A grouting effect evaluation method based on digital image processing

Abstract

A grouting effect evaluation method based on digital image processing, vertical drilling holes are set in the grouting area, then a drilling hole viewer is used for observation, and drilling hole viewing images are collected; the drilling hole viewing images are digitally processed; image grayscale processing is performed to obtain a drilling hole viewing image grayscale histogram; the image segmentation feature condition is judged, if it is obvious, the pixel values corresponding to the crack area and the grouting area in the grayscale image are directly obtained, if it is not obvious, the original image is processed, and then the pixel values corresponding to the crack area and the grouting area in the grayscale image are obtained; the grayscale image of the drilling hole viewing image is subjected to image segmentation processing to obtain the image of the crack area and the image of the grouting area; the areas of the separated crack area and the grouting area are calculated, and the crack grouting plugging and consolidation ratio is obtained, and then the grouting effect is quantitatively described according to the crack grouting plugging and consolidation ratio. The evaluation parameters of the method can be quantitatively described, and the accuracy of the evaluation can be significantly improved.

Description

Technical Field

[0001] The invention belongs to the technical field of grouting effect evaluation, and particularly relates to a grouting effect evaluation method based on digital image processing. Background Art

[0002] In recent years, as coal mines have successively entered the deep mining stage, the hydrogeological environment they face has become more complex. In order to effectively improve the safety of coal mine production operations, grouting technology has been widely used in coal mining. Such as grouting reinforcement (working face coal wall reinforcement, coal pillar reinforcement, tunnel anchor grouting, tunnel broken roof grouting) and water hazard prevention and control. Grouting is one of the most effective and quickest engineering measures to solve the stability problem of broken surrounding rock in tunnels and to control water-rich broken zones. The quality of grouting directly determines the effect of improving the integrity and bearing capacity of the surrounding rock and water hazard prevention. At present, the evaluation method of grouting effect is mainly evaluated by indirect parameters, such as water discharge, comparison of geological radar survey results, etc. These methods have a series of problems such as high labor costs and evaluation costs, and long evaluation cycles.

[0003] Borehole peep is a common method for detecting stratum structure and surrounding rock conditions. It generally involves drilling a hole perpendicular to the rock formation, then using a borehole peep instrument to photograph or record the interior of the hole to form a borehole peep view and other image data. The grouting and consolidation effect is then directly judged manually based on the image. Currently, the analysis of borehole image data is mainly based on rough analysis and judgment directly by the naked eye, and there is no quantitative description method. Therefore, in order to solve the problems existing in the existing technical methods for evaluating grouting effects, it is urgent to provide a grouting effect evaluation method using digital image processing to quantitatively evaluate and analyze the grouting effect. Summary of the Invention

[0004] In response to the problems existing in the above-mentioned prior art, the present invention provides a grouting effect evaluation method based on digital image processing. The method has a simple operation process, reliable evaluation results, quantitative description of evaluation parameters, a short evaluation cycle, and can significantly improve the accuracy of grouting effect evaluation. At the same time, it can also facilitate operators to directly observe the grouting sealing consolidation effect.

[0005] The present invention provides a grouting effect evaluation method based on digital image processing, comprising the following steps:

[0006] Step 1: Collect field data. First, vertically drill peepholes at randomly selected locations within the grouting area. Then, use a borehole peephole instrument to observe the cracks and grouting consolidation conditions, and collect color borehole peephole images.

[0007] Step 2: Use an image processor to digitize the borehole viewing image to obtain a digitized image f(x,y);

[0008] Step 3: First, perform grayscale processing on the digitized image f(x,y), and calculate the grayscale value Y of the grayscale image according to formula (1), and then obtain the relative frequency p(r k ), and then obtain the grayscale histogram of the drill hole peek image;

[0009] Y=0.299*R+0.587*G+0.114*B (1);

[0010] Where R, G, and B represent the values ​​of the three component vectors of the color image respectively;

[0011]

[0012] Where r k represents the kth gray level, n k Represents the number of pixels of this gray level, and N is the total number of pixels in the image;

[0013] Step 4: Analyze the grayscale distribution in the grayscale histogram and judge the image segmentation features. If the image segmentation features are obvious, directly obtain the pixel values ​​corresponding to the crack area and the grouting area in the grayscale image. If the image segmentation features are not obvious, first perform image enhancement, image smoothing and image sharpening on the original image with unclear segmentation features of the crack area and the grouting area, and then obtain the pixel values ​​corresponding to the crack area and the grouting area in the grayscale image.

[0014] The image enhancement processing method is as follows:

[0015] According to formula (3), the grayscale values ​​of the selected crack area and grouting area are mapped into a new output value g(x,y);

[0016] g(x,y)=T[f(x,y)] (3);

[0017] Where T represents a grayscale transformation function;

[0018] Step 5: performing image segmentation processing on the grayscale image of the borehole peek view, separating the image of the crack area and the image of the grouting area in the crack from the borehole peek image;

[0019] Step 6: Calculate the area A1 of the separated crack region according to formula (4), and calculate the area A2 of the separated grouting region according to formula (5);

[0020]

[0021]

[0022] Where g(x,y)′ represents the image of the crack area after segmentation, and g(x,y)″ represents the image of the grouting area after segmentation.

[0023] Step 7: Divide the obtained grouting area A2 by the crack area A1 to obtain the crack grouting plugging consolidation ratio, and then quantitatively describe the grouting effect based on the crack grouting plugging consolidation ratio.

[0024] Furthermore, in order to facilitate the subsequent grayscale processing process, the digital processing method in step 2 is as follows:

[0025] The continuous signal of an image with continuous spatial position and light intensity changes is converted into a discrete digital signal, that is, the image f(x, y) is discretized into a pixel matrix with M pixels containing different brightness and darkness information in each row and N pixels containing different brightness and darkness information in each column.

[0026] Furthermore, in order to make the segmentation features of the image more obvious, the image smoothing method in step 4 is to perform filtering on the image.

[0027] Furthermore, in order to make the segmentation features of the image more obvious, the image sharpening and smoothing processing method in step 4 is to perform edge detection on the image.

[0028] Furthermore, in order to accurately evaluate the grouting plugging effect, in step seven, when the fissure grouting plugging consolidation ratio is greater than or equal to 80%, the grouting effect is ideal; when the fissure grouting plugging consolidation ratio is less than 80%, the grouting effect is not ideal and the grouting operation needs to be repeated.

[0029] Furthermore, in order to accurately and quickly segment the image, in step 4, the method for judging the image segmentation features is as follows:

[0030] When the grayscale histogram shows obvious peaks and valleys and the grayscale value can be selected as the threshold, the segmentation feature is determined to be obvious; otherwise, the segmentation feature is determined to be unclear.

[0031] In step five, the image of the crack area is segmented by using the threshold segmentation method. Specifically, a value between the pixel values ​​of the crack area and the surrounding rock is selected as the threshold, and the pixel points greater than the threshold are set to 1, and the pixel points less than the threshold are set to 0, so that a binary image of the crack area can be obtained; the image of the grouting area is segmented by using the regional growing method. Specifically, a pixel in the grouting area is selected as the seed point, and the maximum absolute value of the difference between the pixel values ​​in the area is determined as the threshold. The growth criterion is determined, and the image is iterated to obtain a binary image of the grouting area.

[0032] The grouting effect evaluation method based on image processing provided by the present invention first converts the acquired borehole peek image into a grayscale histogram, and then judges the segmentation features of the image based on the grayscale distribution. When the segmentation features are obvious, the crack image and the grouting area image are separated from the borehole peek image, and then the crack grouting plugging consolidation ratio is obtained by the ratio of the grouting area area to the crack area area. The crack grouting plugging consolidation ratio can be used to quantitatively evaluate the grouting effect, which can significantly improve the accuracy of the grouting effect evaluation and effectively solve the problem that the existing technology cannot quantitatively evaluate and analyze the grouting effect. In the digital processing process, the borehole peek image is processed in the image processor, which can effectively improve the efficiency of the grouting effect evaluation and help save manpower and material resources. When the segmentation features are not obvious, different grayscale transformations are used for different areas to obtain the enlarged or reduced truncation interval of the corresponding area, so that the segmentation features can be made more obvious, which is beneficial to the subsequent segmentation operation and area summation operation, greatly improving the accuracy of the grouting effect evaluation. This method has a simple operation process, reliable evaluation results, quantitative description of evaluation parameters, a short evaluation cycle, and can significantly improve the accuracy of grouting effect evaluation. At the same time, it can also facilitate operators to directly observe the grouting sealing and consolidation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic flow diagram of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be further described below with reference to the accompanying drawings.

[0035] like Figure 1 As shown, the present invention provides a grouting effect evaluation method based on digital image processing, comprising the following steps:

[0036] Step 1: Collect field data. First, vertically drill peepholes at randomly selected locations within the grouting area. Then, use a borehole peephole instrument to observe the cracks and grouting consolidation conditions, and collect color borehole peephole images.

[0037] Each pixel value in the colored drillhole image is a vector containing three components, which respectively constitute the RGB value of the color;

[0038] Step 2: Use an image processor to digitize the borehole viewing image to obtain a digitized image f(x,y);

[0039] Step 3: First, perform grayscale processing on the digitized image f(x,y), and calculate the grayscale value Y of the grayscale image according to formula (1), and then obtain the relative frequency p(r k ), and then obtain the grayscale histogram of the drill hole peek image;

[0040] Among them, grayscale uses the brightness value of the pixel as the pixel value, and the brightness value is calculated by the color model in formula (1);

[0041] Y=0.299*R+0.587*G+0.114*B (1);

[0042] Where R, G, and B represent the values ​​of the three component vectors of the color image respectively;

[0043]

[0044] Where r k represents the kth gray level, n k Represents the number of pixels of this gray level, and N is the total number of pixels in the image;

[0045] Step 4: Analyze the grayscale distribution and judge the image segmentation characteristics. If the image segmentation characteristics are obvious, directly obtain the pixel values ​​corresponding to the crack area and grouting area in the grayscale image. If the image segmentation characteristics are not obvious, first perform image enhancement, image smoothing and image sharpening on the original image with unclear segmentation characteristics of the crack area and grouting area, and then obtain the pixel values ​​corresponding to the crack area and grouting area in the grayscale image.

[0046] Among them, the pixel values ​​corresponding to the crack area and the grouting area in the grayscale image can be obtained by viewing the pixel values ​​of the relevant areas in the image viewer through the human-computer interaction method;

[0047] In order to make the segmentation features of the image more obvious, the image enhancement processing mainly adopts grayscale transformation. Grayscale transformation is to map the grayscale values ​​of the selected crack area and grouting area into a new output value by changing the function. Specifically, the image enhancement processing method is as follows:

[0048] According to formula (3), the grayscale values ​​of the selected crack area and grouting area are mapped into a new output value g(x,y);

[0049] g(x,y)=T[f(x,y)] (3);

[0050] Where T represents a grayscale transformation function;

[0051] Step 5: performing image segmentation processing on the grayscale image of the borehole peek view, separating the image of the crack area and the image of the grouting area in the crack from the borehole peek image;

[0052] As a preferred method, the crack area image is segmented by using a threshold segmentation method. Specifically, one of the pixel values ​​of the crack area and the surrounding rock is selected as the threshold. Pixels greater than the threshold are set to 1, and pixels less than the threshold are set to 0, thereby obtaining a binary image of the crack area.

[0053] As a preferred method, the grouting area image is segmented by using a region growing method. Specifically, a pixel in the grouting area is selected as a seed point, the maximum absolute value of the difference between the pixel values ​​in the area is determined as a threshold, a growth criterion is determined, and image iteration is performed to obtain a binary image of the grouting area.

[0054] Step 6: The area of ​​the region is usually calculated by counting the number of pixels inside the boundary. For a binary image, if 1 represents the object and 0 represents the background, its area is the number of pixels where g(x,y) = 1.

[0055] The area of ​​the separated crack region A1 is calculated according to formula (4), and the area of ​​the separated grouting region A2 is calculated according to formula (5);

[0056]

[0057]

[0058] Where g(x,y)′ represents the image of the crack area after segmentation, and g(x,y)″ represents the image of the grouting area after segmentation.

[0059] Step 7: Divide the obtained grouting area A2 by the crack area A1 to obtain the crack grouting plugging consolidation ratio, and then quantitatively describe the grouting effect based on the crack grouting plugging consolidation ratio.

[0060] In order to facilitate the subsequent grayscale processing, the digital processing method in step 2 is as follows:

[0061] The continuous signal of an image with continuous spatial position and light intensity changes is converted into a discrete digital signal, that is, the image f(x, y) is discretized into a pixel matrix with M pixels containing different brightness and darkness information in each row and N pixels containing different brightness and darkness information in each column.

[0062] The image size of the image f(x, y) is M*N pixels, and different brightness and darkness information uses values ​​of 0 to 255 to describe the brightness value from black to white.

[0063] As a preferred embodiment, when the grayscale transformation is a window linear transformation, the T function is expressed as shown in formula (6);

[0064]

[0065] Where a and b are the grayscale values ​​of the two endpoints of the grayscale in the original image; c and d are the grayscale values ​​of the processed image and the original image a and b respectively;

[0066] In order to make the segmentation features of the image more obvious, the image smoothing method in step 4 is to perform filtering on the image.

[0067] In order to make the segmentation features of the image more obvious, the image sharpening and smoothing processing method in step 4 is to perform edge detection on the image.

[0068] In order to accurately evaluate the grouting plugging effect, in step seven, the fissure grouting plugging consolidation ratio is compared and analyzed with historical data from previous engineering examples to evaluate the grouting effect; when the fissure grouting plugging consolidation ratio is greater than or equal to 80%, the grouting effect is ideal; when the fissure grouting plugging consolidation ratio is less than 80%, the grouting effect is unsatisfactory and grouting needs to be repeated.

[0069] In order to accurately and quickly segment the image, in step 4, the method for judging the image segmentation features is as follows:

[0070] When the grayscale histogram shows obvious peaks and valleys, and the grayscale value at the appropriate position can be selected as the threshold, the segmentation feature is determined to be obvious; otherwise, the segmentation feature is determined to be unclear.

[0071] The grouting effect evaluation method based on image processing provided by the present invention first converts the acquired borehole peek image into a grayscale histogram, and then judges the segmentation features of the image based on the grayscale distribution. When the segmentation features are obvious, the crack image and the grouting area image are separated from the borehole peek image, and then the crack grouting plugging consolidation ratio is obtained by the ratio of the grouting area area to the crack area area. The crack grouting plugging consolidation ratio can be used to quantitatively evaluate the grouting effect, which can significantly improve the accuracy of the grouting effect evaluation and effectively solve the problem that the existing technology cannot quantitatively evaluate and analyze the grouting effect. In the digital processing process, the borehole peek image is processed in the image processor, which can effectively improve the efficiency of the grouting effect evaluation and help save manpower and material resources. When the segmentation features are not obvious, different grayscale transformations are used for different areas to obtain the enlarged or reduced truncation interval of the corresponding area, so that the segmentation features can be made more obvious, which is beneficial to the subsequent segmentation operation and area summation operation, greatly improving the accuracy of the grouting effect evaluation. This method has a simple operation process, reliable evaluation results, quantitative description of evaluation parameters, a short evaluation cycle, and can significantly improve the accuracy of grouting effect evaluation. At the same time, it can also facilitate operators to directly observe the grouting sealing and consolidation effect.

Claims

1. A grouting effect evaluation method based on digital image processing, characterized in that: The following steps are involved: Step 1: Collect field data. First, vertically drill peepholes at randomly selected locations within the grouting area. Then, use a borehole peephole instrument to observe the cracks and grouting consolidation conditions, and collect color borehole peephole images. Step 2: Use the image processor to digitize the borehole viewing image to obtain a digitized image ; Step 3: First digitize the image Perform image grayscale processing and calculate the grayscale value Y of the grayscale image according to formula (1), and then obtain the relative frequency of grayscale occurrence according to formula (2) p ( r k ), and then obtain the grayscale histogram of the drill hole peek image; Where R, G, and B represent the values ​​of the three component vectors of the color image respectively; Where, r k represents the kth gray level, n k represents the number of pixels at that gray level, N is the total number of pixels in the image; Step 4: Analyze the grayscale distribution in the grayscale histogram and judge the image segmentation features. If the image segmentation features are obvious, directly obtain the pixel values ​​corresponding to the crack area and the grouting area in the grayscale image. If the image segmentation features are not obvious, first perform image enhancement, image smoothing and image sharpening on the original image with unclear segmentation features of the crack area and the grouting area, and then obtain the pixel values ​​corresponding to the crack area and the grouting area in the grayscale image. The method for judging the image segmentation features is as follows: When the grayscale histogram shows obvious peaks and valleys, and the grayscale value can be selected as the threshold, the segmentation feature is judged to be obvious, otherwise it is judged to be unclear. The image enhancement processing method is as follows: According to formula (3), the grayscale values ​​of the selected crack area and grouting area are mapped into a new output value ; Where, represents a grayscale transformation function; Step 5: performing image segmentation processing on the grayscale image of the borehole peek view, separating the image of the crack area and the image of the grouting area in the crack from the borehole peek image; Step 6: Calculate the area of ​​the separated crack region according to formula (4) A 1 , calculate the separated grouting area according to formula (5) A 2 ; Where, represents the crack area image after segmentation processing, Represents the grouting area image after segmentation processing; Step 7: The grouting area A 2 Divide by the crack area A 1 , obtain the fracture grouting plugging consolidation ratio, and then quantitatively describe the grouting effect based on the fracture grouting plugging consolidation ratio.

2. A grouting effect evaluation method based on digital image processing according to claim 1, characterized in that: The digital processing method in step 2 is as follows: The continuous signal of the image whose spatial position and light intensity change are continuous is converted into a discrete digital signal. The image is discretized into a pixel matrix with M pixels in each row and N pixels in each column.

3. A grouting effect evaluation method based on digital image processing according to claim 2, characterized in that: The image smoothing method in step 4 is to perform filtering on the image.

4. A grouting effect evaluation method based on digital image processing according to claim 3, characterized in that: The image sharpening processing method in step 4 is to perform edge detection on the image.

5. A grouting effect evaluation method based on digital image processing according to claim 4, characterized in that: In step seven, when the crack grouting plugging and consolidation ratio is greater than or equal to 80%, the grouting effect is ideal; when the crack grouting plugging and consolidation ratio is less than 80%, the grouting effect is unsatisfactory and the grouting operation needs to be repeated.

6. A grouting effect evaluation method based on digital image processing according to claim 5, characterized in that: In step five, the image of the fracture area is segmented by using the threshold segmentation method. A value between the pixel values ​​of the fracture area and the surrounding rock is selected as the threshold. Pixels greater than the threshold are set to 1, and pixels less than the threshold are set to 0, thereby obtaining a binary image of the fracture area. The image of the grouting area is segmented by using the regional growing method. Specifically, a pixel in the grouting area is selected as the seed point, and the maximum absolute value of the difference between the pixel values ​​in the area is determined as the threshold. The growth criterion is determined, and the image is iterated to obtain a binary image of the grouting area.

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