Microscope image analysis method for pulveryte rock based on ImageJ

Abstract

The invention discloses an ImageJ-based microscopy image analysis method for pulveryte rock, which comprises the following steps of: shooting a shale slice containing silty sand under a single polarizing microscope, and storing an image; the method comprises the following steps: decomposing and converting a microscope image into a 8bit image according to red, yellow and blue three colors, respectively adjusting color thresholds capable of reflecting silty minerals and organic matters, and converting the 8bit image into a binary image; constructing a two-dimensional coordinate system of the binary image, performing statistics on a target region containing silty minerals and organic matters in the binary image, and determining an image area ratio occupied by the target region and a distribution trend of the target region; according to the method, the content ratio of the coarse-grained minerals to the organic matters in the microscopic images of the fine-grained rocks such as shale and siltstone can be automatically counted and identified, and the mineral particle size distribution, the average particle size data, the image area ratio and the mineral distribution trend of the rocks can be obtained.

Description

Technical Field

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

[0002] The rock mass has undergone deformation and destruction during the geological process. It is composed of certain rock components, has a certain structure and exists in a certain geological environment. According to its origin, it is divided into igneous rocks, sedimentary rocks and metamorphic rocks. The origin of the rock determines the rock's connection type and the original structure or occurrence of the rock mass.

[0003] The weathering of rocks under the action of flowing water, groundwater, gravity, waves, glaciers, wind and sand, etc., causes changes in the mineral composition and structural structure of the rocks, which in turn changes the physical and mechanical properties of the rocks, resulting in the existence of a large number of micro-cracks, micro-pores and other micro-defects and mineral inclusions inside the rocks, making the rocks show significant discontinuity, non-uniformity and anisotropy. Therefore, the strength of the rocks is reduced, the deformation resistance is weakened, the porosity is increased, and the permeability is enhanced.

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

[0005] Currently, most microscope image analyses of sedimentary rocks can only obtain mineral particle size distribution and average particle size data, but cannot further determine the distribution trend of mineral particle size, and therefore cannot determine the weathering direction of the rock mass. Summary of the invention

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

[0007] In order to solve the above technical problems, the present invention specifically provides the following technical solutions: A microscopic image analysis method for fine-grained sedimentary rocks based on ImageJ, comprising the following steps: Step 100, photographing the silty shale slice under a single polarizing microscope and saving the picture; Step 200, decomposing the microscope image into an 8-bit image according to the three primary colors of red, yellow and blue, adjusting the color thresholds that can reflect the presence of silty minerals and organic matter, and converting the 8-bit image into a binary image; Step 300: construct a two-dimensional coordinate system of the binary image, count the target areas containing silty 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.

[0008] As a preferred solution of the present invention, in step 200, in the binary map, the pixel values ​​of the pixels corresponding to the areas containing silty minerals and organic matter are 0, and the pixel values ​​of the pixels corresponding to other areas are 255.

[0009] As a preferred solution of the present invention, in step 300, the method for realizing the image area ratio occupied by the target area is: Constructing a two-dimensional coordinate axis with the image corner position of the binary image as the origin; Perform calendar statistics on the binary image to determine the number m of pixels with a pixel value of 0 in the binary image and the number n of pixels 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).

[0010] As a preferred solution of the present invention, a method for determining the distribution trend of the target area is as follows: First, determine the coordinate set corresponding to each target area containing silty minerals and organic matter; Determine the coordinate value corresponding to the central particle of each target area, and represent the coordinate value corresponding to the central particle as the position of the target area; The coordinate values ​​corresponding to the central particles of all target areas are fitted to determine the coordinate values ​​on the same straight line, and the inclination direction corresponding to the coordinate values ​​on the same straight line is determined to determine the distribution trend of the target area.

[0011] As a preferred solution of the present invention, the method for determining the coordinate set corresponding to each target area containing silty minerals and organic matter is as follows: Determine the coordinate value (x, y) of the pixel point whose pixel value is 0 in the binary image; Taking the coordinate value (x, y) of one pixel point with a pixel value of 0 as the reference point, filter out the pixel points with coordinate values ​​of (x±1, y) and coordinate values ​​of (x, y±1) and pixel values ​​of 0; Randomly select one of the pixel points with a pixel value of 0 from the filtered out pixel points as the reference point again, and filter out the pixel points with a coordinate value of (x±1, y) and a coordinate value of (x, y±1) and a pixel value of 0 around the reference point; Repeat the above steps until only one pixel has a pixel value of 0 among the four pixels around the reference point (x, y); The coordinate values ​​of all the screened pixel points with a pixel value of 0 are integrated into a set to generate a coordinate set corresponding to each target area containing silty minerals and organic matter.

[0012] As a preferred solution of the present invention, the method for determining the coordinate value corresponding to each of the target area center particles is as follows: Filter out the minimum horizontal coordinate value x in the coordinate set corresponding to each target area min With the maximum value of the horizontal coordinate x max ; Filter out the minimum ordinate value y in the coordinate set corresponding to each target area min With the maximum value of the vertical coordinate y max ; The coordinate values ​​corresponding to the center point of each target area are: (x md ,y md ) = ((|x max |-|x min |) / 2, (|y max |-|y min |) / 2).

[0013] As a preferred embodiment of the present invention, the number of pixel points in a coordinate set corresponding to each target area containing silty minerals and organic matter is determined, wherein each coordinate point represents a pixel point, and the number of pixel points in the coordinate set corresponding to each target area represents the distribution of mineral particle size; The number of pixels in all coordinate sets is formed into discrete data, and the average number of pixels in the coordinate sets of all target areas is calculated. The average number of pixels in the coordinate sets of all target areas represents the average particle size data, and the coordinate sets with the number of pixels less than the average number of pixels are screened out, and the coordinate sets with the number of pixels greater than or equal to the average number of pixels are retained; Further determine the coordinate values ​​of the central mass point corresponding to each retained coordinate set: (x md ,y md ) = ((|x max |-|x min |) / 2, (|y max |-|y min |) / 2).

[0014] As a preferred solution of the present invention, determine the coordinate values ​​(x md ,y md ) The central mass points corresponding to the coordinate sets with large spacing are screened out, and the central mass points corresponding to the coordinate sets with small spacing are retained, based on the coordinate values ​​of the central mass points corresponding to the retained coordinate sets with small spacing.

[0015] As a preferred solution of the present invention, the coordinate values ​​corresponding to the central particles of all retained target areas are integrated into a set, and the relationship between the horizontal coordinate and the vertical coordinate is determined for all the coordinate values ​​in the set using regression analysis to determine the central particles on the same straight line, and based on the inclination directions corresponding to all straight lines, the overall distribution trend of all target areas is determined.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention can automatically statistically 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 and mineral distribution trend of the rock, and then determine the weathering degree and weathering direction of the rock. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0018] Figure 1 Schematic diagram of the process of the microscope image analysis method according to an embodiment of the present invention; Figure 2 A schematic diagram of a single polarizing microscope image according to an embodiment of the present invention; Figure 3 It is an 8-bit image of an embodiment of the present invention; Figure 4 is a binary image according to an embodiment of the present invention; DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] like Figure 1 As shown, the present invention provides a microscope image analysis method of fine-grained sedimentary rocks based on ImageJ, comprising the following steps: Step 100, photographing the silty shale slice under a single polarizing microscope and saving the picture; Step 200, decomposing the microscope image into an 8-bit image according to the three primary colors of red, yellow and blue, adjusting the color thresholds that can reflect the presence of silty minerals and organic matter, and converting the 8-bit image into a binary image; Step 300: construct a two-dimensional coordinate system of the binary image, count the target areas containing silty 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.

[0021] This implementation is based on the image processing program ImageJ, which can automatically statistically identify the proportion of coarse-grained minerals and organic matter in microscopic images of fine-grained rocks such as shale and siltstone, and can obtain mineral particle size distribution and average particle size data. The statistical results are accurate and objective, the operation is simple, and it is easy to implement.

[0022] Among them, single polarizing microscope images such as Figure 2 As shown, the 8-bit image is Figure 3 As shown, the binary image is Figure 4 shown.

[0023] In step 200 , in the binary image, the pixel values ​​of the pixels corresponding to the regions containing silty minerals and organic matter are 0, and the pixel values ​​of the pixels corresponding to other regions are 255.

[0024] In step 300, the method for realizing the image area ratio occupied by the target area is: Construct a two-dimensional coordinate axis with the image corner position of the binary image as the origin; Perform calendar statistics on the binary image to determine the number of pixels m with a pixel value of 0 and the number of pixels n 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).

[0025] In this embodiment, it is assumed that the origin of the two-dimensional coordinate axis is set at the lower left corner of the binary image, so that the binary image is in the first quadrant of the two-dimensional coordinate axis.

[0026] The black pixels with a pixel value of 0 were taken as the target areas containing silty minerals and organic matter. The number of black pixels with a pixel value of 0 in the binary image of the single polarizing microscope image was determined according to the ImageJ image processing program. The distribution inclination direction of the silty minerals and organic matter was determined according to the distribution trend of the black pixels.

[0027] Among them, the implementation method of determining the distribution trend of the target area is: (1) First determine the coordinate set corresponding to each target area containing silty minerals and organic matter; (2) Determine the coordinate value corresponding to the center mass point of each target area, and represent the coordinate value corresponding to the center mass point as the position of the target area; (3) Fitting the coordinate values ​​corresponding to the central particles of all target areas to determine the coordinate values ​​on the same straight line and the inclination direction corresponding to the coordinate values ​​on the same straight line to determine the distribution trend of the target area.

[0028] The method for determining the coordinate set corresponding to each target area containing silty minerals and organic matter is as follows: Determine the coordinates (x, y) of the pixel points in the binary image whose pixel value is 0; Taking the coordinate value (x, y) of one pixel point with a pixel value of 0 as the reference point, filter out the pixel points with coordinate values ​​of (x±1, y) and coordinate values ​​of (x, y±1) and pixel values ​​of 0; Randomly select one of the pixel points with a pixel value of 0 from the filtered out pixel points as the reference point again, and filter out the pixel points with a coordinate value of (x±1, y) and a coordinate value of (x, y±1) and a pixel value of 0 around the reference point; Repeat the above steps until only one pixel has a pixel value of 0 among the four pixels around the reference point (x, y); The coordinate values ​​of all the screened pixel points with a pixel value of 0 are integrated into a set to generate a coordinate set corresponding to each target area containing silty minerals and organic matter.

[0029] For example, for a target area containing silty minerals and organic matter, the coordinates of the pixel points with a pixel value of 0 in the target area are (x, y). Determine whether there are pixel points with a pixel value of 0 at the four points (x+1, y), (x-1, y), (x, y+1), and (x, y-1) around the control point (x, y).

[0030] When there are pixels with a pixel value of 0 and the number of pixels is more than one, randomly select a pixel point as the reference point (x, y) to further determine whether there are new pixels with a pixel value of 0 at the four points (x+1, y), (x-1, y), (x, y+1), and (x, y-1) around the reference point (x, y).

[0031] Repeat the above steps until only one pixel value among the four points (x+1, y), (x-1, y), (x, y+1), and (x, y-1) around a reference point (x, y) is found to be 0. Then the pixel is considered to be the last pixel of a target area.

[0032] The coordinate set of all the screened pixel points is taken as the target area containing silty minerals and organic matter.

[0033] Furthermore, since the shapes of the target areas containing silty minerals and organic matter are different, in order to precisely determine the distribution trends corresponding to all target areas, it is necessary to determine the coordinate values ​​corresponding to the central mass point of each target area, and then determine the distribution trends of all central mass points according to the regression analysis method. The specific method for determining the coordinate values ​​corresponding to the central mass point of each target area is as follows: Filter out the minimum horizontal coordinate value x in the coordinate set corresponding to each target area min With the maximum value x of the horizontal axis max ; Filter out the minimum ordinate value y in the coordinate set corresponding to each target area min With the maximum value of the vertical coordinate y max ; The coordinate values ​​corresponding to the center point of each target area are: (x md ,y md ) = ((|x max |-|x min |) / 2, (|y max |-|y min |) / 2).

[0034] Furthermore, in order to determine the distribution trend of the target area containing silty minerals and organic matter, it is necessary to screen out the discrete target areas and analyze the distribution trend of the concentrated target areas. The specific method for screening out the discrete target areas is as follows: The number of pixels in the coordinate set corresponding to each target area containing silt minerals and organic matter is determined, wherein each coordinate point represents a pixel point, and the number of pixels in the coordinate set corresponding to each target area represents the mineral particle size distribution.

[0035] The number of pixels in all coordinate sets is formed into discrete data, and the average number of pixels in the coordinate sets of all target areas is calculated. The average number of pixels in the coordinate sets of all target areas represents the average particle size data. The coordinate sets with the number of pixels less than the average number of pixels are screened out, and the coordinate sets with the number of pixels greater than or equal to the average number of pixels are retained.

[0036] Further determine the coordinate values ​​of the central mass point corresponding to each retained coordinate set: (x md ,y md ) = ((|x max |-|x min |) / 2, (|y max |-|y min |) / 2).

[0037] Determine the coordinate values ​​(x md ,y md ) between the two.

[0038] The central mass points corresponding to the coordinate sets with large spacing are screened out, and the central mass points corresponding to the coordinate sets with small spacing are retained, based on the coordinate values ​​of the central mass points corresponding to the retained coordinate sets with small spacing.

[0039] The coordinate values ​​corresponding to the central particles of all retained target areas are integrated into a set. The relationship between the horizontal coordinate and the vertical coordinate is determined by regression analysis for all coordinate values ​​in the set. The central particles on the same straight line are determined. Based on the inclination directions corresponding to all straight lines, the overall distribution trend of all target areas is determined.

[0040] Therefore, this embodiment can automatically statistically 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 and mineral distribution trend of the rock. The weathering degree of the rock can be determined according to the image area ratio, and the weathering direction of the rock can be determined according to the distribution trend of mud and sand minerals in the rock. Among them, the mineral particle size distribution data in the direction of weathering starting is large, and the mineral particle size distribution data in the direction of weathering process is small.

[0041] The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and protection scope of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present application.

Claims

1. A microscope image analysis method for fine-grained sedimentary rocks based on ImageJ, characterized in that: The following steps are involved: Step 100, photographing the silty shale slice under a single polarizing microscope and saving the picture; Step 200, decomposing the microscope image into an 8-bit image according to the three primary colors of red, yellow and blue, adjusting the color thresholds that can reflect the presence of silty minerals and organic matter, and converting the 8-bit image into a binary image; Step 300: construct a two-dimensional coordinate system of the binary image, count the target areas containing silty 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.

2. The method for analyzing microscopic images of fine-grained sedimentary rocks based on ImageJ according to claim 1, characterized in that: In step 200 , in the binary image, the pixel values ​​of the pixels corresponding to the regions containing silty minerals and organic matter are 0, and the pixel values ​​of the pixels corresponding to other regions are 255.

3. The method for analyzing microscopic images of fine-grained sedimentary rocks based on ImageJ according to claim 2, characterized in that: In step 300, the method for realizing the image area ratio occupied by the target area is: Constructing a two-dimensional coordinate axis with the image corner position of the binary image as the origin; Perform calendar statistics on the binary image to determine the number m of pixels with a pixel value of 0 in the binary image and the number n of pixels 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).

4. The method for analyzing microscopic images of fine-grained sedimentary rocks based on ImageJ according to claim 2, characterized in that: The method for determining the distribution trend of the target area is as follows: First, determine the coordinate set corresponding to each target area containing silty minerals and organic matter; Determine the coordinate value corresponding to the central particle of each target area, and represent the coordinate value corresponding to the central particle as the position of the target area; The coordinate values ​​corresponding to the central particles of all target areas are fitted to determine the coordinate values ​​on the same straight line, and the inclination direction corresponding to the coordinate values ​​on the same straight line is determined to determine the distribution trend of the target area.

5. The method for analyzing microscopic images of fine-grained sedimentary rocks based on ImageJ according to claim 4, characterized in that: The method for determining the coordinate set corresponding to each target area containing silty minerals and organic matter is as follows: Determine the coordinate value (x, y) of the pixel point whose pixel value is 0 in the binary image; Taking the coordinate value (x, y) of one pixel point with a pixel value of 0 as the reference point, filter out the pixel points with coordinate values ​​of (x±1, y) and coordinate values ​​of (x, y±1) and pixel values ​​of 0; Randomly select one of the pixel points with a pixel value of 0 from the filtered out pixel points as the reference point again, and filter out the pixel points with a coordinate value of (x±1, y) and a coordinate value of (x, y±1) and a pixel value of 0 around the reference point; Repeat the above steps until only one pixel has a pixel value of 0 among the four pixels around the reference point (x, y); The coordinate values ​​of all the screened pixel points with a pixel value of 0 are integrated into a set to generate a coordinate set corresponding to each target area containing silty minerals and organic matter.

6. The method for analyzing microscopic images of fine-grained sedimentary rocks based on ImageJ according to claim 4, characterized in that: The method for determining the coordinate value corresponding to each of the target area center particles is as follows: Filter out the minimum horizontal coordinate value x in the coordinate set corresponding to each target area min With the maximum value x of the horizontal axis max ; Filter out the minimum ordinate value y in the coordinate set corresponding to each target area min With the maximum value of the vertical coordinate y max ; The coordinate values ​​corresponding to the center point of each target area are: (x) md ,and md )=((|x max |-|x min |) / 2,(|and max |-|and min |) / 2)。 7. The method for analyzing microscopic images of fine-grained sedimentary rocks based on ImageJ according to claim 6, characterized in that: Determine the number of pixels in the coordinate set corresponding to each target area containing silty minerals and organic matter, wherein each coordinate point represents a pixel, and the number of pixels in the coordinate set corresponding to each target area represents the mineral particle size distribution; The number of pixels in all coordinate sets is formed into discrete data, and the average number of pixels in the coordinate sets of all target areas is calculated. The average number of pixels in the coordinate sets of all target areas represents the average particle size data, and the coordinate sets with the number of pixels less than the average number of pixels are screened out, and the coordinate sets with the number of pixels greater than or equal to the average number of pixels are retained; Further determine the coordinate values ​​of the central mass point corresponding to each retained coordinate set: (x) md ,and md )=((|x max |-|x min |) / 2,(|and max |-|and min |) / 2)。 8. A microscope image analysis method for fine-grained sedimentary rocks based on ImageJ according to claim 6 or 7, characterized in that: Determine the coordinate values ​​(x md ,y md ) between the The central mass points corresponding to the coordinate sets with large spacing are screened out, and the central mass points corresponding to the coordinate sets with small spacing are retained, based on the coordinate values ​​of the central mass points corresponding to the retained coordinate sets with small spacing.

9. The method for analyzing microscopic images of fine-grained sedimentary rocks based on ImageJ according to claim 8, characterized in that: The coordinate values ​​corresponding to the central particles of all retained target areas are integrated into a set. The relationship between the horizontal coordinate and the vertical coordinate is determined by regression analysis for all coordinate values ​​in the set. The central particles on the same straight line are determined. Based on the inclination directions corresponding to all straight lines, the overall distribution trend of all target areas is determined.

Images