A multiple interpolation 3D geological modeling method based on borehole data

Abstract

The present invention relates to the field of three-dimensional geological modeling of coal mines, and specifically to a multiple interpolation three-dimensional geological modeling method based on borehole data, comprising determining a three-dimensional geological modeling range and exploration data of multiple boreholes therein, determining the plane coordinates of different boreholes, and converting the boundary points of the same stratum of different boreholes into spatial z coordinates; using different interpolation operation methods to perform interpolation operations to obtain point cloud data of each stratum; performing comprehensive evaluation and analysis to determine a suitable interpolation operation method for each stratum; exporting the point cloud data to Rhino modeling software; extruding entities in the Rhino modeling software, performing grid division according to the Griddle plug-in, and generating a multi-stratum three-dimensional geological model; and importing the generated multi-stratum three-dimensional geological model into other software for analysis. The present invention can consider the advantages and disadvantages of various interpolation operation methods through the established evaluation indicators, ensuring that the generated model is more in line with actual geological conditions; and is particularly suitable for modeling work in complex geological environments.

Description

Technical Field

[0001] The present invention relates to the field of three-dimensional geological modeling of coal mines, and in particular to a multiple interpolation three-dimensional geological modeling method based on drilling data. Background Art

[0002] Three-dimensional geological modeling is of great significance in a variety of fields, including geological exploration, mining, and geotechnical engineering. With the increasing demand for natural resource development, 3D geological modeling has become a crucial tool for understanding underground structures, assessing resource reserves, and formulating development plans. Currently, numerical simulation analysis using 3D geological modeling is very popular. By visually displaying underground structures, it helps researchers understand complex geological bodies and their interrelationships. Furthermore, it can predict and assess geological hazards caused by activities such as mining, providing a scientific basis for underground engineering design and risk assessment.

[0003] However, although a variety of 3D geological modeling technologies and methods have been proposed and applied, current technologies still have many shortcomings and limitations. First, traditional modeling methods often rely on a single borehole data, which leads to incomplete extraction of stratum characteristics and no undulation in the modeled strata. In fact, according to multiple borehole data in the exploration area, the same stratum often presents an undulating state, which results in inaccurate modeling. Secondly, current 3D geological modeling technology lacks flexibility in the selection of interpolation methods. Although there are many interpolation methods such as Kriging interpolation and inverse distance weighted method, different interpolation methods have different focuses in specific applications. How to select the most suitable interpolation method based on actual site conditions and numerical simulation content remains a difficult problem. In addition, most existing interpolation methods focus on a single interpolation process and lack quantitative analysis of interpolation data, making it difficult to intuitively evaluate the rationality of the results.

[0004] Therefore, how to make full use of the multiple borehole data in the modeling area and perform interpolation processing by selecting an appropriate interpolation method to generate a three-dimensional geological model that conforms to multi-borehole exploration and field reality has become an urgent problem to be solved. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention proposes a three-dimensional geological modeling method based on multiple interpolation of borehole data, which includes the following steps:

[0006] S1: Determine the scope of 3D geological modeling and the exploration data of multiple boreholes within it, determine the plane coordinates (x, y) of different boreholes, and convert the boundary points of the same stratum in different boreholes into spatial z coordinates;

[0007] S2: For each stratum, based on the spatial z coordinates obtained from multiple boreholes, different interpolation methods are used to perform interpolation operations to obtain the point cloud data of each stratum;

[0008] S3: Comprehensively evaluate and analyze the point cloud data obtained by each interpolation method, determine the appropriate interpolation method for each stratum, and then determine the point cloud data obtained by using the appropriate interpolation method for each stratum; export the point cloud data to Rhino modeling software;

[0009] S4: In Rhino modeling software, the point cloud data is converted into a mesh surface and laid out into a uniform smooth surface. The edge curve of the smooth surface is extracted and the entity is extruded downward. The grid is divided according to the Griddle plug-in to generate a multi-stratum 3D geological model.

[0010] S5: Import the generated multi-stratum 3D geological model into other software for analysis.

[0011] Preferably, in step S1, the same stratum space z coordinate includes the top plate space z coordinate and the bottom plate space z coordinate.

[0012] Preferably, in step S2, the point cloud data at least includes the spatial z coordinates of the strata between the boreholes and the spatial z coordinates of the strata at the boundaries of the three-dimensional geological modeling range.

[0013] Preferably, in step S2, the interpolation operation includes Kriging interpolation method, inverse distance weighted method IDW, spline function interpolation method, trend surface interpolation method, radial basis function interpolation method and local polynomial interpolation method.

[0014] Preferably, step S3 specifically includes S31: constructing evaluation indicators; the evaluation indicators include local deviation, boundary smoothness, uniformity index and root mean square error;

[0015] S311: The local deviation calculation method is:

[0016]

[0017] Where, represents the value of the interpolation point i, represents the nearest borehole exploration data to the interpolation point, and n is the number of interpolation points near the borehole;

[0018] S312: The boundary smoothness calculation method is:

[0019]

[0020] Where Z i and Z i+1 They represent the values ​​of adjacent interpolation points in the boundary area; m is the total number of interpolation points in the boundary area;

[0021] S313: The uniformity index calculation method is:

[0022]

[0023] Where Z represents the value of all interpolation points; σ(Z) represents the standard deviation of all interpolation points; μ(Z) represents the mean of all interpolation points;

[0024] S314: The root mean square error RMSE is calculated as follows:

[0025]

[0026] Where, represents the value of the interpolation point i, represents the nearest drilling exploration data to the interpolation point, and j is the total number of interpolation points;

[0027] S32: Normalization processing; the calculation formula is:

[0028]

[0029] Where A is the set of results of all interpolation methods calculated using a certain evaluation index, min(A) and max(A) are the minimum and maximum values ​​in A respectively, a is the result of a certain interpolation method, a norm It is the normalized result of a certain interpolation method calculated using a certain evaluation index;

[0030] S33: assigning corresponding weights to different evaluation indicators according to different focuses; using radar charts to present the performance of different interpolation operation methods on different evaluation indicators; determining a suitable interpolation operation method for each stratum, and then determining point cloud data obtained using the interpolation operation method for each stratum;

[0031] S34: Export point cloud data to Rhino modeling software.

[0032] Preferably, in step S5, the other software includes numerical simulation software FLAC3D, 3DEC, ABAOUS, and ANSYS.

[0033] The beneficial effects of the present invention are as follows: 1. This invention effectively solves the problem of traditional 3D modeling methods' insufficient utilization of multiple borehole exploration data. Previous 3D modeling methods often relied solely on single-borehole exploration data, resulting in incomplete extraction of stratum characteristics, a lack of undulation in the modeled strata, and an inability to truly reflect the complexity of the underground structure. However, by integrating multiple borehole exploration data, the present invention generates a 3D geological model that more accurately reflects the undulation of the actual strata, thereby improving the model's authenticity and reliability.

[0034] 2. The present invention comprehensively considers a variety of interpolation operation methods and enhances the flexibility of interpolation operation method selection. The existing technology often adopts a single interpolation operation method and lacks comparative analysis of multiple interpolation operation methods. The present invention constructs new evaluation indicators (local deviation, boundary smoothness, uniformity index), and establishes a new comprehensive evaluation and analysis method, which can more intuitively understand the interpolation operation results. The present invention can consider the advantages and disadvantages of various interpolation operation methods through the established evaluation indicators to ensure that the generated model is more in line with the actual geological conditions; this feature is particularly suitable for modeling work in complex geological environments, allowing users to effectively model in changing geological conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of the method for three-dimensional geological modeling based on multiple interpolation of borehole data according to the present invention;

[0036] Figure 2 It is a flow chart of the interpolation operation comprehensive evaluation model of the present invention;

[0037] Figure 3 This is a comprehensive evaluation result diagram of a stratum interpolation operation of the present invention; DETAILED DESCRIPTION

[0038] The specific calculation method of the present invention is described in detail below with reference to the accompanying drawings.

[0039] The present invention proposes a three-dimensional geological modeling method based on multiple interpolation of borehole data, comprising the following steps:

[0040] S1: Determine the scope of the 3D geological modeling and the exploration data of multiple boreholes within it, determine the plane coordinates (x, y) of different boreholes, and convert the boundary points of the same stratum in different boreholes into spatial z coordinates, including the spatial z coordinates of the top plate and bottom plate of each stratum;

[0041] S2: For each stratum, based on the spatial z coordinates obtained from multiple boreholes, different interpolation methods are used to perform interpolation operations to obtain point cloud data for each stratum, wherein the point cloud data includes at least the spatial z coordinates of the strata between the boreholes and the spatial z coordinates of the strata at the boundaries of the three-dimensional geological modeling range; the interpolation operations include Kriging interpolation, inverse distance weighted IDW, spline function interpolation, trend surface interpolation, radial basis function interpolation, and local polynomial interpolation; these interpolation methods are all well known in the art and will not be described in detail here;

[0042] S3: Comprehensively evaluate and analyze the point cloud data (a collection of interpolation points) obtained by each interpolation method, determine the appropriate interpolation method for each stratum, and then determine the point cloud data obtained by using the appropriate interpolation method for each stratum; export the point cloud data to Rhino modeling software;

[0043] The comprehensive evaluation analysis includes S31: constructing evaluation indicators; the evaluation indicators include local deviation, boundary smoothness, uniformity index and root mean square error;

[0044] S311: The local deviation reflects the prediction accuracy of the interpolation method in the local area by comparing the difference between the interpolation point near the borehole and the borehole exploration data. The absolute value of the difference between the interpolation point near the borehole and the borehole exploration data closest to the interpolation point is taken, and then the absolute value of all the difference values ​​is averaged to obtain the local deviation. The specific calculation formula is:

[0045]

[0046] Where, represents the value of the interpolation point i, represents the nearest borehole exploration data to the interpolation point, and n is the number of interpolation points near the borehole;

[0047] S312: The boundary smoothness refers to the smoothness of the interpolation points in the modeling boundary area. The interpolation points in the modeling boundary area are extracted, and the absolute values ​​of the differences between adjacent interpolation points are calculated. The boundary smoothness is obtained by averaging all the absolute values ​​after the differences. The specific calculation formula is:

[0048]

[0049] Where Z i and Z i+1 They represent the values ​​of adjacent interpolation points in the boundary area; m is the total number of interpolation points in the boundary area;

[0050] S313: The uniformity index is an indicator that measures the uniformity of the distribution of interpolation points. The ratio of the standard deviation of the interpolation points to the mean reflects the degree of spatial dispersion of the interpolation points. A uniformity index close to 1 indicates a more uniform interpolation result, while a uniformity index close to 0 indicates a larger distribution fluctuation. First, the standard deviation σ(Z) of all interpolation points is calculated. The standard deviation can measure the dispersion of the interpolation points. The smaller the standard deviation, the more concentrated the distribution. Then, the mean μ(Z) of the interpolation points is calculated. The mean is used to normalize the standard deviation so that the uniformity index is comparable in value. The uniformity index is calculated by subtracting the ratio of the standard deviation to the uniformity from 1. The specific calculation formula is as follows:

[0051]

[0052] Where Z represents the value of all interpolation points; σ(Z) represents the standard deviation of all interpolation points; μ(Z) represents the mean of all interpolation points;

[0053] S314: The root mean square error (RMSE) is the square root mean of the difference between the interpolation point and the nearest borehole exploration data, and is used to measure the overall deviation between the interpolation result and the actual situation. First, the difference between each interpolation point and the nearest borehole exploration data is calculated and squared. Then, the sum is averaged and the square root is taken. The specific calculation formula is:

[0054]

[0055] Where, represents the value of the interpolation point i, represents the nearest drilling exploration data to the interpolation point, and j is the total number of interpolation points;

[0056] S32: Normalization processing: Since different evaluation indicators have different dimensions, normalization is required to convert all evaluation indicators to the same dimension. The specific calculation formula is:

[0057]

[0058] Where A is the set of results of all interpolation methods calculated using a certain evaluation index, min(A) and max(A) are the minimum and maximum values ​​in the set of results of all interpolation methods, respectively, a is the result of a certain interpolation method, that is, a data in the set, a norm It is the normalized result of a certain interpolation method calculated using a certain evaluation index;

[0059] S33: assign corresponding weights to different evaluation indicators according to different focuses; use radar charts and other methods to present the performance of different interpolation methods on different evaluation indicators to intuitively understand the advantages and disadvantages of each interpolation method; determine the appropriate interpolation method for each stratum, and then determine the point cloud data obtained using the interpolation method for each stratum;

[0060] S34: Export point cloud data to Rhino modeling software;

[0061] S4: In Rhino modeling software, the point cloud data is converted into a mesh surface and laid out into a uniform smooth surface. The edge curve of the smooth surface is extracted and the entity is extruded downward. The grid is divided according to the Griddle plug-in to generate a multi-stratum 3D geological model.

[0062] S5: Import the generated multi-stratum 3D geological model into other software for analysis, such as numerical simulation software FLAC3D, 3DEC, ABAOUS, ANSYS, etc.

Claims

1. A three-dimensional geological modeling method based on multiple interpolation of borehole data, characterized in that: The steps include: S1: Determine the scope of 3D geological modeling and the exploration data of multiple boreholes within it, determine the plane coordinates of different boreholes, and convert the boundary points of the same stratum in different boreholes into spatial z coordinates; S2: For each stratum, based on the spatial z coordinates obtained from multiple boreholes, different interpolation methods are used to perform interpolation operations to obtain the point cloud data of each stratum; S3: Comprehensively evaluate and analyze the point cloud data obtained by each interpolation method, determine the appropriate interpolation method for each stratum, and then determine the point cloud data obtained by using the appropriate interpolation method for each stratum; export the point cloud data to Rhino modeling software; The method comprises the steps of: constructing evaluation indicators, wherein the evaluation indicators include local deviation, boundary smoothness, uniformity index and root mean square error; S311: The local deviation calculation method is: Where, represents the value of the interpolation point i, represents the nearest borehole exploration data to the interpolation point, and n is the number of interpolation points near the borehole; S312: The boundary smoothness calculation method is: Where Z i and Z i+1 They represent the values ​​of adjacent interpolation points in the boundary area; m is the total number of interpolation points in the boundary area; S313: The uniformity index calculation method is: Where Z represents the value of all interpolation points; σ(Z) represents the standard deviation of all interpolation points; μ(Z) represents the mean of all interpolation points; S314: The root mean square error RMSE is calculated as follows: Where, represents the value of the interpolation point i, represents the nearest drilling exploration data to the interpolation point, and j is the total number of interpolation points; S32: normalization processing; The calculation formula is: Where A is the set of results of all interpolation methods calculated using a certain evaluation index, min(A) and max(A) are the minimum and maximum values ​​in set A, respectively, a is the result of a certain interpolation method, a norm It is the normalized result of a certain interpolation method calculated using a certain evaluation index; S33: assigning corresponding weights to different evaluation indicators according to different focuses; using radar charts to present the performance of different interpolation operation methods on different evaluation indicators; determining a suitable interpolation operation method for each stratum, and then determining point cloud data obtained using the interpolation operation method for each stratum; S34: Export point cloud data to Rhino modeling software; S4: In Rhino modeling software, the point cloud data is converted into a mesh surface and laid out into a uniform smooth surface. The edge curve of the smooth surface is extracted and the entity is extruded downward. The grid is divided according to the Griddle plug-in to generate a multi-stratum 3D geological model. S5: Import the generated multi-stratum 3D geological model into other software for analysis.

2. The multiple interpolation 3D geological modeling method based on borehole data according to claim 1, characterized in that: In step S1 , the z coordinates of the same stratum space include the z coordinates of the top plate space and the z coordinates of the bottom plate space.

3. The multiple interpolation 3D geological modeling method based on borehole data according to claim 1, characterized in that: In step S2, the point cloud data includes at least the spatial z coordinates of the strata between the boreholes and the spatial z coordinates of the strata at the boundaries of the three-dimensional geological modeling range.

4. The multiple interpolation 3D geological modeling method based on borehole data according to any one of claims 1 to 3, characterized in that: In step S2, the interpolation operation includes Kriging interpolation method, inverse distance weighted method IDW, spline function interpolation method, trend surface interpolation method, radial basis function interpolation method and local polynomial interpolation method.

5. The multiple interpolation 3D geological modeling method based on borehole data according to claim 4, characterized in that: In step S5, the other software includes numerical simulation software FLAC3D, 3DEC, ABAOUS, and ANSYS.

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