Soil three-dimensional pore structure characteristic distribution rule comprehensive index construction method

Abstract

The invention discloses a soil three-dimensional pore structure feature distribution rule comprehensive index construction method. Relates to the technical field of crossing of soil science and computer technology, in particular to the technical field of analyzing soil three-dimensional pore structure feature distribution rules by constructing comprehensive indexes. According to the method, the three-dimensional pore structure feature distribution rule index is constructed based on the three-dimensional Minkowski functional estimation value, and key parameters such as soil porosity, specific surface area and connectivity can be comprehensively and visually reflected. The method comprises the following steps: obtaining a binarized image and carrying out the preprocessing of the binarized image; cutting the pixel matrix data set to obtain a representative basic volume data set of the soil pore structure; the Minkowski functional estimation value in the three-dimensional space is calculated; analyzing Minkowski functional estimation values on the basis of skewness and kurtosis characteristics in statistics; and based on the pore structure distribution characteristic parameters of different soil types, constructing a three-dimensional pore structure characteristic distribution rule index.

Description

Technical Field

[0001] The present invention relates to the interdisciplinary technical field of soil science and computer technology, and in particular to a technical field of analyzing the distribution law of soil three-dimensional pore structure characteristics by constructing a comprehensive index. Background Art

[0002] The three-dimensional pore structure of soil plays a vital role in regulating soil functions such as water migration, nutrient exchange and microbial activity. Studying the characteristics and distribution patterns of the three-dimensional pore structure of soil helps to reveal the intrinsic relationship between the physical, chemical and biological properties of soil, and is of great significance to soil improvement, farmland production management and ecosystem research. Traditional soil pore structure research methods are mainly based on two-dimensional slice observations, which are difficult to fully and truly reflect the morphology and distribution of three-dimensional pores.

[0003] In recent years, with the development of computed tomography (CT), three-dimensional reconstruction and image processing technology, new technical means have been provided for the study of soil three-dimensional pore structure. However, existing research mainly focuses on single soil or simple geometric models, which makes it difficult to fully reveal the morphology, distribution and connectivity characteristics of multi-scale soil pores. In addition, when characterizing pore characteristics, existing methods are often limited to macroscopic indicators such as porosity, and cannot deeply explore the pore shape, surface characteristics, connectivity and spatial distribution of various types of pores.

[0004] At the same time, the effects of different soil types, farming methods and environmental conditions on soil pore structure are not yet fully understood. Especially in the process of long-term farming or ecological restoration, the evolution law and driving mechanism of soil pore structure still lack systematic research. This research limitation restricts the widespread application of soil pore characteristics in precision agriculture, environmental protection and soil health assessment.

[0005] Therefore, researchers in this field are committed to adopting new methods based on advanced technologies that can efficiently and accurately integrate large amounts of soil pore structure data to generate accurate soil health and performance prediction models, providing strong technical support for future soil management and sustainable agriculture. Summary of the invention

[0006] In view of the above problems, the purpose of the present invention is to propose a method for constructing a comprehensive index of soil three-dimensional pore structure characteristic distribution law. The three-dimensional pore structure characteristic distribution law index is constructed based on the three-dimensional Minkowski functional estimation value, which can comprehensively and intuitively reflect key parameters such as soil porosity, specific surface area and connectivity.

[0007] The method comprises the following steps: S1. Acquisition Binarize the image and preprocess it to get a pixel matrix data set; S2. Cut the pixel matrix dataset to obtain a representative basic volume dataset of soil pore structure , ,in, represents the pore substructure pixel matrix, represents a set of real numbers, N represents the number of soil types, M represents the number of samples contained in each soil type, Indicates the number of overlapping sub-matrices obtained by cutting the pixel matrix data set; S3. Calculation Minkowski functional estimate in three dimensions: ,in, Represents the pixel matrix of the pore substructure The porosity, Represents the pixel matrix of the pore substructure The pore surface area, Represents the pixel matrix of the pore substructure The pore connectivity of S4. Based on the characteristics of skewness and kurtosis in statistics, the Minkowski functional estimation value is analyzed to obtain the characteristic parameters of pore structure distribution of different soil types; S5. Based on the pore structure distribution characteristic parameters of different soil types, a three-dimensional pore structure characteristic distribution law index is constructed.

[0008] Further, the preprocessing is specifically as follows: Select from the binary image Continuous and of the same size A region of interest of pixels is converted into a pixel matrix dataset.

[0009] Furthermore, the pixel matrix data set is specifically cut as follows: the pixel matrix data set is cut in combination with the representative basic volume size and the sliding window; the size of the sliding window is set to , with a step size of L pixels.

[0010] Further, the By formula: Calculate, where Represents a three-dimensional matrix The volume of the mesoporous part, Represents a three-dimensional matrix The total volume of By formula: Calculate, where Represents a three-dimensional matrix the total area of By formula: Calculate, where Represents a three-dimensional matrix The number of connected components of the mesopores, Represents a three-dimensional matrix The number of holes in .

[0011] Furthermore, the pore structure distribution characteristic parameters of the different soil types are specifically: and ,in, Indicates The kurtosis of the estimated value, Indicates The skewness of the estimate, where , Indicates The porosity, Indicates The pore surface area, Indicates pore connectivity.

[0012] Further, the By formula: Calculate, where Representing a collection The number of elements in By formula: calculate.

[0013] Furthermore, the three-dimensional pore structure characteristic distribution law index is specifically: ,in , hour, represents the kurtosis function, hour, represents the skewness function, Indicates Soil type Soil porosity of characteristic samples, Indicates Soil type The soil pore surface area of ​​the characteristic sample, Indicates Soil type Soil pore connectivity of characteristic samples, , and represent the fitting weight parameters of soil porosity, soil pore surface area and soil pore connectivity, respectively.

[0014] Furthermore, the size of the pixel matrix data set is: .

[0015] The beneficial effects of the method of the present invention are: (1) The method for analyzing the distribution law of soil three-dimensional pore structure characteristics provided by the present invention can accurately and efficiently characterize the microscopic characteristics of soil pore structure, and provides a scientific and reliable solution for studying soil structural characteristics and their functional relationships.

[0016] (2) By utilizing the representative elementary volume (REV) cutting theory, the present invention significantly expands the sample size and improves the representativeness and diversity of the overall data distribution, providing solid data support for studying the statistical characteristics of soil pore structure and the laws of parameter changes.

[0017] (3) The present invention proposes a characteristic parameter calculation method based on three-dimensional Minkowski functional, which can comprehensively and intuitively reflect key parameters such as soil porosity, specific surface area and connectivity, and analyze their distribution laws in combination with statistical methods, providing an important reference for studying soil water storage, air permeability and nutrient transfer characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0019] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. 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] This embodiment provides a method for constructing a comprehensive index of soil three-dimensional pore structure characteristics and distribution law. The flowchart of the method is as follows: Figure 1 As shown, the method comprises the following steps: S1. Acquisition Binarized images are preprocessed to obtain pixel matrix data sets.

[0021] The relevant operations of step S1 are introduced with a specific example: As shown in Table 1, the black soil in the corn planting area of ​​Jilin Province , Chernozem , meadow soil Sandy soil Soil sample data for four different soil types.

[0022] Table 1

[0023] As shown in Tables 2, 3 and 4, based on the soil sample data, an X-ray micro-CT system was used to perform CT image data scanning, parameter adjustment, reconstruction, ring removal and hard-bundling operations on the soil sample data. Three samples were randomly selected for CT scanning in each processing action, and image reconstruction was performed using Image J software, and finally 36 data samples were obtained.

[0024] Table 2

[0025] Table 3

[0026] Table 4

[0027] For each data sample, 1000 binary 2D slice images were generated. During the binarization process, the white area was represented by a value of 1 to represent the pores, and the black area was represented by a value of 0 to represent the soil matrix. 300 consecutive images of the same size were selected. The region of interest (ROI) of the pixel is converted into a pixel matrix to obtain a pixel matrix dataset of the original soil pore structure. The value of is 300. The value of should be a positive integer.

[0028] S2. Cut the pixel matrix dataset to obtain a representative basic volume dataset of soil pore structure , ,in, represents the pore substructure pixel matrix, represents a set of real numbers, N represents the number of soil types, M represents the number of samples contained in each soil type, It represents the number of overlapping sub-matrices obtained by cutting the pixel matrix data set. In this embodiment, the values ​​of N, M and K are 4, 9 and 100 respectively.

[0029] The relevant operations of step S2 are introduced with a specific example: Based on the representative elementary volume (REV) cutting theory, the pixel matrix dataset is cut by determining the representative elementary volume size and combining the sliding window method to expand the sample size. The sliding window size is set to , with a step size of 25 pixels to ensure partial overlap between samples and improve the representativeness of the overall distribution. The pixel image matrix generates 100 overlapping sub-matrices to further increase sample diversity. In this embodiment The value of is 50. The value of should be a positive integer.

[0030] S3. Calculation Minkowski functional estimate in three dimensions: ,in, Represents the pixel matrix of the pore substructure The porosity, Represents the pixel matrix of the pore substructure The pore surface area, Represents the pixel matrix of the pore substructure pore connectivity.

[0031] The relevant operations of step S3 are introduced with a specific example: Characterization of soil porosity using zero-order Minkowski functional , represents the ability of soil aggregates to store fluid. By formula: Calculate, where Represents a three-dimensional matrix The volume of the mesoporous part, , Represents three-dimensional matrices The first, second, and third dimensions of Represents a three-dimensional matrix The total volume of ; Characterization of pore surface area by first-order Minkowski functional , Represents the adsorption and dissolution processes of soil aggregates; traverses the three-dimensional matrix For each element of , check the values ​​of the surrounding elements to find all the porous elements (value 1). For each porous element, check the element values ​​in the six directions of up, down, left, right, front and back. If the adjacent element is a soil element (value 0), then the porous element has a boundary in that direction. Calculate the number of boundaries of each porous element in each direction and add them up to get the total surface area contribution of the porous element. By adding up the surface area contributions of all porous elements, you can get the surface area of ​​the entire three-dimensional binary image matrix. ; The definition of is: ,in, Represents the three-dimensional matrix by integration Find the first-order surface area; By formula: Calculate, where Represents a three-dimensional matrix Total area; The third-order Minkowski functional characterizes pore connectivity , Represents the connectivity between pores in the soil; using connectivity analysis, calculate the three-dimensional matrix The number of connected components of the mesopores is: Starting from a pore element in the , we gradually search for the pore elements around it and mark them as the same connected component; by traversing all pore elements, we can calculate the number of connected components. ; Use the hole filling method to calculate the three-dimensional matrix The number of holes in Starting from the soil element in, gradually search for the soil elements around it and mark them as the same hole; by traversing all soil elements, the number of holes can be calculated. The definition of is: ; By formula: calculate.

[0032] S4. Based on the characteristics of skewness and kurtosis in statistics, the Minkowski functional estimation value is analyzed to obtain the characteristic parameters of pore structure distribution of different soil types.

[0033] The relevant operations of step S4 are introduced with a specific example: For Soil type, calculate the estimated values ​​of the three Minkowski functionals for all soil samples belonging to this type, and get three sets:

[0034]

[0035]

[0036] Calculate the distribution characteristic parameters of these three sets and obtain the pore structure distribution characteristic parameters of different soil types: and ,in, Indicates The kurtosis of the estimated value, Indicates The skewness of the estimate, , Indicates The porosity, Indicates The pore surface area, Indicates pore connectivity.

[0037] Said By formula: Calculate, where Representing a collection The number of elements in , Indicates a peak, Indicates a flat peak. Indicates that it is consistent with the normal distribution; By formula: Calculate, where Indicates right deviation, Indicates left deviation, Indicates complete symmetry.

[0038] S5. Based on the pore structure distribution characteristic parameters of different soil types, a three-dimensional pore structure characteristic distribution law index is constructed.

[0039] The relevant operations of step S5 are introduced with a specific example: The three-dimensional pore structure characteristic distribution law index is specifically: ,in , hour, represents the kurtosis function, hour, represents the skewness function, Indicates Soil type Soil porosity of characteristic samples, Indicates Soil type The soil pore surface area of ​​the characteristic sample, Indicates Soil type Soil pore connectivity of characteristic samples, , and represent the fitting weight parameters of soil porosity, soil pore surface area and soil pore connectivity, respectively. , and The calculation formula is as follows:

[0040]

[0041]

[0042] in, represents the variance function, Indicates The variance of soil porosity for each soil type, , Indicates Variance of soil pore connectivity for each soil type.

[0043] Finally got black soil , Chernozem , meadow soil Sandy soil Three-dimensional pore structure characteristic distribution regularity index of four different soil types ,Right now , , , .

Claims

1. A method for constructing a comprehensive index of soil three-dimensional pore structure characteristic distribution law, characterized in that: The method comprises the following steps: S1. Acquisition Binarize the image and preprocess it to obtain a pixel matrix data set; S2. Cut the pixel matrix dataset to obtain a representative basic volume dataset of soil pore structure , ,in, represents the pore substructure pixel matrix, represents a set of real numbers, N represents the number of soil types, M represents the number of samples contained in each soil type, Indicates the number of overlapping sub-matrices obtained by cutting the pixel matrix data set; S3. Calculation Minkowski functional estimate in three dimensions: ,in, Represents the pixel matrix of the pore substructure The porosity, Represents the pixel matrix of the pore substructure The pore surface area, Represents the pixel matrix of the pore substructure The pore connectivity of S4. Based on the characteristics of skewness and kurtosis in statistics, the Minkowski functional estimation value is analyzed to obtain the characteristic parameters of pore structure distribution of different soil types; S5. Based on the pore structure distribution characteristic parameters of different soil types, a three-dimensional pore structure characteristic distribution law index is constructed.

2. The method for constructing a comprehensive index of soil three-dimensional pore structure characteristic distribution law according to claim 1, characterized in that: The pre-processing is specifically as follows: Select from the binary image Continuous and of the same size A region of interest of pixels is converted into a pixel matrix dataset.

3. The method for constructing a comprehensive index of soil three-dimensional pore structure characteristic distribution law according to claim 1, characterized in that: The pixel matrix data set is specifically cut as follows: the pixel matrix data set is cut in combination with the representative basic volume size and the sliding window; the size of the sliding window is set to , with a step size of L pixels.

4. The method for constructing a comprehensive index of soil three-dimensional pore structure characteristic distribution law according to claim 1, characterized in that: Said By formula: Calculate, where Represents a three-dimensional matrix The volume of the mesoporous part, Represents a three-dimensional matrix The total volume of By formula: Calculate, where Represents a three-dimensional matrix the total area of By formula: Calculate, where Represents a three-dimensional matrix The number of connected components of the mesopores, Represents a three-dimensional matrix The number of holes in .

5. The method for constructing a comprehensive index of soil three-dimensional pore structure characteristic distribution law according to claim 1, characterized in that: The pore structure distribution characteristic parameters of different soil types are specifically: and ,in, Indicates The kurtosis of the estimated value, Indicates The skewness of the estimate, where , Indicates The porosity, Indicates The pore surface area, Indicates pore connectivity.

6. A method for constructing a comprehensive index of soil three-dimensional pore structure characteristic distribution law according to claim 5, characterized in that: Said By formula: Calculate, where Representing a collection The number of elements in By formula: calculate.

7. A method for constructing a comprehensive index of soil three-dimensional pore structure characteristic distribution law according to claim 6, characterized in that: The three-dimensional pore structure characteristic distribution law index is specifically: ,in , hour, represents the kurtosis function, hour, represents the skewness function, Indicates Soil type Soil porosity of characteristic samples, Indicates Soil type The soil pore surface area of ​​the characteristic sample, Indicates Soil type Soil pore connectivity of characteristic samples, , and represent the fitting weight parameters of soil porosity, soil pore surface area and soil pore connectivity, respectively.

8. According to the method for constructing a comprehensive index of soil three-dimensional pore structure characteristic distribution law according to claim 2, the size of the pixel matrix data set is: .

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