Rock core fluorescence brightness and area calculation grading method based on deep learning

Abstract

The invention discloses a grading method for core fluorescence brightness and area calculation based on deep learning. The grading method comprises the following steps: S1, forming a labeled fluorescence region data set and an unlabeled fluorescence region data set; s2, preprocessing the core fluorescence images in the labeled fluorescence region data set and the unlabeled fluorescence region data set; s3, segmenting and marking the fluorescent region; s4, constructing a semi-supervised model; s5, based on the label data and the pseudo label data in the extended data set, performing quantitative calculation on the brightness and the area of the fluorescent region by using an improved deep convolutional neural network model; s6, outputting a fluorescence brightness grade and a geological characteristic evaluation result of the rock core; and S7, storing the grading result and related calculation data in a database, and generating an analysis report. The method can improve the efficiency of large-scale data processing while improving the fluorescence brightness and area calculation precision, and can dynamically adapt to the characteristics of different rock core samples.

Description

Technical Field

[0001] The present invention relates to the field of core technology, and in particular to a grading method for core fluorescence brightness and area calculation based on deep learning. Background Art

[0002] In the existing technology, the calculation of core fluorescence brightness and area mainly relies on traditional manual observation and empirical judgment. The traditional method is usually for geological engineers to evaluate the fluorescence brightness and coverage area of ​​the core surface with the naked eye or with the help of simple physical instruments. In this process, the observation results are easily affected by the subjective factors of the observer, resulting in poor accuracy and consistency of the results. Manual observation is inefficient and not scalable in large-scale resource assessment.

[0003] In order to improve the accuracy and consistency of calculations, some technical solutions have introduced automated image processing technology. Automated image processing technology usually uses the brightness and contrast information of fluorescent images combined with some basic image segmentation algorithms to perform preliminary calculations on the brightness and area of ​​core fluorescence. However, existing image processing methods still have obvious technical defects when dealing with complex image backgrounds and refined identification of fluorescent areas. On the one hand, simple image segmentation algorithms are difficult to cope with the strong interference between fluorescent areas and backgrounds, especially in core samples with complex fluorescence brightness changes, which are prone to over-segmentation or missed segmentation, resulting in large errors in the brightness and area calculations of fluorescent areas. On the other hand, existing image processing algorithms lack the ability to process large data sets. When processing a large number of unlabeled core images, the algorithm's generalization ability is insufficient, which can easily lead to inaccurate grading results.

[0004] In addition, the existing technology also has shortcomings in the classification of fluorescence brightness levels and the evaluation of geological characteristics. Most schemes rely on preset brightness and area thresholds for simple classification and evaluation, and are unable to make dynamic adjustments based on the diversity of fluorescent areas, which results in low reliability of the results. At the same time, the existing technology is difficult to adapt to the geological differences of different core samples, resulting in a lack of sufficient flexibility and adaptability in practical applications.

[0005] In summary, the existing technologies have the following main shortcomings: first, manual observation and simple image processing techniques are highly subjective and lack precision; second, the existing image segmentation algorithms have poor robustness under complex backgrounds, resulting in large calculation errors of fluorescence brightness and area; finally, the existing fluorescence brightness level classification and geological property evaluation methods are difficult to adapt to the different characteristics of core samples and lack the ability of dynamic adjustment, which affects their application effect in large-scale resource assessment. Summary of the invention

[0006] One purpose of the present invention is to propose a grading method for core fluorescence brightness and area calculation based on deep learning. The present invention can improve the efficiency of large-scale data processing while improving the accuracy of fluorescence brightness and area calculation, and can dynamically adapt to the characteristics of different core samples.

[0007] A grading method for core fluorescence brightness and area calculation based on deep learning according to an embodiment of the present invention comprises the following steps:

[0008] S1. Use imaging equipment to obtain core fluorescence images with multiple angles and resolutions to form labeled fluorescence area datasets and unlabeled fluorescence area datasets;

[0009] S2, preprocessing the core fluorescence images in the labeled fluorescence area dataset and the unlabeled fluorescence area dataset;

[0010] S3. Use the pre-trained improved deep convolutional neural network model to analyze the pre-processed labeled fluorescence area dataset, automatically identify the fluorescence area in the core fluorescence image, and segment and annotate the fluorescence area;

[0011] S4. Construct a semi-supervised model to predict different enhanced versions of the same unlabeled core fluorescence image for the unlabeled fluorescence area dataset using the consistency regularization technology, apply consistency loss so that the prediction results of the core fluorescence image under different enhancement conditions remain consistent, generate pseudo labels for the unlabeled data, and merge the pseudo label data with the labeled fluorescence area dataset to form an extended dataset;

[0012] S5. Based on the annotated data and pseudo-labeled data in the extended dataset, the brightness and area of ​​the fluorescent area are quantitatively calculated using the improved deep convolutional neural network model;

[0013] S6. Input the calculated results of fluorescence brightness and area into the trained semi-supervised deep learning model, grade the extended data set through the semi-supervised deep learning model, and output the fluorescence brightness level and geological characteristic evaluation results of the core;

[0014] S7. Store the grading results and related calculation data in a database, and generate an analysis report, which includes fluorescence brightness, area and grading result information.

[0015] Optionally, the S1 step specifically includes:

[0016] S11. Using a multi-angle rotating imaging device to obtain a core fluorescence image, the multi-angle rotating imaging device captures the core fluorescence images of the core at different angles and integrates them into a core fluorescence image set I m,n , m represents the imaging angle of the core fluorescence image, and n represents the core fluorescence images with different resolutions;

[0017] S12. Annotate the acquired core fluorescence image set to form an annotated fluorescence region data set:

[0018] D l ={(I m,n ,L m,n )|m∈M,n∈N};

[0019] Among them, I m,n is the core fluorescence image, L m,n is the label for the fluorescence area corresponding to the core fluorescence image, M is the imaging angle set of the core fluorescence image, and N is the resolution set;

[0020] S13. Based on the acquired core fluorescence image set, construct an unlabeled fluorescence area dataset:

[0021] D u = {I m,n |m∈M,n∈N}.

[0022] Optionally, the S2 step specifically includes:

[0023] S21. Use Gaussian filtering algorithm to annotate the fluorescence area dataset D l and the unlabeled fluorescence region dataset D u The core fluorescence images in are denoised to obtain the denoised core fluorescence image set I′ m,n ;

[0024] S22, the denoised core fluorescence image set I′ m,n The brightness and contrast are adjusted, and the contrast of the core fluorescence image is optimized using the histogram equalization method, so that the fluorescence area in the core fluorescence image is clear. The adjusted core fluorescence image set is:

[0025] I″ m,n =HE(I′ m,n );

[0026] Among them, HE represents the histogram equalization operation, I″ m,n represents a set of core fluorescence images after brightness and contrast adjustment;

[0027] S23, adjusting the core fluorescence image set I″ m,n Perform image enhancement processing to obtain the enhanced core fluorescence image set I″′ m,n ;

[0028] S24. Output the preprocessed labeled fluorescence area dataset:

[0029] D′ l ={(I″′ m,n,L m,n )|m∈M,n∈N};

[0030] Output the preprocessed unlabeled fluorescence area dataset:

[0031] D′ u ={I″′ m,n |m∈M,n∈N}.

[0032] Optionally, the S3 step specifically includes:

[0033] S31. Analyze the preprocessed labeled fluorescence area dataset using the pre-trained improved deep convolutional neural network model. The improved deep convolutional neural network model includes a fluorescence area guided attention mechanism module. The attention mechanism module generates a weight matrix A by identifying the area with concentrated fluorescence brightness in the core fluorescence image. m,n , the weight matrix emphasizes the areas with large changes in fluorescence brightness, so that the convolution operation focuses on the fluorescent area and ignores background interference:

[0034]

[0035] Among them, A m,n (x,y) represents the weight value at the coordinate (x,y), I″′ m,n (x, y) is the brightness value of the core fluorescence image after preprocessing, G(x, y) is the local gradient of the fluorescence area, Z m,n is the normalization factor, the sum of all weights in the fluorescent area is 1;

[0036] S32, the core fluorescence image I″′ m,n Input the improved deep convolutional neural network model to extract preliminary feature representation F m,n :

[0037]

[0038] Among them, F m,n (x,y) represents the eigenvalue at the coordinate (x,y), W i k ,j is the weight of convolution kernel k, b k is the bias term;

[0039] S33. The attention mechanism module guided by the fluorescent region is used to represent the preliminary feature F. m,n Perform weighted processing to generate weighted feature map F′ m,n :

[0040] F′ m,n (x,y)=A m,n (x,y)·F m,n (x,y);

[0041] Among them, A m,n (x,y) is the fluorescence area weight, F m,n (x, y) is the preliminary feature representation, F′ m,n (x, y) represents the eigenvalue after weighting by the weight matrix;

[0042] S34, based on weighted feature map F′ m,n By improving the segmentation module in the deep convolutional neural network model, the fluorescent area is segmented using a pixel-level segmentation algorithm to generate a segmented fluorescent area image S m,n , the segmentation process is optimized by the energy minimization function:

[0043] E(S m,n )=∑ x,y ((S m,n (x,y)-F′ m,n (x,y) 2 +λ∑ (x′,y′)∈N(x,y) |S m,n (x,y)-S m,n (x′,y′)|);

[0044] Among them, S m,n (x, y) represents the value of the segmented fluorescence area image at the coordinate (x, y), F′ m,n (x, y) is the weighted feature representation, N(x, y) is the neighborhood pixel set, and λ is the smoothness parameter;

[0045] S35, the segmented fluorescence area image S m,n Perform annotation and match the segmentation results with the annotated fluorescence area dataset to generate an annotated fluorescence area dataset:

[0046]

[0047] Among them, δ(S m,n (x, y), T) is the decision function, which divides the segmented fluorescence area S according to the set threshold T. m,n Marking, L m,n is the generated annotation label matrix, D″ l This is the final annotated fluorescence area dataset.

[0048] Optionally, the S4 step specifically includes:

[0049] S41. Construct a semi-supervised deep learning model to annotate the fluorescence area dataset D″ l and the unlabeled fluorescence region dataset D′ uThe semi-supervised model includes a main network and an auxiliary network for training. The main network is used to classify and segment the input core fluorescence image, and the auxiliary network is used for consistency regularization.

[0050] S42. For the unlabeled fluorescence region dataset D′ u Core fluorescence image I″′ m,n , generate multiple different enhanced versions Each enhanced version is generated by different core fluorescence image transformation operations, including rotation, scaling and noise addition;

[0051] S43, input the enhanced core fluorescence image version into the main network of the semi-supervised model for prediction, and obtain the corresponding prediction results Represents the prediction result of the i-th enhanced version:

[0052]

[0053] Among them, P i m,n (x,y) represents the predicted output of the i-th enhanced version at the coordinate (x,y); f θ is the activation function of the main network, and the parameter θ represents the weight and bias of the network; is the weight of the convolution kernel applied to the pixel position (p,q) in the i-th enhanced version; I″′ i (xp,yq) m,n is the input image value of the pixel position (xp, yq) in the i-th enhanced version;

[0054] S44, in the consistency regularization process, by applying the consistency loss function L cons To keep the prediction results of different enhanced versions consistent, the loss function is defined as:

[0055]

[0056] in, represents the average prediction result of all enhanced versions, k is the number of enhanced versions;

[0057] S45. Generate pseudo labels for the unlabeled fluorescence region dataset based on the prediction results after consistency regularization

[0058]

[0059] Among them, arg max is a function that selects the maximum value in the prediction result, which is used to determine the category of the pseudo label;

[0060] S46. Generate pseudo-label dataset and the labeled fluorescence region dataset D″ l Merge to form an extended data set:

[0061]

[0062] Optionally, the step S5 specifically includes:

[0063] S51, based on the labeled fluorescence area dataset D″ l and pseudo-labeled data The brightness B of the core fluorescence area is analyzed using an improved convolutional neural network model. m,n (x,y) and area A m,n Quantitative calculation, brightness B m,n (x,y) is the brightness value at the pixel point (x,y), area A m,n is the total number of pixels in the fluorescent area;

[0064] S52, using the improved convolutional neural network model to input the core fluorescence image I″′ m,n Perform pixel-by-pixel calculations to obtain the brightness prediction value of each pixel

[0065]

[0066] in, is the weight of the convolution kernel for brightness prediction, b b is the bias term, is the brightness prediction value at pixel (x, y);

[0067] S53, the brightness value B of each fluorescent area m,n (x, y) is accumulated to calculate the total brightness of the fluorescent area

[0068]

[0069] in, It represents the total brightness value of the fluorescent area in the core fluorescence image;

[0070] S54, calculating the area A according to the total number of pixels in the fluorescent area m,n :

[0071]

[0072] in, is the indicator function, when the pixel brightness When it exceeds the set threshold T, it is recorded as 1, otherwise it is recorded as 0, which is used to count the total area of ​​the fluorescent area.

[0073] Optionally, the step S6 specifically includes:

[0074] S61, the fluorescence brightness B m,n (x,y) and fluorescence area A m,n The calculation results are input into the trained semi-supervised deep learning model, and the semi-supervised deep learning model is used to expand the dataset D ext Carry out grading and output the fluorescence brightness level and geological characteristics evaluation results of the core samples;

[0075] S62. Fluorescence brightness B is improved by improving the fully connected layer of the deep convolutional neural network model m,n (x,y) and area A m,n Perform feature fusion to generate feature vector F m,n :

[0076] F m,n =αB m,n (x,y)+βA m,n ;

[0077] Among them, α and β are the weight coefficients of brightness and area, respectively, which are used to adjust the relative importance between features. m,n is the fused feature vector;

[0078] S63, the fused feature vector F m,n Passed to the classification layer, the trained semi-supervised model outputs the fluorescence brightness level G m,n :

[0079]

[0080] Among them, G m,n is the predicted fluorescence brightness level, P represents the number of feature maps, and W (p) is the weight matrix, is the pth feature map value, γ is the adjustment coefficient, Indicates the combined area A m,n and brightness B m,n The nonlinear function of b (p) is the bias term, σ is the activation function;

[0081] S64, conduct a comprehensive evaluation of geological characteristics, according to the fluorescence brightness level G m,n and the fluorescence area A m,n The calculation results generate the geological characteristics evaluation results R m,n :

[0082]

[0083] Among them, R m,n is the geological characteristic evaluation result, Q is the characteristic dimension, λ is the weight attenuation factor, Represents the gradient change of area in the feature dimension, represents the influence function of brightness level, λ is the control factor, Ψ(G m,n ,A m,n ) represents the comprehensive influence of brightness and area.

[0084] S65, the fluorescence brightness level of the core G m,n and geological characteristics evaluation results R m,n The output is the final result, which is used for resource assessment and geological analysis.

[0085] The beneficial effects of the present invention are:

[0086] (1) The present invention introduces an improved deep convolutional neural network model. In the process of calculating the fluorescence brightness and area, a pixel-by-pixel refined calculation method is adopted to accurately identify the fluorescent area under a complex background. The attention mechanism is used to guide the convolution operation. The improved deep convolutional neural network model can perform weighted processing on the fluorescent area with obvious brightness changes, ignore background interference, and ensure the accuracy and consistency of the calculation. Compared with the traditional image segmentation algorithm, the present method can better process the core images with unclear brightness contrast, significantly improve the quantization accuracy of fluorescence brightness and area, and ensure the robustness in different complex scenes.

[0087] (2) The present invention adopts a semi-supervised deep learning model and generates pseudo labels through consistency regularization technology, so that the semi-supervised deep learning model can make full use of unlabeled data during training. Different from the traditional fully supervised learning that relies on large-scale labeled data, the present invention ensures the consistency of prediction results under different enhancement conditions by constraining the enhanced version of the unlabeled data, thereby generating high-quality pseudo labels, greatly improving data utilization and reducing dependence on manually labeled data. This pseudo-label generation strategy can improve the generalization ability of the model and still maintain a high grading accuracy when processing unlabeled core data.

[0088] (3) The present invention introduces a feature fusion mechanism to dynamically fuse the calculation results of fluorescence brightness and fluorescence area, and combines the semi-supervised learning model to grade the geological characteristics of core samples. Through the feature weighting mechanism, it can adaptively adjust the importance of brightness and area in the evaluation of geological characteristics, and use nonlinear functions to deeply fuse these features to generate more accurate core fluorescence brightness levels and geological characteristics evaluation results. Different from the traditional fixed threshold division method, the present invention can flexibly divide and evaluate the core samples according to the diversity, so that it can provide a more reliable basis for geological analysis in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0090] Figure 1 A flowchart of a deep learning-based core fluorescence brightness and area calculation grading method proposed by the present invention;

[0091] Figure 2 This is a schematic diagram of using a convolutional neural network to segment the fluorescence area in a deep learning-based core fluorescence brightness and area calculation grading method proposed in the present invention. DETAILED DESCRIPTION

[0092] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0093] refer to Figure 1-2 , a deep learning-based core fluorescence brightness and area calculation grading method, comprising the following steps:

[0094] S1. Use imaging equipment to obtain core fluorescence images with multiple angles and resolutions to form labeled fluorescence area datasets and unlabeled fluorescence area datasets;

[0095] S2, preprocessing the core fluorescence images in the labeled fluorescence area dataset and the unlabeled fluorescence area dataset;

[0096] S3. Use the pre-trained improved deep convolutional neural network model to analyze the pre-processed labeled fluorescence area dataset, automatically identify the fluorescence area in the core fluorescence image, and segment and annotate the fluorescence area;

[0097] S4. Construct a semi-supervised model to predict different enhanced versions of the same unlabeled core fluorescence image for the unlabeled fluorescence area dataset using the consistency regularization technology, apply consistency loss so that the prediction results of the core fluorescence image under different enhancement conditions remain consistent, generate pseudo labels for the unlabeled data, and merge the pseudo label data with the labeled fluorescence area dataset to form an extended dataset;

[0098] S5. Based on the annotated data and pseudo-labeled data in the extended dataset, the brightness and area of ​​the fluorescent area are quantitatively calculated using the improved deep convolutional neural network model;

[0099] S6. Input the calculated results of fluorescence brightness and area into the trained semi-supervised deep learning model, grade the extended data set through the semi-supervised deep learning model, and output the fluorescence brightness level and geological characteristic evaluation results of the core;

[0100] S7. Store the grading results and related calculation data in a database, and generate an analysis report, which includes fluorescence brightness, area and grading result information.

[0101] In this implementation, step S1 specifically includes:

[0102] S11. Using a multi-angle rotating imaging device to obtain a core fluorescence image, the multi-angle rotating imaging device captures the core fluorescence images of the core at different angles and integrates them into a core fluorescence image set I m,n , m represents the imaging angle of the core fluorescence image, and n represents the core fluorescence images with different resolutions;

[0103] S12. Annotate the acquired core fluorescence image set to form an annotated fluorescence region data set:

[0104] D l ={(I m,n ,L m,n )|m∈M,n∈N};

[0105] Among them, I m,n is the core fluorescence image, L m,n is the label for the fluorescence area corresponding to the core fluorescence image, M is the imaging angle set of the core fluorescence image, and N is the resolution set;

[0106] S13. Based on the acquired core fluorescence image set, construct an unlabeled fluorescence area dataset:

[0107] D u = {I m,n |m∈M,n∈N}.

[0108] In this implementation, step S2 specifically includes:

[0109] S21. Use Gaussian filtering algorithm to annotate the fluorescence area dataset D l and the unlabeled fluorescence region dataset D u The core fluorescence images in are denoised to obtain the denoised core fluorescence image set I′ m,n ;

[0110] S22, the denoised core fluorescence image set I′ m,n The brightness and contrast are adjusted, and the contrast of the core fluorescence image is optimized using the histogram equalization method, so that the fluorescence area in the core fluorescence image is clear. The adjusted core fluorescence image set is:

[0111] I″ m,n =HE(I′ m,n );

[0112] Among them, HE represents the histogram equalization operation, I″ m,n represents a set of core fluorescence images after brightness and contrast adjustment;

[0113] S23, adjusting the core fluorescence image set I″ m,n Perform image enhancement processing to obtain the enhanced core fluorescence image set I″′ m,n ;

[0114] S24. Output the preprocessed labeled fluorescence area dataset:

[0115] D′ l ={(I″′ m,n ,L m,n )|m∈M,n∈N};

[0116] Output the preprocessed unlabeled fluorescence area dataset:

[0117] D′ u ={I″′ m,n |m∈M,n∈N}.

[0118] In this implementation, step S3 specifically includes:

[0119] S31. Analyze the preprocessed labeled fluorescence area dataset using the pre-trained improved deep convolutional neural network model. The improved deep convolutional neural network model includes a fluorescence area guided attention mechanism module. The attention mechanism module generates a weight matrix A by identifying the area with concentrated fluorescence brightness in the core fluorescence image. m,n , the weight matrix emphasizes the areas with large changes in fluorescence brightness, so that the convolution operation focuses on the fluorescent area and ignores background interference:

[0120]

[0121] Among them, A m,n (x,y) represents the weight value at the coordinate (x,y), I″′ m,n (x, y) is the brightness value of the core fluorescence image after preprocessing, G(x, y) is the local gradient of the fluorescence area, Z m,n is the normalization factor, the sum of all weights in the fluorescent area is 1;

[0122] S32, the core fluorescence image I″′ m,n Input the improved deep convolutional neural network model to extract preliminary feature representation F m,n :

[0123]

[0124] Among them, F m,n(x,y) represents the eigenvalue at the coordinate (x,y), is the weight of convolution kernel k, b k is the bias term;

[0125] S33. The attention mechanism module guided by the fluorescent region is used to represent the preliminary feature F. m,n Perform weighted processing to generate weighted feature map F′ m,n :

[0126] F′ m,n (x,y)=A m,n (x,y)·F m,n (x,y);

[0127] Among them, A m,n (x,y) is the fluorescence area weight, F m,n (x, y) is the preliminary feature representation, F′ m,n (x, y) represents the eigenvalue after weighting by the weight matrix;

[0128] S34, based on weighted feature map F′ m,n By improving the segmentation module in the deep convolutional neural network model, the fluorescent area is segmented using a pixel-level segmentation algorithm to generate a segmented fluorescent area image S m,n , the segmentation process is optimized by the energy minimization function:

[0129] E(S m,n )=∑ x,y ((S m,n (x,y)-F′ m,n (x,y) 2 +λ∑ (x′,y′)∈N(x,y) |S m,n (x,y)-S m,n (x′,y′)|);

[0130] Among them, S m,n (x, y) represents the value of the segmented fluorescence area image at the coordinate (x, y), F′ m,n (x, y) is the weighted feature representation, N(x, y) is the neighborhood pixel set, and λ is the smoothness parameter;

[0131] S35, the segmented fluorescence area image S m,n Perform annotation and match the segmentation results with the annotated fluorescence area dataset to generate an annotated fluorescence area dataset:

[0132]

[0133] Among them, δ(S m,n(x, y), T) is the decision function, which divides the segmented fluorescence area S according to the set threshold T. m,n Marking, L m,n is the generated annotation label matrix, D″ l This is the final annotated fluorescence area dataset.

[0134] In this implementation, step S4 specifically includes:

[0135] S41. Construct a semi-supervised deep learning model to annotate the fluorescence area dataset D″ l and the unlabeled fluorescence region dataset D′ u The semi-supervised model includes a main network and an auxiliary network for training. The main network is used to classify and segment the input core fluorescence image, and the auxiliary network is used for consistency regularization.

[0136] S42. For the unlabeled fluorescence region dataset D′ u Core fluorescence image I″′ m,n , generate multiple different enhanced versions Each enhanced version is generated by different core fluorescence image transformation operations, including rotation, scaling and noise addition;

[0137] S43, input the enhanced core fluorescence image version into the main network of the semi-supervised model for prediction, and obtain the corresponding prediction results Represents the prediction result of the i-th enhanced version:

[0138]

[0139] in, represents the predicted output of the i-th enhanced version at the coordinate (x, y); f θ is the activation function of the main network, and the parameter θ represents the weight and bias of the network; is the weight of the convolution kernel applied to the pixel position (p,q) in the i-th enhanced version; I″′ i (xp,yq) m,n is the input image value of the pixel position (xp, yq) in the i-th enhanced version;

[0140] S44, in the consistency regularization process, by applying the consistency loss function L cons To keep the prediction results of different enhanced versions consistent, the loss function is defined as:

[0141]

[0142] in, represents the average prediction result of all enhanced versions, k is the number of enhanced versions;

[0143] S45. Generate pseudo labels for the unlabeled fluorescence region dataset based on the prediction results after consistency regularization

[0144]

[0145] Among them, arg max is a function that selects the maximum value in the prediction result, which is used to determine the category of the pseudo label;

[0146] S46. Generate pseudo-label dataset and the labeled fluorescence region dataset D″ l Merge to form an extended data set:

[0147]

[0148] In this implementation, step S5 specifically includes:

[0149] S51, based on the labeled fluorescence area dataset D″ l and pseudo-labeled data The brightness B of the core fluorescence area is analyzed using an improved convolutional neural network model. m,n (x,y) and area A m,n Quantitative calculation, brightness B m,n (x,y) is the brightness value at the pixel point (x,y), area A m,n is the total number of pixels in the fluorescent area;

[0150] S52, using the improved convolutional neural network model to input the core fluorescence image I″′ m,n Perform pixel-by-pixel calculations to obtain the brightness prediction value of each pixel

[0151]

[0152] in, is the weight of the convolution kernel for brightness prediction, b b is the bias term, is the brightness prediction value at pixel (x, y);

[0153] S53, the brightness value B of each fluorescent area m,n (x, y) is accumulated to calculate the total brightness of the fluorescent area

[0154]

[0155] in, It represents the total brightness value of the fluorescent area in the core fluorescence image;

[0156] S54, calculating the area A according to the total number of pixels in the fluorescent area m,n :

[0157]

[0158] in, is the indicator function, when the pixel brightness When it exceeds the set threshold T, it is recorded as 1, otherwise it is recorded as 0, which is used to count the total area of ​​the fluorescent area.

[0159] In this implementation, step S6 specifically includes:

[0160] S61, the fluorescence brightness B m,n (x,y) and fluorescence area A m,n The calculation results are input into the trained semi-supervised deep learning model, and the semi-supervised deep learning model is used to expand the dataset D ext Carry out grading and output the fluorescence brightness level and geological characteristics evaluation results of the core samples;

[0161] S62. Fluorescence brightness B is improved by improving the fully connected layer of the deep convolutional neural network model m,n (x,y) and area A m,n Perform feature fusion to generate feature vector F m,n :

[0162] F m,n =αB m,n (x,y)+βA m,n ;

[0163] Among them, α and β are the weight coefficients of brightness and area, respectively, which are used to adjust the relative importance between features. m,n is the fused feature vector;

[0164] S63, the fused feature vector F m,n Passed to the classification layer, the trained semi-supervised model outputs the fluorescence brightness level G m,n :

[0165]

[0166] Among them, G m,n is the predicted fluorescence brightness level, P represents the number of feature maps, and W (p) is the weight matrix, is the pth feature map value, γ is the adjustment coefficient, Indicates the combined area A m,n and brightness B m,n The nonlinear function of (p) is the bias term, σ is the activation function;

[0167] S64, conduct a comprehensive evaluation of geological characteristics, according to the fluorescence brightness level G m,n and the fluorescence area A m,n The calculation results generate the geological characteristics evaluation results R m,n :

[0168]

[0169] Among them, R m,n is the geological characteristic evaluation result, Q is the characteristic dimension, λ is the weight attenuation factor, Represents the gradient change of area in the feature dimension, represents the influence function of brightness level, λ is the control factor, Ψ(G m,n ,A m,n ) represents the comprehensive influence of brightness and area.

[0170] S65, the fluorescence brightness level of the core G m,n and geological characteristics evaluation results R m,n The output is the final result, which is used for resource assessment and geological analysis.

[0171] Embodiment 1:

[0172] Example 1 is applied to a large oil field in western China. On September 10, 2023, during resource exploration in a large oil field in western China, the geological team analyzed the fluorescence brightness and area of ​​500 core samples collected. The core samples were obtained from a reservoir at a depth of 3,500 meters by drilling equipment. The fluorescence brightness of the core samples in the reservoir varies complexly, and there is background noise interference. It is difficult for traditional methods to accurately segment the fluorescent area. The geological engineer team decided to use the core fluorescence brightness and area calculation and grading method based on deep learning of the present invention to automatically analyze these core samples.

[0173] On September 12, 2023, the geological team used high-precision imaging equipment to image the fluorescent area of ​​the core at different angles and resolutions, and generated an initial core fluorescence image data set. Through the multi-angle rotation function of the imaging equipment, the geological team took fluorescence images of each core under different lighting conditions. The data volume reached 1TB, and a preliminary labeled data set and an unlabeled data set were formed. Geological engineers found that the brightness changes in the fluorescent area of ​​the core were more obvious, but due to the presence of background noise, traditional image processing tools found it difficult to accurately identify the fluorescent area.

[0174] In order to improve the accuracy of recognition, the team decided to input the data into the deep convolutional neural network model of the present invention for analysis. By preprocessing the images of 500 core samples, the system removed the background noise and performed contrast enhancement. The boundaries of the fluorescent area became clearer. The preprocessed core fluorescence images were input into the improved deep convolutional neural network model, which used the attention mechanism guided by the fluorescent area to successfully identify and segment the fluorescent area in the core.

[0175] On September 15, 2023, the system generated pseudo-label data for 100 unlabeled core samples. The pseudo-labels were generated using consistency regularization technology to ensure that the calculation results of fluorescence brightness and area remained consistent under different image enhancement conditions. The generated pseudo-label data were fused with the labeled data set, and the expanded data set reached 1,200 core samples.

[0176] On the same day, the geological team used the expanded data set to train the semi-supervised deep learning model. The training was conducted on the GPU cluster in the oil field data center. The entire training process took 36 hours. After the training was completed, the system began to automatically calculate and grade the fluorescence brightness and area of ​​300 newly collected core samples.

[0177] At 9:00 am on September 17, the team selected a core sample numbered "XY00123" for detailed analysis. The fluorescence image of this sample showed a significant brightness area, but there was also obvious noise interference in the background area. Through the improved convolutional neural network model processing, the fluorescence area was accurately segmented, and the total fluorescence brightness was calculated to be 3500cd / m 2 , the fluorescent area is 200mm 2 The system further inputs these calculation results into the semi-supervised learning model for grading and generates a report with a fluorescence brightness level of "A2", indicating that the core sample has moderate oil and gas content.

[0178] On September 18, the geological team graded the core sample numbered "XY00456". The fluorescence area of ​​the sample was relatively complex and the brightness range was large. Through automated calculation, the system concluded that the fluorescence brightness of the sample was 4900cd / m 2 , area is 310mm 2 , and was finally graded as “A1”, indicating that it has a high oil and gas content.

[0179] To verify the effectiveness of the method of the present invention, the geological team compared the fluorescence brightness and area grading results of 50 core samples with those of the traditional manual method. The following Table 1 is the comparison data:

[0180] Table 1 Local comparison data of the present invention and the traditional manual method

[0181]

[0182] From the data in Table 1 above, it can be seen that the average grading time for each sample by the traditional method is 27.7 minutes, while the grading time of the method of the present invention is only 1.23 minutes, which greatly improves the efficiency. In addition, the grading consistency of the traditional manual method is 77.7%, while the grading consistency of the method of the present invention reaches 97%. The calculation errors of fluorescence brightness and area are reduced to ±1.5% and ±2%, respectively, which are much lower than the error range of the traditional method. Taking the sample number "XY00456" as an example, the error of fluorescence brightness obtained by the traditional manual method is ±10%, while the error of the method of the present invention is only ±2%. In area calculation, the error of the traditional method is ±14%, while the error of the method of the present invention is only ±2.5%. These data clearly show the significant improvement of the accuracy of fluorescence brightness and area calculation of the present invention.

[0183] The application of this invention shows a very high level of automation and data processing capabilities in the oilfield exploration process, especially when processing complex fluorescent images, it can quickly and accurately segment the fluorescent area and generate reliable fluorescence brightness and area calculation results. Through the application of semi-supervised deep learning models, the system can generate high-quality pseudo-labels in the case of a large amount of unlabeled data, significantly improving data utilization and grading accuracy. The successful application of this oilfield project provides reliable technical support for the geological team in the subsequent exploration process, greatly improving the efficiency and accuracy of resource assessment.

[0184] Through the simulation process of this embodiment, we can see the strong application potential of the present invention in large-scale core analysis. It solves the problems of large subjective judgment errors, poor grading consistency and low processing efficiency in traditional methods, and provides a strong technical guarantee for reservoir evaluation in oil and gas exploration.

[0185] The present invention introduces an improved deep convolutional neural network model. In the process of calculating the fluorescence brightness and area, a pixel-by-pixel refined calculation method is adopted to accurately identify the fluorescent area under a complex background. The attention mechanism is used to guide the convolution operation. The improved deep convolutional neural network model can perform weighted processing on the fluorescent area with obvious brightness changes, ignore background interference, and ensure the accuracy and consistency of the calculation. Compared with the traditional image segmentation algorithm, the method can better process the core images with unclear brightness contrast, significantly improve the quantization accuracy of the fluorescence brightness and area, and ensure the robustness in different complex scenes.

[0186] The present invention adopts a semi-supervised deep learning model and generates pseudo labels through consistency regularization technology, so that the semi-supervised deep learning model can make full use of unlabeled data during training. Different from traditional fully supervised learning that relies on large-scale labeled data, the present invention ensures the consistency of prediction results under different enhancement conditions by constraining the enhanced version of the unlabeled data, thereby generating high-quality pseudo labels, greatly improving data utilization and reducing dependence on manually labeled data. This pseudo-label generation strategy can improve the generalization ability of the model and still maintain a high grading accuracy when processing unlabeled core data.

[0187] The present invention introduces a feature fusion mechanism to dynamically fuse the calculation results of fluorescence brightness and fluorescence area, and combines a semi-supervised learning model to grade the geological characteristics of core samples. Through the feature weighting mechanism, the importance of brightness and area in geological characteristic evaluation can be adaptively adjusted, and these features are deeply fused using nonlinear functions to generate more accurate core fluorescence brightness levels and geological characteristic evaluation results. Different from the traditional fixed threshold division method, the present invention can flexibly divide and evaluate the grades according to the diversity of core samples, so that a more reliable basis for geological analysis can be provided in practical applications.

[0188] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A deep learning-based core fluorescence brightness and area calculation grading method, characterized in that: The steps include: S1. Use imaging equipment to obtain core fluorescence images with multiple angles and resolutions to form labeled fluorescence area datasets and unlabeled fluorescence area datasets; S2, preprocessing the core fluorescence images in the labeled fluorescence area dataset and the unlabeled fluorescence area dataset; S3. Use the pre-trained improved deep convolutional neural network model to analyze the pre-processed labeled fluorescence area dataset, automatically identify the fluorescence area in the core fluorescence image, and segment and annotate the fluorescence area; S4. Construct a semi-supervised model to predict different enhanced versions of the same unlabeled core fluorescence image for the unlabeled fluorescence area dataset using the consistency regularization technology, apply consistency loss so that the prediction results of the core fluorescence image under different enhancement conditions remain consistent, generate pseudo labels for the unlabeled data, and merge the pseudo label data with the labeled fluorescence area dataset to form an extended dataset; S5. Based on the annotated data and pseudo-labeled data in the extended dataset, the brightness and area of ​​the fluorescent area are quantitatively calculated using the improved deep convolutional neural network model; S6. Input the calculated results of fluorescence brightness and area into the trained semi-supervised deep learning model, grade the extended data set through the semi-supervised deep learning model, and output the fluorescence brightness level and geological characteristic evaluation results of the core; S7. Store the grading results and related calculation data in a database, and generate an analysis report, which includes information on fluorescence brightness, area and grading results.

2. The method for grading core fluorescence brightness and area calculation based on deep learning according to claim 1 is characterized in that: The S1 step specifically includes: S11. Using a multi-angle rotating imaging device to obtain a core fluorescence image, the multi-angle rotating imaging device captures the core fluorescence images of the core at different angles and integrates them into a core fluorescence image set I m,n , m represents the imaging angle of the core fluorescence image, and n represents the core fluorescence images with different resolutions; S12. Annotate the acquired core fluorescence image set to form an annotated fluorescence region data set: D l ={(I m,n ,L m,n )|m∈M,n∈N}; Among them, I m,n is the core fluorescence image, L m,n is the label for the fluorescent area corresponding to the core fluorescence image, M is the imaging angle set of the core fluorescence image, and N is the resolution set; S13. Based on the acquired core fluorescence image set, construct an unlabeled fluorescence area dataset: D u ={I m,n |m∈M,n∈N}。 3. The method for grading core fluorescence brightness and area calculation based on deep learning according to claim 1 is characterized in that: The S2 step specifically includes: S21. Use Gaussian filtering algorithm to annotate the fluorescence area dataset D l and the unlabeled fluorescence region dataset D u The core fluorescence images in are denoised to obtain the denoised core fluorescence image set I′ m,n ; S22, the denoised core fluorescence image set I′ m,n The brightness and contrast are adjusted, and the contrast of the core fluorescence image is optimized using the histogram equalization method, so that the fluorescence area in the core fluorescence image is clear. The adjusted core fluorescence image set is: I″ m,n =HE(I′ m,n ); Among them, HE represents the histogram equalization operation, I″ m,n represents a set of core fluorescence images after brightness and contrast adjustment; S23, adjusting the core fluorescence image set I″ m,n Perform image enhancement processing to obtain the enhanced core fluorescence image set I″′ m,n ; S24. Output the preprocessed labeled fluorescence area dataset: D′ l ={(I″′ m,n ,L m,n )|m∈M,n∈N}; Output the preprocessed unlabeled fluorescence area dataset: D′ u ={I″′ m,n |m∈M,n∈N}。 4. The method for grading core fluorescence brightness and area calculation based on deep learning according to claim 1 is characterized in that: The S3 step specifically includes: S31. Analyze the preprocessed labeled fluorescence area dataset using the pre-trained improved deep convolutional neural network model. The improved deep convolutional neural network model includes a fluorescence area guided attention mechanism module. The attention mechanism module generates a weight matrix A by identifying the area with concentrated fluorescence brightness in the core fluorescence image. m,n , the weight matrix emphasizes the areas with large changes in fluorescence brightness, so that the convolution operation focuses on the fluorescent area and ignores background interference: Among them, A m,n (x,y) represents the weight value at the coordinate (x,y), I″′ m,n (x, y) is the brightness value of the core fluorescence image after preprocessing, G(x, y) is the local gradient of the fluorescence area, Z m,n is the normalization factor, the sum of all weights in the fluorescent area is 1; S32, the core fluorescence image I″′ m,n Input the improved deep convolutional neural network model to extract preliminary feature representation F m,n : Among them, F m,n (x,y) represents the eigenvalue at the coordinate (x,y), is the weight of convolution kernel k, b k is the bias term; S33. The attention mechanism module guided by the fluorescent region is used to represent the preliminary feature F. m,n Perform weighted processing to generate weighted feature map F′ m,n : F′ m,n (x,y)=A m,n (x,y)·F m,n (x,y); Among them, A m,n (x,y) is the fluorescence area weight, F m,n (x, y) is the preliminary feature representation, F′ m,n (x, y) represents the eigenvalue after weighting by the weight matrix; S34, based on weighted feature map F′ m,n By improving the segmentation module in the deep convolutional neural network model, the fluorescent area is segmented using a pixel-level segmentation algorithm to generate a segmented fluorescent area image S m,n , the segmentation process is optimized by the energy minimization function: E(S m,n )=∑ x,y ((S m,n (x,y)-F′ m,n (x,y)) 2 +λ∑ (x′,y′)∈N(x,y) |S m,n (x,y)-S m,n (x′,y′)|); Among them, S m,n (x, y) represents the value of the segmented fluorescence area image at the coordinate (x, y), F′ m,n (x, y) is the weighted feature representation, N(x, y) is the neighborhood pixel set, and λ is the smoothness parameter; S35, the segmented fluorescence area image S m,n Perform annotation and match the segmentation results with the annotated fluorescence area dataset to generate an annotated fluorescence area dataset: Among them, δ(S m,n (x, y), T) is the decision function, which divides the segmented fluorescence area S according to the set threshold T. m,n Marking, L m,n is the generated annotation label matrix, D″ l This is the final annotated fluorescence area dataset.

5. The method for grading core fluorescence brightness and area calculation based on deep learning according to claim 1 is characterized in that: The S4 step specifically includes: S41. Construct a semi-supervised deep learning model to annotate the fluorescence area dataset D″ l and the unlabeled fluorescence region dataset D′ u The semi-supervised model includes a main network and an auxiliary network for training. The main network is used to classify and segment the input core fluorescence image, and the auxiliary network is used for consistency regularization. S42. For the unlabeled fluorescence region dataset D′ u Core fluorescence image I″′ m,n , generate multiple different enhanced versions Each enhanced version is generated by different core fluorescence image transformation operations, including rotation, scaling and noise addition; S43, input the enhanced core fluorescence image version into the main network of the semi-supervised model for prediction, and obtain the corresponding prediction results Represents the prediction result of the i-th enhanced version: in, represents the predicted output of the i-th enhanced version at the coordinate (x, y); f θ is the activation function of the main network, and the parameter θ represents the weight and bias of the network; is the weight of the convolution kernel applied to the pixel position (p,q) in the i-th enhanced version; I″′ i (xp,yq) m,n is the input image value of the pixel position (xp, yq) in the i-th enhanced version; S44, in the consistency regularization process, by applying the consistency loss function L cons To keep the prediction results of different enhanced versions consistent, the loss function is defined as: in, represents the average prediction result of all enhanced versions, k is the number of enhanced versions; S45. Generate pseudo labels for unlabeled fluorescence region datasets based on the prediction results after consistency regularization Among them, arg max is a function that selects the maximum value in the prediction result, which is used to determine the category of the pseudo label; S46. Generate pseudo-label dataset and the labeled fluorescence region dataset D″ l Merge to form an extended data set:

6. The method for grading core fluorescence brightness and area calculation based on deep learning according to claim 1 is characterized in that: The S5 step specifically includes: S51, based on the labeled fluorescence area dataset D″ l and pseudo-labeled data The brightness B of the core fluorescence area is analyzed using an improved convolutional neural network model. m,n (x,y) and area A m,n Quantitative calculation, brightness B m,n (x,y) is the brightness value at the pixel point (x,y), area A m,n is the total number of pixels in the fluorescent area; S52, using the improved convolutional neural network model to input the core fluorescence image I″′ m,n Perform pixel-by-pixel calculations to obtain the brightness prediction value of each pixel in, is the weight of the convolution kernel for brightness prediction, b b is the bias term, is the brightness prediction value at pixel (x, y); S53, the brightness value B of each fluorescent area m,n (x, y) is accumulated to calculate the total brightness of the fluorescent area in, It represents the total brightness value of the fluorescent area in the core fluorescence image; S54, calculating the area A according to the total number of pixels in the fluorescent area m,n : in, is the indicator function, when the pixel brightness When it exceeds the set threshold T, it is recorded as 1, otherwise it is recorded as 0, which is used to count the total area of ​​the fluorescent area.

7. The method for grading core fluorescence brightness and area calculation based on deep learning according to claim 1 is characterized in that: The S6 step specifically includes: S61, the fluorescence brightness B m,n (x,y) and fluorescence area A m,n The calculation results are input into the trained semi-supervised deep learning model, and the semi-supervised deep learning model is used to expand the dataset D ext Carry out grading and output the fluorescence brightness level and geological characteristics evaluation results of the core samples; S62. Fluorescence brightness B is improved by improving the fully connected layer of the deep convolutional neural network model m,n (x,y) and area A m,n Perform feature fusion to generate feature vector F m,n : F m,n =αB m,n (x,y)+βA m,n ; Among them, α and β are the weight coefficients of brightness and area, respectively, which are used to adjust the relative importance between features. m,n is the fused feature vector; S63, the fused feature vector F m,n Passed to the classification layer, the trained semi-supervised model outputs the fluorescence brightness level G m,n : Among them, G m,n is the predicted fluorescence brightness level, P represents the number of feature maps, and W (p) is the weight matrix, is the pth feature map value, γ is the adjustment coefficient, Indicates the combined area A m,n and brightness B m,n The nonlinear function of (p) is the bias term, σ is the activation function; S64, conduct a comprehensive evaluation of geological characteristics, according to the fluorescence brightness level G m,n and the fluorescence area A m,n The calculation results generate the geological characteristics evaluation results R m,n : Among them, R m,n is the geological characteristic evaluation result, Q is the characteristic dimension, λ is the weight attenuation factor, Represents the gradient change of area in the feature dimension, represents the influence function of brightness level, λ is the control factor, Ψ(G m,n ,A m,n ) represents the comprehensive influence of brightness and area. S65, the fluorescence brightness level of the core G m,n and geological characteristics evaluation results R m,n The output is the final result, which is used for resource assessment and geological analysis.

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