Method for acquiring pore distribution characteristics of transparent soil

Abstract

The invention provides a method for obtaining transparent soil pore distribution characteristics, and the method comprises the following steps: designing a transparent soil pore structure image obtaining visual test system, carrying out a visual experiment, and obtaining a transparent soil pore structure image; performing image processing on the acquired image, and extracting pore size distribution data; and based on Weibull distribution quantification, characterizing pore distribution characteristics, and drawing a pore size distribution curve. The test system comprises a main experimental device and image acquisition devices, the main experimental device is provided with a sample loading chamber, and the image acquisition devices comprise planar laser transmitters, CCD cameras and computers which are arranged on two sides of the sample loading chamber. According to the method, the in-situ, continuous and high-precision observation of the evolution process of the pore structure of the transparent soil is realized by adopting a planar laser-induced fluorescence imaging technology and combining image processing and Weibull distribution fitting, the pore distribution characteristics can be quantitatively analyzed, the evolution rule of the pore structure in the seepage erosion process can be disclosed, and an effective means is provided for observation and research of the pore structure of the soil body.

Description

Technical Field

[0001] This invention relates to the field of hydraulic geotechnical engineering technology, and in particular to a method for obtaining the pore distribution characteristics of transparent soil. Background Technology

[0002] Soil pore structure plays a crucial role in numerous physical, chemical, and biological processes within soil. It is a core intrinsic factor determining the physical and mechanical properties of soil, directly influencing the transport patterns of water and air within the soil. Quantitative analysis of pore structure helps reveal the response mechanisms of soil structural evolution and changes in its macroscopic hydraulic properties. Obtaining information on pore structure and its distribution is not only a key approach to understanding the multi-scale characteristics of soil but also an important foundation for scientific predictions ranging from microscopic structure to macroscopic behavior.

[0003] Currently, the following methods are used to obtain pore distribution characteristics: ① Scanning electron microscopy (SEM): Using a high-energy electron beam to scan the sample surface, it obtains images of pore morphology at the micrometer to nanometer scale. It allows for direct observation of pore morphology, arrangement, and intergranular bonding characteristics. However, it only obtains surface information and is difficult to represent the overall pore structure distribution characteristics. ② X-ray micro-CT: Using X-rays to penetrate the sample and reconstruct a three-dimensional structural image, it achieves three-dimensional visualization of the pore space. It can obtain the three-dimensional structure, connectivity, and volume distribution of pores non-destructively. However, the imaging cost is high, and the resolution is limited by the equipment. ③ Mercury intrusion porosimetry (MIP): Mercury enters the pores under external pressure, and the pore size distribution is estimated based on the mercury intrusion curve. The test operation is simple, and the results are intuitive. However, high pressure can easily damage the soil structure, and there is a "throat master effect," which can lead to results biased towards large pores. ④ Nuclear magnetic resonance (NMR): It uses the relaxation time (T2) of water molecules to reflect the pore size, achieving quantitative characterization of pore structure. Non-destructive testing can reflect pore connectivity and moisture distribution, and the pore size distribution needs to be calculated through model assumptions (such as T2-pore size conversion).

[0004] The above methods are all limited to some extent. Therefore, there is an urgent need to develop a method and device for obtaining the pore distribution characteristics of transparent soil that can be visualized and has high precision. This method and device can be directly used to study and observe the pore structure of soil, and provide strong technical support for solving complex structural problems in geotechnical engineering. Summary of the Invention

[0005] The purpose of this invention is to provide a method for obtaining the pore distribution characteristics of transparent soil. This method is based on transparent soil and planar laser-induced fluorescence imaging technology. A set of experimental systems for obtaining images of transparent soil pore structure is designed. With the help of digital image recording and data acquisition methods, the synchronous observation of pore structure evolution can be achieved, and the pore distribution of transparent soil can be obtained in a visualized and high-precision manner.

[0006] To achieve the above-mentioned technical features, the objective of this invention is as follows: a method for obtaining the pore distribution characteristics of transparent soil, the method comprising the following steps: Step 1, Obtain an image of the pore structure of transparent soil: A visualization experimental system for acquiring images of the pore structure of transparent soil was constructed, and visualization experiments were conducted to obtain images of the pore structure of transparent soil. Step 2, obtain pore size distribution data: Image processing is performed on the transparent soil pore structure image obtained in step 1 to obtain pore size distribution data; Step 3, plot the pore size distribution curve: Based on the Weibull distribution, the pore distribution characteristics are quantitatively characterized, and the pore size distribution curve is plotted.

[0007] Preferably, step 1 specifically includes the following steps: Step 1.1: Construct a visualization experimental system for acquiring images of the pore structure of transparent soil; The visualization test system in step 1.1 includes a main experimental device and an image acquisition device. The main experimental device includes a sample chamber for holding transparent soil samples. The image acquisition device includes a planar laser emitter, a CCD camera, a filter, and a computer. The planar laser emitter and the CCD camera are placed on both sides of the sample chamber. The filter is installed in front of the CCD camera lens. A water inlet pipe is connected to the side of the sample chamber. A flow meter is installed on the water inlet pipe. The flow meter is connected to the computer through a data acquisition card and collects the flow rate of the pore fluid entering the sample chamber. Step 1.2: Conduct a visualization experiment to acquire images of the pore structure of transparent soil, using a CCD camera to capture images of the pore structure of transparent soil.

[0008] Preferably, step 1.2 specifically includes: Experimental preparation: Set up the visualization experimental system, prepare plexiglass plates, prepare transparent soil molds, and prepare fused silica sand, n-dodecane and No. 15 white mineral oil of different particle size ranges for preparing transparent soil samples; Sample preparation: Prepare a mixed oil solution by mixing n-dodecane and 15# white mineral oil in a certain proportion to serve as a pore fluid. According to the required particle size distribution scheme, stir the fused silica sand evenly and add it to the mixed oil solution to ensure complete coverage of the particle surface. Place the beaker containing the fused silica sand and the mixed oil solution into a vacuum drying oven for vacuum treatment. Prepare a transparent soil sample by layering the samples. Each layer is filled with the transparent soil sample in the beaker. Repeat the above operation until the transparent soil sample is filled, and finally obtain a transparent soil sample. Data collection: Turn on the planar laser emitter, adjust the filter to cover the entire cross section of the sample chamber, and continuously capture images at 5s to 10s intervals using the CCD camera. Use Matlab software to write an image acquisition program to control the CCD camera and perform image processing. End of experiment: Organize and collect experimental data, make relevant experimental records, and clean up the experimental site.

[0009] Preferably, during the sample preparation process, the prepared transparent soil sample should ensure that the refractive index of the fused silica sand and the mixed oil solution is the same, so as to ensure that light propagates in a straight line within the transparent soil sample, thereby enabling visualization of the interior of the transparent soil sample.

[0010] Preferably, during the sample preparation process, Nile Red dye is added to the mixed oil solution. After the fluorescence is filtered through a filter, the fluorescence appears bright yellow, while the particles appear black, thus distinguishing the particles from the pore fluid.

[0011] Preferably, step 2 specifically includes the following steps: Step 2.1, Image framing and color gamut partitioning: The captured image is screened. By manually picking some color blocks in the solid particles and pore areas, the inaccurate pore areas automatically divided by the software are corrected. By repeatedly adjusting the RGB values ​​of different color gamut depths in the image until the two parts are correctly divided, the RGB rules are statistically analyzed, and the solid and liquid phases of the color image are numerically labeled into two regions. Step 2.2, grayscale normalization: Normalize the R, G, and B color thresholds of the original color image to grayscale values, and use the formula: I = grayscale value / 255 to accurately divide the solid and liquid regions. Step 2.3, Black and White Highlight Binarization Processing: After simplifying the partition by normalizing the critical I value, a binary image is obtained, in which the pore area is white and the solid particle area is black; image filtering and noise reduction processing is performed, and then image recognition is performed. Step 2.4, Secondary layer overlay rendering: On the basis of the binarized image, the pore feature parameter labels and the original image information are overlaid.

[0012] Preferably, step 3 specifically includes the following steps: Step 3.1: Recognize the image and convert the extracted pore area into the equivalent pore radius; Step 3.2: Plot the cumulative pore volume percentage curve; Step 3.3, Weibull function fitting.

[0013] Preferably, step 3.1 specifically includes: After image recognition using ImageJ software, each extracted irregular pore is approximated as a region with a value equal to its area.S For equal circular holes, the equivalent radius of the hole is calculated according to the formula for the area of ​​a circle. R Expressed as: (1)

[0014] Preferably, step 3.2 specifically involves: classifying the identified pores according to their equivalent radii, accumulating the pore surfaces with the same equivalent radii, and dividing by the total area of ​​the image recognition region to calculate the pore area ratio corresponding to each equivalent pore diameter; based on this, sorting the pores by their equivalent radii from smallest to largest, and plotting a curve showing the cumulative pore volume ratio not exceeding a certain pore diameter.

[0015] Preferably, step 3.3 specifically includes: The Weibull function was used to fit the cumulative pore volume distribution curve. The expression of the fitting function is as follows: (2) In the formula, Indicates the equivalent pore radius. For scale parameters, and These are model parameters; Differentiating formula (2) yields the volume percentage of pores of each pore size, and the expression for its pore volume distribution density function is: (3) Based on the image recognition results, the following was fitted: , , Using three model parameters, plot the cumulative porosity and pore size distribution of each group of samples.

[0016] The present invention has the following beneficial effects: 1. This invention can continuously observe changes in pore structure in situ, and realize the continuous and non-destructive capture of the three-dimensional distribution and evolution process of pore structure, so as to more realistically reflect the pore distribution characteristics inside the soil. 2. This invention employs planar laser-induced imaging, which can form a high-resolution two-dimensional optical section inside a transparent soil sample, enabling precise capture of fine pore structures and fluid migration details. Compared to ordinary illumination imaging, PLIF has stronger spatial focusing capabilities and a higher signal-to-noise ratio, resulting in higher imaging accuracy and the ability to achieve microscale resolution. 3. The image processing technology of this invention can convert experimental images into computable digital models. Through grayscale threshold segmentation, edge detection and other means, it can quantitatively extract porosity and pore morphology features, thereby improving the accuracy and efficiency of experimental data analysis. 4. The Weibull distribution of this invention has the advantages of flexible form, clear physical meaning, stable parameters, and high fitting accuracy. It can not only reflect the heterogeneity and scale characteristics of pore structure, but also provide a solid theoretical foundation for establishing a relationship model between microstructure and macro hydraulic behavior. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0018] Figure 1 This is a flowchart of the transparent soil pore structure image processing of the present invention.

[0019] Figure 2 This invention relates to a device for obtaining the pore structure of transparent soil.

[0020] Figure 3 This is a comparison image before and after binarization processing according to the present invention.

[0021] Figure 4 This is the cumulative porosity distribution of a sample with a fine particle content of 20% according to the present invention.

[0022] In the diagram, 1 is the sample loading chamber, 2 is the flow meter, 3 is the data acquisition card, 4 is the CCD camera, 5 is the computer, 6 is the planar laser emitter, 7 is the water inlet pipe, and 8 is the filter. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0024] Example 1: like Figure 1 As shown in the figure, this embodiment provides a method for obtaining the pore distribution characteristics of transparent soil, including the following steps: Step 1, Obtain an image of the pore structure of transparent soil: Step 1.1: Construct a visualization experimental system for acquiring images of the pore structure of transparent soil; The visualization test system includes a main experimental device and an image acquisition device. The main experimental device includes a sample chamber 1 for holding transparent soil samples. The image acquisition device includes a planar laser emitter 6, a CCD camera 4, a filter 8, and a computer 5. The planar laser emitter 6 and the CCD camera 4 are placed on both sides of the sample chamber 1. The filter 8 is installed in front of the lens of the CCD camera 4. A water inlet pipe 7 is connected to the side of the sample chamber 1. A flow meter 2 is installed on the water inlet pipe 7. The flow meter 2 is connected to the computer 5 through a data acquisition card 3 and collects the pore fluid flow rate entering the sample chamber 1. Step 1.2: Conduct a visualization experiment to acquire images of the pore structure of transparent soil. Use a CCD camera 4 to capture images of the pore structure of the transparent soil; specifically including: Experimental preparation: Set up the visualization experimental system, prepare plexiglass plates, prepare transparent soil molds, and prepare fused silica sand, n-dodecane and No. 15 white mineral oil of different particle size ranges for preparing transparent soil samples; Sample preparation: Prepare a mixed oil solution by mixing n-dodecane and 15# white mineral oil in a certain proportion to serve as a pore fluid. According to the required particle size distribution scheme, stir the fused silica sand evenly and add it to the mixed oil solution to ensure complete coverage of the particle surface. Place the beaker containing the fused silica sand and the mixed oil solution into a vacuum drying oven for vacuum treatment. Prepare a transparent soil sample by layering the samples. Each layer is filled with the transparent soil sample in the beaker. Repeat the above operation until the transparent soil sample is filled, and finally obtain a transparent soil sample. Data collection: Turn on the planar laser emitter 6, adjust the filter 8 to cover the entire cross section of the sample chamber 1, and continuously capture images at 5-second intervals using the CCD camera 4. Use Matlab software to write an image acquisition program to control the operation of the CCD camera 4 and perform image processing. End of experiment: Organize and collect experimental data, make relevant experimental records, and clean up the experimental site.

[0025] During the sample preparation process, the transparent soil sample should be prepared with the same refractive index as the fused silica sand and the mixed oil solution to ensure that light propagates in a straight line within the transparent soil sample, thereby enabling visualization of the interior of the transparent soil sample.

[0026] In the sample preparation process, a small amount of Nile Red dye is added to the mixed oil solution. The addition of Nile Red dye does not change the refractive index of the mixed oil solution, but it makes the mixed oil solution appear transparent orange-yellow under natural light. Under laser irradiation, the Nile Red-containing mixed oil solution will emit fluorescence. After the fluorescence is filtered by filter 8, the fluorescence appears bright yellow, while the particles appear black. This method effectively separates particles from pore fluid.

[0027] Step 2, Obtain pore size distribution data: Perform image processing on the transparent soil pore structure image obtained in Step 1 to obtain pore size distribution data; Step 2.1, Image framing and color gamut partitioning: The captured image is screened. By manually picking some color blocks in the solid particles and pore areas, the inaccurate pore areas automatically divided by the software are corrected. By repeatedly adjusting the RGB values ​​of different color gamut depths in the image until the two parts are correctly divided, the RGB rules are statistically analyzed, and the solid and liquid phases of the color image are numerically labeled into two regions. Step 2.2, grayscale normalization: In order to accurately identify the area of ​​each region of the pores, the color threshold (R, G, B values) of the original color image is normalized to grayscale values, and the solid and liquid regions are accurately divided using the formula (I = grayscale value / 255). Step 2.3, Black and White Highlight Binarization Processing: After simplifying the partition by normalizing the critical I value, a binary image is obtained, in which the pore area is white and the solid particle area is black; in order to make the image display smoother, image filtering and noise reduction processing is required, and then image recognition is performed. Step 2.4, Secondary Layer Overlay Rendering: This step involves overlaying pore feature parameter labels and original image information onto the binarized image to achieve the dual functions of "visual observation + quantitative analysis".

[0028] Step 3: Plot the pore size distribution curve: Based on the Weibull distribution, quantify the pore distribution characteristics and plot the pore size distribution curve. This includes the following steps: Step 3.1: Recognize the image and convert the extracted pore area into the equivalent pore radius; After image recognition using ImageJ software, statistical analysis of the pore size distribution is required to accurately characterize changes in the soil's internal structure. To obtain richer and more accurate pore parameter information, the pore area data extracted from the recognition results needs to be converted into the corresponding equivalent pore radius. Considering that pore shapes are usually irregular, to simplify calculations, each irregular pore is approximated as a circular cavity with an area S equal to its size. According to the formula for the area of ​​a circle, the equivalent pore radius R can be expressed as: (1) Step 3.2: Plot the cumulative pore volume percentage curve; The identified pores are classified according to their equivalent radii. The pore surfaces with the same equivalent radius are accumulated and divided by the total area of ​​the image recognition region to calculate the pore area percentage corresponding to each equivalent pore diameter. Based on this, pores are sorted from smallest to largest by their equivalent radii, and a curve showing the cumulative volume percentage of pores not exceeding a certain pore diameter is plotted.

[0029] Step 3.3, Weibull function fitting.

[0030] To further quantitatively characterize the pore size distribution, the Weibull function was used to fit the cumulative pore volume distribution curve. The fitting function expression is as follows: (2) In the formula, Indicates the equivalent pore radius. For scale parameters, and These are model parameters; Differentiating formula (2) yields the volume percentage of pores of each pore size, and the expression for its pore volume distribution density function is: (3) Based on the image recognition results, the following was fitted: , , Using three model parameters, plot the cumulative porosity and pore size distribution of each group of samples.

[0031] Example 2: like Figure 1 As shown, a method for obtaining the pore distribution characteristics of transparent soil includes the following steps: Step 1: Design a visualization experimental system for acquiring transparent soil pore structure images, conduct visualization experiments, and acquire transparent soil pore structure images; Step 1.1: Construct an experimental system for acquiring images of the pore structure of transparent soil; such as... Figure 2 As shown, the transparent soil pore structure image acquisition visualization experimental system constructed in step 1 includes a main experimental device and an image acquisition device. The main experimental device has a sample loading chamber 1. The image acquisition device includes a planar laser 6, a CCD camera 4, a filter 8, and a computer 5. The planar laser emitter 6 and the CCD camera 4 are placed on both sides of the sample loading chamber 1, and the filter 8 is installed in front of the lens of the CCD camera 4.

[0032] In this embodiment, a test apparatus is designed and fabricated. The sample chamber 1 has dimensions of 100mm × 20mm × 140mm (length × width × height). The inlet pipe 7 is connected to a flow meter 2 for measuring the water head. The flow meter 2 is connected to a DC power supply and a data acquisition card 3. A CCD camera 4 is arranged on the front of the sample chamber 1 and connected to a computer 5 together with the data acquisition card 3 for taking pictures of a laser plane at a fixed position to obtain a digital image of the soil pore structure. A filter 8 is installed in front of the lens of the CCD camera 4. The filter 8 is made of orange optical glass, and specific elements in the material can absorb the spectrum before 550nm, limiting the imaging to the wavelength range of the Nile red fluorescent agent to eliminate the influence of scattered light.

[0033] Step 1.2: Conduct a visualization experiment to acquire images of the pore structure of transparent soil. A CCD camera 4 is used to capture digital images of the soil pore structure. Specifically: Experimental preparation: Set up the experimental equipment, prepare transparent soil material and plexiglass plate, prepare transparent soil mold, and prepare fused silica sand with different particle size ranges for preparing transparent soil samples; in this embodiment, four types of fused silica sand with particle size ranges of 15~5mm, 3~1mm, 1~0.5mm, and 0.5~0.2mm are prepared to prepare transparent soil samples and fill the samples; Sample preparation: n-Dodecane and 15# white mineral oil were mixed in a certain proportion to form a mixed oil solution, which was used as a pore fluid. A small amount of Nile Red dye was added to it. According to the required particle size distribution scheme, fused silica sand was stirred evenly and then added to the mixed oil solution to ensure that the oil completely covered the particle surface. The beaker containing fused silica sand and mixed oil solution was placed in a vacuum drying oven for vacuum treatment. Transparent soil samples were prepared by layering the samples in the beaker. Each layer was filled with the sample in the beaker. The above operation was repeated until the transparent soil sample was filled and the transparent soil sample was finally obtained. Data collection: In this embodiment, the planar laser emitter 6 is turned on, the sheet light is adjusted to cover the entire cross section of the sample chamber, and the CCD camera 4 continuously captures images at 5-second intervals. The image acquisition program is written using Matlab software to control the operation of the CCD camera 4 and perform image processing. End of experiment: Organize and collect experimental data, make relevant experimental records, and clean up the experimental site.

[0034] Step 2 involves processing the acquired transparent soil pore structure image to obtain pore size distribution data, specifically: Step 2.1, Image Frame Segmentation and Color Gamut Partitioning: The captured image is screened. By manually picking some color blocks in the solid particle and pore areas respectively, the inaccurate pore areas automatically divided by the software are corrected. By repeatedly adjusting the RGB values ​​of different color gamut depths in the image until the two parts are correctly divided, the RGB rules are statistically analyzed, and the solid and liquid phases of the color image are numerically labeled into two regions.

[0035] Step 2.2, grayscale normalization: In order to accurately identify the area of ​​each region of the pores, the color threshold (R, G, B values) of the original color image is normalized to grayscale values, and the solid and liquid regions are accurately divided using the formula (I = grayscale value / 255).

[0036] Step 2.3, Black and White Highlight Binarization Processing: After simplifying the partition by normalizing the critical I value, a binary image is obtained, in which the pore area is white and the solid particle area is black; in order to make the image display smoother, image filtering and noise reduction processing is required, and then image recognition is performed.

[0037] Step 2.4, Secondary Layer Overlay Rendering: This step involves overlaying pore feature parameter labels and original image information onto the binarized image to achieve the dual functions of "visual observation + quantitative analysis".

[0038] Step 3: Based on the Weibull distribution, quantitatively characterize the pore distribution features and plot the pore size distribution curve. Specifically, based on the captured images of the soil's internal structure, identify and analyze the changes in soil porosity and pore size distribution during the three stages of seepage erosion initiation, development, and failure under the influence of different fine particle contents and coarse-to-fine particle size ratios. Step 3.1: Use ImageJ software to recognize the image, and convert the pore area data extracted from the recognition results into the corresponding equivalent pore radius. This invention approximates each irregular pore as a circular hole with an area S equal to its size. According to the formula for the area of ​​a circle, the equivalent pore radius R can be expressed as: ; Step 3.2: Sort the pores by equivalent radius from smallest to largest and plot the cumulative volume ratio curve of pores not exceeding a certain pore diameter.

[0039] Step 3.3: To further quantitatively characterize the pore size distribution, the Weibull function is used to fit the cumulative pore volume distribution curve. The fitting function expression is as follows: ; In the formula, Indicates the equivalent pore radius. For scale parameters, and These are the model parameters.

[0040] Differentiating the above equation yields the pore volume percentage for each pore size, and the expression for its pore volume distribution density function is: ; Based on the image recognition results, a fitting can be obtained. , , The three model parameters can be used to plot the cumulative porosity and pore size distribution of each group of samples at different stages of seepage erosion, further revealing the evolution of the pore structure of cohesive soil during seepage erosion.

[0041] Figure 4The cumulative porosity distribution and pore size distribution density function curves are shown for the seepage erosion development process under a fine particle content of 20%. It can be seen that in the initial stage of seepage erosion, the porosity is 0.383, the peak pore size corresponds to 0.4 mm, and the corresponding pore volume ratio is 40%. In the development stage of seepage erosion, the pore distribution shifts to the right and the peak value decreases, while the porosity increases to 0.413. This indicates that the migration and loss of some fine particles causes the soil pores to transform into larger pores overall, increasing the pore volume and making the pore size distribution more uneven. When seepage fails, the porosity further increases to 0.456, and the pores continue to develop towards larger pores and uneven distribution. This is mainly because the fine particles fill less, allowing the fluid to more easily carry away the fine particles, promoting the formation of large pores. Accompanied by large-scale and severe erosion, the fine particles undergo significant redistribution, leading to an even more uneven pore size distribution.

[0042] Although the preferred embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many specific modifications under the guidance of the present invention without departing from the spirit of the invention and the scope of protection of the claims, and these modifications all fall within the scope of protection of the present invention.

Claims

1. A method for obtaining the pore distribution characteristics of transparent soil, characterized in that, The method includes the following steps: Step 1, Obtain an image of the pore structure of transparent soil: A visualization experimental system for acquiring images of the pore structure of transparent soil was constructed, and visualization experiments were conducted to obtain images of the pore structure of transparent soil. Step 2, obtain pore size distribution data: Image processing is performed on the transparent soil pore structure image obtained in step 1 to obtain pore size distribution data; Step 3, plot the pore size distribution curve: Based on the Weibull distribution, the pore distribution characteristics are quantitatively characterized, and the pore size distribution curve is plotted.

2. The method for obtaining the pore distribution characteristics of transparent soil according to claim 1, characterized in that, Step 1 specifically includes the following steps: Step 1.1: Construct a visualization experimental system for acquiring images of the pore structure of transparent soil; The visualization test system in step 1.1 includes a main experimental device and an image acquisition device; the main experimental device includes a sample chamber (1) for holding transparent soil samples; the image acquisition device includes a planar laser emitter (6), a CCD camera (4), a filter (8) and a computer (5); the planar laser emitter (6) and the CCD camera (4) are placed on both sides of the sample chamber (1), the filter (8) is installed in front of the lens of the CCD camera (4), the side of the sample chamber (1) is connected to a water inlet pipe (7), a flow meter (2) is installed on the water inlet pipe (7), the flow meter (2) is connected to the computer (5) through a data acquisition card (3) and collects the pore fluid flow rate entering the sample chamber (1); Step 1.2: Conduct a visualization experiment to acquire images of the pore structure of transparent soil. Use a CCD camera (4) to capture images of the pore structure of transparent soil.

3. The method for obtaining the pore distribution characteristics of transparent soil according to claim 2, characterized in that, Step 1.2 specifically includes: Experimental preparation: Set up the visualization experimental system, prepare plexiglass plates, prepare transparent soil molds, and prepare fused silica sand, n-dodecane and No. 15 white mineral oil of different particle size ranges for preparing transparent soil samples; Sample preparation: Prepare a mixed oil solution by mixing n-dodecane and 15# white mineral oil in a certain proportion to serve as a pore fluid. According to the required particle size distribution scheme, stir the fused silica sand evenly and add it to the mixed oil solution to ensure complete coverage of the particle surface. Place the beaker containing the fused silica sand and the mixed oil solution into a vacuum drying oven for vacuum treatment. Prepare a transparent soil sample by layering the samples. Each layer is filled with the transparent soil sample in the beaker. Repeat the above operation until the transparent soil sample is filled, and finally obtain a transparent soil sample. Data collection: Turn on the planar laser emitter (6), adjust the filter (8) to cover the entire cross section of the sample chamber (1), and the CCD camera (4) continuously captures images at intervals of 5s to 10s. The image acquisition program is written using Matlab software to control the CCD camera (4) and perform image processing. End of experiment: Organize and collect experimental data, make relevant experimental records, and clean up the experimental site.

4. The method for obtaining the pore distribution characteristics of transparent soil according to claim 3, characterized in that, During the sample preparation process, the prepared transparent soil sample should ensure that the refractive index of the fused silica sand and the mixed oil solution is the same, so as to ensure that light propagates in a straight line within the transparent soil sample, thereby enabling visualization of the interior of the transparent soil sample.

5. The method for obtaining the pore distribution characteristics of transparent soil according to claim 3, characterized in that, During the sample preparation process, Nile Red dye is added to the mixed oil solution. After the fluorescence is filtered through the filter (8), the fluorescence appears bright yellow, while the particles appear black, thus distinguishing the particles from the pore fluid.

6. The method for obtaining the pore distribution characteristics of transparent soil according to claim 3, characterized in that, Step 2 specifically includes the following steps: Step 2.1, Image framing and color gamut partitioning: The captured image is screened. By manually picking some color blocks in the solid particles and pore areas, the inaccurate pore areas automatically divided by the software are corrected. By repeatedly adjusting the RGB values ​​of different color gamut depths in the image until the two parts are correctly divided, the RGB rules are statistically analyzed, and the solid and liquid phases of the color image are numerically labeled into two regions. Step 2.2, grayscale normalization: Normalize the R, G, and B color thresholds of the original color image to grayscale values, and use the formula: I = grayscale value / 255 to accurately divide the solid and liquid regions. Step 2.3, Black and White Highlight Binarization Processing: After simplifying the partition by normalizing the critical I value, a binary image is obtained, in which the pore area is white and the solid particle area is black; image filtering and noise reduction processing is performed, and then image recognition is performed. Step 2.4, Secondary layer overlay rendering: On the basis of the binarized image, the pore feature parameter labels and the original image information are overlaid.

7. The method for obtaining the pore distribution characteristics of transparent soil according to claim 6, characterized in that, Step 3 specifically includes the following steps: Step 3.1: Recognize the image and convert the extracted pore area into the equivalent pore radius; Step 3.2: Plot the cumulative pore volume percentage curve; Step 3.3, Weibull function fitting.

8. The method for obtaining the pore distribution characteristics of transparent soil according to claim 7, characterized in that, Step 3.1 specifically involves: After image recognition using ImageJ software, each extracted irregular pore is approximated as a region with a value equal to its area. S For equal circular holes, the equivalent radius of the hole is calculated according to the formula for the area of ​​a circle. R Expressed as: ；（1） 9. The method for obtaining the pore distribution characteristics of transparent soil according to claim 8, characterized in that, Step 3.2 specifically involves classifying the identified pores according to their equivalent radii, accumulating the pore surfaces with the same equivalent radius, and dividing by the total area of ​​the image recognition region to calculate the pore area ratio corresponding to each equivalent pore diameter; based on this, sorting the pores by their equivalent radii from smallest to largest, and plotting the cumulative pore volume ratio curve for pores not exceeding a certain pore diameter.

10. The method for obtaining the pore distribution characteristics of transparent soil according to claim 8, characterized in that, Step 3.3 specifically involves: The Weibull function was used to fit the cumulative pore volume distribution curve. The expression of the fitting function is as follows: ；（2） In the formula, Indicates the equivalent pore radius. For scale parameters, and These are model parameters; Differentiating formula (2) yields the volume percentage of pores of each pore size, and the expression for its pore volume distribution density function is: ； （3） Based on the image recognition results, the following was fitted: , , Using three model parameters, plot the cumulative porosity and pore size distribution of each group of samples.

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