Three-dimensional rock stratum structural plane visual model construction method

Abstract

The invention relates to the technical field of data processing, in particular to a three-dimensional rock stratum structural plane visual model construction method which comprises the following steps: S1, importing a three-dimensional stratum model; s2, rock stratum structural plane extraction and structural plane occurrence calculation; and S3, carrying out visual expression on the rock stratum attitude. The invention provides a method for directly identifying a structural plane from a three-dimensional stratum model, so that designers can directly and preliminarily know stratum structural plane information according to model data; according to the invention, the structural surface of the rock mass model is visualized, so that the structural surface of the rock mass and geologic features thereof can be visually seen; according to the method, not only can the structure be identified and extracted, but also occurrence analysis can be carried out on the imported structural surface, and visual display is carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method for constructing a three-dimensional rock layer structural surface visualization model. Background Art

[0002] With the gradual application of 3D geological models, the use scenarios of stratigraphic information models have gradually diversified, which has put forward higher requirements for the comprehensiveness and authenticity of the stratigraphic information model expression content. The stratigraphic models created by current 3D geological modeling tools are mostly expressed in colors. This expression is suitable for soil models, but rock models should contain structural surface information, and color differentiation cannot meet the needs of structural surface information expression.

[0003] Structural surface is a general term for various geological interfaces that cut rock mass. It is an important basis for determining the quality and stability of rock mass. The acquisition and analysis of structural surface information is the basis for studying the stability of rock mass. The description of structural surface is often expressed by the occurrence. The occurrence of rock formations is the spatial position of the structural surface of the rock formation. Except for horizontal rock formations that are produced in a horizontal state, the occurrence of all inclined rock formations is expressed by their strike, dip and dip, i.e. the three elements of occurrence.

[0004] The main method for obtaining structural surface information in the field is compass measurement, which is time-consuming and labor-intensive, and difficult to measure in areas with large slopes. Currently, three-dimensional laser scanning technology is commonly used for information collection. This method provides accurate and comprehensive data, but it cannot be well combined with the formation model, and designers need to combine the two sets of data for analysis.

[0005] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a method for constructing a three-dimensional rock structure surface visualization model to solve the technical problems existing in the prior art.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] The present invention provides a method for constructing a three-dimensional rock layer structure surface visualization model, which is characterized by comprising the following steps:

[0009] S1. Import three-dimensional stratum model;

[0010] S2. Extraction of rock formation structural plane and calculation of structural plane occurrence;

[0011] S3. Visual expression of rock formation occurrence.

[0012] Preferably, in step S1, the three-dimensional stratum model is constructed using an irregular triangulated network.

[0013] Preferably, in step S2, the following steps are included:

[0014] S21, obtaining normal vectors of all vertices in the stratum model;

[0015] S22, vertex normal vector cluster analysis;

[0016] S23, coplanar detection;

[0017] S24, plane fitting;

[0018] S25. Obtaining occurrence information.

[0019] Preferably, in step S21, the following steps are included:

[0020] S211, calculating the normal vectors of all triangles;

[0021] S212. Calculate the normal vectors of all vertices.

[0022] Preferably, in step S22, the following steps are included:

[0023] S221, determining the number of mesh vertex groups of the formation model based on the silhouette coefficient;

[0024] S222. Determine the initial cluster center based on K-means++ algorithm;

[0025] S223, using a K-means clustering algorithm to determine an approximate mesh vertex group.

[0026] Preferably, in step S24, the following steps are included:

[0027] S241, randomly selecting three points from the coplanar points to form a plane equation, and defining a plane;

[0028] S242, setting a threshold according to the accuracy required by the actual situation, calculating the distance from all coplanar points to the plane, and defining them as external points if the distance is greater than the threshold, and defining them as internal points if the distance is less than the threshold;

[0029] S243, repeat steps S241 and S242, and take the plane with the most internal points as the best fitting plane.

[0030] Preferably, in step S3, the following steps are included:

[0031] S31, visualization of the structural surface of the above intelligent identification;

[0032] S32, Visualization of externally inserted structural surfaces.

[0033] Preferably, in step S31, the following steps are included:

[0034] S311, obtaining a structural surface;

[0035] S312. Place the occurrence mark.

[0036] By adopting the above technical solution, the present invention has the following beneficial effects:

[0037] (I) Structural surfaces are usually identified by image maps. The present invention provides a method for identifying structural surfaces directly from a three-dimensional stratum model, so that designers can directly obtain preliminary information on stratum structural surfaces based on model data.

[0038] (ii) Current stratum models are mostly represented by colors, which has a poor display effect on rock models. The present invention visualizes the structural surface of the rock model, so that the rock structural surface and its geological characteristics can be intuitively seen.

[0039] (III) The present invention can not only identify and extract structures, but also analyze the occurrence of imported structural surfaces and display them visually. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 A flowchart of a method for constructing a three-dimensional rock formation structural surface visualization model provided by an embodiment of the present invention;

[0042] Figure 2 A schematic diagram of a simple mesh surface provided by an embodiment of the present invention;

[0043] Figure 3 A schematic diagram of structural surface types and symbolic expressions provided by an embodiment of the present invention;

[0044] Figure 4 A schematic diagram of a stratum model of an inserted structural surface provided by an embodiment of the present invention;

[0045] Figure 5 A schematic diagram of the structural surface occurrence marking provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0048] Combination Figures 1 to 5 As shown, the present invention provides a method for constructing a three-dimensional rock layer structure surface visualization model, which specifically includes the following steps:

[0049] S1 imports 3D stratigraphic model

[0050] The three-dimensional stratigraphic model is a three-dimensional visual expression of a real geological body, and a good stratigraphic model will retain as much geological information as possible. Most stratigraphic models are expressed using irregular triangulated networks (TINs), which are a series of discrete points in space that do not intersect and overlap, and connect three points in a group in an orderly manner according to a certain rule to form adjacent triangular facet networks. These triangulated networks are surrounded by a mesh body to represent the three-dimensional strata, and because the triangular facets are irregular shapes, the state of the stratum surface and the morphology of the geological body can be better simulated. Therefore, the stratigraphic models used in the present invention are all constructed using irregular triangulated networks.

[0051] S2 rock formation structural plane extraction and structural plane occurrence calculation

[0052] S21 obtains the normal vectors of all vertices in the stratum model

[0053] Since there are cases where multiple triangles contain the same mesh vertex, when calculating the normal vector of the vertex, it is necessary to combine the normal vectors of adjacent faces to calculate it.

[0054] S211 calculates the normal vectors of all triangles

[0055] Traverse all the triangular patches of the stratum model and obtain the three vertices (represented by A, B, and C) that make up the triangular face. Obtain the face normal vector by vector cross multiplication normal = (BA) × (CA), then normalize the normal vector by dividing each component of the normal vector by the length of the normal vector. Finally, in order to facilitate the calculation of the normal vectors of subsequent points, the face normal vectors are uniformly changed to point outside the mesh.

[0056] S212 calculates the normal vectors of all vertices

[0057] While traversing the triangles, number each face and record the adjacent triangle face numbers in the triangle vertices. Figure 1 As shown in , the adjacent face sets of points A and C include faces 1 and 2, while point B only includes face 1, and point D only includes face 2. After obtaining the normal vectors of the adjacent faces, the mean of the normal vectors is taken as the normal vector of the point.

[0058] Cluster analysis of S22 vertex normal vectors

[0059] After obtaining the normalized normal vectors of each vertex, the same normal vectors cannot be directly classified as the same structural surface, because the structural surface is not necessarily completely flat and smooth, and there may be slight differences. When the difference is less than a certain threshold, it should also be regarded as a point on the same structural surface. Therefore, it is necessary to perform cluster analysis on the vertex normal vectors and group points with similar normal vectors into one group.

[0060] The present invention adopts K-means cluster analysis because of its high computational efficiency and wide application, but K-means clustering is sensitive to the initial cluster center, and the first step to accurately identify this is to determine a good initial cluster center. In view of the limitations of the K-means clustering algorithm, the present invention adopts an improved K-means algorithm based on the silhouette coefficient and the K-means++ algorithm for cluster analysis.

[0061] S221 Determine the number of mesh vertex groups (k value) of the formation model based on the silhouette coefficient

[0062] An important factor affecting K-means clustering is the K value. If the K value is too small, it may lead to overfitting, that is, the samples in the cluster are too close and cannot reflect the diversity of the samples; however, if the K value is too large, it may lead to underfitting, that is, the samples in the cluster are too dispersed, thus losing the meaning of clustering.

[0063] Considering the distance between a sample and other samples in the same cluster and the distance to the nearest cluster sample, the present invention adopts the silhouette coefficient method to determine the optimal K value, and the process is shown as follows:

[0064] Assume that all mesh vertices are classified into n groups, using x i Represents a vertex in a group.

[0065] Calculate x i The distance from the normal vector of all other vertices in this group is expressed as a(x i )express.

[0066] Calculate x i The minimum distance to the average normal vector of other groups of vertices is b(x i )express.

[0067] Calculate the silhouette coefficient: When S(xi ) is larger, the better the vertex clustering effect is, so take S(x i ) value is the number of groups corresponding to the maximum value as the K value.

[0068] Considering that the stratum area is generally large, the value of n is set to 2-15, and the silhouette coefficient is calculated for each n value, and the maximum silhouette coefficient is obtained as the K value.

[0069] S222 determines the initial cluster center based on K-means++ algorithm

[0070] Another important factor affecting K-means clustering is the initial cluster center. If the selection is not standardized, it will lead to a local optimal solution. In order to improve the clustering quality, considering the initial center at a longer distance, the present invention uses the K-means++ algorithm to determine the initial cluster center. The process is as follows:

[0071] A vertex normal vector is randomly selected from all mesh vertices as the initial cluster center.

[0072] For each point i in the mesh vertex, calculate the minimum normal vector distance D from each point to all selected cluster centers ij .

[0073] Calculate the probability that each vertex will be the next cluster center: Take P i The vertex normal vector corresponding to the maximum value is used as the next cluster center

[0074] Repeat steps 2 and 3 until K cluster centers are selected.

[0075] S223 K-means clustering algorithm to determine approximate mesh vertex groups

[0076] Combining step S221 and step S222, relatively standardized K initial cluster centers can be obtained. The steps for obtaining the approximate mesh vertex group are as follows:

[0077] Calculate the normal vector distance from each vertex in the mesh model to the initial cluster, and classify each vertex into the group with the closest distance.

[0078] Calculate the mean of all vertex normal vectors in each group to obtain a new cluster center;

[0079] Compare the cluster center to see if they are the same as the previous cluster center or if the difference is within an acceptable error. If so, the point center is the best cluster center. Otherwise, repeat steps 1 and 2 until the best cluster center is found.

[0080] S23 coplanarity detection

[0081] Through step S22, K approximate normal vector vertex groups are obtained. Due to the differences in vertex normal vectors within a group, not every group can be approximately placed on the same structural surface. Therefore, it is necessary to extract points on the same surface from the cluster. The eigenvalue (λ) of each cluster is obtained through principal component analysis. 1 ,λ 2 ,λ 3 ), and then get the deviation parameter according to the feature value After many instance tests, the maximum value of the deviation parameter is determined. When the deviation values ​​of a cluster point set are all smaller than the maximum value of the deviation parameter, it means that the point set is coplanar and plane fitting can be performed.

[0082] S24 plane fitting

[0083] After extracting the points on the plane, the best plane is fitted using the coplanar points to obtain the plane normal vector. The fitting process is as follows:

[0084] S241, randomly selecting three points from the coplanar points to form a plane equation, and defining a plane;

[0085] S242, setting a threshold according to the accuracy required by the actual situation, calculating the distance from all coplanar points to the plane, and defining them as external points if the distance is greater than the threshold, and defining them as internal points if the distance is less than the threshold;

[0086] S243, repeat steps 1 and 2, and take the plane with the most internal points as the best fitting plane.

[0087] The fitting plane obtained above is borderless and needs to be clipped and segmented to obtain the structural surface on the formation model. The contour of the inner point on the best fitting plane is calculated according to the alpha shape algorithm, and the area surrounded by the contour is the structural surface area on the formation model. Finally, the points in the area are constructed into a mesh to form the structural surface.

[0088] S25 occurrence information acquisition

[0089] The above best fitting surface can be expressed by the equation ax+by+cz+d=0, where a, b, c are the three coordinate axis direction parameters of the plane unit normal vector, and d is the distance from the origin to the plane. Calculate the external normal vector of the structural surface, that is, n=(-a,-b,1). Through the external normal vector of the structural surface, the inclination and dip angle of the structural surface can be calculated by the following formula:

[0090]

[0091] Where α is the inclination of the structural surface, in degrees, and β is the inclination angle of the structural surface, in degrees.

[0092] S3 Visualization of rock formation occurrence

[0093] The present invention provides two ways to visualize the occurrence of rock formations: one is to use the structural plane identified by the above intelligence to directly perform visualization. The other is to insert the structural plane, cut the formation according to the structural plane, and then visualize the occurrence of the cut surface.

[0094] S31 Visualization of the above intelligently identified structural surfaces

[0095] For the structural surface intelligently identified by the present invention, the basic information of the structural surface has been stored in the model, and only the occurrence mark needs to be made within the range of the structural surface.

[0096] S311 Get structure surface

[0097] Click the structural surface in the model that needs to be visualized, or select all structural surfaces for visualization to extract the occurrence information of the selected structural surface.

[0098] S312 Placement of occurrence mark

[0099] Different types of structural surfaces require different marking symbols, and the angle value of the inclination angle is written above the symbol. The present invention provides three types of structural surface marking symbols, as shown in the attached figure. Figure 2 As shown. Bedding is a parallel or nearly parallel layered structure formed by the accumulation of sediments layer by layer, so bedding mainly represents sedimentary rock layers; foliation is a parallel or nearly parallel layered structure formed by the rearrangement of rocks after being subjected to tectonic forces, which mainly occurs in metamorphic rocks, so foliation mainly represents metamorphic rock layers; joints are geological structures in which rocks are broken under the action of tectonic forces, and there is no obvious displacement on both sides of the break, which can appear in sedimentary rocks, igneous rocks and metamorphic rocks.

[0100] Among the three types of structural surface symbols, take the bedding symbol ┴ as an example. The long straight line below is the strike, the broken line perpendicular to the long line is the dip, and the angle between the surface composed of the symbol and the horizontal plane is the dip. The marking symbol that fits the structural surface can be obtained through the dip and dip.

[0101] The symbol placement starts from the lower left corner of the structure surface and traverses all mesh vertices on the structure surface from the right to the top. Since the positions of mesh points are often unevenly distributed, it is necessary to adjust the symbol placement frequency to optimize the visualization effect. When the mesh points are too dense, you can choose to place them at intervals; when the mesh points are too sparse, you can choose to insert a few points between two points.

[0102] Visualization of S32 externally inserted structural surfaces

[0103] For externally inserted structural surfaces, the stratigraphic model must first be cut according to the structural surfaces so that it can be displayed on the structural surfaces. Then different visualizations are performed according to different structural surface conditions. There are two types of imported structural surfaces: one is that there is both plane information and structural surface occurrence information, and the other is that there is only plane information.

[0104] S321 For the first type of case, the visualization step is similar to S31. Taking the boundary of the structural surface as the range, the mesh vertices of the stratum model within the range are extracted, these vertices are marked, and then different types of structural surfaces are marked with corresponding symbols according to the type of structural surface.

[0105] S322 For the second case, it is necessary to first perform step S25 to obtain the occurrence information, and then perform step S321 to complete the visualization.

[0106] In summary, by adopting the above technical solution, the present invention has the following beneficial effects:

[0107] (I) Structural surfaces are usually identified by image maps. The present invention provides a method for identifying structural surfaces directly from a three-dimensional stratum model, so that designers can directly obtain preliminary information on stratum structural surfaces based on model data.

[0108] (ii) Current stratum models are mostly represented by colors, which has a poor display effect on rock models. The present invention visualizes the structural surface of the rock model, so that the rock structural surface and its geological characteristics can be intuitively seen.

[0109] (III) The present invention can not only identify and extract structures, but also analyze the occurrence of imported structural surfaces and display them visually.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a three-dimensional rock structure surface visualization model, characterized in that: The steps include: S1. Import three-dimensional stratum model; S2. Extraction of rock formation structural plane and calculation of structural plane occurrence; S3. Visual expression of rock formation occurrence.

2. The method for constructing a three-dimensional rock structure surface visualization model according to claim 1, characterized in that: In step S1, the three-dimensional stratum model is constructed using an irregular triangulated network.

3. The method for constructing a three-dimensional rock structure surface visualization model according to claim 1, characterized in that: In step S2, the following steps are included: S21, obtaining normal vectors of all vertices in the stratum model; S22, vertex normal vector cluster analysis; S23, coplanar detection; S24, plane fitting; S25. Obtaining occurrence information.

4. The method for constructing a three-dimensional rock structure surface visualization model according to claim 1, characterized in that: In step S21, the following steps are included: S211, calculating the normal vectors of all triangles; S212. Calculate the normal vectors of all vertices.

5. The method for constructing a three-dimensional rock structure surface visualization model according to claim 1, characterized in that: In step S22, the following steps are included: S221, determining the number of mesh vertex groups of the formation model based on the silhouette coefficient; S222. Determine the initial cluster center based on K-means++ algorithm; S223, using a K-means clustering algorithm to determine an approximate mesh vertex group.

6. The method for constructing a three-dimensional rock structure surface visualization model according to claim 1, characterized in that: In step S24, the following steps are included: S241, randomly selecting three points from the coplanar points to form a plane equation, and defining a plane; S242, setting a threshold according to the accuracy required by the actual situation, calculating the distance from all coplanar points to the plane, and defining them as external points if the distance is greater than the threshold, and defining them as internal points if the distance is less than the threshold; S243, repeat steps S241 and S242, and take the plane with the most internal points as the best fitting plane.

7. The method for constructing a three-dimensional rock structure surface visualization model according to claim 1, characterized in that: In step S3, the following steps are included: S31, visualization of the structural surface of the above intelligent identification; S32, Visualization of externally inserted structural surfaces.

8. The method for constructing a three-dimensional rock structure surface visualization model according to claim 7, characterized in that: In step S31, the following steps are included: S311, obtaining a structural surface; S312. Place the occurrence mark.

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