CN112348831B - Shale SEM image segmentation method based on machine learning - Google Patents
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Abstract
The invention discloses a shale SEM image segmentation method based on machine learning, which belongs to the technical field of shale component characterization and comprises the following steps: s1, obtaining an SEM secondary electron gray image of shale; s2, establishing component categories to be segmented; s3, manually selecting image areas of all the component categories as training pixels; s4, filtering the image areas of the component classes through functions, extracting characteristic values and constructing a training data set; s5, training a random forest classifier by using a training data set; and S6, segmenting the shale SEM image to be segmented by adopting the trained random forest classifier. According to the shale SEM image segmentation method based on machine learning, each pixel point in an original image is processed by using a specific function, each characteristic value in a training data set is obtained, the training data set effectively improves the accuracy of a trained random forest classifier, and the image segmentation precision is improved.
Description
Technical Field
The invention relates to a shale SEM image segmentation method based on machine learning, and belongs to the technical field of shale component characterization.
Background
The demand of national economy development on energy sources is rising day by day, and unconventional oil and gas resources including shale oil and gas are paid more and more attention by people. Compared with the conventional reservoir, the shale has ultralow porosity and ultralow permeability and is rich in organic matters. Shale reservoir pore types are various, a large number of nanometer pores and a small number of natural fractures develop in organic matters, and the matrix pores can be divided into organic pores and inorganic pores due to the formation factor. The content of the micro-pore components and the pore size distribution thereof are key parameters influencing the physical property, permeability and electrical property of the shale reservoir, and the accurate evaluation of the shale microstructure is beneficial to improving the evaluation precision and development efficiency of the reservoir.
The SEM test has high resolution, and can be used for counting the surface porosity and the pore radius distribution and developing into an important method for analyzing the unconventional oil and gas reservoir micro-pore structure. The derivation developed a wide ion beam scanning electron microscope (BIB-SEM) based on SEM. The method is characterized in that the surface of a sample is polished by adopting an ion beam through a wide ion beam scanning electron microscope, the surface is divided into a series of grids, each grid is scanned by adopting SEM to establish a high-resolution two-dimensional image, the two-dimensional images of all the grids are spliced to establish a large-view-field high-precision two-dimensional gray image, the characteristics of high resolution and large view field are achieved, and a new way is provided for quantitatively analyzing the organic matter content, porosity and pore structure of the shale.
In the practice of testing the microstructure of the shale by the BIB-SEM, the shale contains various components such as organic matters, clay, quartz and the like, so that the hardness difference is large, and the polished end face of a sample is difficult to ensure to be completely flat. The imaging mode of the secondary electrons is affected by the surface topography, such as a high brightness in sharp portions and a low brightness in flat portions, in addition to the surface composition. There is therefore a problem of grey scale anomalies at the composition boundaries in the acquired SEM two-dimensional image, for example regions of abnormally high grey scale values at the edges of the pores, which makes accurate segmentation of the image difficult.
Therefore, the present invention is directed to provide an advanced shale image segmentation method to solve the problems in the prior art.
The above description is included in the technical recognition scope of the inventors, and does not necessarily constitute the prior art.
Disclosure of Invention
The invention aims to solve the problems in the prior art, provides a shale SEM image segmentation method based on machine learning, solves the problem of segmentation of pore edge gray abnormal areas in a shale image, and is high in image segmentation accuracy.
The invention adopts the following technical scheme to realize the purpose:
a shale SEM image segmentation method based on machine learning comprises the following steps:
s1, obtaining an SEM secondary electron gray image of shale;
s2, establishing a classification to be segmented according to the component characteristics of the shale;
s3, manually and respectively selecting image areas with the gray features of all the component classes from the obtained gray images as training pixels, and labeling class labels;
s4, filtering image areas belonging to each component category through a Gaussian filter function, a film projection filter function, a bilateral filter function, a Kuwahara filter function and a mean filter function to obtain a plurality of characteristic values based on the gray level of pixel points, using the characteristic values as training data sets, and obtaining at least 1 characteristic through each filter function;
s5, training a random forest classifier by using a training data set;
and S6, segmenting the shale image to be segmented by adopting the trained random forest classifier.
Further, when the original image is processed by the Gaussian filter function, a normally distributed 3 × 3 convolution kernel is calculated by the Gaussian function, and the convolution kernel and the gray value of each pixel in the original image are subjected to convolution operation to obtain a feature image after Gaussian filtering; gaussian function
The middle sigma values respectively take 5 values of 1, 2, 4, 8 and 16, convolution operation is respectively carried out on the convolution kernels corresponding to the sigma values and the original image, and 5 characteristic values are obtained.
Further, when the original image is processed by the film projection filter function, a 19 × 19 matrix is used as an initial matrix, all the middle columns of the initial matrix are set to 1, and the rest are set to zero, and the initial matrix is rotated by 6 ° to 180 ° each time to create 30 convolution kernels; performing convolution operation on the original images and 30 convolution kernels to obtain 30 images, and projecting the 30 images to a single image in the following 6 ways to obtain 6 characteristic values;
(1) The sum of the gray levels of pixel points at the same position in each image;
(2) Average value of gray levels of pixel points at the same position in each image;
(3) The standard deviation of the gray level of the pixel point at the same position in each image;
(4) The median of the gray levels of the pixel points at the same position in each image;
(5) The maximum gray scale of the pixel point at the same position in each image;
(6) And the minimum gray scale of the pixel point at the same position in each image.
Further, when the bilateral filter function is used for processing, pixel points with the space radius of 5 and pixel points within 10 are respectively selected from the neighborhood pixel points, the proximity between the gray value of the neighborhood pixel point and the gray value of the current pixel point is respectively set to be 50 and 100, namely the space radius and the proximity value are respectively set to be four conditions of 5&50, 5&100, 10 &50and 10&100, and the average value is calculated to obtain 4 characteristic values.
Further, when the original image is processed by the Kuwahara filter function, a 19 × 19 matrix is used as an initial matrix, all the middle columns of the initial matrix are set to 1, and the rest are set to zero; each time, the initial matrix is rotated by 6 degrees to 180 degrees to create 30 templates, the 30 templates are respectively used for masking the pixel points in the original image to obtain 30 regions, the variance and the mean of the 30 regions are calculated, and variance, variance/mean and variance/mean are respectively selected 2 The minimum area is used as a target area, the gray value of the central pixel point of the template is equal to the average value of the pixel points in the target area, and 3 characteristic values are obtained.
When the original image is processed through the mean filtering function, the gray value of the target pixel point is respectively set as the average value of the gray values of the pixel points within the radius of 1, 2, 4, 8 and 16 pixel points away from the target pixel point, and 5 characteristic values are obtained.
Further, the component categories include one or more of porosity, organic matter, inorganic matter framework and pyrite.
Further, the training pixels with the gray features of the component classes are manually selected to include pore edge gray abnormal parts, and the pore edge gray abnormal parts are divided into non-pore component classes adjacent to the area.
Further, in step S5, the parameters of the random forest classifier are set as: the number of the pixel points processed in each batch is 100-500, preferably 200; the number of the classification trees is 100-500, preferably 200; the number of randomly used characteristic variables is 5-20, preferably 10.
The beneficial effects of the invention include but are not limited to:
the shale SEM image segmentation method based on machine learning provided by the invention comprises the steps of (1) segmenting and converting a gray level image into pixel point classification operation, describing each pixel point and an adjacent region thereof in an original image in a plurality of ways based on space and proportion related information by using a Gaussian filter function, a membrane projection filter function, a bilateral filter function, a Kuwahara filter function and a mean filter function, obtaining a plurality of characteristic values of each pixel point based on gray level, constructing an efficient training data set, and improving the accuracy of a trained random forest classifier. (2) The method has the advantages that the area with abnormal pore edge gray level in the gray level image is classified into the training pixels and reasonably divided into corresponding categories, comprehensive and complete information is provided for extracting features for classification, the problem of abnormal pore edge gray level in the SEM secondary electron gray level image caused by low surface flatness of a sample is effectively solved, the image segmentation precision is improved, and the method is suitable for segmentation of the SEM secondary electron gray level images of different shale polishing planes.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is an enlarged image of an SEM secondary electron grayscale image of an obtained shale end face (an interpolated image is a partially enlarged detail);
fig. 2 is a pixel selected for training in the shale end face grayscale image provided in fig. 1 in the shale SEM image segmentation method based on machine learning according to embodiment 1 of the present invention (an interpolation image is a partially enlarged detail);
fig. 3 is a result of performing learning segmentation on the shale end face grayscale image provided in fig. 1 by using the shale SEM image segmentation method based on machine learning according to embodiment 1 of the present invention;
fig. 4 is a result of shale image segmentation on the shale end face grayscale image provided in fig. 1 by using the segmentation method provided in comparative example 1 (an interpolation graph is a partially enlarged detail);
FIG. 5 shows selected training pixels in the shale end-face grayscale image provided in FIG. 1 for the segmentation method provided in comparative example 2;
fig. 6 is a result of shale image segmentation performed on the shale end face grayscale image provided in fig. 1 by using the method provided in comparative example 2 (an interpolation graph is a partially enlarged detail);
fig. 7 shows the result of segmenting the shale end face grayscale image provided in fig. 1 by using the conventional grayscale threshold segmentation method described in comparative example 3 (the interpolated graph is a partially enlarged detail).
Detailed Description
The present invention will be described in further detail in the following. It should be noted, however, that the following detailed description merely gives specific operation examples of the present invention by way of example, and the scope of the present invention is not limited thereto. The scope of the invention is limited only by the claims. It will be obvious to those skilled in the art that various other modifications and substitutions can be made to the described embodiments of the invention within the scope of the claims of the invention and still achieve the same technical result as the final technical object of the invention.
The shale image segmentation method of the present invention will be described in detail below in a specific embodiment.
Example 1:
the shale SEM image automatic segmentation method based on machine learning provided by the embodiment is applied to the SEM secondary electron grayscale image segmentation, and specifically includes the following steps:
s1, obtaining an SEM secondary electron gray image of shale:
specifically, in this embodiment, a BIB polished core end face is used for a shale, and a secondary electron mode of SEM is used for imaging to obtain an SEM secondary electron grayscale image of the shale, which includes 1411 × 950 pixels.
Fig. 1 is an enlarged image (an interpolated graph is a partially enlarged detail) of the obtained SEM secondary electron grayscale image of the shale end face, and the darkest (the lowest grayscale value) area in fig. 1 is a pore, and then an organic matter, an inorganic framework and pyrite are sequentially included.
After the pores surrounded by the organic matters in the graph 1 are enlarged, the gray scale at the pore boundaries is found to be abnormal, highlight areas exist around the pores, the gray scale value of the highlight areas is obviously higher than that of the surrounding organic matters, and the gray scale value is greatly overlapped with the gray scale interval of the pyrite in the graph.
And S2, determining the component classes to be segmented according to the component characteristics of the shale end face gray level image in the graph 1, wherein the component classes comprise pores, organic matters, inorganic substance frameworks and pyrites.
S3, manually selecting an image area with the gray features of all the component classes as training pixels from the obtained shale end face gray image, and labeling class labels; the training pixels contain abnormal-gray portions at the edges of the pores, as shown in fig. 2, the pores are labeled as an a region, the organic matter is labeled as a B region, the inorganic matter framework is labeled as a C region, and the pyrite is labeled as a D region; when selecting, the part with abnormal pore edge gray needs to be divided into non-pore component categories adjacent to the area.
S4, filtering image areas belonging to each component category through a Gaussian filter function, a film projection filter function, a bilateral filter function, a Kuwahara filter function and a mean filter function to obtain a plurality of characteristic values based on pixel gray levels for constructing a training data set, and obtaining at least 1 characteristic through each filter function; the Gaussian filter function, the film projection filter function, the bilateral filter function, the Kuwahara filter function and the mean filter function are used for processing the original image, and the parameters are as follows:
the Gaussian filtering reduces the noise level by using a Gaussian function, when the original image is processed by the Gaussian filtering function, a normally distributed 3 x 3 convolution kernel is calculated by using the Gaussian function, and the convolution kernel and the gray value of each pixel in the original image are subjected to convolution operation to obtain a characteristic value after the Gaussian filtering; gaussian function
And the middle sigma value takes 5 values of 1, 2, 4, 8 and 16 respectively, convolution operation is carried out on the convolution kernel corresponding to each sigma value and the original image respectively, and 5 characteristic values are obtained.
Membrane projection filtering enhances the membrane-like structure of the image by directional filtering, when the original image is processed by the membrane projection filtering function, a 19 × 19 matrix is used as an initial matrix, all the middle columns of the initial matrix are set to 1, the rest are set to zero, and the initial matrix is rotated by 6 degrees to 180 degrees each time to create 30 convolution kernels; respectively performing convolution operation on 30 convolution kernels and an original image to obtain 30 images, and respectively projecting the 30 images into a single image in the following 6 ways to obtain 6 characteristic values;
(1) The sum of the gray levels of pixel points at the same position in each image;
(2) Average value of gray levels of pixel points at the same position in each image;
(3) Standard deviation of gray levels of pixel points at the same position in each image;
(4) The median of the gray levels of the pixel points at the same position in each image;
(5) The maximum gray scale of the pixel point at the same position in each image;
(6) And the minimum gray scale of the pixel point at the same position in each image.
The bilateral filtering is to average the gray value of each pixel close to the current pixel, when the bilateral filtering is performed, the neighborhood pixel selects the pixel within 5 and 10 of the spatial radius of the current pixel respectively, the proximity of the gray value of the neighborhood pixel to the gray value of the current pixel is set to 50 and 100 respectively, namely the spatial radius and the proximity value are set to be four conditions of 5&50, 5&100, 10 &50and 10 &100respectively, and the average value is calculated to obtain 4 characteristic values.
Kuwahara filtering selects the mean value of a region with more uniform image gray value to replace the central pixel gray value of a template by calculating the mean value and the variance in the neighborhood of an image template, and when an original image is processed by a Kuwahara filtering function, a 19 multiplied by 19 matrix is used as an initial matrix, all middle columns of the initial matrix are set to be 1, and the rest are set to be zero; the initial matrix is rotated by 6 degrees to 180 degrees each time to create 30 templates, the 30 templates are respectively used for masking the pixel points in the original image to obtain 30 areas, the variance and the mean of the 30 areas are calculated, and the variance, the variance/mean and the variance/mean are respectively selected 2 The minimum area is used as a target area, the gray value of the central pixel point of the template is equal to the average value of the pixel points in the target area, and 3 characteristic values are obtained.
And when the original image is processed by the mean filtering function, the gray value of the target pixel point is respectively set as the average value of the gray values of the pixel points within the radius of 1, 2, 4, 8 and 16 pixel points away from the target pixel point, and 5 characteristic values are obtained.
As can be seen, in this embodiment, the total number of features obtained by five image processing functions, namely, a gaussian filter function, a film projection filter function, a bilateral filter function, a Kuwahara filter function, and a mean filter function, is 23.
S5, training a random forest classifier by using a training data set; the training data set is an unbalanced data set; the parameters of the random forest classifier are set as follows: the number of pixels processed in each batch is 200; the number of the classification trees is 200; the number of features used randomly was 10. In the training process of the random forest classifier, a plurality of decision trees are constructed by using information gain, information gain ratio or a Kini index as a criterion, extracted samples returned from a training data set are used as a training set when each decision tree is constructed, for example, the classification tree is split by using the characteristic of the minimum Kini index until the Kini index is smaller than a threshold value, a plurality of decision trees are constructed to form a random forest, and a final classification result is determined according to voting of a plurality of trees.
S6, segmenting the shale image to be segmented by adopting a trained random forest classifier, comparing the segmentation result with that shown in figure 3 and showing in figure 1, wherein the classifier can automatically identify and segment pores, organic matters, inorganic substance frameworks and pyrite, the phenomenon of pore edge gray scale abnormity does not occur in the segmented image, and the region with pore edge gray scale abnormity is accurately classified.
Comparative example 1:
comparative example 1 differs from example 1 in that: in steps S4 and S5, the original image is processed by using image processing methods such as gaussian blur, sobel filtering, hessian matrix, difference of gaussians, and variance, and the result of learning segmentation is obtained after the training data set is constructed by extracting features, and it can be seen from fig. 4 that the pores in the organic matter are wrongly classified as inorganic frameworks.
Comparative example 2:
comparative example 2 differs from example 1 in that: as shown in fig. 5, the region with abnormal edge gradation is not divided when the training pixel is selected in step S3. It can be seen from the segmentation result of fig. 6 that the phenomenon of edge gray level abnormality in the segmentation result is not solved, and different selection modes of the training pixels can bring distinct results.
Comparative example 3:
in comparative example 3, fig. 1 was divided by a conventional gray threshold division method, and it can be seen from fig. 7 that the conventional gray threshold division method wrongly divides a portion belonging to an organic matter into pyrite and divides a region belonging to an inorganic skeleton into pyrite, resulting in a wrong division of an image.
Comparing the segmentation method provided by the embodiment 1 of the invention with the comparative examples 1 to 3, it can be known that the segmentation result of the segmentation method provided by the embodiment 1 of the invention is in accordance with the actual situation, and the problem of pore edge gray scale abnormality caused by the surface flatness of the sample is solved.
The above-described embodiments should not be construed as limiting the scope of the present invention, and any alternative modifications or alterations to the embodiments of the present invention will be apparent to those skilled in the art.
The present invention is not described in detail, but is known to those skilled in the art.
Claims (7)
1. The shale SEM image segmentation method based on machine learning is characterized by comprising the following steps of:
s1, obtaining an SEM secondary electron gray image of shale;
s2, establishing component categories to be segmented according to the component characteristics of the shale, wherein the component categories comprise one or more of pores, organic matters, inorganic substance frameworks and pyrite;
s3, in the obtained gray level image, manually selecting an image area with the gray level features of each component type as a training pixel, and labeling a type label, wherein the training pixel with the gray level features of each component type comprises a pore edge gray level abnormal part, and the pore edge gray level abnormal part is divided into non-pore component types adjacent to the area;
s4, filtering the training pixels through a Gaussian filter function, a film projection filter function, a bilateral filter function, a Kuwahara filter function and a mean filter function respectively to obtain a plurality of characteristic values based on the gray level of the pixel points to construct a training data set, and at least obtaining 1 characteristic through each filter function;
s5, training a random forest classifier by using a training data set;
and S6, segmenting the shale SEM image to be segmented by adopting the trained random forest classifier.
2. The shale SEM image segmentation method based on machine learning of claim 1, characterized in thatWhen the original image is processed by the Gaussian filter function, calculating a normally distributed 3 x 3 convolution kernel by using the Gaussian function, and performing convolution operation on the convolution kernel and the gray value of each pixel in the original image to obtain the characteristic value of each pixel after Gaussian filtering; gaussian function
The middle sigma values respectively take 5 values of 1, 2, 4, 8 and 16, convolution operation is respectively carried out on the convolution kernels corresponding to the sigma values and the original image, and 5 characteristic values are obtained.
3. The shale SEM image segmentation method based on machine learning of claim 1, wherein when processing the original image through the membrane projection filter function, using a 19 x 19 matrix as an initial matrix, the middle columns of the initial matrix are all set to 1, the rest are set to zero, and each time the initial matrix is rotated by 6 ° up to 180 ° to create 30 convolution kernels; performing convolution operation on the original images and 30 convolution kernels to obtain 30 images, and projecting the 30 images to a single image in the following 6 ways to obtain 6 characteristic values;
(1) The sum of the gray levels of the pixel points at the same position in each image;
(2) Average value of gray levels of pixel points at the same position in each image;
(3) The standard deviation of the gray level of the pixel point at the same position in each image;
(4) The median of the gray levels of the pixel points at the same position in each image;
(5) The maximum gray scale of the pixel point at the same position in each image;
(6) And the minimum gray scale of the pixel point at the same position in each image.
4. The shale SEM image segmentation method based on machine learning of claim 1 is characterized in that when bilateral filter function processing is carried out, pixel points within 5 and 10 space radiuses of current pixel points are respectively selected by neighborhood pixel points, the closeness between the gray value of the neighborhood pixel points and the gray value of the current pixel points is respectively set to be 50 and 100, namely four conditions that the space radiuses and the closeness values are respectively set to be 5 and 50, 5 and 100, 10 and 50 and 10 and 100 are respectively set, an average value is calculated, and 4 feature values are obtained.
5. The shale SEM image segmentation method based on machine learning of claim 1, wherein when the original image is processed by the Kuwahara filter function, a 19 x 19 matrix is used as an initial matrix, all the middle columns of the initial matrix are set to 1, and the rest are set to zero; the initial matrix is rotated by 6 degrees to 180 degrees each time to create 30 templates, the 30 templates are respectively used for masking the pixel points in the original image to obtain 30 areas, the variance and the mean of the 30 areas are calculated, and the variance, the variance/mean and the variance/mean are respectively selected 2 The minimum area is used as a target area, the gray value of the central pixel point of the template is equal to the average value of the pixel points in the target area, and 3 characteristic values are obtained.
6. The shale SEM image segmentation method based on machine learning of claim 1, wherein when the original image is processed through the mean filtering function, the gray values of target pixel points are respectively set as the average of the gray values of the pixel points within 1, 2, 4, 8 and 16 pixel point radii from the target pixel, and 5 feature values are obtained.
7. The shale SEM image segmentation method based on machine learning as claimed in claim 1, wherein in step S5, the parameters of the random forest classifier are set as: the number of pixels processed in each batch is 100-500; the number of the classification trees is 100-500; the number of randomly used characteristic variables is 5-20.
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| CN108961246A (en) * | 2018-07-10 | 2018-12-07 | 吉林大学 | A kind of scanning electron microscope image hole recognition methods based on artificial intelligence |
| CN110910444A (en) * | 2019-11-14 | 2020-03-24 | 中国科学院力学研究所 | RES scale shale equivalent three-dimensional pore parameter rapid extraction method |
| CN111028094A (en) * | 2019-11-14 | 2020-04-17 | 中国科学院力学研究所 | Equivalent TOC calculation method based on shale scanning electron microscope image extraction |
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