Method and apparatus for soil microstructure dataset construction

Abstract

The application provides a method and device for soil microstructure dataset construction, and relates to the technical field of land resources and environment, and the method comprises the following steps: scanning a soil sample by using XCT to obtain a scanning image; generating a three-dimensional microstructure image of the soil based on the scanning data; extracting soil pore structure in the three-dimensional microstructure image of the soil, calculating soil pore related parameters based on the soil pore structure, and performing network analysis on the soil pore structure to generate network analysis related parameters of the soil pore structure; determining soil permeability according to the soil pore structure; generating a two-dimensional image stack based on the three-dimensional microstructure of the soil, and calculating soil morphological parameters and soil organic matter content based on the two-dimensional image stack. The three-dimensional microstructure of the soil is obtained by scanning through XCT, the soil microstructure dataset is generated based on the three-dimensional microstructure of the soil, and compared with the traditional soil structure research method, the application has the characteristics of nondestructive testing and high resolution.

Description

Technical Field

[0001] This invention relates to the field of land resources and environmental technology, and in particular to a method and apparatus for constructing a soil microstructure dataset. Background Technology

[0002] Soil is the loose, fertile surface layer of the Earth's landmass, capable of supporting plant growth. It is a natural body formed under the combined influence of factors such as climate, parent material, organisms, topography, and time. As the material basis of terrestrial ecosystems, soil not only maintains the stable operation of these ecosystems but is also a core natural resource upon which human society depends for survival and development. Soil microstructure refers to the arrangement and connection between soil particles, as well as the morphology, size, and spatial distribution of surrounding pores. It reflects the interactions between soil particles and how these interactions affect the macroscopic properties of the soil. Soil pores, as a key component of soil microstructure, are the sites for the transport and storage of water, air, and nutrients in the soil. Soil pore structure is a comprehensive reflection of the morphology, size, quantity, and spatial distribution of pores, embodying their geometric characteristics (pore volume, surface area, convexity, etc.) and topological characteristics (connectivity, Euler number, etc.). These two characteristics together determine soil moisture migration, gas diffusion, biological activity, and the acquisition of available water by plants. Without considering other complex physicochemical processes at the solid-liquid interface, soil hydraulic properties are primarily influenced by pore structure. Accurate identification and characterization of soil porosity are fundamental to a deeper understanding and prediction of soil moisture migration and solute transport processes. Quantitative research on soil microstructure is crucial for predicting soil hydraulic properties, controlling groundwater pollution, increasing soil fertility, and improving crop yield. However, soil microstructure is highly complex and heterogeneous, and its dynamic changes are often influenced by biotic and abiotic factors, such as soil microbial and animal activity (e.g., earthworms, ants), plant root growth, agricultural machinery tillage and compaction, wet-dry cycles, and freeze-thaw processes. Therefore, directly observing and quantitatively describing soil microstructure presents a significant scientific challenge.

[0003] Currently, most methods for obtaining soil microstructure datasets are laboratory analysis methods. These methods involve collecting soil samples and measuring their physical and chemical properties in the laboratory, which can yield accurate data. However, these methods can damage the original pore structure of the soil, making it difficult to reconstruct the three-dimensional pores inside the soil.

[0004] Therefore, there is an urgent need for a method to obtain soil microstructure datasets without damaging the three-dimensional pores inside the soil. Summary of the Invention

[0005] To address the technical problem that existing methods for acquiring soil microstructure datasets destroy the three-dimensional pores within the soil, this invention provides a method and apparatus for constructing soil microstructure datasets. The technical solution is as follows:

[0006] On the one hand, a method for constructing a soil microstructure dataset is provided, the method comprising:

[0007] Soil samples were scanned using X-ray computed tomography (CT) equipment, and three-dimensional reconstruction of the soil samples was performed to obtain the scanning data.

[0008] The scanned data is imported into image processing software for threshold segmentation and RGB rendering to generate a three-dimensional microstructure image of the soil.

[0009] The soil pore structure is extracted from the three-dimensional microstructure image of the soil, a ball-and-stick model of the soil pore structure is established, pore throat analysis is performed on the ball-and-stick model to generate pore throat analysis related parameters, and network analysis is performed based on the pore throat analysis related parameters to generate network analysis related parameters of the soil pore structure.

[0010] Based on the pore throat analysis parameters, an STL format mesh file is generated. The soil moisture migration process is numerically simulated based on the mesh file to determine the soil permeability.

[0011] A two-dimensional image stack is generated based on the three-dimensional microstructure image of the soil. The two-dimensional image stack is binarized to obtain two phases: solid and porous. Soil morphological parameters are determined based on the two phases: solid and porous. Soil organic matter is identified through machine learning based on the two-dimensional image stack, and the soil organic matter content is generated.

[0012] A soil microstructure dataset was constructed based on the three-dimensional soil microstructure images, pore-throat analysis parameters, network analysis parameters, soil permeability, soil morphological parameters, and soil organic matter content. A two-dimensional image stack was also constructed based on the pore-throat analysis parameters.

[0013] Optionally, the soil morphological parameters include: pore volume fraction, pore surface area, pore roundness, and pore Euler number; wherein the pore volume fraction is determined based on a zero-order Minkowski functional, the pore surface area is determined based on a first-order Minkowski functional, the pore roundness is determined based on a second-order Minkowski functional, and the pore Euler number is determined based on a third-order Minkowski functional.

[0014] Optionally, the soil morphological parameters further include:

[0015] Perimeter, perimeter density, convex surface area, and convexity;

[0016] Wherein, perimeter is the boundary length of solid particles in the cross-sectional image of soil sample, perimeter density is the ratio of the perimeter to the area of ​​solid particles in the cross-sectional image of soil sample, convex area refers to the area of ​​the smallest convex polygon formed by connecting the points on the pore boundary around the solid particle, and convexity represents the ratio of the actual area of ​​the solid particle to the convex area of ​​the solid particle.

[0017] Optionally, the pore throat analysis parameters include pore throat radius and coordination number, and the pore throat analysis includes:

[0018] Identify the throats between pores and determine the throat radius of each pore;

[0019] Determine the coordination number of each pore, which is the number of neighboring pores connected to each pore.

[0020] Optionally, the network analysis parameters include clustering coefficients, average distance, average degree, and node centrality, wherein the clustering coefficients are determined according to Formula 1:

[0021] Formula 1: ,

[0022] in Clustering coefficient, For those directly connected to node i The number of connection methods between node i and node i / 2 is the term The number of connection methods between nodes. N represents the total number of void nodes in the network, and the clustering coefficient ranges from 0 to 1. N is the number of void nodes involved in calculating the clustering coefficient.

[0023] The average distance is determined according to Formula 2:

[0024] Formula 2:

[0025] in The average distance, For nodes and nodes The shortest distance between them The shortest path between any pair of nodes is determined by Dijkstra's algorithm, where M is the set of pore nodes.

[0026] Determine the average degree using formula 3:

[0027] Formula 3: ,

[0028] in, The average degree, It refers to the number of throats in a porous network. This represents the total number of void nodes in the network.

[0029] The nodal centrality of the pore nodes is determined according to Formula 4:

[0030] Formula 4: ,

[0031] Where NBC represents the node centrality of the pore nodes. Indicates from node To the node The total number of paths, For the node To the node The path passes through the node The number of paths.

[0032] Optionally, the step of generating an STL format mesh file based on the pore throat analysis parameters, and performing a numerical simulation of the soil moisture migration process based on the mesh file to determine the soil permeability includes:

[0033] Based on the pore throat analysis parameters, an STL format mesh file is generated, which contains geometric information about soil pores and their skeleton.

[0034] The holes and overlapping areas in the STL format mesh file are repaired to generate a repaired STL file;

[0035] Based on the repaired STL file, the infiltration process of water flow in the soil pore structure was simulated, and the soil permeability was obtained by using Darcy's law and the Stokes equation.

[0036] Optionally, the step of identifying soil organic matter and generating soil organic matter content based on the two-dimensional image stack through machine learning includes:

[0037] The two-dimensional image stack is denoised and its contrast is enhanced to generate a first image;

[0038] The first image is input into a machine learning model, which is used to identify organic matter in the soil.

[0039] The soil organic matter content is determined based on the volume of the identified soil organic matter and the total soil volume.

[0040] On the other hand, embodiments of the present invention also provide an apparatus for constructing a soil microstructure dataset. The apparatus is used to implement the method for constructing a soil microstructure dataset provided in embodiments of the present invention. The apparatus includes:

[0041] The acquisition module is used to scan soil samples using X-ray computed tomography equipment, perform three-dimensional reconstruction of the soil samples, and acquire scan data.

[0042] The first generation module is used to import the scanned data into image processing software, perform threshold segmentation and RGB rendering, and generate a three-dimensional microstructure image of the soil.

[0043] The second generation module is used to extract the soil pore structure from the three-dimensional microstructure image of the soil, establish a ball-and-stick model of the soil pore structure, perform pore throat analysis on the ball-and-stick model, generate pore throat analysis related parameters, and perform network analysis based on the pore throat analysis related parameters to generate network analysis related parameters of the soil pore structure.

[0044] The third generation module is used to generate an STL format mesh file based on the pore throat analysis parameters, and to perform numerical simulation of the soil moisture migration process based on the mesh file to determine the soil permeability.

[0045] The fourth generation module is used to generate a two-dimensional image stack based on the three-dimensional microstructure image of the soil, perform binarization operation on the two-dimensional image stack to obtain solid and porous phases, determine soil morphological parameters based on the solid and porous phases, and identify soil organic matter based on the two-dimensional image stack through machine learning to generate soil organic matter content.

[0046] The module is used to construct a soil microstructure dataset based on the three-dimensional soil microstructure image, pore throat analysis parameters, network analysis parameters, soil permeability, soil morphological parameters, and soil organic matter content.

[0047] On the other hand, embodiments of the present invention also provide an apparatus for constructing a soil microstructure dataset, the apparatus comprising:

[0048] processor;

[0049] A memory storing computer-readable instructions, which, when executed by the processor, implement the method provided in the embodiments of the present invention.

[0050] On the other hand, embodiments of the present invention also provide a computer-readable storage medium storing program code, which can be invoked by a processor to execute the method provided in the embodiments of the present invention.

[0051] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0052] This invention utilizes X-ray computed tomography (CT) technology to scan and obtain the three-dimensional microstructure of soil. Based on the scanned data, it generates a visualized image of the soil microstructure, pore throat analysis parameters, network analysis parameters, soil permeability, soil morphological parameters, and soil organic matter content, thus constructing a soil microstructure dataset. This achieves the generation of a soil microstructure dataset without damaging the three-dimensional pores inside the soil. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart of a method for constructing a soil microstructure dataset provided in an embodiment of the present invention;

[0055] Figure 2 This is a three-dimensional structural rendering of a soil sample after X-ray computed tomography, provided in an embodiment of the present invention. Figure 2 In the middle, (a), (b), (c), and (d) are, respectively, slices of soil samples along the z-axis, y-axis, and x-axis, and a three-dimensional structural diagram;

[0056] Figure 3a This is one of the schematic diagrams of numerical simulation results of soil sample moisture migration provided in an embodiment of the present invention;

[0057] Figure 3b This is the second schematic diagram of a numerical simulation result of soil sample moisture migration provided in an embodiment of the present invention;

[0058] Figure 4 This is a schematic diagram of the network analysis results of a soil sample provided in an embodiment of the present invention;

[0059] Figure 5 This is a schematic diagram of a device for constructing a soil microstructure dataset according to an embodiment of the present invention;

[0060] Figure 6 This is a schematic diagram of a structure for constructing a soil microstructure dataset provided in an embodiment of the present invention. Detailed Implementation

[0061] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0062] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0063] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0064] In this embodiment of the invention, sometimes a subscript such as W1 may be represented in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0065] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0066] To address the technical problem that existing methods for acquiring soil microstructure datasets destroy the three-dimensional pores within the soil, this invention provides a method and apparatus for constructing soil microstructure datasets. The technical solution is as follows:

[0067] like Figure 1 As shown, this embodiment of the invention provides a method for constructing a soil microstructure dataset, the method comprising:

[0068] S1. Use X-ray computed tomography (XCT) to scan soil samples, perform three-dimensional reconstruction of the soil samples, and obtain scanning data;

[0069] S2. Import the scanned data into image processing software, perform threshold segmentation and RGB rendering, and generate a three-dimensional microstructure image of the soil.

[0070] S3. Extract the soil pore structure from the three-dimensional microstructure image of the soil, establish a ball-and-stick model of the soil pore structure, perform pore throat analysis on the ball-and-stick model, generate pore throat analysis related parameters, and perform network analysis based on the pore throat analysis related parameters to generate network analysis related parameters of the soil pore structure.

[0071] S4. Based on the pore throat analysis parameters, generate an STL format mesh file, and perform numerical simulation of the soil moisture migration process based on the mesh file to determine the soil permeability.

[0072] S5. Generate a two-dimensional image stack based on the three-dimensional microstructure image of the soil, perform binarization operation on the two-dimensional image stack to obtain solid and porous phases, determine soil morphological parameters based on the solid and porous phases, and identify soil organic matter based on the two-dimensional image stack through machine learning to generate soil organic matter content.

[0073] S6. Construct a soil microstructure dataset based on the visualized soil microstructure image, pore throat analysis parameters, network analysis parameters, soil permeability, soil morphological parameters, and soil organic matter content.

[0074] Among them, XCT is based on intensity. A cone-shaped beam of radiation irradiates a uniformly rotating sample. After penetrating the sample, the radiation strikes a receiver with an intensity of I. The receiver transmits the radiation intensity signal to a computer, which then reconstructs the image structure information using the following formula:

[0075] .

[0076] In the formula, It is the intensity of the radiation after it penetrates the sample; It is the initial intensity of the incident ray; It is the linear attenuation coefficient of the sample, which is related to the sample material and the radiation energy, and varies with position (it is a function of position), reflecting the difference in radiation attenuation ability at different positions; The thickness of the sample along the path of the X-ray penetration is given.

[0077] Before scanning, XCT scanning parameters can be set, including voltage, current, exposure time, and gain factor.

[0078] In some embodiments, after generating a three-dimensional soil microstructure image, the present invention further includes visualizing the soil microstructure based on the three-dimensional soil microstructure image. In some embodiments, the present invention performs threshold segmentation on the three-dimensional soil microstructure image and renders it with RGB colors, using different colors to represent different components in the soil, thereby achieving visualization of the three-dimensional soil microstructure.

[0079] Optionally, the soil morphological parameters include: pore volume fraction, pore surface area, pore roundness, pore Euler number, perimeter, perimeter density, convex surface area, and convexity, wherein the perimeter... The boundary length and perimeter density of solid particles in the cross-sectional image of the soil sample. Perimeter density is the ratio of the perimeter to the area of ​​solid particles in a cross-sectional image of a soil sample. It comprehensively considers both the boundary length and the area occupied by the solid particles, and can more accurately reflect the complexity of the particle shape. (Convex surface area) The convexity refers to the area of ​​the smallest convex polygon formed by connecting the points on the pore boundary surrounding a solid particle. The convexity represents the ratio of the actual area of ​​the solid particle to the area of ​​its convex surface; it is a relative proportion and describes how closely the solid particle's shape approximates a convex shape. The pore volume fraction... The pore surface area is determined based on the zero-order Minkowski functional. The pore roundness is determined based on a first-order Minkowski functional. The porosity Euler number is determined based on a second-order Minkowski functional. Determined based on the third-order Minkowski functional.

[0080] In some implementations, convexity can be expressed by formula Confirmed, among which It represents the area of ​​soil particles on a two-dimensional plane, representing the actual shape and outline of the soil particles. The convex hull of X is the smallest convex polygon containing X; pore volume fraction. Based on formula It is confirmed that, among them, Total volume of pores It is the total volume of the sample; the pore surface area SA is based on the formula Determine, where dS is the area of ​​a micro-element on the pore surface. To perform a surface integral, the total surface area is obtained by traversing the entire surface of the pores and summing the areas of the infinitesimal elements; pore roundness based on Confirmed, among which The curvature of the pore surface, Integrating the curvature over the pore surface reflects the overall torsional characteristics of the pores; the pore Euler number χ is based on... Confirmed, among which The first principal radius of curvature of the pore surface is . The second principal radius of curvature of the pore surface. and A scale used to describe localized bending.

[0081] Optionally, the pore throat analysis parameters include pore throat radius and coordination number, and the pore throat analysis includes:

[0082] Identify the throats between pores and determine the throat radius of each pore;

[0083] Determine the coordination number of each pore, which is the number of neighboring pores connected to each pore.

[0084] In some implementations, Avizo's image segmentation function can be used to distinguish between the pore and solid portions of the soil. Subsequently, the Pore Network analysis tool is used to identify the connecting channels (i.e., throats) between pores, calculate the throat radius of each pore, and use the Axis Connectivity analysis tool to analyze the connectivity between pores and calculate the coordination number, i.e., the number of neighboring pores each pore connects to. The coordination number reflects the structural characteristics of the pore network; a high coordination number generally indicates a more interconnected pore network and higher soil permeability. The throat radius is a key parameter describing the size of the channels between pores, directly affecting fluid flow characteristics. The coordination number reflects the structural characteristics of the pore network; a high coordination number generally indicates a more interconnected pore network and better soil permeability.

[0085] Optionally, the parameters related to the pore throat analysis include the pore throat radius and coordination number.

[0086] Network analysis views the research object as a collection of many interconnected individuals. By calculating network indicators, it can reflect the overall attributes and individual characteristics of the research object. The interconnected pores of soil can form a complex network. Soil seepage can be regarded as "information" transmitted within the network, and the topological characteristics and seepage properties of soil can be quantified through network analysis.

[0087] Optionally, network analysis generates water flow-related features in the soil pore structure, including clustering coefficients, average distance, average degree, and node centrality. The average clustering coefficient of all nodes in the network is the clustering coefficient C of the entire network, determined according to Formula 1.

[0088] Formula 1: ,

[0089] in Clustering coefficient, For those directly connected to node i The number of connection methods between node i and node i / 2 is the The number of connection methods between nodes. The clustering coefficient represents the total number of porous nodes in the network, ranging from 0 to 1, where N is the number of porous nodes involved in calculating the clustering coefficient. The value of is between 0 and 1 (if the clustering coefficient is 1, then all nodes in the network are interconnected, which is almost non-existent in real networks);

[0090] The average distance is determined according to Formula 2:

[0091] Formula 2: ,

[0092] in The average distance, For nodes and nodes The shortest distance between them The shortest path between any pair of nodes is determined using Dijkstra's algorithm. The average distance D characterizes the connectivity and fluidity of the fluid flow in the porous network and is the average of the shortest distances between nodes. The smaller the average distance D, the shorter the distance between pairs of porous nodes in the network. M is the set of porous nodes.

[0093] The average degree K reflects the development level of throats in the pore network. A higher average degree K indicates a more developed seepage channel and better seepage connectivity in the pore network. The average degree is determined according to Formula 3:

[0094] Formula 3: ,

[0095] in It refers to the number of throats in a porous network. This represents the total number of void nodes in the network.

[0096] Node centrality (NBC) describes the centrality of a porosity node. In a porosity network, the higher the NBC value of a porosity node, the more critical it is in the overall network. The node centrality of a porosity node is determined according to Equation 4:

[0097] Formula 4: ,

[0098] in Indicates from node To the node The total number of paths, For the node To the node The path passes through the node The number of paths.

[0099] Optionally, the step of generating an STL format mesh file based on the pore throat analysis parameters, and simulating the soil moisture migration process based on the mesh file to determine soil permeability includes:

[0100] Based on the pore-throat analysis parameters, an STL format mesh file is generated.

[0101] The STL format mesh file contains geometric information about soil pores and their framework.

[0102] The holes and overlapping areas in the STL format mesh file are repaired to generate a repaired STL file;

[0103] Based on the repaired STL file, the infiltration process of water flow in the soil pore structure was simulated, and the soil permeability was obtained by using Darcy's law and the Stokes equation.

[0104] In some implementations, Avizo 4.1 can be used to extract the soil pore structure from high-resolution 3D image data and generate an STL-formatted mesh file. The STL mesh file contains geometric information about the soil pores and their framework, providing fundamental data for subsequent hydrodynamic simulations. However, since the pore structure may contain defects or irregular meshes during image reconstruction, holes and overlapping areas in the mesh file need to be repaired to ensure a suitable triangular mesh for simulation. The repaired STL mesh file can then be imported into COMSOL Multiphysics 6.3 for permeability calculations, using Darcy's law and the Stokes equation. Due to the relatively low Reynolds number, the "creeping flow" interface can be used to simulate the infiltration process of water through the soil pore structure, thereby obtaining the soil permeability.

[0105] Optionally, the step of identifying soil organic matter and generating soil organic matter content based on the two-dimensional image stack through machine learning includes:

[0106] The two-dimensional image stack is denoised and its contrast is enhanced to generate a first image;

[0107] The first image is input into a machine learning model, which is used to identify organic matter in the soil.

[0108] The soil organic matter content is determined based on the volume of the identified soil organic matter and the total soil volume.

[0109] In some embodiments, the organic matter in soil can be classified and identified by ilastik 1.4.0. First, the two-dimensional image stack is denoised and the contrast is enhanced to improve the image quality, generating a first image. Subsequently, the first image is imported into ilastik 1.4.0 to identify the organic matter and other components in the soil. There is an organic matter identification model trained using machine learning algorithms in ilastik 1.4.0. In some embodiments, by annotating different regions (such as organic matter, minerals, pores, etc.) in the soil image, a training data set can be created. ilastik 1.4.0 can use the training data set to train a model for extracting image features (such as color, texture, and shape, etc.) through machine learning algorithms. By continuously optimizing the training data and adjusting the model parameters, the trained model can automatically distinguish the organic matter in the soil from other components based on the input image data. Adding up the volumes of all the identified soil organic matter fragments and dividing by the total soil volume can obtain the ratio of the soil organic matter volume to the total soil volume.

[0110] The soil microstructure visualization images, pore throat analysis related parameters, network analysis related parameters, soil permeability, soil morphology parameters, and soil organic matter content can be combined to construct a soil microstructure data set.

[0111] An embodiment of the present invention provides an implementation manner of a method for constructing a soil microstructure data set. The steps of the implementation manner are as follows:

[0112] (1) Select six representative soil samples taken from different ecosystems (agricultural ecosystem, forest ecosystem, grassland ecosystem, desert ecosystem, lake ecosystem, and wetland ecosystem) for X-ray computed tomography, numbered as soil samples 1-6. In this embodiment, a large industrial XCT (ZEISS METROTOM 1500 225kV G3) is used to scan the soil samples. The setting of the scanning parameters and the three-dimensional reconstruction of the scanned images are realized through the METROTOM software supporting the device. Set the XCT scanning parameters. In some embodiments, the voltage is 280 kV, the current is 280 μA, the exposure time is 1000 ms, the gain multiple is 3.0, and all scans are performed with a spatial resolution of 46.31 µm. A total of 3000 projection images are collected for each soil sample. The XCT irradiates the uniformly rotating sample with a conical ray of intensity I, and then irradiates the ray with intensity I after passing through the sample onto the receiver. The receiver transmits the ray intensity signal to the computer, and the PhoenixDatosx software is used to reconstruct the projection image based on the ray intensity signal and generate a three-dimensional volume file of the soil sample.

[0113] After the scanning is completed, the scanned files are imported into the image processing software VG StudioMAX 3.5 for visualizing the soil microstructure. The gray-scale images are subjected to threshold segmentation and rendered with RGB colors to characterize different components in the soil, thus realizing the visualization of the three-dimensional soil microstructure, as Figure 2 shown. The three-dimensional soil microstructure is exported as a stack of two-dimensional gray-scale images for further parameter calculation and analysis.

[0114] (3) Analyze the data of the stack of two-dimensional gray-scale soil images. Binarize the 16-bit gray-scale images to divide them into two phases, namely solids and pores, and calculate the soil morphological parameters. The soil morphological parameters include: Minkowski functional (pore volume fraction, surface area, roundness, Euler number), perimeter, perimeter density, convex area, and convexity.

[0115] (4) Extract the soil pores, establish a ball-and-stick model of the soil pore structure, and conduct throat analysis on this model. Abstract the pores as balls and the throats as sticks to construct the ball-and-stick model. The size of each ball can be determined according to the size (equivalent diameter) of the corresponding pore, and the position of the ball corresponds to the position of the pore in the soil. The length of the stick corresponds to the length of the throat, and its connection relationship reflects the connectivity between the pores. In the embodiment of the present invention, Avizo software is used to extract pore information from high-resolution three-dimensional soil images and calculate the throat radius and coordination number of the soil pores. The throat radius TR is a key parameter describing the size of the channels between pores, which directly affects the flow characteristics of fluids in the soil. The coordination number CN is an important indicator reflecting the characteristics of the pore network structure. A higher coordination number often indicates that the pore network has better connectivity, which in turn means that the soil has stronger permeability. In practical applications, an operation process is as follows. First, through the image segmentation function of Avizo, distinguish the soil pores and the solid part. Subsequently, use the Pore Network analysis tool to identify the connection channels (i.e., throats) between the pores, calculate the throat radius of each pore, and finally, use the Axis Connectivity analysis tool to analyze the connectivity between the pores and calculate the coordination number, that is, the number of adjacent pores connected to each pore, to obtain the parameters related to throat analysis.

[0116] (5) Use Python software for network analysis. Network analysis can obtain the characteristics related to water flow in the soil pore structure. The parameters related to network analysis include: clustering coefficient, average distance, average degree, and node centrality. As Figure 3a shown in (a-c) and Figure 3b shown in (d-f) are the network analysis diagrams of 6 soil samples. The color of the nodes represents the magnitude of the node centrality NBC value of the pore.

[0117] (6) Based on the relevant parameters of the pore throat analysis, an STL format mesh file was generated, and the soil moisture migration process was numerically simulated in the computational fluid dynamics software to solve for the soil permeability. First, the pore structure of the soil was extracted from the high-resolution three-dimensional image data of the soil by Avizo 4.1 and exported as an STL (Stereolithography) format mesh file. The STL format mesh file contains the geometric information of the soil pores and their skeleton, providing basic data for subsequent fluid dynamics simulation. However, since the pore structure may have defects or irregular meshes during the image reconstruction process, the holes and overlapping areas in the mesh file can be repaired by Netfabb 2024 to ensure that a triangular mesh suitable for simulation is obtained. The repaired STL format mesh file was imported into COMSOL Multiphysics 6.3 for permeability calculation, and Darcy's law and Stokes equations were used for solution. Since the Reynolds number is small, the "Pneumatic Flow Interface" was used to simulate the infiltration process of water flow in the soil pore structure, and then the permeability and other related parameters were calculated. Figure 4 The diagram in (af) shows the velocity field streamlines from a numerical simulation of water migration in six soil samples.

[0118] (7) The determination of soil organic matter adopts a machine learning-based identification method. Ilastik 1.4.0 can be used to classify and identify soil organic matter. First, the two-dimensional image stack is denoised and contrast-enhanced to improve image quality, generating a first image. Then, the first image is imported into Ilastik 1.4.0 to identify soil organic matter and other components. Ilastik 1.4.0 contains an organic matter identification model trained using machine learning algorithms. In some implementations, a training dataset can be created by labeling different regions (such as organic matter, minerals, pores, etc.) in the soil image. Ilastik 1.4.0 can use the training dataset to train a model that extracts image features (such as color, texture, and shape) using machine learning algorithms. By continuously optimizing the training data and adjusting the model parameters, the trained model can automatically distinguish soil organic matter from other components based on the input image data. The ratio of soil organic matter volume to total soil volume is obtained by summing the volumes of all identified soil organic matter fragments and dividing by the total soil volume.

[0119] The parameters of the six soil samples calculated through the above steps are summarized in Table 1.

[0120] Table 1 Summary of Soil Sample Parameters

[0121]

[0122] On the other hand, such as Figure 5 As shown, this embodiment of the invention also provides an apparatus for constructing a soil microstructure dataset. The apparatus is used to implement the method for constructing a soil microstructure dataset provided in this embodiment of the invention. The apparatus includes:

[0123] The acquisition module 501 is used to scan soil samples using an X-ray computed tomography (CT) scanner, perform three-dimensional reconstruction of the soil samples, and acquire scan data.

[0124] The first generation module 502 is used to import the scanned data into image processing software, perform threshold segmentation and RGB rendering, and generate a three-dimensional microstructure image of the soil.

[0125] The second generation module 503 is used to extract the soil pore structure from the three-dimensional microstructure image of the soil, establish a ball-and-stick model of the soil pore structure, perform pore throat analysis on the ball-and-stick model, generate pore throat analysis related parameters, and perform network analysis based on the pore throat analysis related parameters to generate network analysis related parameters in the soil pore structure.

[0126] The third generation module 504 is used to generate an STL format mesh file based on the pore throat analysis parameters, and to perform numerical simulation of the soil moisture migration process based on the mesh file to determine the soil permeability.

[0127] The fourth generation module 505 is used to generate a two-dimensional image stack based on the three-dimensional microstructure image of the soil, perform binarization operation on the two-dimensional image stack to obtain solid and porous phases, determine soil morphological parameters based on the solid and porous phases, and identify soil organic matter based on the two-dimensional image stack through machine learning to generate soil organic matter content.

[0128] The construction module 506 is used to construct a soil microstructure dataset based on the visualized soil microstructure image, pore throat analysis parameters, network analysis parameters, soil permeability, soil morphological parameters, and soil organic matter content.

[0129] On the other hand, embodiments of the present invention also provide an apparatus for constructing a soil microstructure dataset, the apparatus comprising:

[0130] processor;

[0131] A memory storing computer-readable instructions, which, when executed by the processor, implement the method provided in the embodiments of the present invention.

[0132] On the other hand, embodiments of the present invention also provide a computer-readable storage medium storing program code, which can be invoked by a processor to execute the method provided in the embodiments of the present invention.

[0133] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0134] This invention uses X-ray computed tomography (CT) to scan and obtain the three-dimensional microstructure of soil. Based on the obtained three-dimensional microstructure, soil morphological parameters, network analysis parameters, and physical property parameters are accurately calculated and analyzed to construct a soil microstructure dataset. Furthermore, the construction of the soil microstructure dataset does not require destroying the three-dimensional pores inside the soil.

[0135] Figure 6 This is a schematic diagram of the structure of a device for constructing a soil microstructure dataset according to an embodiment of the present invention, as shown below. Figure 6 As shown, optionally, the device 610 for constructing a soil microstructure dataset may include a first processor 2001.

[0136] Optionally, the device 610 for constructing soil microstructure datasets may also include a memory 2002 and a transceiver 2003.

[0137] The first processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0138] The following is combined Figure 6 A detailed description of the various components of the device 610 used for constructing soil microstructure datasets is provided below:

[0139] The first processor 2001 is the control center of the device 610 used for constructing soil microstructure datasets. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0140] Optionally, the first processor 2001 can perform various functions of the device 610 for constructing soil microstructure datasets by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0141] In a specific implementation, as one example, the first processor 2001 may include one or more CPUs, for example... Figure 6 CPU0 and CPU1 are shown in the diagram.

[0142] In a specific implementation, as one example, the device 610 for constructing soil microstructure datasets may also include multiple processors, such as... Figure 6 The first processor 2001 and the second processor 2004 are shown in the diagram. Each of these processors can be a single-core processor or a multi-core processor. Here, a processor can refer to one or more devices, circuits, and / or processing cores used to process data (such as computer program instructions).

[0143] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the first processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0144] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently, and may be connected via an interface circuit of the device 610 for constructing the soil microstructure dataset. Figure 6 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0145] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0146] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 6 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0147] Alternatively, the transceiver 2003 can be integrated with the first processor 2001 or exist independently, and can be connected via the interface circuit of the device 610 for constructing soil microstructure datasets. Figure 6 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0148] It should be noted that, Figure 6 The structure of the device 610 shown for constructing a soil microstructure dataset does not constitute a limitation on the router. Actual knowledge structure identification devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0149] Furthermore, the technical effects of the device 610 used for constructing soil microstructure datasets can be referred to the technical effects of the method for constructing soil microstructure datasets described in the above method embodiments, and will not be repeated here.

[0150] It should be understood that the first processor 2001 in the embodiments of the present invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0151] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0152] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, motor drive, or data center to another website, computer, motor drive, or data center via infrared, microwave, or other means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a motor drive or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0153] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0154] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0155] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0156] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0157] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0158] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0159] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0160] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0161] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a motor driver, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0162] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for constructing a soil microstructure dataset, characterized in that, The method includes: Soil samples were scanned using X-ray computed tomography (CT) equipment, and three-dimensional reconstruction of the soil samples was performed to obtain the scanning data. The scanned data is imported into image processing software for threshold segmentation and RGB rendering to generate a three-dimensional microstructure image of the soil. The soil pore structure is extracted from the three-dimensional microstructure image of the soil, a ball-and-stick model of the soil pore structure is established, pore throat analysis is performed on the ball-and-stick model to generate pore throat analysis related parameters, and network analysis is performed based on the pore throat analysis related parameters to generate network analysis related parameters of the soil pore structure. Based on the pore throat analysis parameters, an STL format mesh file is generated. The soil moisture migration process is numerically simulated based on the mesh file to determine the soil permeability. A two-dimensional image stack is generated based on the three-dimensional microstructure image of the soil. The two-dimensional image stack is binarized to obtain two phases: solid and porous. Soil morphological parameters are determined based on the two phases: solid and porous. Soil organic matter is identified through machine learning based on the two-dimensional image stack, and the soil organic matter content is generated. A soil microstructure dataset was constructed based on the three-dimensional soil microstructure images, pore throat analysis parameters, network analysis parameters, soil permeability, soil morphological parameters, and soil organic matter content. The network analysis parameters include clustering coefficient, average distance, average degree, and node centrality. The clustering coefficient is determined according to Formula 1: Official 1: , in Clustering coefficient, For those directly connected to node i The number of connection methods between node i and node i / 2 is the The number of connection methods between nodes. N represents the total number of void nodes in the network, and the clustering coefficient ranges from 0 to 1. N is the number of void nodes involved in calculating the clustering coefficient. The average distance is determined according to Formula 2: Official 2: , in The average distance, For nodes and nodes The shortest distance between them The shortest path between any pair of nodes is determined by Dijkstra's algorithm, where M is the set of pore nodes. Determine the average degree using formula 3: Official 3: , in, The average degree, It refers to the number of throats in a porous network. This represents the total number of void nodes in the network. The nodal centrality of the pore nodes is determined according to Formula 4: Official 4: , Wherein, NBC represents the node centrality of the pore nodes. Indicates from node To the node The total number of paths, For the node To the node The path passes through the node The number of paths.

2. The method according to claim 1, characterized in that, The soil morphological parameters include: pore volume fraction, pore surface area, pore roundness, and pore Euler number; The pore volume fraction is determined based on a zero-order Minkowski functional, the pore surface area is determined based on a first-order Minkowski functional, the pore roundness is determined based on a second-order Minkowski functional, and the pore Euler number is determined based on a third-order Minkowski functional.

3. The method according to claim 2, characterized in that, The soil morphological parameters also include: perimeter, perimeter density, convexity area, and convexity. Wherein, perimeter is the boundary length of solid particles in the cross-sectional image of soil sample, perimeter density is the ratio of the perimeter to the area of ​​solid particles in the cross-sectional image of soil sample, convex area refers to the area of ​​the smallest convex polygon formed by connecting the points on the pore boundary around the solid particle, and convexity represents the ratio of the actual area of ​​the solid particle to the convex area of ​​the solid particle.

4. The method according to claim 1, characterized in that, The parameters related to the pore throat analysis include the pore throat radius and coordination number. The pore throat analysis includes: Identify the throats between pores and determine the throat radius of each pore; Determine the coordination number of each pore, which is the number of neighboring pores connected to each pore.

5. The method according to claim 1, characterized in that, The process of generating an STL format mesh file based on the pore throat analysis parameters, and then performing a numerical simulation of the soil moisture migration process based on the mesh file to determine the soil permeability includes: Based on the pore throat analysis parameters, an STL format mesh file is generated, which contains geometric information about soil pores and their skeleton. The holes and overlapping areas in the STL format mesh file are repaired to generate a repaired STL file; Based on the repaired STL file, the infiltration process of water flow in the soil pore structure was simulated, and the soil permeability was obtained by using Darcy's law and the Stokes equation.

6. The method according to claim 1, characterized in that, The process of identifying soil organic matter and generating soil organic matter content based on the two-dimensional image stack through machine learning includes: The two-dimensional image stack is denoised and its contrast is enhanced to generate a first image; The first image is input into a machine learning model, which is used to identify organic matter in the soil. The soil organic matter content is determined based on the volume of the identified soil organic matter and the total soil volume.

7. An apparatus for constructing a soil microstructure dataset, said apparatus for implementing the method for constructing a soil microstructure dataset as described in any one of claims 1-6, characterized in that, The device includes: The acquisition module is used to scan soil samples using X-ray computed tomography equipment, perform three-dimensional reconstruction of the soil samples, and acquire scan data. The first generation module is used to import the scanned data into image processing software, perform threshold segmentation and RGB rendering, and generate a three-dimensional microstructure image of the soil. The second generation module is used to extract the soil pore structure from the three-dimensional microstructure image of the soil, establish a ball-and-stick model of the soil pore structure, perform pore throat analysis on the ball-and-stick model, generate pore throat analysis related parameters, and perform network analysis based on the pore throat analysis related parameters to generate network analysis related parameters of the soil pore structure. The third generation module is used to generate an STL format mesh file based on the pore throat analysis parameters, and to perform numerical simulation of the soil moisture migration process based on the mesh file to determine the soil permeability. The fourth generation module is used to generate a two-dimensional image stack based on the three-dimensional microstructure image of the soil, perform binarization operation on the two-dimensional image stack to obtain solid and porous phases, determine soil morphological parameters based on the solid and porous phases, and identify soil organic matter based on the two-dimensional image stack through machine learning to generate soil organic matter content. The module is used to construct a soil microstructure dataset based on the three-dimensional soil microstructure image, pore throat analysis parameters, network analysis parameters, soil permeability, soil morphological parameters, and soil organic matter content.

8. An apparatus for constructing a soil microstructure dataset, characterized in that, The device used for constructing the soil microstructure dataset includes: processor; A memory storing computer-readable instructions that, when executed by the processor, implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 6.

Images