Method for automatically processing complex fault intersection relationship
By automating the processing of fault model intersection relationships and utilizing fault model feature value analysis and collision detection, the problem of low efficiency in processing fault intersection relationships has been solved, achieving more efficient and accurate fault model editing.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA PETROLEUM & CHEMICAL CORP
- Filing Date
- 2024-11-22
- Publication Date
- 2026-05-22
AI Technical Summary
In existing technologies, the handling of complex fault junction relationships requires manual editing, resulting in large-scale and wide-span faults in large exploration models. Inaccurate three-dimensional spatial editing and movement positions lead to abnormal fault morphology and low work efficiency.
By analyzing the feature values of fault models based on geological exploration datasets, the system automatically determines the intersection relationships of fault models, generates shared columns, including the extension and cutting of fault models, and uses covariance matrix and oriented bounding boxes for collision detection to achieve automatic connection and cutting of fault models.
It significantly reduces the time and labor required for manually editing fault models, improves the efficiency of processing fault junction relationships, ensures the true geological morphology of fault models, and enhances the accuracy and reliability of fault models.
Smart Images
Figure CN122072379A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of petroleum exploration and development technology, specifically a method for automatically processing complex fault junction relationships. Background Technology
[0002] Faults, as an important geological element, are influenced by multiple phases of tectonic movement, causing complex deformations of strata or rock masses. The irregularity and discontinuity of fault geometry at the surface and deep depths, coupled with the scarcity of original three-dimensional data describing their spatial morphology, increase the difficulty of understanding faults and related complex geological structures. The existence of faults often has a significant impact on the migration and accumulation of oil and gas, as well as controlling the formation of other minerals. Computer simulation technology is used to dynamically demonstrate the formation process of faults and their control over oil and gas resources. Fault models are the foundation of three-dimensional geological models; only by establishing high-quality fault models can a good mesh model be built, and only then can the phase and property models built upon it be reliable. The handling of fault contact relationships usually cannot be automatically determined and requires manual definition of the contact relationships between each fault. Fault contact relationships mainly include connectivity and cutting relationships.
[0003] Connecting faults, also known as branching faults, involve a relatively simple contact relationship. It simply requires manually defining two pillars connecting the two faults; after definition, the two faults will share one pillar. Cross-connecting faults are achieved by manually cutting one fault into two parts, each of which is then manually connected to the other fault. The shared pillars generated from two connected faults are often relatively upright, inconsistent with the curvature of the fault surface. This can cause mesh intersections during skeleton mesh generation, requiring manual editing of the shared pillar shape. In current large-scale exploration models, faults are large in scale and span, and inaccurate position picking in 3D space editing can lead to abnormal fault shapes, resulting in low efficiency in manually handling fault junctions and editing shared pillar shapes. Summary of the Invention
[0004] To address the shortcomings of existing technologies, this invention provides a method for automatically processing complex fault junction relationships. This method solves the problem that current exploration models often have large fault scales and spans, and the inaccurate picking of editing and moving positions in three-dimensional space leads to abnormal fault morphology, resulting in low efficiency in manually processing fault junction relationships and editing shared fault column morphology.
[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for automatically processing complex fault junction relationships, comprising the following steps: obtaining fault model feature values based on geological exploration fault datasets, and screening fault models that need to be processed for junction relationships; extending the fault models that need to be processed for junction relationships at both ends based on the extension distance and cutting distance set in the database to obtain junction fault models, and determining whether the junction fault models intersect; if they intersect, generating a shared column at the intersection position of the junction fault models, and determining whether the distance from the end of the junction fault model to the shared column is less than or equal to the cutting distance set in the database; if it is less than or equal to, cutting is performed; if it is greater, no processing is performed; if they do not intersect, the extension distance is automatically canceled.
[0006] Furthermore, based on the acquired geological exploration fault dataset, the fault model feature values are obtained through comprehensive analysis. The specific analysis process is as follows: First, acquire the fault edge data, fault polygon data, and fault point data of the geological exploration fault. Second, quantize the fault edge data, fault polygon data, and fault point data to obtain quantized values for the fault edge data, fault polygon data, and fault point data of the fault model. Third, comprehensively process these quantized values to obtain the fault model feature values. Fourth, analyze the fault model feature values to determine whether the fault model conforms to the geological data. If the fault model feature values conform to the geological data, define crossbeams in the fault model to form the three-dimensional geometric structure of the fault model. If the fault model feature values do not conform to the geological data, no processing is performed.
[0007] Furthermore, if the fault model characteristic values are determined to conform to the geological data, then crossbeams are defined in the fault model to form the three-dimensional geometric structure of the fault model. The specific analysis process is as follows: obtain the top crossbeam data, middle crossbeam data, bottom crossbeam data, and column data of the geological exploration fault; construct the three-dimensional geometric structure of the fault model using the top crossbeam data, middle crossbeam data, bottom crossbeam data, and column data of the geological exploration fault.
[0008] Furthermore, the fault model feature values are analyzed to determine whether the fault model conforms to the geological data. The specific analysis process is as follows: the fault model feature values are compared with the fault model geological data evaluation values in the database; if the fault model feature values are greater than or equal to the fault model geological data evaluation values in the database, then the fault model conforms to the fault model geological data; if the fault model feature values are less than the fault model geological data evaluation values in the database, then the fault model does not conform to the fault model geological data.
[0009] Furthermore, the formula for calculating the feature values of the fault model is as follows:
[0010]
[0011] In the formula, F is the characteristic value of the fault model, and Q is... L Q is the quantized value of the fault edge data in the fault model. P Q is the quantized value of the polygonal data of the fault model. D α is the quantized value of the fault model breakpoint data, β is the weight factor of the quantized value of the fault model edge data stored in the database, γ is the weight factor of the quantized value of the fault model polygon data stored in the database, and δ is the weight factor of the interaction term between the quantized value of the fault model edge data and the quantized value of the fault model polygon data stored in the database.
[0012] Furthermore, based on the extension distance and cutting distance set in the database, the fault models that need to be processed for the intersection relationship are extended at both ends to obtain the intersection fault models. The specific analysis process is as follows: obtain the extension distance and cutting distance set in the database; adjust the fault models that need to be processed for the intersection relationship according to the determined extension distance so that each fault model that needs to be processed for the intersection relationship intersects with the others; delete the redundant extension parts according to the determined cutting distance to obtain the intersection fault models.
[0013] Furthermore, the adjustment of the fault model that needs to be processed according to the determined extension distance is specifically analyzed as follows: the fault model that needs to be processed is extended towards the tangent direction of the two columns at the end of the fault model.
[0014] Further, the determination of whether the cross-fault models intersect is performed through the following analysis: The separation axis is obtained by analyzing the covariance matrix, and the optimal direction of the oriented bounding boxes is determined; the oriented bounding boxes of the two cross-fault models are projected along the separation axis, and the projection interval of the oriented bounding boxes on each separation axis is calculated; if the oriented bounding boxes on the separation axis overlap, the two cross-fault models are determined to intersect; if the oriented bounding boxes on the separation axis do not overlap, the two cross-fault models are determined to not intersect; collision detection is performed using the oriented bounding boxes to obtain the intersecting triangles, and these intersecting triangles are stored.
[0015] Furthermore, the collision detection is performed using oriented bounding boxes to obtain the triangles where the oriented bounding boxes intersect, and the number of intersecting triangles is stored. The specific analysis process is as follows:
[0016] Collision detection is performed on all the triangular bounding boxes of every two fault models to obtain the triangles where the bounding boxes intersect.
[0017] Iterate through all pairs of triangles that intersect the bounding box, find the intersection of the sides of one triangle and the faces of the other triangle to obtain the valid intersection points, and connect all the valid intersection points in sequence to obtain the intersection line of the two fault models.
[0018] Furthermore, the formula for calculating the separation axis is obtained by analyzing the covariance matrix as follows:
[0019]
[0020] In the formula, C is the covariance matrix, p i Let be the i-th point in the dataset, μ be the average position of all points, n be the number of data points, T be the transpose of the matrix, and i be an index used to represent the i-th point in the dataset.
[0021] The present invention has the following beneficial effects:
[0022] This method for automatically processing complex fault junction relationships significantly reduces the time and labor required for manually editing fault models by automating the connection and cutting relationships of fault models. The automated tools can more accurately identify and process fault junctions, automatically determine intersection positions and create shared pillars, which helps maintain the true geological morphology of faults. It solves the problem that current exploration models with large fault scales and spans often have inaccurate three-dimensional editing and movement position picking, resulting in abnormal fault morphology and thus low efficiency in manually processing fault junction relationships and editing the morphology of shared fault pillars.
[0023] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description
[0024] Figure 1 This is a flowchart of the method for automatically processing complex fault junction relationships according to the present invention.
[0025] Figure 2 This is a schematic diagram of a fault model for the automatic processing of complex fault junction relationships according to the present invention.
[0026] Figure 3 This is a schematic diagram showing the extension of both ends of the fault model for the automatic processing of complex fault junction relationships in this invention.
[0027] Figure 4 This is a schematic diagram of the directional bounding box for the automatic processing of complex fault junction relationships in this invention.
[0028] Figure 5This is a schematic diagram of the intersection fault model before cutting off the redundant part of the intersection fault model for automatic processing of complex fault intersection relationships according to the present invention.
[0029] Figure 6 This is a schematic diagram of the cross-fault model after the redundant parts have been cut off in the automatic processing of complex fault cross-connection relationships according to the present invention. Detailed Implementation
[0030] This application embodiment achieves automatic processing of fault model connection and cutting relationships through a method for automatically processing complex fault junction relationships, which significantly reduces the time and labor required for manually editing fault models.
[0031] The problem addressed in this application's embodiments can be summarized as follows:
[0032] Select the fault model whose intersection relationship needs to be processed, set the extension distance and cutting distance, extend the fault model whose intersection relationship needs to be processed at both ends based on the extension distance and cutting distance set in the database, and determine whether the fault models intersect based on the intersection relationship of the fault models. If they intersect, a shared column will be generated at the intersection position. If they do not intersect, the extension distance will be automatically canceled.
[0033] Example 1;
[0034] Please see Figure 1-6 As shown, the present invention provides a technical solution: a method for automatically processing complex fault junction relationships, comprising the following steps: based on the acquired geological exploration fault dataset, comprehensively analyzing the fault model feature values, filtering fault models that need to be processed for junction relationships through visualization, and not visualizing fault models that do not need to be processed; extending the fault models that need to be processed for junction relationships at both ends based on the extension distance and cutting distance set in the database to obtain junction fault models, and determining whether the junction fault models intersect; if it is determined that the junction fault models intersect, generating a shared column at the intersection position of the junction fault models, and determining whether the distance from the end of the junction fault model to the shared column is less than or equal to the cutting distance set in the database, if it is less than or equal to, cutting is performed, if it is greater, no processing is performed; if it is determined that the junction fault models do not intersect, automatically canceling the extension distance.
[0035] Specifically, based on the acquired geological exploration fault dataset, the fault model feature values are obtained through comprehensive analysis. The specific analysis process is as follows: acquiring fault edge data, fault polygon data, and fault point data of the geological exploration fault; quantifying the fault edge data, fault polygon data, and fault point data to obtain quantized values for the fault edge data, fault polygon data, and fault point data of the fault model; comprehensively processing these quantized values to obtain fault model feature values; analyzing the fault model feature values to determine whether the fault model conforms to the geological data; if the fault model feature values conform to the geological data, defining crossbeams in the fault model to form the three-dimensional geometric structure of the fault model; if the fault model feature values do not conform to the geological data, no processing is performed.
[0036] In this implementation plan, fault edge data of geological exploration faults refers to the fault boundary lines identified in geological exploration. These data are usually obtained through seismic exploration, surface surveying, or drilling, and are crucial for determining the precise location and shape of the fault. Fault polygon data of geological exploration faults describes the projected area of the fault on the surface or a specific geological stratum. It is obtained by measuring the closed polygons formed by the fault boundaries. Fault point data of geological exploration faults refers to the key intersections on the fault line, such as the fault's start point, end point, or point of change of direction. These points are highly important in geological modeling because they may indicate key areas of geological activity. Fault model edge data quantization converts the length of the fault edge or its relative position with other geological features into numerical values to represent the geometric characteristics of the fault model edge. Fault model polygon data quantization calculates the area or perimeter of the fault polygon, quantifying the spatial extension of the fault model. Fault model breakpoint data quantization assesses the importance or geological activity of the breakpoint. Based on the geological location of the breakpoint or its role in the fault network, the quantized values of fault model edge data, fault model polygon data, and fault model breakpoint data are comprehensively processed to obtain the fault model feature value. By converting complex geometric and geological data into quantified values, the data analysis and processing workflow can be simplified. Quantified data can be compared across different faults and geological structures. For example, by comparing the feature values of different fault models, geological similarities and differences can be quickly identified.
[0037] Specifically, if the fault model's characteristic values are determined to match the geological data, then crossbeams are defined in the fault model to form its three-dimensional geometric structure. The specific analysis process is as follows: obtain the top crossbeam data, middle crossbeam data, bottom crossbeam data, and column data of the geological exploration fault; construct the three-dimensional geometric structure of the fault model using the top crossbeam data, middle crossbeam data, bottom crossbeam data, and column data of the geological exploration fault.
[0038] In this implementation scheme, the top crossbeam data of the geological exploration fault represents the uppermost layer of the fault model, typically near the surface or the shallowest known geological layer. The middle crossbeam data represents the middle part of the fault model, connecting the top and bottom, and usually covers the main geological activity areas. The bottom crossbeam data relates to the bottom of the fault model, which is usually the deepest part of the fault and is crucial for understanding the overall structure of the fault. The column data of the geological exploration fault is used to connect the crossbeams and provide vertical support in the fault model, ensuring the precise positioning of the crossbeams in the appropriate geological layers. Using the collected data, the top, middle, bottom, and column data of the geological exploration fault are imported into 3D modeling software such as Petre I, Leapfrog Geo, and Leapfrog. In 3D modeling software such as Geo, top, middle, and bottom crossbeams are positioned. Columns are added between the top and bottom crossbeams, and between the middle crossbeams and either the top or bottom crossbeams. The position and angle of these columns need to be precisely set based on actual geological data to ensure correct alignment and connection between the crossbeams. Modeling tools are used to create connections between the crossbeams and columns, forming a complete 3D fault model. Precise placement of these columns in the 3D space of the fault model based on geological data ensures the model's vertical integrity and continuity, while the columns provide structural support and stability. By using detailed geological data to construct the 3D model, the physical and geological characteristics of the fault can be simulated more accurately. Fault models with precise 3D structures provide more reliable data support for geological analysis and prediction.
[0039] Example 2;
[0040] The fault model feature values are analyzed to determine whether the fault model conforms to geological data. The specific analysis process involves comparing the fault model feature values with the fault model geological data evaluation values in the database. In one specific embodiment, the fault model geological data evaluation values are obtained by comparing the similarity of the fault geological map and fault topographic map with the various reference fault geological maps and reference fault topographic map combinations stored in the database. The combination of reference fault geological maps and reference fault topographic maps corresponding to the minimum similarity is obtained. A mapping set is constructed based on the relationship between the reference fault geological map and reference fault topographic map combinations and the fault model geological data evaluation values in the historical data of the database. The fault model geological data evaluation value in the database corresponding to the reference fault geological map and reference fault topographic map combination with the minimum similarity is obtained through the mapping set. If the fault model feature value is greater than or equal to the fault model geological data evaluation value in the database, the fault model conforms to the fault model geological data; if the fault model feature value is less than the fault model geological data evaluation value in the database, the fault model does not conform to the fault model geological data.
[0041] In this implementation plan, fault geological maps and fault topographic maps are first imported into a database. These maps typically contain information on the surface features, stratigraphic distribution, fault lines, fault points, and other geological structures of the fault. The fault geological maps and fault topographic maps are then compared with the reference fault geological maps and reference fault topographic maps stored in the database for similarity analysis. Computer vision algorithms (such as SIFT, SURF, or ORB) are used to extract key features from the fault geological maps and fault topographic maps. These features represent the basic attributes and structure of the images, such as edges, corners, and textures. Feature matching techniques (such as FLANN or KD-Tree) are used to calculate the similarity between different images. Commonly used similarity metrics include cosine similarity and Euclidean distance. The calculated similarity is compared with the similarity of the reference fault geological maps and topographic maps pre-stored in the database. The match with the highest similarity or the smallest error is identified, thus obtaining the geological data evaluation value of the fault model in the database. The database contains records of various fault geological maps and fault topographic maps. Each record is associated with a feature value and a geological data evaluation value. The combinations of reference fault geological maps and reference fault topographic maps in the database are derived from historical data analysis, previous geological studies, or simulation data generated by geological modeling software. The geological data evaluation values of the fault models in the database are compared with the fault model feature values. By comparing, it is determined whether the current fault model feature value is greater than or equal to the expected geological data evaluation value of the fault model in the database. If it is greater than or equal to, it indicates that the fault model is accurate in terms of geological data. If it is less than, it indicates that the fault model may have errors or the data needs to be updated. By comparing with real geological data, deviations or errors in the model can be identified and corrected, improving the accuracy and reliability of the model. Accurate fault models help to more effectively explore and manage resources such as oil and natural gas.
[0042] Specifically, the formula for calculating the feature values of the fault model is as follows:
[0043]
[0044] In the formula, F is the characteristic value of the fault model, and Q is... L Q is the quantized value of the fault edge data in the fault model. P Q is the quantized value of the polygonal data of the fault model. D α is the quantized value of the fault model breakpoint data, β is the weight factor of the quantized value of the fault model edge data stored in the database, γ is the weight factor of the quantized value of the fault model polygon data stored in the database, and δ is the weight factor of the interaction term between the quantized value of the fault model edge data and the quantized value of the fault model polygon data stored in the database.
[0045] In this implementation scheme, the aforementioned fault model feature values are obtained through the quantification values of fault edge data, fault polygon data, and fault breakpoint data. The fault model feature values are comprehensive numerical values that represent the overall geological characteristics of the fault and are used to assess or compare the geological activity and structural complexity of the fault. The quantification values of fault edge data quantify the linear characteristics of the fault, such as the length, shape, or linear direction of the fault. The fault edge is the direct physical representation of the fault. The quantification values of fault polygon data represent the directional characteristics of the fault, such as the area or perimeter, reflecting the geographical range or spatial morphology covered by the fault. The quantification values of fault breakpoint data quantify the data of breakpoints, such as the starting point, ending point, or key turning point of the fault. Each weighting factor determines the influence of each quantified data in the total feature value, and each weighting factor reflects the importance of the corresponding geological data in assessing the fault characteristics. and Introducing nonlinearity enhances the balance of fault model sensitivity to data of different magnitudes. For example, squaring emphasizes the influence of quantized values of fault edge data, while square rooting mitigates the influence of large quantized values of polygonal data. The logarithmic function ln(1+Q) D This ensures that the quantized values of fault breakpoint data contribute little to the fault model feature values at low values, while increasing slowly at high values, thus preventing the quantized values of fault breakpoint data from excessively influencing the fault model feature values; the interaction term δ*Q L *Q P This formula represents the interaction between the quantized values of fault edge data and fault polygon data in the fault model. It reflects the influence of the relationship between the fault edge and the fault polygon data on the fault model eigenvalues. By analyzing the relationship between the quantized values of the fault edge data and the fault polygon data in historical data, a suitable value for δ is obtained. This formula provides a method to integrate multiple geological data and provide a comprehensive fault assessment through a single fault model eigenvalue. The comprehensive eigenvalues provided can be used to quickly assess the potential risks and resource potential of faults.
[0046] The weighting factors are obtained from the database. In a specific embodiment, by establishing the relationship between the quantized values of fault edge data, fault polygon data, fault breakpoint data, and fault feature values in historical data, a mapping set between the quantized values of fault edge data, fault polygon data, fault breakpoint data, and weighting factors is established. The current quantized values of fault edge data, fault polygon data, and fault breakpoint data are input into this mapping set to obtain the weighting factors corresponding to the quantized values of fault edge data, fault polygon data, and fault breakpoint data.
[0047] Example 3: Based on the extension distance and cutting distance set in the database, the fault models that need to be processed for the intersection relationship are extended at both ends to obtain the intersection fault models. The specific analysis process is as follows: obtain the extension distance and cutting distance set in the database; adjust the fault models that need to be processed for the intersection relationship according to the determined extension distance so that each fault model that needs to be processed for the intersection relationship intersects with the others; delete the excess extension part according to the determined cutting distance to obtain the intersection fault models.
[0048] In this implementation scheme, extension distance and cutting distance are set, with a default extension distance of 150 meters and a cutting distance of 20 meters. For two faults that initially do not intersect but the user believes they should intersect based on geological understanding, a certain extension distance can be set to ensure the two faults intersect after extension. If two faults intersect and partially extend, a cutting distance can be set to delete the extended portion, helping to maintain the simplicity of the fault model while preserving necessary geological features. Following the set extension distance, each fault model's ends will extend by the specified distance, ensuring connection or overlap between models. This extension simulates the natural expansion and interaction of faults in real geological processes. Setting and adjusting the extension and cutting distances can precisely control the geometry of the fault model, ensuring accurate representation in three-dimensional space. Through reasonable extension and cutting, the model more realistically reflects the natural state and behavior of faults, and removing unnecessary extensions reduces model complexity.
[0049] Specifically, the adjustment of the fault model that needs to be processed according to the determined extension distance is as follows: the fault model that needs to be processed is extended to the tangent direction of the two columns at the end of the fault model.
[0050] In this implementation scheme, the terminal posts of the fault model are determined. These posts are typically located at the outermost end of the fault model, marking the endpoint of the fault line or boundary. Based on a predetermined extension distance, the fault model is extended outwards along a standardized tangential direction. This not only covers the terminal region of the model but also provides potential interaction areas for inter-model junctions. During the extension process, new data points or nodes are inserted to realistically reflect the extended geometry. The coordinates of these new points are determined by adding the extension distance to the coordinates of the original terminal point and multiplying by the direction vector. By extending along the fault tangent direction, the geological continuity of the fault model is ensured. This extension process enables the model to more realistically simulate the fault characteristics in the geological environment. The extension operation not only enhances the coverage of the fault model but also ensures its geometric integrity.
[0051] Specifically, the process for determining whether the cross-fault models intersect is as follows: The separation axis is obtained by analyzing the covariance matrix, and the optimal direction of the oriented bounding boxes is determined; the oriented bounding boxes of the two cross-fault models are projected along the separation axis, and the projection interval of the oriented bounding boxes on each separation axis is calculated; if the oriented bounding boxes on the separation axis overlap, the two cross-fault models are determined to intersect; if the oriented bounding boxes on the separation axis do not overlap, the two cross-fault models are determined to not intersect; collision detection is performed using the oriented bounding boxes to obtain the intersecting triangles, and these triangles are stored.
[0052] In this implementation scheme, the oriented bounding box (OBB box) is determined by the geometry of the fault model. It does not need to be parallel to the coordinate axes. The center of the OBB box is determined by the coordinate mean, and the orientation is found using the principal covariance axis determination method. This allows for the selection of the most suitable and compact OBB box for the object, enabling the use of the separation axis theorem to determine whether two OBB boxes intersect. First, 15 separation axes are determined for the two OBB boxes. These 15 axes include the six coordinate axes of the two OBB boxes and nine vectors obtained by cross products of three axes with the other three axes. Then, the two OBB boxes are projected onto these separation axes, and the overlap of these projection intervals is checked sequentially to determine if the two OBB boxes intersect. If they do not overlap in one direction, the two fault models are considered non-intersecting; if they overlap in one direction, they are considered intersecting. Using the OBB box and separation axis method reduces the number of geometric elements that need to be compared. The separation axis theorem quickly eliminates non-intersecting cases through simple interval overlap checks.
[0053] Specifically, the collision detection is performed using oriented bounding boxes to obtain the triangles where the oriented bounding boxes intersect, and the number of intersecting triangles is stored. The specific analysis process is as follows:
[0054] Collision detection is performed on all the triangular bounding boxes of every two fault models to obtain the triangles where the bounding boxes intersect.
[0055] Iterate through all pairs of triangles that intersect the bounding box, find the intersection of the sides of one triangle and the faces of the other triangle to obtain the valid intersection points, and connect all the valid intersection points in sequence to obtain the intersection line of the two fault models.
[0056] In this implementation, before determining the intersection of the triangulation, a hierarchical binary tree can be used to construct the bounding boxes of the triangles. Collision detection is performed first using these hierarchical bounding boxes, reducing the number of triangles to be intersected and thus accelerating the process. The indices of the intersecting triangles are stored for later use. The stored pairs of potentially intersecting triangles are traversed, and their intersection segments are calculated. The calculation process is as follows: for each triangle's edge, check if it intersects the plane of another triangle, calculate the possible intersection points, and represent the edge as a parameterized line r = r o +td, where r o Let be the sides of the triangle, d be the direction vector of the side, and t be the parameter. The plane of the triangle can be represented as n*(rr'). o ) = 0, where n is the normal vector of the triangle plane, r' o It is a point on the plane. Solve the equation: n*(r) o +td-r' o The parameter t is obtained by setting t = 0, and then t is substituted into the equation of the line to obtain the intersection point. Using the centroid coordinates or the projection of the point, it is determined whether the intersection point is within the boundary of the triangle to determine if it is a valid intersection point. If the intersection point is outside the boundary, it is not considered a valid intersection point. Connecting the valid intersection points of all triangle pairs in sequence yields the intersection line of the two cross sections. Preliminary filtering using OBB reduces the number of triangles requiring detailed inspection, while the subsequent edge-face intersection check provides high-precision intersection results. This method combines rapid macroscopic detection with fine microscopic inspection, ensuring computational efficiency and accuracy. By traversing each cross section and all its triangles, it can handle complex geometric interactions and is suitable for various irregular geological structures.
[0057] Specifically, the formula for calculating the separation axis is obtained by analyzing the covariance matrix as follows:
[0058]
[0059] In the formula, C is the covariance matrix, p i Let be the i-th point in the dataset, μ be the average position of all points, n be the number of data points, T be the transpose of the matrix, and i be an index used to represent the i-th point in the dataset.
[0060] In this implementation, the covariance matrix is a 3x3 matrix. It describes the spread and interrelationships of the data in different directions. For example, the diagonal elements are the variance of each dimension, while the off-diagonal elements are the covariance between different dimensions. `i` is an index representing the i-th point in the dataset, used to iterate through each point `p` in the dataset. i T represents the matrix transpose operation. In the formula, (pi -μ) T It is a vector (p) i The transpose of -μ) is used to form the outer product of the two vectors into a matrix. i It is the coordinate vector of the i-th point in the dataset, represented as (x i y i z i ) represents a specific location in three-dimensional space, and μ is the average location of all points, expressed as (μ x μ y μ z ), where each element is the average of the corresponding dimension, calculated using the following formula:
[0061]
[0062] The average vector μ represents the center position of the point set and is used to calculate the deviation of each point relative to the center.
[0063] p i -μ is the transpose vector, which is the transpose of the bias vector. It is usually used in matrix multiplication to generate elements of the covariance matrix. The normalization factor ensures that the elements of the covariance matrix are unbiased estimates, that is, accurately reflect the overall characteristics of the data without being affected by the sample size. In this formula, n is the total number of data points.
[0064] The separation axis is determined through eigenvalue and eigenvector analysis of the covariance matrix. These eigenvectors provide the main direction of the data distribution, used to determine the principal axis direction of the bounding box. Eigenvalues and eigenvectors are found by performing eigenvalue decomposition on the covariance matrix. Eigenvalues represent the degree of variation or spread of the data along the corresponding eigenvector direction. Specifically, larger eigenvalues correspond to data directions with larger variance, meaning the data is more diffused in that direction.
[0065] The eigenvalues λ of the covariance matrix are obtained by solving the following equation:
[0066] det(C-λI)=0
[0067] Where det represents the determinant of the matrix, I is the identity matrix of the same order as C, and λ is the eigenvalue.
[0068] An eigenvector is a vector associated with an eigenvalue, representing the main direction of data diffusion. Each eigenvector corresponds to an eigenvalue, and the length of the eigenvector is usually normalized.
[0069] For a given eigenvalue λ, the eigenvector v satisfies:
[0070] Cv = λv;
[0071] Where C is the covariance matrix, v is the eigenvector, and λ is the eigenvalue.
[0072] The eigenvalues are sorted in descending order to determine the direction of maximum variance in the dataset. The direction corresponding to the largest eigenvalue is the principal direction of the data, representing the greatest extensibility or variability. Based on the sorted eigenvalues, the corresponding eigenvectors are selected as the principal axes of the oriented bounding boxes. The eigenvector of the largest eigenvalue is used as the first principal axis of the oriented bounding box, and the eigenvectors corresponding to the remaining eigenvalues determine the secondary and tertiary principal axes of the oriented bounding boxes in order. Through eigenvalue and eigenvector analysis, the representation of complex datasets can be simplified, highlighting the main extensibility direction of the data, thereby optimizing subsequent geometric analysis and calculations.
[0073] By automating the connection and cutting relationships of fault models, the time and labor required for manual editing of fault models are significantly reduced. The automated tools can more accurately identify and process fault junctions, automatically determine intersection positions and create shared pillars, which helps maintain the true geological morphology of faults. This solves the problem that current exploration models with large fault scales and spans often have inaccurate three-dimensional editing and movement position picking, resulting in abnormal fault morphology and thus low efficiency in manually processing fault junction relationships and editing the morphology of shared fault pillars.
[0074] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.
[0075] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0076] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0077] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0078] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.
[0079] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.
Claims
1. A method for automatically processing complex fault junction relationships, characterized in that, Includes the following steps: Based on the fault dataset from geological exploration, fault model feature values are obtained, and fault models that need to be processed for intersection relationships are selected. Based on the extension distance and cutting distance set in the database, the fault model that needs to be processed for the intersection relationship is extended at both ends to obtain the intersection fault model, and it is determined whether the intersection fault models intersect. If they intersect, a shared column will be generated at the intersection of the fault models. It will be determined whether the distance from the end of the fault model to the shared column is less than or equal to the cutting distance set in the database. If it is less than or equal to the cutting distance, then cutting will be performed; if it is greater than the cutting distance, then no processing will be performed. If they do not intersect, the extended distance will be automatically canceled.
2. The method for automatically processing complex fault junction relationships according to claim 1, characterized in that: The specific analysis process for obtaining fault model feature values based on geological exploration fault datasets is as follows: Acquire fault edge data, fault polygon data, and fault point data of geological exploration faults; The fault edge data, fault polygon data, and fault point data of geological exploration faults are quantized to obtain the quantized values of fault edge data, fault polygon data, and fault point data of fault models. The fault model feature values are obtained by comprehensively processing the quantized values of fault edge data, fault polygon data, and fault point data of the fault model. Analyze the characteristic values of the fault model to determine whether the fault model conforms to the geological data; If the fault model's characteristic values are determined to match the geological data, then a crossbeam is defined in the fault model to form the three-dimensional geometric structure of the fault model. If the fault model's characteristic values are determined to be inconsistent with the geological data, no action will be taken.
3. The method for automatically processing complex fault junction relationships according to claim 2, characterized in that: If the fault model's characteristic values are determined to match the geological data, then a crossbeam is defined in the fault model to form its three-dimensional geometric structure. The specific analysis process is as follows: Acquire the top crossbeam data, middle crossbeam data, bottom crossbeam data, and column data of the geological exploration fault; The three-dimensional geometric structure of the fault model is constructed using the top crossbeam data, middle crossbeam data, bottom crossbeam data, and column data of the geological exploration fault.
4. The method for automatically processing complex fault junction relationships according to claim 2, characterized in that: The fault model's characteristic values are analyzed to determine whether the fault model conforms to geological data. The specific analysis process is as follows: The fault model feature values are compared with the fault model geological data evaluation values in the database; If the fault model characteristic value is greater than or equal to the fault model geological data evaluation value in the database, then the fault model conforms to the fault model geological data. If the fault model eigenvalue is less than the fault model geological data evaluation value in the database, then the fault model does not conform to the fault model geological data.
5. The method for automatically processing complex fault junction relationships according to claim 2, characterized in that: The formula for calculating the feature values of the fault model is as follows: In the formula, F is the characteristic value of the fault model, and Q is... L Q is the quantized value of the fault edge data in the fault model. P Q is the quantized value of the polygonal data of the fault model. D α is the quantized value of the fault model breakpoint data, β is the weight factor of the quantized value of the fault model edge data stored in the database, γ is the weight factor of the quantized value of the fault model polygon data stored in the database, and δ is the weight factor of the interaction term between the quantized value of the fault model edge data and the quantized value of the fault model polygon data stored in the database.
6. The method for automatically processing complex fault junction relationships according to claim 1, characterized in that: Based on the extension and cutting distances set in the database, the fault model that needs to be addressed in terms of intersection relationships is extended at both ends to obtain the intersection fault model. The specific analysis process is as follows: Retrieve the extension distance and cutting distance set in the database; Adjust the fault models that need to be addressed based on the determined extension distance, so that each fault model that needs to be addressed intersects with the others. The excess extensions are deleted according to the determined cutting distance to obtain the junction fault model.
7. The method for automatically processing complex fault junction relationships according to claim 6, characterized in that: The adjustment of the fault model requiring the handling of junction relationships according to the determined extension distance is specifically analyzed as follows: The fault model that needs to be addressed for the junction relationship is extended towards the tangent direction of the two pillars at the end of the fault model.
8. The method for automatically processing complex fault junction relationships according to claim 6, characterized in that: The specific analysis process for determining whether the fault models intersect is as follows: The separation axis is obtained by analyzing the covariance matrix, and the optimal orientation of the bounding box is determined. Project the bounding boxes of the two junction fault models along the separation axis and calculate the projection range of the bounding boxes on each separation axis; If the oriented bounding boxes on the separation axis overlap, then the two intersecting fault models are determined to intersect. If the oriented bounding boxes on the separation axis do not overlap, then the two intersecting fault models are determined to be non-intersecting. Collision detection is performed using oriented bounding boxes to obtain the triangles where the oriented bounding boxes intersect, and these triangles are then stored.
9. The method for automatically processing complex fault junction relationships according to claim 8, characterized in that: The collision detection is performed using oriented bounding boxes to obtain the triangles where the oriented bounding boxes intersect. The number of intersecting triangles is stored. The specific analysis process is as follows: Collision detection is performed on all the triangular bounding boxes of every two fault models to obtain the triangles where the bounding boxes intersect. Iterate through all pairs of triangles that intersect the bounding box, find the intersection of the sides of one triangle and the faces of the other triangle to obtain the valid intersection points, and connect all the valid intersection points in sequence to obtain the intersection line of the two fault models.
10. The method for automatically processing complex fault junction relationships according to claim 8, characterized in that: The formula for calculating the separation axis is obtained by analyzing the covariance matrix as follows: In the formula, C is the covariance matrix, p i Let be the i-th point in the dataset, μ be the average position of all points, n be the number of data points, T be the transpose of the matrix, and i be an index used to represent the i-th point in the dataset.