The purpose of the present invention is to provide a microscopic image analysis method for fine-grained sedimentary rocks based on ImageJ to solve the technical problems in the prior art that the distribution trend of mineral grain sizes cannot be further determined and the weathering direction of rock masses cannot be determined.

[0007] To solve the above technical problems, the present invention specifically provides the following technical solutions:

[0008] A microscopic image analysis method for fine-grained sedimentary rocks based on ImageJ, comprising the following steps:

[0009] Step 100: Photograph a shale thin section containing silt under a single-polarized light microscope and save the picture;

[0010] Step 200: Decompose and convert the microscope image into an 8-bit image according to the three primary colors of red, yellow, and blue, respectively adjust the color thresholds that can reflect the silt-bearing minerals and organic matter, and convert the 8-bit image into a binary image;

[0011] Step 300: Construct a two-dimensional coordinate system for the binary image, count the target areas containing silt-bearing minerals and organic matter in the binary image, and determine the image area ratio occupied by the target areas and the distribution trend of the target areas.

[0012] As a preferred solution of the present invention, in the step 200, in the binary image, the pixel values of the pixel points corresponding to the areas containing silt-bearing minerals and organic matter are 0, and the pixel values of the pixel points corresponding to other areas are 255.

[0013] As a preferred solution of the present invention, in the step 300, the implementation method of the image area ratio occupied by the target area is as follows:

[0014] Construct a two-dimensional coordinate axis with the image corner position of the binary image as the origin;

[0015] Perform a calendar-style statistics on the binary image to determine the number m of pixel points with a pixel value of 0 in the binary image and the number n of pixel points with a pixel value of 255 in the binary image, and calculate the image area ratio P occupied by the target area:

[0016] P = m / (m + n).

[0017] As a preferred solution of the present invention, the implementation method of determining the distribution trend of the target area is as follows:

[0018] First, determine the coordinate set corresponding to each target area containing silt-bearing minerals and organic matter;

[0019] Determine the coordinate value corresponding to the central mass point of each target area, and represent the coordinate value corresponding to the central mass point as the position of the target area;

[0020] Perform a fitting process on the coordinate values corresponding to the central mass points of all target areas, determine the coordinate values on the same straight line, and determine the inclination direction corresponding to the coordinate values on the same straight line to determine the distribution trend of the target areas.

[0021] As a preferred solution of the present invention, the implementation method of determining the coordinate set corresponding to each target area containing silt-bearing minerals and organic matter is as follows:

[0022] Determine the coordinate values (x, y) of the pixel points with a pixel value of 0 in the binary image;

[0023] Taking the coordinate value (x, y) of the pixel point with one of the pixel values being 0 as a reference point, screening out the pixel points with coordinate values (x±1, y) and (x, y±1) and pixel values being 0;

[0024] Arbitrarily selecting one of the pixel points with pixel value 0 screened out as a new reference point, and screening out the pixel points with coordinate values (x±1, y) and (x, y±1) and pixel values being 0 around the reference point;

[0025] Repeating the above steps until only one pixel point corresponding to a pixel value of 0 exists among the four pixel points around the reference point (x, y);

[0026] Integrating the coordinate values of all pixel points with pixel value 0 screened out into a set, generating a coordinate set corresponding to each target area containing silt minerals and organic matter.

[0027] As a preferred solution of the present invention, the implementation method for determining the coordinate value corresponding to the central mass point of each target area is as follows:

[0028] Screening out the minimum abscissa x in the coordinate set corresponding to each target area min and the maximum abscissa x max ;

[0029] Screening out the minimum ordinate y in the coordinate set corresponding to each target area min and the maximum ordinate y max ;

[0030] The coordinate value corresponding to the central mass point of each target area:

[0031] (x md , y md ) = ((|x max | - |x min |) / 2, (|y max | - |y min |) / 2).

[0032] As a preferred solution of the present invention, determining the number of pixel points in the coordinate set corresponding to each target area containing silt minerals and organic matter, where each coordinate point represents a pixel point, and the number of pixel points in the coordinate set corresponding to each target area represents the mineral particle size distribution;

[0033] Forming discrete data from the number of pixel points in all coordinate sets, and calculating the average number of pixel points in the coordinate sets of all target areas. The average number of pixel points in the coordinate sets of all target areas represents the average particle size data. Screening out the coordinate sets with the number of pixel points less than the average number of pixel points, and retaining the coordinate sets with the number of pixel points greater than or equal to the average number of pixel points;

[0034] Further determine the coordinate values corresponding to the central mass points of each set of coordinates to be retained:

[0035] (x md , y md ) = ((|x max | - |x min |) / 2, (|y max | - |y min |) / 2).

[0036] As a preferred embodiment of the present invention, among all the sets of coordinates determined to be screened and retained, determine the distance between the coordinate values (x md , y md ) corresponding to the central mass points of every two of the said sets of coordinates;

[0037] Screen out the central mass points corresponding to the sets of coordinates with large distances, and retain the central mass points corresponding to the sets of coordinates with small distances, based on the coordinate values of the central mass points corresponding to the retained sets of coordinates with small distances.

[0038] As a preferred embodiment of the present invention, integrate the coordinate values corresponding to all the central mass points of the retained target areas into a set, use the regression analysis method to determine the relationship between the abscissa and the ordinate for all the coordinate values in this set, determine the central mass points on the same straight line, and based on the inclination directions corresponding to all the straight lines, determine the overall distribution trend of all the target areas.

[0039] The present invention has the following beneficial effects compared with the prior art:

[0040] The present invention can automatically statistically identify the proportion of coarse-grained minerals and organic matter content in microscopic images of fine-grained rocks such as shale and siltstone, and can obtain the mineral particle size distribution, average particle size data, image area ratio of the rock, and the mineral distribution trend of the rock, and further determine the weathering degree and weathering direction of the rock. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary, and for those of ordinary skill in the art, without creative efforts, other implementation drawings can also be obtained according to the provided drawings.

[0042] Figure 1 It is a schematic flowchart of the microscopic image analysis method according to an embodiment of the present invention;

[0043] Figure 2 It is a schematic diagram of a single-polarized light microscope image according to an embodiment of the present invention;

[0044] Figure 3 This is the 8-bit image of the embodiment of the present invention;

[0045] Figure 4 This is the binary image of the embodiment of the present invention; Detailed implementation manners

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0047] As Figure 1 shown, the present invention provides a microscopic image analysis method for fine-grained sedimentary rocks based on ImageJ, including the following steps:

[0048] Step 100: Photograph the silt-bearing shale thin section under a single-polarized light microscope and save the picture;

[0049] Step 200: Decompose and convert the microscope image into an 8-bit image according to the three primary colors of red, yellow, and blue, and respectively adjust the color thresholds that can reflect the silt-bearing minerals and organic matter, and convert the 8-bit image into a binary image;

[0050] Step 300: Construct a two-dimensional coordinate system of the binary image, count the target areas of the silt-bearing minerals and organic matter in the binary image, and determine the image area ratio occupied by the target areas and the distribution trend of the target areas.

[0051] This embodiment is based on the image processing program ImageJ, which can automatically count and identify the proportion of coarse-grained minerals and organic matter in the microscopic images of fine-grained rocks such as shale and siltstone, and can obtain the mineral particle size distribution and average particle size data. The statistical results are accurate, objective, simple to operate, and easy to implement.

[0052] Among them, the single-polarized light microscope image is as Figure 2 shown, the 8-bit image is as Figure 3 shown, and the binary image is as Figure 4 shown.

[0053] In step 200, in the binary image, the pixel values of the pixel points corresponding to the areas containing silt-bearing minerals and organic matter are 0, and the pixel values of the pixel points corresponding to other areas are 255.

[0054] In step 300, the implementation method of the image area ratio occupied by the target area is:

[0055] Construct a two-dimensional coordinate axis with the image corner position of the binary image as the origin;

[0056] Perform a sequential statistical analysis on the binary image to determine the number m of pixel points with a pixel value of 0 in the binary image and the number n of pixel points with a pixel value of 255 in the binary image, and calculate the image area ratio P occupied by the target area: P = m / (m + n).

[0057] In this embodiment, assume that the origin of the two-dimensional coordinate axis is set at the lower left corner of the binary image, such that the binary image is within the first quadrant of the two-dimensional coordinate axis.

[0058] Take the black pixel points with a pixel value of 0 as the target area containing silt minerals and organic matter. According to the ImageJ image processing program, determine the number of black pixel points with a pixel value of 0 in the binary image of the single polarized light microscope image, and determine the distribution inclination direction of the silt minerals and organic matter according to the distribution trend of the black pixel points.

[0059] Among them, the implementation method for determining the distribution trend of the target area is as follows:

[0060] (1) First, determine the coordinate set corresponding to each target area containing silt minerals and organic matter;

[0061] (2) Determine the coordinate value corresponding to the central mass point of each target area, and represent the coordinate value corresponding to the central mass point as the position of the target area;

[0062] (3) Perform a fitting process on the coordinate values corresponding to the central mass points of all target areas, determine the coordinate values on the same straight line, and determine the inclination direction corresponding to the coordinate values on the same straight line to determine the distribution trend of the target area.

[0063] The implementation method for determining the coordinate set corresponding to each target area containing silt minerals and organic matter is as follows:

[0064] Determine the coordinate values (x, y) of the pixel points with a pixel value of 0 in the binary image;

[0065] Taking the coordinate values (x, y) of one of the pixel points with a pixel value of 0 as a reference point, screen out the pixel points with coordinate values (x±1, y) and coordinate values (x, y±1) and a pixel value of 0;

[0066] Arbitrarily select one of the pixel points with a pixel value of 0 screened out as a new reference point, and screen out the pixel points with coordinate values (x±1, y) and coordinate values (x, y±1) and a pixel value of 0 around the reference point;

[0067] Repeat the above steps until there is only one pixel point with a pixel value of 0 among the four pixel points around the reference point (x, y);

[0068] Integrate the coordinate values of all pixel points with a pixel value of 0 screened out into a set, and generate a coordinate set corresponding to each target area containing silt minerals and organic matter.

[0069] For example, for a certain target area containing silt minerals and organic matter, the coordinate values of the pixel points with a pixel value of 0 in this target area are (x, y). Determine whether there are pixel points with a pixel value of 0 among the four surrounding points (x + 1, y), (x - 1, y), (x, y + 1), and (x, y - 1) of this reference point (x, y).

[0070] When there are pixel points with a pixel value of 0 and the number of pixel points is more than one, randomly select one pixel point as the new reference point (x, y), and further determine whether there are new pixel points with a pixel value of 0 among the four surrounding points (x + 1, y), (x - 1, y), (x, y + 1), and (x, y - 1) of this reference point (x, y).

[0071] Repeat the above steps until there is only one pixel point with a pixel value of 0 among the four surrounding points (x + 1, y), (x - 1, y), (x, y + 1), and (x, y - 1) of a certain reference point (x, y). Then, consider this pixel point as the last pixel point of a certain target area.

[0072] Take the coordinate set of all the screened pixel points as the target areas containing silt minerals and organic matter.

[0073] Furthermore, since the shapes of the target areas containing silt minerals and organic matter are diverse, in order to precisely determine the distribution trend corresponding to all target areas, it is necessary to determine the coordinate values corresponding to the central mass points of each target area, and then determine the distribution trend of all central mass points according to the regression analysis method. The specific implementation method for determining the coordinate values corresponding to the central mass points of each target area is as follows:

[0074] Screen out the minimum abscissa x in the coordinate set corresponding to each target area min and the maximum abscissa x max ;

[0075] Screen out the minimum ordinate y in the coordinate set corresponding to each target area min and the maximum ordinate y max ;

[0076] The coordinate values corresponding to the central mass points of each target area:

[0077] (x md , y md ) = ((|x max | - |x min |) / 2, (|ymax |-|y min |) / 2).

[0078] Furthermore, in order to determine the distribution trend of the target areas containing silt minerals and organic matter, it is necessary to screen out the discrete target areas and analyze the distribution trend of the centrally distributed target areas. The specific method for realizing the screening of discrete target areas is as follows:

[0079] Determine the number of pixel points in the coordinate set corresponding to each target area containing silt minerals and organic matter. Here, each coordinate point represents a pixel point, and the number of pixel points in the coordinate set corresponding to each target area represents the mineral particle size distribution.

[0080] Form discrete data from the number of pixel points in all coordinate sets, and calculate the average number of pixel points in the coordinate sets of all target areas. The average number of pixel points in the coordinate sets of all target areas represents the average particle size data. Screen out the coordinate sets with the number of pixel points less than the average number of pixel points, and retain the coordinate sets with the number of pixel points greater than or equal to the average number of pixel points.

[0081] Further determine the coordinate values of the central mass points corresponding to each retained coordinate set:

[0082] (x md , y md ) = ((|x max |-|x min |) / 2, (|y max |-|y min |) / 2).

[0083] Determine the distance between the coordinate values (x md , y md ) of the central mass points corresponding to every two coordinate sets among all the screened and retained coordinate sets.

[0084] Screen out the central mass points corresponding to the coordinate sets with large distances, and retain the central mass points corresponding to the coordinate sets with small distances, based on the coordinate values of the central mass points corresponding to the retained coordinate sets with small distances.

[0085] Integrate the coordinate values of the central mass points corresponding to all the retained target areas into a set, use the regression analysis method to determine the relationship between the abscissa and the ordinate for all the coordinate values in this set, determine the central mass points on the same straight line, and determine the overall distribution trend of all the target areas based on the inclination directions corresponding to all the straight lines.

[0086] Therefore, this embodiment can automatically count and identify the proportion of coarse-grained minerals and organic matter in the microscopic images of fine-grained rocks such as shale and siltstone, and can obtain the mineral particle size distribution, average particle size data, image area ratio of the rock, and the mineral distribution trend of the rock. According to the image area ratio, the weathering degree of the rock can be determined, and according to the sediment mineral distribution trend of the rock, the weathering direction of the rock can be determined. Among them, the mineral particle size distribution data in the initial weathering direction is large, and the mineral particle size distribution data in the weathering process direction is small.

[0087] The above embodiments are only exemplary embodiments of the present application and are not used to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art can make various modifications or equivalent replacements within the essence and protection scope of the present application, and such modifications or equivalent replacements should also be regarded as falling within the protection scope of the present application.

Claims

1. A microscopic image analysis method for fine-grained sedimentary rocks based on ImageJ, characterized in that, It includes the following steps: Step 100: Photograph the shale thin slice containing siltstone under a single-polarized light microscope and save the picture; Step 200: Decompose and convert the microscope image into an 8-bit image according to the three primary colors of red, yellow, and blue, respectively adjust the color thresholds that can reflect the siltstone minerals and organic matter, and convert the 8-bit image into a binary image; In the step 200, in the binary image, the pixel values of the pixel points corresponding to the areas containing siltstone minerals and organic matter are 0, and the pixel values of the pixel points corresponding to other areas are 255; Step 300: Construct a two-dimensional coordinate system of the binary image, count the target areas containing siltstone minerals and organic matter in the binary image, and determine the image area ratio occupied by the target areas and the distribution trend of the target areas; The implementation method for determining the distribution trend of the target areas is: First, determine the coordinate set corresponding to each target area containing siltstone minerals and organic matter; Determine the coordinate value corresponding to the central mass point of each target area, and represent the coordinate value corresponding to the central mass point as the position of the target area; Perform fitting processing on the coordinate values corresponding to the central mass points of all target areas, determine the coordinate values on the same straight line, and determine the inclination direction corresponding to the coordinate values on the same straight line to determine the distribution trend of the target areas.

2. The microscopic image analysis method of fine-grained sedimentary rocks based on ImageJ according to claim 1, wherein In the step 300, the implementation method for the image area ratio occupied by the target areas is: Construct a two-dimensional coordinate axis with the image corner position of the binary image as the origin; Perform a traversal statistics on the binary image, determine the number m of pixel points with a pixel value of 0 in the binary image and the number n of pixel points with a pixel value of 255 in the binary image, and calculate the image area ratio P occupied by the target areas: P = m / (m + n).

3. The microscopic image analysis method of fine-grained sedimentary rocks based on ImageJ according to claim 1, wherein The implementation method for determining the coordinate set corresponding to each target area containing siltstone minerals and organic matter is: Determine the coordinate values (x, y) of the pixel points with a pixel value of 0 in the binary image; Taking the coordinate values (x, y) of one pixel point with a pixel value of 0 as a reference point, screen out the pixel points with coordinate values (x±1, y) and coordinate values (x, y±1) and a pixel value of 0; Arbitrarily select one of the screened pixel points with a pixel value of 0 as a new reference point, and screen out the pixel points with coordinate values (x±1, y) and coordinate values (x, y±1) and a pixel value of 0 around the reference point; Repeat the above steps until only one pixel point corresponding to a pixel value of 0 exists among the four pixel points around the reference point (x, y); Integrate the coordinate values of all the screened pixel points with a pixel value of 0 into a set to generate the coordinate set corresponding to each target area containing siltstone minerals and organic matter.

4. The microscopic image analysis method of fine-grained sedimentary rocks based on ImageJ according to claim 1, wherein The implementation method for determining the coordinate values corresponding to the central mass points of each of the said target regions is as follows: Filter out the minimum abscissa value x in the coordinate set corresponding to each target area min and the maximum abscissa value x max ; Filter out the minimum ordinate value y in the coordinate set corresponding to each target area min and the maximum ordinate value y max ; The coordinate values corresponding to the central mass points of each of the said target regions: (x md , y md ) = ((|x max | - |x min |) / 2, (|y max | - |y min |) / 2).

5. A microscopic image analysis method for fine-grained sedimentary rocks based on ImageJ according to claim 4, wherein: Determine the number of pixel points within the coordinate set corresponding to each target region containing silt-bearing minerals and organic matter, where each coordinate point represents a pixel point, and the number of pixel points within the coordinate set corresponding to each target region represents the mineral grain size distribution; Form discrete data from the number of pixel points within all coordinate sets, and calculate the average number of pixel points in the coordinate sets of all target regions. The average number of pixel points in the coordinate sets of all target regions represents the average grain size data. Screen out the coordinate sets with the number of pixel points less than the average number of pixel points, and retain the coordinate sets with the number of pixel points greater than or equal to the average number of pixel points; Further determine the coordinate values corresponding to the central mass points of each retained coordinate set: (x md , y md ) = ((|x max | - |x min |) / 2, (|y max | - |y min |) / 2).

6. A microscopic image analysis method for fine-grained sedimentary rocks based on ImageJ according to claim 4 or 5, wherein: Determine the distance between the coordinate values (x md , y md ) of the central mass points corresponding to every two of the coordinate sets among all the coordinate sets selected and retained; Screen out the central mass points corresponding to the coordinate sets with large spacings, and retain the central mass points corresponding to the coordinate sets with small spacings, based on the coordinate values of the central mass points corresponding to the retained coordinate sets with small spacings.

7. A microscopic image analysis method for fine-grained sedimentary rocks based on ImageJ according to claim 6, wherein: Integrate the coordinate values corresponding to the central mass points of all retained target regions into a set, use regression analysis on all coordinate values within this set to determine the relationship between the abscissa and the ordinate, determine the central mass points on the same straight line, and based on the inclination directions corresponding to all the straight lines, determine the overall distribution trend of all target regions.

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