Full-vision field digital slice analysis system and method for geological core analysis

Abstract

The invention relates to a full-view digital slice analysis system and method for geological core analysis, and relates to the technical field of geological exploration, and the system comprises a matrix construction module which is used for obtaining a multi-band image of a core slice and establishing a multi-scale image collection matrix; the first function construction module is used for constructing a three-dimensional space structure function; the feature fusion module is used for generating a feature fusion matrix; the second function construction module is used for defining a splicing optimization function and calculating a splicing optimization result; the integrity evaluation module is used for evaluating the continuity of the three-dimensional structure of the core slice; the image updating module is used for dynamically adjusting the learning rate and the time attenuation coefficient based on the gradient direction of the integrity evaluation function, and updating the image parameters until convergence; and the three-dimensional image generation module is used for selecting key feature points of the core slice and generating a three-dimensional visual image of the core slice through the spatial distance attenuation coefficient. The system can improve the accuracy of digital slice analysis.

Description

Technical Field

[0001] The present invention relates to the field of geological exploration technology, and in particular to a geological core analysis full-view digital thin-section analysis system, method, electronic equipment and non-transient computer-readable storage medium. Background Art

[0002] Currently, core thin section analysis, as a key method in geological exploration, reservoir evaluation and oil and gas reservoir description, widely uses traditional optical microscopy combined with manual identification methods, supplemented by digital image stitching and image recognition algorithms for image acquisition and analysis.

[0003] However, existing digital thin section analysis methods generally have problems such as limited field of view, lack of spatial structure information, and fragmented processing flow. Most methods are still based on local field of view splicing, which makes it difficult to restore the continuous spatial structure and phase distribution characteristics of core thin sections at a macroscopic scale, resulting in distortion or loss of core structure information during the splicing process. Summary of the invention

[0004] In view of the technical problems existing in the prior art, the present invention provides a geological core analysis full-view digital thin section analysis system, method, electronic device and non-transitory computer-readable storage medium capable of improving the accuracy of digital thin section analysis.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: The present invention provides a geological core analysis full-view digital thin-section analysis system, the system comprising: Matrix building module, used to obtain multi-band images of core slices and establish a multi-scale image acquisition matrix; The first function construction module is used to suppress noise and enhance edge features by using a gradient operator, and to construct a three-dimensional spatial structure function in combination with a feature point distribution function; A feature fusion module, used to perform a joint transformation on the multi-scale image acquisition matrix and the three-dimensional space structure function, calculate the fusion weight by measuring the feature similarity, and generate a feature fusion matrix; The second function building module is used to define a splicing optimization function and calculate a splicing optimization result according to the reference point coordinates and the smoothing coefficient; An integrity assessment module, used to calculate the absolute value of the gradient of the splicing optimization result, and to assess the continuity of the three-dimensional structure of the core slice in combination with a structural integrity metric function; An image update module is used to dynamically adjust the learning rate and time decay coefficient based on the gradient direction of the integrity assessment function and update the image parameters until convergence; The three-dimensional image generation module is used to select key feature points of the core slice and generate a three-dimensional visualization image of the core slice through a spatial distance attenuation coefficient.

[0006] Optionally, the matrix building module is further used for: Acquiring the intensity distribution of the multi-band image; Determining each image matrix element according to the intensity distribution of the multi-band image; The multi-scale image acquisition matrix is ​​constructed according to the image matrix elements.

[0007] Optionally, the multi-scale image acquisition matrix is ​​expressed as: ; Where M is the multi-scale image acquisition matrix, is the image matrix element, is the weight coefficient for different image channels or scales, is the image intensity at the corresponding position and band, is the wavelength parameter, is the image matrix dimension.

[0008] Optionally, the first function building module is further used for: Performing a three-dimensional integration on the multi-scale image acquisition matrix and performing noise suppression using a gradient operator to obtain a corresponding integral term; The feature point distribution function is superimposed on the integral term to obtain a space construction function.

[0009] Optionally, the space construction function is expressed as: ; in, is the spatial structure reconstruction function, is the voxel representation of the multi-scale image acquisition matrix, is the gradient operator, is the feature point distribution function, m is the number of feature points, They are the first adjustment parameter, the second adjustment parameter and the third adjustment parameter respectively.

[0010] Optionally, the feature fusion module is further used for: Obtaining a spatial structure function for describing the structural integrity and three-dimensional distribution of the core slice; defining a joint transform operator for spatially aligning the multiscale image acquisition matrix with the spatial structure function; Calculate the attenuation factor based on the feature distance to generate the feature values ​​of each fused image; The feature fusion matrix is ​​generated according to the feature values ​​of each image.

[0011] Optionally, the second function building module is further used for: According to the reference point coordinates, calculating the corresponding spatial attenuation factor; The splicing optimization function is determined according to the spatial attenuation factor, the smoothing coefficient and the feature fusion matrix.

[0012] Optionally, the integrity assessment module is further used to: Obtaining the local pore volume, connected path length and fracture density of the core slice; The structural integrity metric function is determined based on the local pore volume, the connected path length and the fracture density of the core slice.

[0013] Optionally, the three-dimensional image generation module is further used for: Determine a corresponding distance attenuation factor according to the key feature points of the core slice and the distance attenuation coefficient; A three-dimensional visualization image of the core slice is determined according to the distance attenuation factor and the splicing optimization function value calculated by the splicing optimization function.

[0014] The present invention also provides a full-view digital thin-section analysis method for geological core analysis, the method comprising: Acquire multi-band images of core slices and establish a multi-scale image acquisition matrix; The gradient operator is used to suppress noise and enhance edge features, and the three-dimensional spatial structure function is constructed by combining the feature point distribution function; Performing a joint transformation on the multi-scale image acquisition matrix and the three-dimensional space structure function, calculating fusion weights by measuring feature similarity, and generating a feature fusion matrix; According to the reference point coordinates and the smoothing coefficient, a splicing optimization function is defined and the splicing optimization result is calculated; Calculating the absolute value of the gradient of the splicing optimization result, and evaluating the continuity of the three-dimensional structure of the core slice in combination with the structural integrity metric function; Based on the gradient direction of the integrity assessment function, the learning rate and time decay coefficient are dynamically adjusted to update the image parameters until convergence; The key feature points of the core slice are selected, and a three-dimensional visualization image of the core slice is generated through a spatial distance attenuation coefficient.

[0015] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby realizing a full-view digital thin section analysis method for geological core analysis as described above.

[0016] In addition, to achieve the above-mentioned purpose, the present invention also proposes a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, a full-view digital thin section analysis method for geological core analysis as described above is implemented.

[0017] The beneficial effects of the present invention are: (1) The present invention establishes a multi-scale image acquisition matrix to effectively integrate image information of different scales and bands within the local field of view, forming a continuous and complete core image set, providing high-quality raw data support for subsequent structural analysis and three-dimensional modeling, and significantly improving the spatial coverage of thin-section images.

[0018] (2) The present invention not only considers the overall image information (through the integral term) through the spatial structure reconstruction function, but also introduces the explicit feature point distribution, and effectively reconstructs the microstructure morphology through the gradient control mechanism, thus overcoming the limitation of traditional methods that only rely on image segmentation or texture judgment, and improving the accuracy and robustness of three-dimensional reconstruction.

[0019] (3) The present invention constructs a feature fusion matrix, jointly encodes image features and structural information, and introduces a feature distance and weight control mechanism, which further enhances the contrast of key structures, mineral boundaries, and fracture features, and helps to achieve more accurate structural recognition and classification analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A scene diagram of a full-view digital thin-section analysis method for geological core analysis provided by the present invention; Figure 2 A structural schematic diagram of a full-view digital thin-section analysis system for geological core analysis provided by the present invention; Figure 3 A flow chart of a full-view digital thin-section analysis method for geological core analysis provided by the present invention; Figure 4 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention; Figure 5 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0022] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0023] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0024] See also Figure 1 , Figure 1 A scene diagram of a geological core analysis full-view digital thin-section analysis method provided by the present invention. Figure 1 As shown, the terminal and the server are connected via a network, such as a wired or wireless network connection. The terminal may include but is not limited to portable terminals such as mobile phones and tablets installed with various network platform applications, as well as fixed terminals such as computers, query machines, and advertising machines. The server provides users with various business services, including service push servers, user recommendation servers, etc.

[0025] It should be noted that Figure 1 The scene diagram of a full-view digital thin section analysis method for geological core analysis shown is only an example. The terminal, server and application scenario described in the embodiment of the present invention are for more clearly illustrating the technical solution of the embodiment of the present invention, and do not generate limitations on the technical solution provided by the embodiment of the present invention. Ordinary technicians in this field can know that with the evolution of the system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.

[0026] Among them, the terminal can be used for: Acquire multi-band images of core slices and establish a multi-scale image acquisition matrix; The gradient operator is used to suppress noise and enhance edge features, and the three-dimensional spatial structure function is constructed by combining the feature point distribution function; Performing a joint transformation on the multi-scale image acquisition matrix and the three-dimensional space structure function, calculating fusion weights by measuring feature similarity, and generating a feature fusion matrix; According to the reference point coordinates and the smoothing coefficient, a splicing optimization function is defined and the splicing optimization result is calculated; Calculating the absolute value of the gradient of the splicing optimization result, and evaluating the continuity of the three-dimensional structure of the core slice in combination with the structural integrity metric function; Based on the gradient direction of the integrity assessment function, the learning rate and time decay coefficient are dynamically adjusted to update the image parameters until convergence; The key feature points of the core slice are selected, and a three-dimensional visualization image of the core slice is generated through a spatial distance attenuation coefficient.

[0027] See also Figure 2 , Figure 2 A structural schematic diagram of a full-view digital thin-section analysis system for geological core analysis provided by the present invention.

[0028] like Figure 2 As shown, a geological core analysis full-view digital thin-section analysis system proposed in an embodiment of the present invention includes: A matrix building module 201 is used to obtain multi-band images of core slices and establish a multi-scale image acquisition matrix; The first function construction module 202 is used to suppress noise and enhance edge features by using a gradient operator, and to construct a three-dimensional spatial structure function in combination with a feature point distribution function; A feature fusion module 203 is used to perform a joint transformation on the multi-scale image acquisition matrix and the three-dimensional space structure function, calculate the fusion weight by measuring the feature similarity, and generate a feature fusion matrix; The second function construction module 204 is used to define a splicing optimization function and calculate a splicing optimization result according to the reference point coordinates and the smoothing coefficient; An integrity evaluation module 205 is used to calculate the absolute value of the gradient of the splicing optimization result and evaluate the continuity of the three-dimensional structure of the core slice in combination with the structural integrity metric function; An image updating module 206, for dynamically adjusting the learning rate and the time decay coefficient based on the gradient direction of the integrity assessment function, and updating the image parameters until convergence; The three-dimensional image generation module 207 is used to select key feature points of the core slice and generate a three-dimensional visualization image of the core slice through a spatial distance attenuation coefficient.

[0029] In some embodiments, the matrix construction module 201 may also be used to: Acquiring the intensity distribution of the multi-band image; Determining each image matrix element according to the intensity distribution of the multi-band image; The multi-scale image acquisition matrix is ​​constructed according to the image matrix elements.

[0030] In some embodiments, the multi-scale image acquisition matrix can be expressed as: ; Where M is the multi-scale image acquisition matrix, is the image matrix element, is the weight coefficient for different image channels or scales, is the image intensity at the corresponding position and band, is the wavelength parameter, is the image matrix dimension.

[0031] In the specific implementation, M is used as the raw data container for subsequent spatial reconstruction and feature fusion. Each element Represents the fusion result of image intensity at a specific location. It is an n×n two-dimensional matrix, each element of which corresponds to an image pixel. Used to adjust the importance of images of different scales / bands, which may be set according to image clarity, contrast or preset rules. Indicates the wavelength at the coordinate (x, y) in the kth and lth channels or scales. Image brightness or reflectivity under certain conditions. Usually used to represent a certain imaging band, such as visible light, ultraviolet, infrared, X-ray, etc.; here it is assumed to be a fixed band . n represents the resolution of the image in the horizontal and vertical directions (i.e. the number of pixels), which can also be understood as the side length of the matrix.

[0032] Represents raw image data at multiple scales or channels (e.g., different magnifications, different imaging instruments, different spectra), with each data source having a different weight Add fusion. All these image information from different sources are weighted and superimposed at each pixel (x, y) to form a unified image matrix M, which provides a unified input for subsequent spatial reconstruction and feature analysis. Different from the choice ,This model can adapt to different types of core image data acquisition equipment and modes (such as microscope images, multi-spectral scanning images, etc.).

[0033] From the above, it can be seen that the process of this formula in the present invention is equivalent to "fusion calibration" for different image sources, the purpose of which is to: unify the image scale and spectral differences, eliminate the imaging differences between devices; improve the expressiveness of local features of the image, and retain key information in each scale; provide a high-information input matrix M for subsequent three-dimensional structure reconstruction.

[0034] In some embodiments, the first function building module 202 is further used to: Performing a three-dimensional integration on the multi-scale image acquisition matrix and performing noise suppression using a gradient operator to obtain a corresponding integral term; The feature point distribution function is superimposed on the integral term to obtain a space construction function.

[0035] In some embodiments, the spatial construction function may be expressed as: ; in, is the spatial structure reconstruction function, is the voxel representation of the multi-scale image acquisition matrix, is the gradient operator, is the feature point distribution function, m is the number of feature points, They are the first adjustment parameter, the second adjustment parameter and the third adjustment parameter respectively.

[0036] In the specific implementation, Describes the structural expression value of a point in three-dimensional space, is the expanded form of the image matrix in three-dimensional space, Indicates the local spatial change rate of the image (such as edge, texture, and structural mutation). Emphasizes areas of dramatic change, often indicating texture complexity or boundary clarity. Characterizes the distribution of key structures (such as cracks, mineral boundaries, etc.) in three-dimensional space, m is the total number of feature points, Control the influence of the integral term in the structure construction (dominate the overall situation), Control the sensitivity to image gradients (such as edge suppression or enhancement), Control the influence of feature point items on the structure (dominant local).

[0037] Image volume integration based on gradient modulation , the purpose is to extract a smooth, continuous structure distribution from the original image. This term extracts structural features from the image intensity matrix M, using an exponential decay term Suppress high gradient areas (such as noise or sharp edges) and enhance areas with strong continuity and clear structure. Acts as an overall strength factor to modulate the influence of global background information.

[0038] The distribution of feature points detected manually or automatically is explicitly added to ensure that important features are not weakened by the smoothing process during the structure reconstruction process and to enhance the expression of key details. It is the representation of feature points in three-dimensional space (such as Gaussian kernel function, Dirac function, etc.). Control the contribution of these key feature points to the overall structure.

[0039] This structure function is a "global-local fusion model": the global structure is constructed by M+ gradient control terms, emphasizing continuity; the local features are constructed by Implantation, retaining key point information of geological structure; three adjustment parameters It can be dynamically adjusted through experiments or optimization to adapt to the characteristics of different core image data.

[0040] Through the above methods, the present invention can be used for tasks such as three-dimensional core reconstruction, structural analysis, and lithofacies identification, providing a spatial data basis for subsequent feature fusion and structural integrity assessment; it is decoupled from the image preprocessing step and has good modularity and controllability.

[0041] In some embodiments, the feature fusion module 203 is further used to: Obtaining a spatial structure function for describing the structural integrity and three-dimensional distribution of the core slice; defining a joint transform operator for spatially aligning the multiscale image acquisition matrix with the spatial structure function; Calculate the attenuation factor based on the feature distance to generate the feature values ​​of each fused image; The feature fusion matrix is ​​generated according to the feature values ​​of each image.

[0042] In some embodiments, the feature fusion matrix can be expressed as: ; Among them, F is the feature fusion matrix, is the fusion image feature value, is the fusion weight coefficient, It is a transformation operator acting on images and structures. is the feature distance (similarity measure), is the distance attenuation coefficient.

[0043] In the specific implementation, each element It is the fused composite image feature, forming an intermediate expression layer for subsequent splicing and modeling. It is used to adjust the contribution of the kth and lth feature fusion units to the whole, which may be set according to the significance or reliability of the image area. It is a function that simultaneously extracts, transforms or encodes image information M and structural information S. Common ones include PCA, CNN, filter bank, attention mechanism, etc. Measures the feature difference between the fusion unit and the central area, which is used to suppress spatial weights and avoid noise influence. Controls the influence of feature distance on fusion contribution. The larger the value, the more sensitive the distance is. Indicates the spatial resolution of the fusion matrix, which is usually smaller than the original image size and is equivalent to a dimensionality reduction or high-level expression layer.

[0044] This formula uses the transformation operator , the information in the image intensity matrix M and the spatial structure function S are integrated and analyzed; this operation can support the fusion of different scales and different semantic levels. For example, M captures the image texture and brightness difference, S emphasizes the structural integrity and three-dimensional distribution, and T realizes joint dimensionality reduction, feature reconstruction or salient area extraction. Control the contribution of distant or different areas to the current feature point, reduce interference from low-correlation areas, and enhance local consistency. It can be automatically set based on image noise, structural complexity, or specific algorithms (such as attention distribution); the fusion strength of different regions is therefore controllable.

[0045] The feature fusion matrix uses image brightness / texture information M and spatial structure information S to integrate multi-source information. and Control the fusion radius and similarity weight to achieve local sensitivity modulation, output dimension Compared with the original image, it is usually more abstract as a high-level representation to achieve feature dimension conversion. It provides a refined feature basis for the splicing optimization function O(x,y) and has strong subsequent adaptability.

[0046] In summary, the feature fusion matrix of the present invention is a "comprehensive expression" of images and structures, which can be used for image stitching and matching, model training input feature layers, spatial pattern recognition (such as fracture network, bedding structure extraction), and improving feature robustness and spatial consistency.

[0047] In some embodiments, the second function building module 204 is further configured to: According to the reference point coordinates, calculating the corresponding spatial attenuation factor; The splicing optimization function is determined according to the spatial attenuation factor, the smoothing coefficient and the feature fusion matrix.

[0048] In some embodiments, the splicing optimization function can be expressed as: ; in, is the splicing optimization function, is the value in the feature fusion matrix, is the splicing coefficient, is the spatial attenuation parameter, and are the reference point coordinates.

[0049] In the specific implementation, Represents the pixel value or feature value after fusion output at position (x, y) to reconstruct the complete view. Contains image and structural information. Control from reference point The contribution strength of is related to the feature confidence or position error. , It is the location of key stitching points or feature points in the original image, defining the central area of ​​overlap / splicing. control , The influence of the distance from the reference point in space on the contribution determines the "fuzzy range" of the fusion.

[0050] Each reference point As a "local fusion center"; through the spatial Gaussian weight Suppress the interference of areas far from the center; the influence ranges of various reference points may overlap, thus achieving smooth transition and continuous splicing. Splicing coefficient It can be set based on the following information: local image quality (blur, exposure), image overlap, importance of structural features, support for non-uniform stitching strategies, and improved edge consistency and geometric alignment.

[0051] In the present invention, the stitching function has multiple values ​​in geological core image analysis: eliminating stitching gaps: buffering the discontinuity between different image blocks through spatial weighting; enhancing structural consistency: using F(x, y) to retain the spatial structural features, so that the stitched image is not only visually coherent, but also consistent in structural information; adapting to complex core data: supporting unified fusion of images under different resolutions, sampling areas, and imaging conditions.

[0052] In some embodiments, integrity assessment module 205 is further configured to: Obtaining the local pore volume, connected path length and fracture density of the core slice; The structural integrity metric function is determined based on the local pore volume, the connected path length and the fracture density of the core slice.

[0053] In some embodiments, the structural integrity metric function may be expressed as: ; Where Q is the structural integrity measurement function, is the three-dimensional gradient, is the evaluation weight parameter, is a structural integrity measurement function.

[0054] In the specific implementation, the gradient of the image change → represents the structure boundary and texture clarity; the spatial structure integrity measure R(x,y,z) → measures whether the local structure is complete and continuous; the attenuation control parameter η → controls the impact of the missing structure area on the overall score. The final Q is an overall evaluation indicator that can be used for quality control, splicing effect evaluation or optimization of the regularization term in the objective function.

[0055] Q is used to measure the integrity and quality of the overall spatial structure or image. It indicates the rate of change of the image in three-dimensional space after stitching optimization, reflecting the edge / texture clarity. Indicates the degree of completeness loss at a spatial point (x, y, z), such as holes, blur, outliers, etc. Controls the influence of missing regions in the overall score. The larger the value, the stronger the suppression of missing regions.

[0056] Gradient as a structural strength indicator, The larger the gradient is, the stronger the image structure changes, with obvious edges and clear textures; it is suitable for identifying bedding, cracks, and lines in core images; if the gradient is small, it means that the area is smooth, which may be a missing structure or a wrong fusion area. R(x,y,z) suppresses the interference of missing areas. The larger the R is, the incomplete regional structure (such as missing, dislocated, or ghosting); Approaching zero, thus reducing the contribution of this area in the overall evaluation; effectively avoiding the defect area "misleading" the overall evaluation. Three-dimensional integration ensures global analysis through triple integration Statistics of the integrity of the entire volume or image domain; suitable for multi-slice data, 3D structure modeling, etc.

[0057] In summary, the present invention provides a quantifiable indicator for integrated image quality. Low Q areas may have stitching errors or structural losses, which can be used for iterative optimization of subsequent steps. As part of the loss function, the results generated by different stitching algorithms or parameter combinations can be objectively compared through Q.

[0058] In some embodiments, the state of the image parameters at the t+1th iteration may be expressed as: ; in, is the state of the tth iteration, is the state of the t+1th iteration, is the learning rate or update step size, is the time decay coefficient, is the gradient of the integrity function Q, is the time decay factor.

[0059] In the specific implementation, this formula defines a state optimization iteration strategy based on the integrity evaluation function Q. Its goal is to perform directional update optimization based on the image / structure integrity evaluation results while retaining the existing state through continuous iteration. Introduced: Gradient guidance ( ): Indicates how to adjust the state to improve overall integrity; time decay factor : Controls the iteration amplitude to decrease over time and tend to converge; Step size parameter (δ): adjusts the intensity of each step update.

[0060] Gradient-driven updates , if the integrity function Q is regarded as the objective function, this formula is a typical gradient ascent method. The gradient indicates in which direction the current state can improve the structural integrity; in fact, It can be a partial derivative with respect to spatial position, image pixel value, or structural parameter.

[0061] Learning rate control , determines the adjustment amplitude of the state at each step; it is often necessary to set it to a small value to ensure stable convergence of the system; an adaptive adjustment mechanism can be used to dynamically adjust δ according to the gradient size.

[0062] Time decay mechanism , the update speed is fast in the initial stage, which is conducive to quickly jumping out of the local minimum; as the number of iterations t increases, the attenuation term decreases, making the optimization stable; it is equivalent to a simulated annealing strategy, avoiding oscillating convergence and improving robustness.

[0063] The initial state I(0) can be an initial image, a structural reconstruction sketch or a blank model. The integrity evaluation function is used to analyze and calculate the gradient Get the sensitive direction of the current state to improve Q, update the state I(t+1) and use the controlled step size and attenuation coefficient to update the model or image.

[0064] In summary, the present invention makes directional adjustments for the integrity target to avoid blind updates. The time decay strategy ensures rapid initialization and stability in the later stage. It can be embedded in neural network training, image enhancement, three-dimensional reconstruction and other links, and is suitable for various state update processes, including image data, transformation matrices, structural models, etc.

[0065] In some embodiments, the 3D image generation module 207 is further configured to: Determine a corresponding distance attenuation factor according to the key feature points of the core slice and the distance attenuation coefficient; A three-dimensional visualization image of the core slice is determined according to the distance attenuation factor and the splicing optimization function value calculated by the splicing optimization function.

[0066] In some embodiments, a three-dimensional visualization of a core slice may be represented as: ; in, is the intensity or feature map of the 3D visualization image, is the splicing optimization function value, is the weight of the i-th feature point, is the spatial distance attenuation coefficient, is the spatial position of the feature point, is a distance metric.

[0067] In the specific implementation, this function is used to generate a 3D visualization image or feature mapping result based on the 3D stitching optimization result O(x,y,z). It integrates the influence of multiple feature points into the 3D space, so that: the visualization effect focuses on the key area of ​​the structure; different feature points are weighted And spatial distance decay σ controls its influence range; constructs a weight-guided, space-sensitive 3D image mapping model.

[0068] Based on the spatial weighting mechanism of feature points, each feature point It will "radiate" within a certain range around it; the closer the distance, the greater the impact on the current point (x, y, z), and vice versa; attenuation function Controls the falloff of influence strength with spatial distance; similar to the Gaussian Weight kernel, but without the squared term, resulting in a larger influence radius and a more gradual transition.

[0069] Feature points guide focus enhancement. Feature points are often key areas for analysis (such as fault zones, pore clusters, sedimentary structures, etc.). Control its "visualization weight" to achieve enhanced display of key areas and weakened processing of non-key areas; it is conducive to subsequent expert identification, model training or interactive analysis.

[0070] Combining the original values ​​with the weighted kernel mapping, this mapping does not "replace" the original spliced ​​data, but "modulates" it and presents it; making the result have both the authenticity of the original structure and the prominence of the feature area.

[0071] In summary, the present invention can enhance the display effect of the area around key feature points, help geological experts quickly locate important areas in three-dimensional structures, construct heat fields and region of interest (ROI) masks, and output them as feature maps for use in machine learning and neural networks. It can also superimpose V(x, y, z) of multiple channels to construct a multi-view visualization model.

[0072] See also Figure 3 , provides a flow chart of a geological core analysis full-view digital thin section analysis method of the present invention, comprising the following steps: Step 301, obtaining a multi-band image of a core slice and establishing a multi-scale image acquisition matrix; Step 302: Use a gradient operator to suppress noise and enhance edge features, and combine the feature point distribution function to construct a three-dimensional space structure function; Step 303: jointly transform the multi-scale image acquisition matrix and the three-dimensional space structure function, calculate the fusion weight by feature similarity measurement, and generate a feature fusion matrix; Step 304: define a splicing optimization function and calculate a splicing optimization result according to the reference point coordinates and the smoothing coefficient; Step 305, calculating the absolute value of the gradient of the splicing optimization result, and evaluating the continuity of the three-dimensional structure of the core slice in combination with the structural integrity metric function; Step 306: Based on the gradient direction of the integrity assessment function, dynamically adjust the learning rate and the time decay coefficient, and update the image parameters until convergence; Step 307: Select key feature points of the core slice and generate a three-dimensional visualization image of the core slice by using a spatial distance attenuation coefficient.

[0073] It should be noted that for the specific implementation and beneficial effects of the above steps 301-307, please refer to the description of modules 201-207, which will not be repeated here.

[0074] See also Figure 4 , Figure 4 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented: Acquire multi-band images of core slices and establish a multi-scale image acquisition matrix; The gradient operator is used to suppress noise and enhance edge features, and the three-dimensional spatial structure function is constructed by combining the feature point distribution function; Performing a joint transformation on the multi-scale image acquisition matrix and the three-dimensional space structure function, calculating fusion weights by measuring feature similarity, and generating a feature fusion matrix; According to the reference point coordinates and the smoothing coefficient, a splicing optimization function is defined and the splicing optimization result is calculated; Calculating the absolute value of the gradient of the splicing optimization result, and evaluating the continuity of the three-dimensional structure of the core slice in combination with the structural integrity metric function; Based on the gradient direction of the integrity assessment function, the learning rate and time decay coefficient are dynamically adjusted to update the image parameters until convergence; The key feature points of the core slice are selected, and a three-dimensional visualization image of the core slice is generated through a spatial distance attenuation coefficient.

[0075] See also Figure 5 , Figure 5 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 5 As shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented: It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0076] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as systems, methods, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0077] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0078] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0079] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0080] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0081] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A geological core analysis full-view digital thin-section analysis system, characterized in that: The system comprises: Matrix building module, used to obtain multi-band images of core slices and establish a multi-scale image acquisition matrix; The first function construction module is used to suppress noise and enhance edge features by using a gradient operator, and to construct a three-dimensional spatial structure function in combination with a feature point distribution function; A feature fusion module is used to perform a joint transformation on the multi-scale image acquisition matrix and the three-dimensional space structure function, calculate the fusion weight by measuring the feature similarity, and generate a feature fusion matrix; The second function building module is used to define a splicing optimization function and calculate a splicing optimization result according to the reference point coordinates and the smoothing coefficient; An integrity assessment module, used to calculate the absolute value of the gradient of the splicing optimization result, and to assess the continuity of the three-dimensional structure of the core slice in combination with a structural integrity metric function; An image update module is used to dynamically adjust the learning rate and time decay coefficient based on the gradient direction of the integrity assessment function and update the image parameters until convergence; The three-dimensional image generation module is used to select key feature points of the core slice and generate a three-dimensional visualization image of the core slice through a spatial distance attenuation coefficient.

2. The geological core analysis full-view digital thin-section analysis system according to claim 1, characterized in that: The matrix building module is also used to: Acquiring the intensity distribution of the multi-band image; Determining each image matrix element according to the intensity distribution of the multi-band image; The multi-scale image acquisition matrix is ​​constructed according to the image matrix elements.

3. The geological core analysis full-view digital thin-section analysis system according to claim 2 is characterized in that: The multi-scale image acquisition matrix is ​​expressed as: ； Where M is the multi-scale image acquisition matrix, is the image matrix element, is the weight coefficient for different image channels or scales, is the image intensity at the corresponding position and band, is the wavelength parameter, is the image matrix dimension.

4. The geological core analysis full-view digital thin-section analysis system according to claim 3 is characterized in that: The first function building module is also used for: Performing a three-dimensional integration on the multi-scale image acquisition matrix and performing noise suppression using a gradient operator to obtain a corresponding integral term; The feature point distribution function is superimposed on the integral term to obtain a space construction function.

5. The geological core analysis full-view digital thin-section analysis system according to claim 4, characterized in that: The space construction function is expressed as: ； in, is the spatial structure reconstruction function, is the voxel representation of the multi-scale image acquisition matrix, is the gradient operator, is the feature point distribution function, m is the number of feature points, They are the first adjustment parameter, the second adjustment parameter and the third adjustment parameter respectively.

6. The geological core analysis full-view digital thin-section analysis system according to claim 5, characterized in that: The feature fusion module is also used for: Obtaining a spatial structure function for describing the structural integrity and three-dimensional distribution of the core slice; defining a joint transform operator for spatially aligning the multiscale image acquisition matrix with the spatial structure function; Calculate the attenuation factor based on the feature distance to generate the feature values ​​of each fused image; The feature fusion matrix is ​​generated according to the feature values ​​of each image.

7. The geological core analysis full-view digital thin-section analysis system according to claim 6, characterized in that: The second function building block is also used for: According to the reference point coordinates, calculating the corresponding spatial attenuation factor; The splicing optimization function is determined according to the spatial attenuation factor, the smoothing coefficient and the feature fusion matrix.

8. The geological core analysis full-view digital thin-section analysis system according to claim 7, characterized in that: The integrity assessment module is also used to: Obtaining the local pore volume, connected path length and fracture density of the core slice; The structural integrity metric function is determined based on the local pore volume, the connected path length and the fracture density of the core slice.

9. The geological core analysis full-view digital thin-section analysis system according to claim 7, characterized in that: The three-dimensional image generation module is also used for: Determine a corresponding distance attenuation factor according to the key feature points of the core slice and the distance attenuation coefficient; A three-dimensional visualization image of the core slice is determined according to the distance attenuation factor and the splicing optimization function value calculated by the splicing optimization function.

10. A method for full-view digital thin-section analysis of geological core analysis, characterized in that: The method comprises: Acquire multi-band images of core slices and establish a multi-scale image acquisition matrix; The gradient operator is used to suppress noise and enhance edge features, and the three-dimensional spatial structure function is constructed by combining the feature point distribution function; Performing a joint transformation on the multi-scale image acquisition matrix and the three-dimensional space structure function, calculating fusion weights by measuring feature similarity, and generating a feature fusion matrix; According to the reference point coordinates and the smoothing coefficient, a splicing optimization function is defined and the splicing optimization result is calculated; Calculating the absolute value of the gradient of the splicing optimization result, and evaluating the continuity of the three-dimensional structure of the core slice in combination with the structural integrity metric function; Based on the gradient direction of the integrity assessment function, the learning rate and time decay coefficient are dynamically adjusted to update the image parameters until convergence; The key feature points of the core slice are selected, and a three-dimensional visualization image of the core slice is generated through a spatial distance attenuation coefficient.

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