A method for evaluating soil water conductivity based on X-ray CT three-dimensional pore reconstruction

Abstract

The application discloses a soil water conductivity evaluation method based on X-ray CT three-dimensional pore reconstruction, and belongs to the technical field of soil physics and agricultural soil structure evaluation. The method comprises the following steps: soil sample collection and culture, X-ray CT scanning, image preprocessing, pore segmentation, three-dimensional pore structure reconstruction and pore structure parameter extraction. The CT porosity and the specific surface area of the pore-solid interface are obtained by using Avizo software, the Kozeny-Carman model is normalized under the condition of a homologous soil system, a dimensionless structure water conductivity index based on the CT structure parameters is constructed, the potential water conductivity of the soil under different treatment conditions is quantitatively evaluated, and the structure water conductivity optimization threshold is identified. The application can improve the accuracy of the evaluation of the water conductivity of a complex soil system, and can be applied to the fields of farmland soil structure evaluation, water regulation and control and agricultural soil optimization management.

Description

Technical Field

[0001] This invention belongs to the field of soil physics and agricultural soil structure evaluation technology, specifically involving a soil water conductivity evaluation method based on three-dimensional pore reconstruction using X-ray computed tomography (X-ray CT) and combined with an improved Kozeny–Carman model. Background Technology

[0002] Soil water conductivity is an important physical indicator reflecting soil moisture transport efficiency, and it is of great significance for farmland water regulation, crop root zone water supply, nutrient migration, and soil structure quality evaluation. Accurate evaluation of soil water conductivity is crucial for improving agricultural water resource utilization efficiency, optimizing farmland management practices, and promoting stable and increased crop yields.

[0003] Currently, the determination of soil hydraulic conductivity mainly includes indoor infiltration experiments and empirical model estimation methods. Indoor infiltration experiments typically use the head method to determine the saturated hydraulic conductivity of soil, which can obtain highly accurate test results. However, these methods generally suffer from problems such as long testing cycles and complex operating procedures, and may alter the soil structure during sample preparation or testing, making it difficult to fully reflect the hydraulic conductivity process under complex pore structure conditions.

[0004] Empirical model estimation methods typically establish a functional relationship between pore structure parameters and hydraulic conductivity to achieve rapid prediction of soil hydraulic conductivity. Among these, the Kozeny–Carman model, as a classic seepage theory model for porous media, is widely used in soil hydraulic conductivity research. This model characterizes the fluid flow resistance inside the medium through parameters such as porosity and the specific surface area of ​​the pore-solid interface, and has good applicability to regular, homogeneous porous systems.

[0005] However, the internal pore structure of farmland soil exhibits significant spatial heterogeneity. The morphology and connectivity of the pore network are easily influenced by factors such as straw return to the field, biological disturbance, organic matter input, and tillage practices, displaying complex three-dimensional structural evolution characteristics. Under these conditions, an increase in total soil porosity does not necessarily lead to a simultaneous increase in water conductivity. Water transport is only beneficial when newly added pores can form continuous and effective water-conducting channels; if the complexity of the pore-solid interface increases simultaneously, it may increase flow resistance, thereby weakening the effect of improving water conductivity. Traditional Kozeny–Carman models are usually based on the assumptions of regular capillary bundles and homogeneous media, making it difficult to effectively describe the aforementioned complex structural effects.

[0006] With the development of X-ray computed tomography (CT) technology, this technique can reduce damage to the soil sample structure during scanning and analysis, enabling three-dimensional visualization and quantitative analysis of the soil's internal pore structure, and providing a new technical means for studying the characteristics of the real pore network structure. X-ray CT technology can directly obtain key structural parameters such as soil porosity, specific surface area of ​​the pore-solid interface, and pore connectivity characteristics, providing a reliable data foundation for optimizing water conductivity models.

[0007] Currently, most related research focuses on the three-dimensional characterization of soil pore structure, and there is still a lack of a standardized method that systematically incorporates the three-dimensional pore structure parameters obtained by CT into the Kozeny-Carman theoretical framework and is applicable to the evaluation of the water conductivity of complex natural soil structures. Therefore, it is necessary to provide a method for evaluating the water conductivity of soil structures based on X-ray CT three-dimensional pore reconstruction combined with an improved Kozeny-Carman model, so as to achieve rapid and quantitative evaluation of the potential water conductivity of complex soil systems. Summary of the Invention

[0008] Objective: This invention addresses the shortcomings of existing methods for evaluating soil hydraulic conductivity by providing a method based on three-dimensional pore reconstruction using X-ray CT. This method enables rapid and quantitative evaluation of the potential hydraulic conductivity of complex soil systems while minimizing damage to the sample structure during scanning analysis. Current methods for evaluating soil hydraulic conductivity primarily rely on indoor infiltration experiments or empirical model calculations. Indoor experiments are time-consuming, complex, and prone to altering soil structure during sample preparation or testing. While empirical models are computationally simple, they are typically based on idealized pore structure assumptions and struggle to accurately reflect the impact of the complex pore network of natural soils on water transport processes.

[0009] The Kozeny–Carman model is a classic model describing the relationship between pore structure and water conductivity. However, its structural constants usually rely on empirical fitting. Under conditions such as straw return to the field, biological disturbance, and organic matter input, it is difficult to accurately characterize the impact of changes in soil three-dimensional pore structure on water conductivity.

[0010] With the development of X-ray computed tomography (XCT) technology, this technology can achieve three-dimensional characterization of the pore structure inside the soil, providing a technical basis for evaluating soil water conductivity based on real pore structure parameters. However, there is still a lack of an evaluation method that effectively combines the three-dimensional pore structure parameters obtained by CT with the water conductivity model.

[0011] To address the aforementioned issues, this invention normalizes the structural constants in the Kozeny–Carman model under homologous soil system conditions, and incorporates the porosity and pore-solid interface specific surface area parameters extracted by CT into the model to construct a dimensionless structural hydraulic conductivity index. This study aims to achieve a quantitative comparison of the potential water conductivity of soil under different treatment conditions, providing technical support for agricultural soil structure evaluation, farmland water management, and soil optimization and regulation.

[0012] Technical solution: To achieve the above objectives, the present invention adopts the following technical solution.

[0013] This invention provides a method for evaluating soil water conductivity based on three-dimensional pore reconstruction using X-ray CT, characterized by the following steps. The overall flow of the method of this invention is as follows: Figure 1 As shown.

[0014] Step 1: Soil sample collection and pretreatment

[0015] Soil sample preparation and controlled culture process as follows Figure 2 As shown, it mainly includes soil sample collection and pretreatment, control and treatment settings, and sample culture process.

[0016] Step 1.1 Soil Sample Collection

[0017] Soil samples were collected from the same farmland area and pretreated using a uniform method to ensure that samples from different treatments had consistent initial conditions.

[0018] Step 1.2 Impurity Removal

[0019] Remove plant debris, stones, and obvious impurities from the collected soil samples and pass them through a 2 mm sieve.

[0020] Step 1.3 Sample loading

[0021] The treated soil samples were uniformly packed into PVC culture columns. Preferably, the inner diameter of the culture column was 10–15 cm, and the height was 15–30 cm. After packing, the column was left to stand for 12–24 hours.

[0022] Step 1.4 Initial Condition Control

[0023] The initial bulk density and moisture content of each sample should be kept consistent. Preferably, the initial bulk density of the soil is controlled to be 1.20–1.40 g / cm³. 3 The initial moisture content is controlled at 55% to 70% of the maximum water holding capacity.

[0024] Step 2: Adjustment and processing settings

[0025] Step 2.1 Setting up control processing

[0026] Different control treatments are set according to the research objectives.

[0027] Step 2.2 Organic matter input processing

[0028] Different organic matter input gradients are set up. Preferably, the amount of organic matter added is set to 0-5% of the soil mass. More preferably, straw decomposed by Bacillus subtilis is used as the organic matter source.

[0029] Step 2.3 Biological Disturbance Treatment

[0030] Different levels of biological disturbance are implemented. Preferably, 0–20 earthworms are inoculated per culture column. More preferably, the earthworm species used is *Eisenia fetida* (red eisenia). Eisenia fetida ).

[0031] Step 2.4 Coupling and Control Processing

[0032] The organic matter input is coupled with biological perturbation. Preferably, a gradient processing method is set along the direction in which the amount of organic matter input increases synchronously with the number of earthworms.

[0033] Step 2.5 Pore Structure Induction

[0034] Soil pore structure is reorganized by inducing different regulatory gradients.

[0035] Step 3: Sample culture

[0036] Step 3.1 Constant temperature and humidity incubation

[0037] The treated samples are then incubated under constant temperature conditions. Preferably, the incubation temperature is controlled at 20–25°C, and the relative humidity is controlled at 50%–80%.

[0038] Step 3.2 Moisture Content Control

[0039] Water is replenished periodically during the cultivation process to maintain a stable moisture content in the sample. Preferably, the moisture content is maintained at 60% ± 5% of the maximum water holding capacity during cultivation.

[0040] Step 3.3 Stable formation of pore structure

[0041] Continuous cultivation promotes the gradual formation and stabilization of soil pore structure. Preferably, the cultivation time is 30–90 days; more preferably, the cultivation time is 45–60 days.

[0042] Step 3.4 Sample Collection

[0043] After the culture was completed, columnar soil samples were collected for subsequent CT scan analysis.

[0044] Step 4: X-ray CT scan

[0045] The process of obtaining pore structure and extracting parameters based on X-ray CT is as follows: Figure 3 As shown, the main processes include CT scanning, image preprocessing, pore segmentation, three-dimensional pore structure reconstruction, and pore structure parameter extraction.

[0046] Step 4.1 Sample Fixation

[0047] The soil sample was fixed at the center of the CT rotating stage.

[0048] Step 4.2 CT Scanning Equipment

[0049] The scan was performed using an X-ray computed tomography (CT) scanner.

[0050] Step 4.3 Tomographic Image Acquisition

[0051] During the scanning process, the sample is rotated 180° continuously to acquire a two-dimensional tomographic grayscale image sequence.

[0052] Step 4.4 Scan Parameter Settings

[0053] The scan parameters are set as follows: 1) The collimation width is 8 × 0.55 mm; 2) The tube voltage is 100–160 kV, preferably 140 kV; 3) The tube current is 100–150 mA, preferably 140 mA; 4) The layer thickness is 0.30–0.60 mm, preferably 0.55 mm; 5) The preferred voxel resolution is 512×512×261 μm 3 ; 6) The preferred field of view is 512×512 mm. 2 ; 7) The single rotation scan time is 1.0 to 2.0 s, preferably 1.50 s.

[0054] Step 4.5 Unified Parameter Control

[0055] Before the formal scanning, the scanning parameters were optimized through preliminary experiments, and the same scanning conditions were used for all samples to ensure the consistency of image quality and spatial resolution.

[0056] Step 5: CT Image Preprocessing

[0057] Step 5.1 Image Import

[0058] Import the two-dimensional tomographic grayscale image obtained in step 4 into Avizo software for processing.

[0059] Step 5.2 Median Filtering

[0060] Median filtering is used to remove random noise.

[0061] Step 5.3 Grayscale Standardization

[0062] Perform grayscale normalization on the image.

[0063] Step 5.4 Ring artifact correction

[0064] Correction is performed on the ring artifacts generated during the scanning process.

[0065] Step 5.5 Image Enhancement

[0066] Image enhancement processing is performed to improve the grayscale difference between the porous phase and the solid phase.

[0067] Step 6: Pore segmentation

[0068] Step 6.1 Threshold Segmentation

[0069] Based on the preprocessed image, a threshold segmentation module is used in Avizo software to identify pores.

[0070] Step 6.2 Threshold Determination

[0071] The optimal segmentation threshold was determined by combining the Otsu automatic thresholding method with manual calibration.

[0072] Step 6.3 Morphological Correction

[0073] After threshold segmentation, morphological corrections are performed, including: 1) Remove isolated noise; 2) Fill tiny holes; 3) Repair local fractures and pores.

[0074] Step 6.4 Binary Image Acquisition

[0075] Finally, a three-dimensional binary image of pores that reflects the continuous pore structure is obtained.

[0076] Step 7: Reconstruction of three-dimensional pore structure

[0077] Step 7.1 Pore Structure Reconstruction

[0078] The pore binary image was reconstructed using the 3D reconstruction module of Avizo software.

[0079] Step 7.2 ROI Region Selection

[0080] To reduce the impact of boundary effects, a region with intact morphology is selected from the center of the sample as the region of interest (ROI) for analysis.

[0081] Step 7.3 Three-dimensional model creation

[0082] Establish a three-dimensional soil pore structure model.

[0083] Step 7.4 Structural Feature Characterization

[0084] The three-dimensional pore structure model is used to reflect: 1) Pore spatial distribution; 2) Pore connectivity paths; 3) Morphology of the pore-solid interface.

[0085] Step 8: Extract pore structure parameters

[0086] The parameters of the three-dimensional pore structure model were extracted using the Quantification module of Avizo software. The extracted parameters included:

[0087] Step 8.1 CT porosity

[0088] This was obtained by statistically analyzing the ratio of the number of pore voxels to the total number of voxels:

[0089] in: This represents the total pore volume; This represents the total sample volume. This parameter is used to characterize the pore volume distribution.

[0090] Step 8.2 Specific surface area of ​​the pore-solid interface

[0091] Obtained through the Surface Area calculation module:

[0092] in: This represents the total area of ​​the pore-solid interface. This parameter is used to characterize the complexity of the pore interface.

[0093] Step 8.3 Pore connectivity parameters

[0094] The Connected Components Analysis module identifies the main connected pore network to help evaluate the continuity of the pore space.

[0095] Step 9: Establish a method for normalizing structural constants.

[0096] Since all samples originated from the same source and had the same mineral and textural composition, the structural constants in the Kozeny–Carman model were consistent under the same soil system and testing conditions. The same value was used across all control treatments and normalized when constructing the structural hydraulic conductivity index. This step was used to reduce the influence of empirically chosen structural constants on the inter-treatment comparison results.

[0097] Step 10: Structural Evolution of the Kozeny–Carman Model

[0098] The traditional Kozeny–Carman model expression is:

[0099] in: Hydraulic conductivity; Porosity; The specific surface area of ​​the pore-solid interface; is a structure constant.

[0100] Under the normalization conditions established in step 9, formula (3) is processed to construct a dimensionless structural hydraulic conductivity index. . The structural hydraulic conductivity index, obtained after normalization of structural constants, is used to characterize the relative hydraulic conductivity of soil pore structure. Its expression is:

[0101] Further incorporating CT structural parameters, the following was established:

[0102] in: The dimensionless structural water conductivity index is constructed based on CT structural parameters; The porosity of CT obtained by three-dimensional pore reconstruction of X-ray CT; The specific surface area of ​​the pore-solid interface is obtained by three-dimensional pore reconstruction using X-ray CT.

[0103] Step 11: Evaluation of structural water conductivity

[0104] Based on the calculation in step 10 The values ​​were used to quantitatively compare the potential water conductivity of soil under different treatment conditions. Among them: The larger the value, the more favorable the pore structure is for forming a continuous and effective water-conducting path; The smaller the value, the more complex the pore interface or the less pore connectivity, which restricts the water conduction process.

[0105] Step 12: Structural Threshold Identification

[0106] analyze By analyzing the changing trend of regulation intensity, the inflection point where it transitions from increasing to decreasing is identified. This inflection point is defined as the optimal threshold for soil structure water conductivity.

[0107] Step 13: Output Results

[0108] The output includes: (1) a three-dimensional pore structure reconstruction map of the soil; (2) CT porosity analysis results; (3) specific surface area results of the pore-solid interface; (4) Value; (5) Structural threshold identification results; (6) Evaluation level of soil structure water conductivity.

[0109] Compared with the prior art, the present invention has the following beneficial effects:

[0110] This invention extends the classic Kozeny-Carman model to complex natural soil systems. Traditional Kozeny-Carman models, typically based on assumptions of regular capillary bundles and homogeneous porous media, struggle to accurately describe the complex three-dimensional pore network structure of natural farmland soils. This invention improves the traditional model by incorporating real three-dimensional pore structure parameters obtained from X-ray CT and combining them with constraints from a homogeneous soil system, thus achieving an adaptive extension of the classic flow conduction theory model to complex natural soil systems.

[0111] A theoretical framework for normalizing structural constants is established to eliminate interference from empirical parameters. Existing empirical models typically rely on empirical fitting of structural constants, resulting in poor parameter applicability across different research subjects. This invention, based on the condition that soil samples from the same source have essentially the same mineral and textural composition, treats the structural constants from the traditional Kozeny–Carman model as normalized parameters in relative comparisons. This reduces the influence of differences in empirical parameters on the comparison results of structural hydraulic conductivity between different treatments, thereby improving the consistency and reliability of model comparisons.

[0112] A dimensionless structural hydraulic conductivity index is constructed to achieve rapid quantitative evaluation. This invention constructs a dimensionless structural hydraulic conductivity index based on CT structural parameters. The relative quantitative evaluation of the potential water conductivity of soil is achieved through formula (5). Compared with traditional permeability test methods, this invention can complete the evaluation without complex permeability tests, and has the advantages of short test cycle, small sample disturbance and high repeatability.

[0113] This invention achieves a quantitative expression of the synergistic effect of pore volume distribution and interface complexity. Traditional soil structure evaluation usually focuses on a single indicator such as porosity or pore size distribution, which is difficult to reflect the actual water conduction process. This invention introduces porosity and the specific surface area of ​​the pore-solid interface into the evaluation system, which can simultaneously reflect: (1) the promoting effect of pore volume distribution on the water conduction process; and (2) the constraining effect of interface complexity on the water conduction process. Thus, a comprehensive evaluation of the functional state of soil structure is achieved.

[0114] It can identify the optimal threshold for soil structure and water conductivity. Through analysis... Based on the changing trend of regulation intensity, this invention can identify the critical threshold at which soil pore structure transitions from promoting to restricting water conduction. This threshold can be used to determine the optimal range of action for agricultural regulation measures.

[0115] This invention has high application value in agricultural production. It can be widely applied to: (1) evaluation of the effect of straw returning to the field; (2) optimization of biological disturbance regulation; (3) evaluation of farmland soil structure and quality; (4) agricultural water management decision-making; and (5) improvement and protection of arable land quality. This invention can provide theoretical basis and technical support for soil structure optimization and regulation under precision agriculture conditions.

[0116] It has good applicability and can be widely promoted. The X-ray CT three-dimensional reconstruction and image analysis method used in this invention has strong versatility. In addition to the black soil system, it can also be applied to the evaluation of the structural water conductivity of saline-alkali soil, albic soil, brown soil and other farmland soil systems. Attached Figure Description

[0117] Figure 1 This is a flowchart of the soil structure water conductivity evaluation method of the present invention;

[0118] Figure 2 Flowchart for soil sample preparation and culture control;

[0119] Figure 3 The flowchart shows the process of obtaining pore structure and extracting parameters based on X-ray CT.

[0120] Figure 4 Three-dimensional visualizations of soil pore structure under different treatments are provided, where (a)~(c), (d)~(f), (g)~(i), (j)~(l), and (m)~(o) correspond to T1, T7, and T8, respectively. 13 T 19 and T 25 The treatments, in order, consisted of a pore network model, a pore body and pore throat ball-and-stick model, and a pore skeleton model. T1 was the control treatment, and T7 and T8 were the control treatments. 13 T 19 and T 25 The treatments were low, medium, relatively high, and high intensity, respectively.

[0121] Figure 5 These are actual images of plant growth under different treatment conditions, where (a) to (e) represent T1, T7, and T8 respectively. 13 T 19 and T 25 The leaf morphologies treated; (f) to (j) are T1, T7, and T, respectively. 13 T 19 and T 25 The morphology of the roots being treated;

[0122] Figure 6 Structural water conductivity index Correlation diagram with leaf dry weight, in which , The leaf dry weight is in grams, and the fitting equation is: Coefficient of determination In the graph, dots represent observed values, and curves represent fitted curves.

[0123] Figure 7 Structural water conductivity index Correlation diagram with root trunk weight, in which , The dry weight is expressed in grams, and the fitted equation is: Coefficient of determination In the figure, the dots represent observed values, and the curves represent fitted curves. Detailed Implementation

[0124] This embodiment provides a method for evaluating soil hydraulic conductivity based on X-ray CT three-dimensional pore reconstruction, including the following steps:

[0125] (1) Soil sample collection and pretreatment

[0126] Farmland topsoil from the same source with similar mineral and texture composition was collected. Preferably, topsoil from the 0–20 cm layer was collected. After collection, plant residues, stones, and obvious impurities were removed, and the soil was sieved through a 2 mm sieve for later use.

[0127] The pretreated soil is filled into a culture column. Preferably, the culture column is a PVC column with an inner diameter of 10–15 cm and a height of 15–30 cm. The soil bulk density is controlled to be 1.20–1.40 g / cm³. 3 And adjust the initial moisture content to 55% to 70% of the maximum water holding capacity.

[0128] (2) Control and processing settings

[0129] Different regulatory treatments were designed according to the research objectives, including: 1) Organic matter input treatment; 2) Treatment of biological disturbance; 3) Coupling of organic matter input with biological disturbance.

[0130] Preferably, the amount of organic matter added is 0-5% of the soil mass; the biological disturbance treatment adopts earthworm inoculation, with 0-20 earthworms inoculated per culture column.

[0131] Soil pore structure is reorganized by inducing different regulatory gradients.

[0132] (3) Sample culture

[0133] The treated culture columns are placed in a constant temperature and humidity environment for cultivation. Preferably, the cultivation temperature is controlled at 20–25℃; the relative humidity is controlled at 50%–80%; and the cultivation period is 30–90 days. During the cultivation period, water is replenished regularly to maintain the soil moisture content at 60% ± 5% of the maximum water holding capacity.

[0134] (4) X-ray CT scan

[0135] After cultivation, columnar soil samples were collected and sealed to prevent moisture loss from affecting the pore structure. The samples were then scanned using an industrial X-ray computed tomography (CT) system. During the scan, the samples were fixed on a rotating stage and rotated 180° along the horizontal axis to acquire a sequence of two-dimensional tomographic grayscale images.

[0136] Preferably, the scanning parameters are as follows: collimation width: 8×0.55 mm; tube voltage: 100~160 kV; tube current: 100~150 mA; layer thickness: 0.30~0.60 mm; field of view: 512×512 mm 2 Single rotation scan time: 1.0–2.0 s. All samples were scanned using uniform parameters to ensure consistent image quality and spatial resolution.

[0137] (5) CT image preprocessing

[0138] The obtained two-dimensional tomographic images are imported into Avizo software for preprocessing, including: 1) Median filtering removes random noise; 2) Grayscale standardization processing; 3) Ring artifact correction; 4) Image enhancement processing. The above treatment improves the grayscale difference between the porous phase and the solid phase.

[0139] (6) Pore segmentation and three-dimensional reconstruction

[0140] Avizo software's Threshold module was used for pore identification. Preferably, the optimal segmentation threshold was determined using the Otsu automatic thresholding method combined with manual calibration. After threshold segmentation, morphological corrections are performed, including: 1) Remove isolated noise; 2) Fill tiny holes; 3) Repair local fractures and pores. Finally, a three-dimensional binary image of pores that reflects the continuous pore structure is obtained.

[0141] We further utilized the 3D reconstruction module of Avizo software to establish a 3D pore structure model of the soil, and selected the central region of the sample as the region of interest (ROI) for analysis to reduce the influence of boundary effects.

[0142] (7) Extraction of pore structure parameters

[0143] Pore ​​structure parameters, including CT porosity, were extracted using the Quantification module of Avizo software. and the specific surface area of ​​the pore-solid interface .

[0144] The expression for CT porosity is as follows:

[0145] in This represents the total pore volume; This represents the total volume of the sample.

[0146] The expression for the specific surface area of ​​the pore-solid interface is as follows:

[0147] in, The specific surface area of ​​the pore-solid interface; This represents the total area of ​​the interface between the pores and the solid. This represents the total volume of the sample.

[0148] (8) Construction of structural water conductivity index

[0149] The traditional Kozeny–Carman model expression is as follows:

[0150] in: Hydraulic conductivity; Porosity; The specific surface area of ​​the pore-solid interface; is a structure constant.

[0151] Since all samples were collected from the same farmland area and subjected to the same sample pretreatment, loading, and testing conditions, the structural constants were consistent. The same value was taken across all control treatments and normalized when constructing the structural hydraulic conductivity index. Furthermore, CT structural parameters were introduced to construct a dimensionless structural hydraulic conductivity index based on these parameters:

[0152] in: The structural water conductivity index; CT porosity; It represents the specific surface area of ​​the pore-solid interface.

[0153] (9) Evaluation of structural water conductivity

[0154] According to the calculation The value is used to quantitatively evaluate the potential water conductivity of soil under different treatment conditions. Among them: The larger the value, the more favorable the pore structure is for forming a continuous and effective water-conducting path; The smaller the value, the more complex the pore interface or the less pore connectivity, which restricts the water transport process.

[0155] Specific Implementation Cases

[0156] The following is a verification case based on a cultivation experiment in Northeast China's black soil region. This embodiment uses topsoil from the Northeast China black soil region for verification. By setting different organic matter inputs and biological disturbance treatments, different pore structure states are constructed, and the water conductivity of these structures is quantitatively evaluated using the method of this invention.

[0157] (1) Soil sample collection

[0158] The soil samples were collected from the 0–20 cm topsoil layer of a typical farmland in the Northeast Black Soil Region. The sampling area was flat, and the soil type was typical black soil. The samples were sealed and preserved immediately after sampling.

[0159] (2) Sample pretreatment

[0160] The collected soil samples were air-dried naturally, and plant debris, gravel, and other impurities were removed. The samples were then sieved through a 2 mm sieve. The basic physicochemical properties of the soil are shown in the table. All samples originated from the same region, therefore their mineral composition and particle size distribution are considered to be basically consistent.

[0161]

[0162] (3) Sample loading and incubation

[0163] Soil was filled into PVC culture columns. The columns had an inner diameter of 100 mm and a height of 150 mm, with an effective soil filling height of 120 mm. A layered compaction method was used, with each layer 20 mm thick, controlling the bulk density to 1.25 g / cm³. 3 The initial moisture content was adjusted to 60%, and the mixture was allowed to stand for 24 hours to promote uniform moisture distribution. Twenty-five control treatments were set up, including a control treatment, a straw decomposition product input treatment, a bio-disturbance treatment, and a coupled control treatment, with five replicates for each treatment.

[0164] Wherein: T1 was the control group; T7 was the low-intensity regulation group; T 13 The moderate intensity regulation group; T 19 This is the higher intensity regulation group; T 25 This is a high-intensity control group.

[0165] The cultivation conditions were: temperature 20–25℃; relative humidity 50%–70%; and cultivation period 45 days.

[0166] (4) X-ray CT scan

[0167] After cultivation, the soil samples were sealed to prevent moisture loss during scanning from affecting the pore structure. An industrial X-ray computed tomography (CT) system was used for scanning. During scanning, the sample was horizontally fixed on a rotating stage and rotated 180° along the horizontal axis for tomographic acquisition. The scanning parameters were set as follows: collimation width: 8 × 0.55 mm; tube voltage: 140 kV; tube current: 140 mA; slice thickness: 0.55 mm; voxel resolution: 512 × 512 × 261 μm. 3 Field of view: 512×512 mm 2 Single rotation scan time: 1.50 s. To ensure consistent image quality for samples with different processing methods, pre-experimental parameter optimization was performed before the formal scan, and uniform scanning parameters were applied to all samples. After scanning, a continuous two-dimensional grayscale tomographic image sequence was obtained and exported as a standard image format for subsequent three-dimensional reconstruction analysis.

[0168] (5) Evaluation results of structural water conductivity

[0169] The results of soil three-dimensional pore structure reconstruction under different treatment conditions are as follows: Figure 4 As shown. Different treatments were calculated using the structural water conductivity index formula established in this invention. The values ​​are as follows:

[0170]

[0171] (6) Agricultural Function Verification

[0172] Plant growth status under different treatment conditions, such as Figure 5 As shown. Plant growth indicators were measured, and the results are as follows. Leaf dry weight: T 13 Root dry weight increased by 32.05% compared to T1. 13 This represents a 60.58% improvement over T1. For example... Figure 6 and Figure 7 As shown, the structural water conductivity index has a good correlation with both leaf dry weight and root dry weight. Correlation analysis results: Leaf dry weight: ; Weight of root and trunk: The results demonstrate that the structural hydraulic conductivity index established in this invention can effectively characterize the impact of soil structure changes on agricultural functions.

[0173] (7) Implementation effect analysis

[0174] The results show that the method of this invention can effectively reflect the changes in the water conductivity of soil pore structure under different regulation measures and can identify the soil structure optimization threshold. Compared with traditional infiltration experiments, this invention has advantages such as short testing cycle, minimal sample disturbance during scanning analysis, high repeatability, and strong structural analysis capability. It can be applied to fields such as agricultural soil structure evaluation, farmland water regulation, and soil structure optimization management.

Claims

1. A method for evaluating soil water conductivity based on X-ray CT three-dimensional pore reconstruction, characterized in that, Includes the following steps: (1) Collect and pre-process soil samples, set different regulatory treatments and then culture them, wherein the regulatory treatments include organic matter input treatment, biological disturbance treatment and organic matter input and biological disturbance coupling treatment; (2) Two-dimensional tomographic images of soil were obtained by X-ray computed tomography. (3) Preprocessing, pore segmentation and three-dimensional pore structure reconstruction are performed on the two-dimensional tomographic grayscale image; (4) Extracting soil CT porosity and the specific surface area of ​​the pore-solid interface ; (5) Establish a dimensionless structural water conductivity index based on the Kozeny–Carman model and CT pore structure parameters. ; (6) Based on the dimensionless structural water conductivity index Quantitatively evaluate the potential water conductivity of soil under different treatment conditions; (7) The dimensionless structural water conductivity index The expression is: 。 2. The method for evaluating soil water conductivity based on X-ray CT three-dimensional pore reconstruction according to claim 1, characterized in that, The porosity of CT Through the total volume of pores Total sample volume The ratio is obtained, and its expression is: ； The specific surface area of ​​the pore-solid interface Through the total area of ​​the interface between pores and solid Total sample volume The ratio is obtained, and its expression is: 。 3. The method for evaluating soil water conductivity based on X-ray CT three-dimensional pore reconstruction according to claim 1, characterized in that, The soil samples were collected from the same farmland area, and the structural constants in the Kozeny–Carman model... The same value was taken across all control treatments, and the structural constant was adjusted when constructing the structural hydraulic conductivity index. Normalization is performed.

4. The method for evaluating soil water conductivity based on X-ray CT three-dimensional pore reconstruction according to claim 1, characterized in that, The two-dimensional tomographic grayscale image was preprocessed, segmented, and reconstructed using Avizo software. The segmentation threshold was determined by combining the Otsu automatic thresholding method with manual calibration, and a stable and continuous three-dimensional pore binary image was obtained through morphological correction.

Images