A method for lithologic analysis of debris based on multi-level fusion image algorithm and X-ray diffraction

Abstract

The present invention discloses a rock fragment lithology analysis method based on a multi-level fusion image algorithm and X-ray diffraction. The method includes: S1: acquiring rock fragment image data at different resolutions; S2: generating a multi-level rock fragment image feature matrix; S3: preliminarily generating a lithology classification result; S4: generating supplementary data for X-ray diffraction analysis; S5: fusing the supplementary lithology analysis data with the preliminarily generated lithology classification result to ultimately generate a comprehensive lithology classification result; S6: inputting the comprehensive lithology classification result into a dynamic adaptive analysis system, which adaptively adjusts the analysis strategy based on the complexity of the rock fragment sample and optimizes the parameters of the dual-path convolutional neural network model and multimodal analysis algorithm through real-time feedback and learning mechanisms; S7: outputting an analysis report containing mineral distribution, crystal structure, and comprehensive lithology classification results. The present invention improves the classification accuracy of different types of rock fragment samples by approximately 15%.

Description

Technical Field

[0001] The present invention relates to the field of rock cuttings technology, and in particular to a rock cuttings lithology analysis method based on a multi-level fusion image algorithm and X-ray diffraction. Background Art

[0002] In existing technologies, rock chip lithology analysis mainly relies on a single image processing technology or X-ray diffraction technology. Traditional image processing technology mainly classifies rock chip samples based on the texture and morphological characteristics of two-dimensional images. Although it can identify the macroscopic characteristics of rock chips to a certain extent, it is difficult to accurately distinguish different mineral components and their crystal structures when processing complex multi-component samples. In addition, image processing technology has technical bottlenecks in resolution improvement and noise control, and is easily affected by image quality, resulting in inaccurate classification results.

[0003] On the other hand, X-ray diffraction technology is often used to analyze the crystal structure and mineral composition of rock cuttings. By calculating diffraction intensity data and interplanar spacing, XRD can identify the mineral composition in the sample and provide relatively accurate crystal structure information. However, XRD technology is limited to providing mineral composition and structural information of rock samples and cannot generate intuitive results of lithology classification in the absence of image information. In addition, XRD technology has a slow sample processing speed and cannot meet the needs of rapid analysis of large-scale samples. In complex geological exploration projects, traditional XRD analysis is time-consuming and difficult to provide real-time feedback on analysis results.

[0004] The main shortcomings or problems of existing technologies include the following: First, single image processing techniques cannot simultaneously account for both the macroscopic morphology and microscopic mineral composition of rock chip samples. In particular, subtle differences in complex rock chip samples are easily overlooked, resulting in insufficient analytical accuracy. Second, while X-ray diffraction technology can provide information on mineral composition and crystal structure, it is disconnected from image processing techniques and lacks a complete description of overall lithologic characteristics. Furthermore, existing technologies lack the ability to automate the processing and real-time analysis of large-scale samples. Traditional techniques struggle to achieve rapid analysis while maintaining high accuracy, hindering the efficiency of decision-making in geological exploration projects. Summary of the Invention

[0005] One purpose of the present invention is to propose a rock chip lithology analysis method based on a hierarchical fusion image algorithm and X-ray diffraction. The present invention improves the classification accuracy of different types of rock chip samples by about 15%.

[0006] According to an embodiment of the present invention, a method for analyzing rock debris lithology based on a hierarchical fusion image algorithm and X-ray diffraction includes the following steps:

[0007] S1. Pre-process rock cuttings samples to remove impurities and contaminants, and perform multispectral image acquisition to obtain rock cuttings image data at different resolutions;

[0008] S2. Use multi-scale image processing technology to process rock chip image data of different resolutions, identify the macroscopic lithologic characteristics of rock chip samples at low resolution, identify the microscopic structure and texture characteristics at high resolution, and generate a multi-level rock chip image feature matrix;

[0009] S3. Perform deep feature extraction on the multi-level rock cuttings image feature matrix using a dual-path convolutional neural network model, automatically learn the image features in the rock cuttings samples, and preliminarily generate lithology classification results.

[0010] S4. Use X-ray diffraction technology to perform crystal structure analysis on rock chip samples, obtain diffraction patterns of samples, identify mineral composition and crystal structure information of rock chip samples, and generate supplementary data for lithology analysis;

[0011] S5. Fusion of the supplementary data from lithologic analysis and the preliminary generated lithologic classification results, and finally generation of comprehensive lithologic classification results through comparison, optimization, and refinement of multimodal analysis algorithms;

[0012] S6. Input the comprehensive lithologic classification results into a dynamic adaptive analysis system, which adaptively adjusts the analysis strategy based on the complexity of the cuttings sample and optimizes the parameters of the dual-path convolutional neural network model and multimodal analysis algorithm through real-time feedback and learning mechanisms;

[0013] S7. Output an analysis report containing mineral distribution, crystal structure and comprehensive lithology classification results. The analysis report includes a three-dimensional mineral distribution map, XRD spectrum interpretation and image analysis flow chart.

[0014] Optionally, S1 includes the following specific steps:

[0015] S11, performing physical pretreatment on the collected rock cuttings sample, using screening equipment to remove particles with a particle size smaller than d1 and larger than d2, where d1 is the minimum allowable diameter of the rock cuttings particle, and d2 is the maximum allowable diameter of the rock cuttings particle. The values ​​of d1 and d2 are set according to the initial particle size distribution of the rock cuttings sample;

[0016] S12, cleaning and drying the pretreated rock cuttings sample to remove impurities and moisture attached to the sample surface;

[0017] S13, use multispectral imaging equipment to collect images of rock cuttings samples, obtain rock cuttings image data at different resolutions, and n Corresponding to r1 to r n The rock chip image data is collected at the resolution level of 100000;

[0018] S14. The collected rock cuttings image data is stored in the form of a three-dimensional matrix T (λ, r, x, y):

[0019]

[0020] Among them, I i (λ j ,r k ,x,y) represents the spectral band λ j , resolution r k And rock chip image data at a spatial position (x, y), where x and y represent the spatial coordinates of the rock chip image data.

[0021] Optionally, S2 includes the following specific steps:

[0022] S21, multi-scale processing of rock chip image data with different resolutions, r low Preprocess the rock cuttings image data to reduce the noise of the rock cuttings image data and obtain the macroscopic lithologic characteristics of the rock cuttings sample F macro :

[0023]

[0024] Among them, I(x′,y′,r low ) indicates that at low resolution r low The original rock chip image pixel value under G(xx′,yy′,σ macro ) is the Gaussian kernel function, and the kernel width is σ macro , represents the scale of the macroscopic features, and the integration range is the macroscopic area Ω of the cuttings image macro ;

[0025] S22, using the image processing method based on wavelet transform to analyze the high resolution r high The cuttings image data is decomposed at multiple scales to extract the microscopic lithologic structure characteristics of the cuttings image. micro :

[0026]

[0027] Among them, I(x+m,y+n,r high ) represents the pixel value of the rock chip image at high resolution, is the wavelet basis function, the scale factor is s, M and N are the spatial window sizes of the wavelet transform;

[0028] S23, the microscopic lithologic structural characteristics F micro and macroscopic lithologic characteristics F macro Fusion is performed to generate a multi-level cuttings image feature matrix T feature (x,y):

[0029]

[0030] Among them, F macro (x,y) represents the macroscopic lithologic characteristics at the spatial position (x,y), F micro (x,y) represents the microscopic lithologic structural characteristics at the same spatial position.

[0031] Optionally, S3 includes the following specific steps:

[0032] S31, multi-level cuttings image feature matrix T feature (x, y) is input into the dual-path convolutional neural network model, which includes two independent feature extraction paths: the spatial feature path and the texture feature path for processing different features of rock cuttings samples;

[0033] S32. Use multiple convolutional layers in the spatial feature path to extract features of the overall shape, structure, and contour of the rock cuttings sample:

[0034]

[0035] in, Represents the convolution output feature map of the l-th layer spatial feature path, is the convolution kernel weight in the l-th layer spatial feature path, is the bias term, k is the convolution kernel size;

[0036] S33. After each convolution layer in the spatial feature path, the ReLU activation function is used to perform nonlinear processing on the convolution result, and the pooling layer is used to reduce the dimensionality of the spatial features:

[0037]

[0038] in, is the size of the pooling window, and the overall shape, structure, and contour information of the rock cuttings sample are preserved through the pooling operation;

[0039] S34. In the texture feature path, multiple convolutional layers are used to extract features of the subtle texture and microstructure of the rock cuttings sample:

[0040]

[0041] in, Represents the convolution output feature map of the texture feature path of the lth layer, is the convolution kernel weight in the texture feature path of the lth layer, is the bias term;

[0042] S35. After each convolution layer in the texture feature path, the convolution result is processed nonlinearly using the ReLU activation function, and the texture feature is reduced in dimension using the pooling layer:

[0043]

[0044] Preserve subtle texture and microstructural information in rock chip samples through pooling operations;

[0045] S36, the feature map F extracted from the spatial feature path and the texture feature path spatial_pool ( x,y ) and F texture_pool ( x,y ) Perform fusion to generate comprehensive feature matrix T combined ( x,y ) :

[0046] T combined ( x,y ) =α·F spatial_pool ( x,y ) +β·F texture_pool ( x,y ) ;

[0047] Among them, α and β are fusion coefficients, which represent the weights of spatial features and texture features;

[0048] S37, the integrated feature matrix T combined (x,y) is input to the fully connected layer, and the Softmax classifier is used to classify the rock cuttings samples:

[0049]

[0050] Among them, P(C k ∣T combined ) represents the probability that the sample belongs to the kth lithology, W k and W j is the weight of the classifier, and K is the number of lithology classifications.

[0051] Optionally, S4 includes the following specific steps:

[0052] S41, placing the pretreated rock cuttings sample in an X-ray diffractometer, irradiating the sample with X-rays and obtaining diffraction intensity data I(θ), where θ represents the diffraction angle;

[0053] S42. Identify the types and contents of various mineral components in the rock chip sample by analyzing the diffraction intensity data I(θ) and position, and generate a mineral composition analysis table C. mineral (x,y):

[0054]

[0055] Among them, I i (θ) is the diffraction angle θ of the i-th mineral component i The diffraction intensity under σ i is the diffraction peak width, Z is the normalization factor used to make the percentage of the total mineral components equal to 1, Indicates that the diffraction angle range θ min to θ max The total diffraction intensity within, f mineral (x,y,θ i ) is the diffraction angle θ i The characteristic function of the relevant mineral composition describes the distribution characteristics of the mineral at the spatial position (x, y);

[0056] S43. Constructing a mineral crystal structure model based on changes in diffraction intensity and diffraction angle crystal (x,y), the mineral crystal structure model is used to describe the crystal structure characteristics of different mineral components:

[0057]

[0058] Among them, V k is the unit cell volume of the kth mineral, F hkl is the structure factor of the crystal plane family, G hkl is the reciprocal vector of the crystal plane family, describing the lattice periodicity, r(x,y) represents the atomic position vector at the spatial position (x,y), D hkl (T) is the temperature factor of the crystal, which indicates the effect of temperature on the diffraction intensity;

[0059] S44, mineral composition analysis table C mineral (x,y) and mineral crystal structure model S crystal (x,y) are fused to generate supplementary data D for lithologic analysis supplement ( x,y ) :

[0060] D supplement ( x,y ) =α1·C mineral ( x,y ) +β1·S crystal ( x,y );

[0061] Among them, α1 and β1 are weight coefficients, which are used to balance the weights of the mineral composition analysis table and the mineral crystal structure model in the supplementary data.

[0062] Optionally, S5 includes the following specific steps:

[0063] S51. Supplementary data D for lithologic analysis supplement ( x,y ) Compared with the preliminary lithologic classification results P(C k ∣T combined ) to fuse and obtain the initial fused feature matrix T fused ( x,y):

[0064] T fused ( x,y ) =α2∈P ( C k ∣T combined ) +β2·D supplement ( x,y ) ;

[0065] Among them, α2 and β2 are weight coefficients, which represent the weights of the preliminary lithologic classification results and the supplementary lithologic analysis data in the fusion process;

[0066] S52, the initial fusion feature matrix T fused (x, y) performs multimodal analysis and calculates the similarity measure S between the preliminary classification results and the supplementary data similarity ( x,y ) :

[0067] S similarity (x,y)=exp(-(P(C k ∣T combined )-D supplement (x,y)) 2 );

[0068] The closer the similarity measure is to 1, the higher the consistency between the preliminary classification results and the supplementary data;

[0069] S53, according to the similarity measure S similarity (x,y) The fused feature matrix T fused (x, y) is optimized and the weight coefficients α2 and β2 are adjusted by the back propagation algorithm to maximize the similarity measure:

[0070]

[0071] Where η is the learning rate, and is the updated weight coefficient;

[0072] S54, the optimized fusion feature matrix T fused (x,y) is input into the classification model, and the Softmax classifier is used to perform the final refinement classification of the lithology category:

[0073]

[0074] Among them, P final (C k ∣T fused_opt ) represents the probability that the sample belongs to the kth lithology under the final fusion feature matrix.

[0075] Optionally, S7 includes the following specific steps:

[0076] S71. Integrate the comprehensive lithology classification results with the mineral composition analysis table and the mineral crystal structure model to generate an analysis report data set;

[0077] S72. Draw a three-dimensional mineral distribution map based on the mineral composition analysis table, where the three-dimensional mineral distribution map represents the distribution of minerals at spatial positions (x, y, z);

[0078] S73. Interpret the diffraction intensity data to generate a spectrum interpretation result including diffraction peak position, peak intensity and mineral composition.

[0079] The beneficial effects of the present invention are:

[0080] (1) The present invention adopts multi-scale image processing technology to capture the macroscopic lithologic features of rock cuttings samples at low resolution and identify their microstructure and texture at high resolution. The dual-path convolutional neural network model performs deep feature extraction on feature matrices at different levels to automatically learn the complex rock cuttings image features in the sample and generate preliminary lithologic classification results. Compared with traditional single image processing technology, the present invention can identify subtle differences in rock cuttings at a wider scale, especially in complex samples, showing higher accuracy. Experimental results show that the present invention improves the classification accuracy of different types of rock cuttings samples by about 15%.

[0081] (2) The present invention innovatively integrates the mineral composition and crystal structure information obtained by X-ray diffraction technology with the lithologic classification results generated by the image processing algorithm. Through optimization by a multimodal analysis algorithm, XRD technology can accurately identify the mineral composition of rock cuttings samples, while the image algorithm can provide detailed macro- and microstructural information. The combination of the two greatly improves the accuracy and robustness of lithologic analysis. Through comparative analysis and optimization, the problem of insufficient classification accuracy in a single technology can be effectively solved. The lithologic classification results finally generated show obvious advantages in refining mineral composition and structural characteristics.

[0082] (3) The present invention can quickly process a large number of rock cuttings samples through automated image processing and XRD analysis processes, and generate high-precision lithology classification reports in a relatively short period of time. The automatic learning of the dual-path convolutional neural network model and the back-propagation adjustment system of the optimization algorithm can adaptively adjust the analysis parameters to ensure the consistency and efficiency of the classification results. Compared with traditional manual analysis methods, the present invention greatly shortens the analysis time and increases the analysis speed by about 30% in large-scale geological exploration projects while maintaining a high classification accuracy, which can provide more timely decision support for the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] 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:

[0084] Figure 1 This is a flow chart of a cuttings lithology analysis method based on a multi-level fusion image algorithm and X-ray diffraction proposed by the present invention;

[0085] Figure 2 This is a schematic diagram of the dual-path convolutional neural network model used in the rock debris lithology analysis method based on a multi-level fusion image algorithm and X-ray diffraction proposed in the present invention for deep feature extraction of multi-level image features. DETAILED DESCRIPTION

[0086] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0087] refer to Figure 1-2 A rock fragment lithology analysis method based on hierarchical fusion image algorithm and X-ray diffraction includes the following steps:

[0088] S1. Pre-process rock cuttings samples to remove impurities and contaminants, and perform multispectral image acquisition to obtain rock cuttings image data at different resolutions;

[0089] S2. Use multi-scale image processing technology to process rock chip image data of different resolutions, identify the macroscopic lithologic characteristics of rock chip samples at low resolution, identify the microscopic structure and texture characteristics at high resolution, and generate a multi-level rock chip image feature matrix;

[0090] S3. Perform deep feature extraction on the multi-level rock cuttings image feature matrix using a dual-path convolutional neural network model, automatically learn the image features in the rock cuttings samples, and preliminarily generate lithology classification results.

[0091] S4. Use X-ray diffraction technology to perform crystal structure analysis on rock chip samples, obtain diffraction patterns of samples, identify mineral composition and crystal structure information of rock chip samples, and generate supplementary data for lithology analysis;

[0092] S5. Fusion of the supplementary data from lithologic analysis and the preliminary generated lithologic classification results, and comparison, optimization, and refinement through multimodal analysis algorithms to ultimately generate the final lithologic classification results;

[0093] S6. Input the final lithologic classification results into the dynamic adaptive analysis system, which adaptively adjusts the analysis strategy based on the complexity of the rock cuttings sample and optimizes the parameters of the dual-path convolutional neural network model and multimodal analysis algorithm through real-time feedback and learning mechanisms;

[0094] S7. Output a comprehensive analysis report including mineral distribution, crystal structure and final lithology classification results. The comprehensive analysis report includes a three-dimensional mineral distribution map, XRD spectrum interpretation and image analysis flow chart.

[0095] In this embodiment, S1 includes the following specific steps:

[0096] S11, performing physical pretreatment on the collected rock cuttings sample, using screening equipment to remove particles with a particle size smaller than d1 and larger than d2, where d1 is the minimum allowable diameter of the rock cuttings particle, and d2 is the maximum allowable diameter of the rock cuttings particle. The values ​​of d1 and d2 are set according to the initial particle size distribution of the rock cuttings sample;

[0097] S12, cleaning and drying the pretreated rock cuttings sample to remove impurities and moisture attached to the sample surface;

[0098] S13, use multispectral imaging equipment to collect images of rock cuttings samples, obtain rock cuttings image data at different resolutions, and n Corresponding to r1 to r n The rock chip image data is collected at the resolution level of 100000;

[0099] S14. The collected rock cuttings image data is stored in the form of a three-dimensional matrix T (λ, r, x, y):

[0100]

[0101] Among them, I i (λ j ,r k ,x,y) represents the spectral band λ j , resolution r k And rock chip image data at a spatial position (x, y), where x and y represent the spatial coordinates of the rock chip image data.

[0102] In this embodiment, S2 includes the following specific steps:

[0103] S21, multi-scale processing of rock chip image data with different resolutions, r low Preprocess the rock cuttings image data to reduce the noise of the rock cuttings image data and obtain the macroscopic lithologic characteristics of the rock cuttings sample F macro :

[0104]

[0105] Among them, I(x′,y′,r low ) indicates that at low resolution r low The original rock chip image pixel value under G(xx′,yy′,σ macro ) is the Gaussian kernel function, and the kernel width is σ macro , represents the scale of the macroscopic features, and the integration range is the macroscopic area Ω of the cuttings image macro ;

[0106] S22, using the image processing method based on wavelet transform to analyze the high resolution r high The cuttings image data is decomposed at multiple scales to extract the microscopic lithologic structure characteristics of the cuttings image. micro :

[0107]

[0108] Among them, I(x+m,y+n,r high ) represents the pixel value of the rock chip image at high resolution, is the wavelet basis function, the scale factor is s, M and N are the spatial window sizes of the wavelet transform;

[0109] S23, the microscopic lithologic structural characteristics F micro and macroscopic lithologic characteristics F macro Fusion is performed to generate a multi-level cuttings image feature matrix T feature (x,y):

[0110]

[0111] Among them, F macro (x,y) represents the macroscopic lithologic characteristics at the spatial position (x,y), F micro (x,y) represents the microscopic lithologic structural characteristics at the same spatial position.

[0112] In this embodiment, S3 includes the following specific steps:

[0113] S31, multi-level cuttings image feature matrix T feature ( x,y) is input into the dual-path convolutional neural network model, which includes two independent feature extraction paths, namely the spatial feature path and the texture feature path for processing different features of rock cuttings samples;

[0114] S32. Use multiple convolutional layers in the spatial feature path to extract features of the overall shape, structure, and contour of the rock cuttings sample:

[0115]

[0116] in, Represents the convolution output feature map of the l-th layer spatial feature path, is the convolution kernel weight in the l-th layer spatial feature path, is the bias term, k is the convolution kernel size;

[0117] S33. After each convolution layer in the spatial feature path, the ReLU activation function is used to perform nonlinear processing on the convolution result, and the pooling layer is used to reduce the dimensionality of the spatial features:

[0118]

[0119] in, is the size of the pooling window, and the overall shape, structure, and contour information of the rock cuttings sample are preserved through the pooling operation;

[0120] S34. In the texture feature path, multiple convolutional layers are used to extract features of the subtle texture and microstructure of the rock cuttings sample:

[0121]

[0122] in, Represents the convolution output feature map of the texture feature path of the lth layer, is the convolution kernel weight in the texture feature path of the lth layer, is the bias term;

[0123] S35. After each convolution layer in the texture feature path, the convolution result is processed nonlinearly using the ReLU activation function, and the texture feature is reduced in dimension using the pooling layer:

[0124]

[0125] Preserve subtle texture and microstructural information in rock chip samples through pooling operations;

[0126] S36, the feature map F extracted from the spatial feature path and the texture feature path spatial_pool ( x,y ) and F texture_pool ( x,y ) Perform fusion to generate comprehensive feature matrix T combined ( x,y ) :

[0127] T combined ( x,y ) =α·F spatial_pool ( x,y ) +β·F texture_pool ( x,y ) ;

[0128] Among them, α and β are fusion coefficients, which represent the weights of spatial features and texture features;

[0129] S37, the integrated feature matrix T combined (x,y) is input to the fully connected layer, and the Softmax classifier is used to classify the rock cuttings samples:

[0130]

[0131] Among them, P(C k ∣T combined ) represents the probability that the sample belongs to the kth lithology, W k and W j is the weight of the classifier, and K is the number of lithology classifications.

[0132] In this embodiment, S4 includes the following specific steps:

[0133] S41, placing the pretreated rock cuttings sample in an X-ray diffractometer, irradiating the sample with X-rays and obtaining diffraction intensity data I(θ), where θ represents the diffraction angle;

[0134] S42. Identify the types and contents of various mineral components in the rock chip sample by analyzing the diffraction intensity data I(θ) and position, and generate a mineral composition analysis table C. mineral (x,y):

[0135]

[0136] Among them, I i (θ) is the diffraction angle θ of the i-th mineral component i The diffraction intensity under σ i is the diffraction peak width, Z is the normalization factor used to make the percentage of the total mineral components equal to 1, Indicates that the diffraction angle range θ min to θ max The total diffraction intensity within, f mineral (x,y,θ i ) is the diffraction angle θ i The characteristic function of the relevant mineral composition describes the distribution characteristics of the mineral at the spatial position (x, y);

[0137] S43. Constructing a mineral crystal structure model based on changes in diffraction intensity and diffraction angle crystal (x,y), the mineral crystal structure model is used to describe the crystal structure characteristics of different mineral components:

[0138]

[0139] Among them, V k is the unit cell volume of the kth mineral, F hkl is the structure factor of the crystal plane family, G hkl is the reciprocal vector of the crystal plane family, describing the lattice periodicity, r(x,y) represents the atomic position vector at the spatial position (x,y), D hkl (T) is the temperature factor of the crystal, which indicates the effect of temperature on the diffraction intensity;

[0140] S44, mineral composition analysis table C mineral (x,y) and mineral crystal structure model S crystal (x,y) are fused to generate supplementary data D for lithologic analysis supplement ( x,y ) :

[0141] D supplement ( x,y ) =α1·C mineral ( x,y ) +β1·S crystal ( x,y );

[0142] Among them, α1 and β1 are weight coefficients, which are used to balance the weights of the mineral composition analysis table and the mineral crystal structure model in the supplementary data.

[0143] In this embodiment, S5 includes the following specific steps:

[0144] S51. Supplementary data D for lithologic analysis supplement ( x,y ) Compared with the preliminary lithologic classification results P(C k ∣T combined ) to fuse and obtain the initial fused feature matrix T fused (x,y):

[0145] T fused ( x,y ) =α2·P ( C k ∣T combined ) +β2·D supplement ( x,y ) ;

[0146] Among them, α2 and β2 are weight coefficients, which represent the weights of the preliminary lithologic classification results and the supplementary lithologic analysis data in the fusion process;

[0147] S52, the initial fusion feature matrix T fused (x, y) performs multimodal analysis and calculates the similarity measure S between the preliminary classification results and the supplementary data similarity (x,y):

[0148] S similarity (x,y)=exp(-(P(C k ∣T combined )-D supplement (x,y)) 2 );

[0149] The closer the similarity measure is to 1, the higher the consistency between the preliminary classification results and the supplementary data;

[0150] S53, according to the similarity measure S similarity (x,y) The fused feature matrix T fused (x, y) is optimized and the weight coefficients α2 and β2 are adjusted by the back propagation algorithm to maximize the similarity measure:

[0151]

[0152] Where η is the learning rate, and is the updated weight coefficient;

[0153] S54, the optimized fusion feature matrix T fused (x,y) is input into the classification model, and the Softmax classifier is used to perform the final refinement classification of the lithology category:

[0154]

[0155] Among them, P final (C k ∣T fused_opt ) represents the probability that the sample belongs to the kth lithology under the final fusion feature matrix.

[0156] In this embodiment, S7 includes the following specific steps:

[0157] S71. Integrate the final lithology classification results with the mineral composition analysis table and the mineral crystal structure model to generate a comprehensive analysis report data set;

[0158] S72. Draw a three-dimensional mineral distribution map based on the mineral composition analysis table, where the three-dimensional mineral distribution map represents the distribution of minerals at spatial positions (x, y, z);

[0159] S73. Interpret the diffraction intensity data to generate a spectrum interpretation result including diffraction peak position, peak intensity and mineral composition.

[0160] Example 1:

[0161] In August 2023, a geological exploration company was faced with the problem of analyzing complex rock chip samples while conducting oil and gas exploration in a certain basin. Due to the complex geological structure of the basin, the exploration depth reached 4,000 to 4,500 meters, the rock types were diverse, and the sample composition was complex. Traditional rock chip lithology analysis methods were unable to cope with the complex situation. Therefore, the exploration team decided to use the rock chip lithology analysis method based on the hierarchical fusion image algorithm and X-ray diffraction technology of the present invention to improve the accuracy and efficiency of lithology classification.

[0162] On August 15, 2023, the exploration team collected a batch of rock chip samples from a depth of 4,200 meters, numbered "XJ4200-15". The samples were analyzed by traditional methods. The results showed that the main components of the samples were quartz and feldspar. The analysis time was about 45 minutes. However, in terms of microstructure, the traditional method failed to identify other mineral components that may exist in it. The classification result was "quartz sandstone". When compared with historical data, analysts found that the rock types in the nearby area were mostly complex mixed rocks containing multiple minerals, which made the accuracy of the analysis results questionable.

[0163] The research team then used the method of this invention to further analyze the sample numbered "XJ4200-15." First, they used multispectral imaging equipment to collect image data of the sample, obtaining rock chip images at different resolutions. After preliminary low-resolution processing, the sample showed obvious macroscopic features of quartz and feldspar, but the researchers noticed that in the high-resolution images, some tiny crystalline textures were also present on the sample surface. Using a convolutional neural network to perform deep learning and feature extraction on the image data, the system automatically generated preliminary classification results, indicating that in addition to quartz and feldspar, the sample may also contain small amounts of calcite and dolomite.

[0164] To verify the preliminary results, the research team further used an X-ray diffractometer to analyze the "XJ4200-15" sample. After calculating the diffraction intensity data, the system identified the crystal structure of calcite and dolomite in the sample, with a mineral content of approximately 13% calcite and 5% dolomite. The comprehensive analysis results showed that the sample should be classified as "calcite-containing quartz sandstone", which is more accurate than traditional methods.

[0165] In the analysis of another batch of rock chip samples numbered "XJ4300-25" on August 18, 2023, the traditional method took 50 minutes, and the analysis results showed that the sample was ordinary feldspar sandstone, but no trace minerals in the sample were identified. Using the method of the present invention, the sample was also subjected to image acquisition, convolutional neural network feature extraction, and X-ray diffraction analysis, and it was finally determined that in addition to feldspar and quartz, the sample also contained a small amount of pyrite. The detection of pyrite was not identified in the traditional method, but under the method of the present invention, the microstructure of pyrite was clearly visible through the refinement of high-resolution images, and was further confirmed by X-ray diffraction data.

[0166] Analysis of the two sets of samples demonstrated that the method of the present invention offers significant advantages over conventional methods. Subsequently, on September 10, 2023, the exploration team processed 250 of the 500 samples collected using conventional methods. The average analysis time per sample was 45 minutes, for a total of approximately 18,750 minutes (312.5 hours). The classification accuracy was 78%, with the identification of trace minerals being particularly weak.

[0167] For the remaining 250 samples, the method of the present invention was used for analysis, with an average analysis time of 25 minutes per sample and a total analysis time of approximately 6,250 minutes (approximately 104.2 hours), a 66% reduction in the total analysis time compared to traditional methods. Classification accuracy was increased to 93%, with outstanding performance in identifying trace mineral components and refining classification. The system accurately detected the microscopic mineral structure of various samples, including the composition of trace minerals such as calcite and pyrite. The accurate identification of trace components provides more precise data support for stratigraphic structure analysis in oil and gas exploration.

[0168] In the sample numbered "XJ4500-35", traditional methods failed to identify the dolomite in the sample. However, through the method of the present invention, after high-resolution image processing, the convolutional neural network model automatically extracted the microstructural characteristics of the dolomite. Subsequently, X-ray diffraction technology was used to further confirm that the sample contained approximately 12% dolomite. The researchers were able to accurately identify the sample as "dolomite-containing quartz sandstone". This result greatly improved the accuracy of sample classification and provided important mineral distribution information for subsequent exploration.

[0169] Table 1 Sample analysis and comparison data table

[0170]

[0171] From the data comparison in Table 1 above, it can be seen that the traditional method has a long analysis time and low classification accuracy when processing complex rock cuttings samples, especially in the identification of microscopic mineral components, the performance is not ideal. The method of the present invention not only significantly shortens the analysis time and improves the classification accuracy, but also shows a higher recognition rate in the detection of subtle mineral components.

[0172] Through practical application in oil and gas exploration in a specific basin, the present invention's rock fragment lithology analysis method has demonstrated excellent performance across a variety of complex samples. Combining multi-scale image processing, deep learning algorithms, and X-ray diffraction technology, it achieves efficient and accurate lithology classification, particularly in microstructure identification and multi-mineral composition analysis. Compared to traditional methods, the present invention shortens analysis time by 66%, improves classification accuracy by 15%, and increases micro-mineral identification by 33%. This improvement significantly enhances the efficiency and data accuracy of geological exploration and has broad application prospects.

[0173] The present invention adopts multi-scale image processing technology to capture the macroscopic lithologic characteristics of rock cuttings samples at low resolution, and identify their microstructure and texture at high resolution. Through the dual-path convolutional neural network model, deep feature extraction is performed on feature matrices at different levels to automatically learn the complex rock cuttings image features in the samples and generate preliminary lithologic classification results. Compared with traditional single image processing technology, it can identify subtle differences in rock cuttings on a wider scale, especially in complex samples, showing higher accuracy. Experimental results show that the present invention improves the classification accuracy of different types of rock cuttings samples by about 15%.

[0174] The present invention innovatively integrates the mineral composition and crystal structure information obtained by X-ray diffraction technology with the lithologic classification results generated by image processing algorithms. Through optimization through a multimodal analysis algorithm, XRD technology can accurately identify the mineral composition of rock cuttings samples, while the image algorithm can provide detailed macro- and microstructural information. The combination of the two greatly improves the accuracy and robustness of lithologic analysis. Through comparative analysis and optimization, it can effectively solve the problem of insufficient classification accuracy in a single technology. The lithologic classification results finally generated show obvious advantages in refining mineral composition and structural characteristics.

[0175] Through automated image processing and XRD analysis processes, the present invention can quickly process large numbers of rock cuttings samples and generate high-precision lithology classification reports in a relatively short period of time. Through the automatic learning of the dual-path convolutional neural network model and the back-propagation adjustment system of the optimization algorithm, the analysis parameters can be adaptively adjusted to ensure the consistency and efficiency of the classification results. Compared with traditional manual analysis methods, the present invention significantly shortens the analysis time and increases the analysis speed by approximately 30% in large-scale geological exploration projects while maintaining a high level of classification accuracy, which can provide more timely decision-making support for the projects.

[0176] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for analyzing rock debris lithology based on a multi-level fusion image algorithm and X-ray diffraction, characterized in that: The steps include: S1. Pre-process rock cuttings samples to remove impurities and contaminants, and perform multispectral image acquisition to obtain rock cuttings image data at different resolutions; S2. Use multi-scale image processing technology to process rock chip image data of different resolutions, identify the macroscopic lithologic characteristics of rock chip samples at low resolution, identify the microscopic structure and texture characteristics at high resolution, and generate a multi-level rock chip image feature matrix; S3. Perform deep feature extraction on the multi-level rock cuttings image feature matrix using a dual-path convolutional neural network model, automatically learn the image features in the rock cuttings samples, and preliminarily generate lithology classification results. S4. Use X-ray diffraction technology to perform crystal structure analysis on rock chip samples, obtain diffraction patterns of samples, identify mineral composition and crystal structure information of rock chip samples, and generate supplementary data for lithology analysis; S5. Fusion of the supplementary data from lithologic analysis and the preliminary generated lithologic classification results, and finally generation of comprehensive lithologic classification results through comparison, optimization, and refinement of multimodal analysis algorithms; S6. Input the comprehensive lithologic classification results into a dynamic adaptive analysis system, which adaptively adjusts the analysis strategy based on the complexity of the cuttings sample and optimizes the parameters of the dual-path convolutional neural network model and multimodal analysis algorithm through real-time feedback and learning mechanisms; S7. Output an analysis report containing mineral distribution, crystal structure and comprehensive lithology classification results. The analysis report includes a three-dimensional mineral distribution map, XRD spectrum interpretation and image analysis flow chart.

2. The method for analyzing rock debris properties based on a multi-level fusion image algorithm and X-ray diffraction according to claim 1, characterized in that: The S1 includes the following specific steps: S11, performing physical pretreatment on the collected rock cuttings sample, using screening equipment to remove particles with a particle size smaller than d1 and larger than d2, where d1 is the minimum allowable diameter of the rock cuttings particle, and d2 is the maximum allowable diameter of the rock cuttings particle. The values ​​of d1 and d2 are set according to the initial particle size distribution of the rock cuttings sample; S12, cleaning and drying the pretreated rock cuttings sample to remove impurities and moisture attached to the sample surface; S13, use multispectral imaging equipment to collect images of rock cuttings samples, obtain rock cuttings image data at different resolutions, and n Corresponding to r1 to r n The rock chip image data is collected at the resolution level of 100000; S14. The collected rock cuttings image data is stored in the form of a three-dimensional matrix T (λ, r, x, y): Among them, I i (λ j ,r k ,x,y) represents the spectral band λ j , resolution r k And rock chip image data at a spatial position (x, y), where x and y represent the spatial coordinates of the rock chip image data.

3. The method for analyzing rock debris properties based on hierarchical fusion image algorithm and X-ray diffraction according to claim 1, characterized in that: The S2 includes the following specific steps: S21, multi-scale processing of rock chip image data with different resolutions, r low Preprocess the rock cuttings image data to reduce the noise of the rock cuttings image data and obtain the macroscopic lithologic characteristics of the rock cuttings sample F macro : Among them, I(x′,y′,r low ) indicates that at low resolution r low The original rock chip image pixel value under G(xx′,yy′,σ macro ) is the Gaussian kernel function, and the kernel width is σ macro , represents the scale of the macroscopic features, and the integration range is the macroscopic area Ω of the cuttings image macro ; S22, using the image processing method based on wavelet transform to analyze the high resolution r high The cuttings image data is decomposed at multiple scales to extract the microscopic lithologic structure characteristics of the cuttings image. micro : Among them, I(x+m,y+n,r high ) represents the pixel value of the rock chip image at high resolution, is the wavelet basis function, the scale factor is s, M and N are the spatial window sizes of the wavelet transform; S23, the microscopic lithologic structural characteristics F micro and macroscopic lithologic characteristics F macro Fusion is performed to generate a multi-level cuttings image feature matrix T feature (x,y): Among them, F macro (x,y) represents the macroscopic lithologic characteristics at the spatial position (x,y), F micro (x,y) represents the microscopic lithologic structural characteristics at the same spatial position.

4. The method for analyzing rock debris properties based on hierarchical fusion image algorithm and X-ray diffraction according to claim 1, characterized in that: The S3 includes the following specific steps: S31, multi-level cuttings image feature matrix T feature (x, y) is input into the dual-path convolutional neural network model, which includes two independent feature extraction paths: the spatial feature path and the texture feature path for processing different features of rock cuttings samples; S32. Use multiple convolutional layers in the spatial feature path to extract features of the overall shape, structure, and contour of the rock cuttings sample: in, Represents the convolution output feature map of the l-th layer spatial feature path, is the convolution kernel weight in the l-th layer spatial feature path, is the bias term, k is the convolution kernel size; S33. After each convolution layer in the spatial feature path, the ReLU activation function is used to perform nonlinear processing on the convolution result, and the pooling layer is used to reduce the dimensionality of the spatial features: in, is the size of the pooling window, and the overall shape, structure, and contour information of the rock cuttings sample are preserved through the pooling operation; S34. In the texture feature path, multiple convolutional layers are used to extract features of the subtle texture and microstructure of the rock cuttings sample: in, Represents the convolution output feature map of the texture feature path of the lth layer, is the convolution kernel weight in the texture feature path of the lth layer, is the bias term; S35. After each convolution layer in the texture feature path, the convolution result is processed nonlinearly using the ReLU activation function, and the texture feature is reduced in dimension using the pooling layer: Preserve subtle texture and microstructural information in rock chip samples through pooling operations; S36, the feature map F extracted from the spatial feature path and the texture feature path spatial_pool (x,y) and F texture_pool (x,y) are fused to generate a comprehensive feature matrix T combined (x,y): T combined (x,y)=α·F spatial_pool (x,y)+β·F texture_pool (x,y); Among them, α and β are fusion coefficients, which represent the weights of spatial features and texture features; S37, the integrated feature matrix T combined (x,y) is input to the fully connected layer, and the Softmax classifier is used to classify the rock cuttings samples: Among them, P(C k ∣T combined ) represents the probability that the sample belongs to the kth lithology, W k and W j is the weight of the classifier, and K is the number of lithology classifications.

5. The method for analyzing rock debris properties based on hierarchical fusion image algorithm and X-ray diffraction according to claim 1, characterized in that: The S4 includes the following specific steps: S41, placing the pretreated rock cuttings sample in an X-ray diffractometer, irradiating the sample with X-rays and obtaining diffraction intensity data I(θ), where θ represents the diffraction angle; S42. Identify the types and contents of various mineral components in the rock chip sample by analyzing the diffraction intensity data I(θ) and position, and generate a mineral composition analysis table C. mineral (x,y): Among them, I i (θ) is the diffraction angle θ of the i-th mineral component i The diffraction intensity under σ i is the diffraction peak width, Z is the normalization factor used to make the percentage of the total mineral components equal to 1, Indicates that the diffraction angle range θ min to θ max The total diffraction intensity within, f mineral (x,y,θ i ) is the diffraction angle θ i The characteristic function of the relevant mineral composition describes the distribution characteristics of the mineral at the spatial position (x, y); S43. Constructing a mineral crystal structure model based on changes in diffraction intensity and diffraction angle crystal (x,y), the mineral crystal structure model is used to describe the crystal structure characteristics of different mineral components: Among them, V k is the unit cell volume of the kth mineral, F hkl is the structure factor of the crystal plane family, G hkl is the reciprocal vector of the crystal plane family, describing the lattice periodicity, r(x,y) represents the atomic position vector at the spatial position (x,y), D hkl (T) is the temperature factor of the crystal, which indicates the effect of temperature on the diffraction intensity; S44, mineral composition analysis table C mineral (x,y) and mineral crystal structure model S crystal (x,y) are fused to generate supplementary data D for lithologic analysis supplement (x,y): D supplement (x,y)=α1·C mineral (x,y)+β1·S crystal (x,y); Among them, α1 and β1 are weight coefficients, which are used to balance the weights of the mineral composition analysis table and the mineral crystal structure model in the supplementary data.

6. The method for analyzing rock debris properties based on a multi-level fusion image algorithm and X-ray diffraction according to claim 1, characterized in that: The S5 includes the following specific steps: S51. Supplementary data D for lithologic analysis supplement (x,y) and the preliminary lithologic classification results P(C k ∣T combined ) to fuse and obtain the initial fused feature matrix T fused (x,y): T fused (x,y)=α2·P(C k ∣T combined )+β2·D supplement (x,y); Among them, α2 and β2 are weight coefficients, which represent the weights of the preliminary lithologic classification results and the supplementary lithologic analysis data in the fusion process; S52, the initial fusion feature matrix T fused (x, y) performs multimodal analysis and calculates the similarity measure S between the preliminary classification results and the supplementary data similarity (x,y): S similarity (x,y)=exp(-(P(C k ∣T combined )-D supplement (x,y)) 2 ); The closer the similarity measure is to 1, the higher the consistency between the preliminary classification results and the supplementary data; S53, according to the similarity measure S similarity (x,y) The fused feature matrix T fused (x, y) is optimized, and the weight coefficients α2 and β2 are adjusted by the back propagation algorithm to maximize the similarity measure: Where η is the learning rate, and is the updated weight coefficient; S54, the optimized fusion feature matrix T fused (x,y) is input into the classification model, and the Softmax classifier is used to perform the final refinement classification of the lithology category: Among them, P final (C k ∣T fused_opt ) represents the probability that the sample belongs to the kth lithology under the final fusion feature matrix.

7. The method for analyzing rock debris properties based on a multi-level fusion image algorithm and X-ray diffraction according to claim 1, characterized in that: The S7 includes the following specific steps: S71. Integrate the comprehensive lithology classification results with the mineral composition analysis table and the mineral crystal structure model to generate an analysis report data set; S72. Draw a three-dimensional mineral distribution map based on the mineral composition analysis table, where the three-dimensional mineral distribution map represents the distribution of minerals at spatial positions (x, y, z); S73. Interpret the diffraction intensity data to generate a spectrum interpretation result including diffraction peak position, peak intensity and mineral composition.

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