Multi-interpolation three-dimensional geological modeling method based on drilling data

Abstract

The invention relates to the field of coal mine three-dimensional geological modeling, in particular to a multi-interpolation three-dimensional geological modeling method based on drill hole data, which comprises the following steps: determining a three-dimensional geological modeling range and exploration data of a plurality of drill holes in the three-dimensional geological modeling range, determining plane coordinates of different drill holes, and converting the same stratum demarcation point of the different drill holes into a space z coordinate; performing interpolation operation by adopting different interpolation operation methods to obtain point cloud data of each stratum; comprehensive evaluation analysis is carried out to determine a proper interpolation operation method for each stratum; exporting the point cloud data to rhinoceros modeling software; extruding an entity in rhinoceros modeling software, performing grid division according to a Griddle plug-in, and generating a multi-stratum three-dimensional geologic model; and importing the generated multi-stratum three-dimensional geologic model into other software for analysis. According to the method, advantages and disadvantages of various interpolation operation methods can be considered through the established evaluation indexes, and it is ensured that the generated model better conforms to actual geological conditions; the method is especially suitable for modeling work in a complex geological environment.

Description

Technical Field

[0001] The 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 many fields such as geological exploration, mining and geotechnical engineering. With the increasing demand for natural resource development, three-dimensional geological modeling has become an important tool for understanding underground structures, evaluating resource reserves and formulating development plans. At present, numerical simulation analysis through three-dimensional geological modeling is very popular. It helps researchers understand complex geological bodies and their interrelationships by intuitively displaying underground structures. In addition, it can predict and evaluate geological-related disasters caused by activities such as human 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, the current technology still has many shortcomings and limitations. First, traditional modeling methods often rely on one borehole data, which leads to incomplete extraction of stratum characteristics and no undulation in the constructed model strata. In fact, according to multiple borehole data in the exploration area, the same stratum is often undulating, which causes inaccurate modeling. Secondly, the 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 according to the actual conditions on site and the content of numerical simulation is still a difficult problem. In addition, most of the existing interpolation methods focus on a single interpolation process, lack quantitative analysis of interpolation data, and it is difficult to intuitively evaluate the rationality of the results.

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

[0005] In view of the above technical problems, the present invention proposes a multiple interpolation three-dimensional geological modeling method based on drilling data, comprising 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 same stratum boundary points of 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 point cloud data of each stratum;

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

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

[0010] S5: Import the generated multi-strata 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, inverse distance weighted IDW, spline function interpolation, trend surface interpolation, radial basis function interpolation and local polynomial interpolation.

[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] In the formula, represents the value of interpolation point i, represents the nearest drilling exploration data to the interpolation point, and n is the number of interpolation points near the drilling hole;

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

[0019]

[0020] In the formula, Z i and Z i+1 They represent the values ​​of adjacent interpolation points in the boundary area respectively; 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 calculation method is:

[0025]

[0026] In the formula, represents the value of 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, and 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 by 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] Beneficial effects of the present invention: 1. The present invention effectively solves the problem of insufficient utilization of multiple borehole exploration data in traditional three-dimensional modeling methods. Previous three-dimensional modeling methods often rely only on single borehole exploration data, resulting in incomplete extraction of stratum characteristics, lack of undulating characteristics in the modeled strata, and failure to truly reflect the complexity of the underground structure. The present invention integrates multiple borehole exploration data to generate a three-dimensional geological model that can more accurately reflect the undulating state of the actual strata, thereby improving the authenticity and reliability of the model.

[0034] 2. The present invention comprehensively considers a variety of interpolation methods and enhances the flexibility of interpolation method selection. The prior art often adopts a single interpolation method and lacks comparative analysis of multiple interpolation 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 results. The present invention can consider the advantages and disadvantages of various interpolation 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 It is a flow chart of the method for three-dimensional geological modeling based on multiple interpolation of borehole data of 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 It 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 multiple interpolation three-dimensional geological modeling method based on drilling data, comprising the following steps:

[0040] 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 of different boreholes into spatial z coordinates, including the spatial z coordinates of the top plate and the bottom plate of each stratum;

[0041] S2: for each stratum, based on the spatial z coordinates obtained from multiple boreholes, different interpolation operation methods are used to perform interpolation operations to obtain point cloud data of each stratum, wherein 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 boundary of the three-dimensional geological modeling range; 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; these interpolation operation methods are all well-known methods in the art and will not be described in detail here;

[0042] S3: Comprehensively evaluate and analyze the point cloud data (a set of interpolation points) obtained by each interpolation operation method, determine a suitable interpolation operation method for each stratum, and then determine the point cloud data obtained by using the suitable interpolation operation 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 operation method in the local area by comparing the difference between the interpolation point near the borehole and the borehole exploration data; the interpolation point near the borehole and the borehole exploration data closest to the interpolation point are subtracted and the absolute value is taken, and then all the absolute values ​​after the difference are averaged to obtain the local deviation; the specific calculation formula is:

[0045]

[0046] In the formula, represents the value of interpolation point i, represents the nearest drilling exploration data to the interpolation point, and n is the number of interpolation points near the drilling hole;

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

[0050] S313: The uniformity index is an indicator to measure the uniformity of the distribution of the interpolation points; the ratio of the standard deviation of the interpolation points to the mean reflects the degree of dispersion of the interpolation points in space; the closer the uniformity index is to 1, the more uniform the interpolation result is; the closer it is to 0, the greater the distribution fluctuation is; 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 is; 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 drilling exploration data, which 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 drilling exploration data is calculated and squared, then the sum is averaged and the square root is taken. The specific calculation formula is:

[0054]

[0055] In the formula, represents the value of 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] In the formula, 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 operation methods on different evaluation indicators, so as to intuitively understand the advantages and disadvantages of each interpolation operation method; determine a suitable interpolation operation method for each stratum, and then determine the point cloud data obtained by using the interpolation operation method for each stratum;

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

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

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

Claims

1. A multiple interpolation three-dimensional geological modeling method based on 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 same stratum boundary points of 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 point cloud data of each stratum; S3: Comprehensively evaluate and analyze the point cloud data obtained by each interpolation operation method, determine the appropriate interpolation operation method for each stratum, and then determine the point cloud data obtained by using the appropriate interpolation operation method for each stratum; export the point cloud data to Rhino modeling software; Comprising S31: constructing evaluation indicators; the evaluation indicators include local deviation, boundary smoothness, uniformity index and root mean square error; S311: The local deviation calculation method is: In the formula, represents the value of interpolation point i, represents the nearest drilling exploration data to the interpolation point, and n is the number of interpolation points near the drilling hole; S312: The boundary smoothness calculation method is: In the formula, Z i and Z i+1 They represent the values ​​of adjacent interpolation points in the boundary area respectively; 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 calculation method is: In the formula, represents the value of 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, and 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 by using the interpolation operation method for each stratum; S34: Export point cloud data to Rhino modeling software. S4: In the Rhino modeling software, the point cloud data is converted into a mesh surface and draped into a uniform smooth surface. The edge curve of the smooth surface is extracted and the entity is extruded downward. The mesh is divided according to the Griddle plug-in and a multi-stratum 3D geological model is generated. S5: Import the generated multi-strata 3D geological model into other software for analysis.

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

3. The multiple interpolation 3D geological modeling method based on drilling data according to claim 1 is characterized in that: 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 boundary of the three-dimensional geological modeling range.

4. The method for three-dimensional geological modeling based on multiple interpolation of 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 method for multiple interpolation 3D geological modeling 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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