Three-dimensional reconstruction method for coal mine karst collapse column based on morphological characteristic analysis

Abstract

According to the coal mine karst collapse column three-dimensional reconstruction method based on morphological characteristic analysis, on the basis of exposed collapse column data of underground excavation, data fusion is carried out on morphological characteristics of exposed collapse columns in a mode of combining an analytic hierarchy process and an entropy weight method, then regression analysis is utilized to carry out collapse column height fitting, and the three-dimensional reconstruction of the exposed collapse columns is realized. And finally, three-dimensional reconstruction of the collapse column is carried out by using an interpolation algorithm and a Dironey triangulation network, so that specific detail features of the collapse column can be accurately expressed, an important collapse column model foundation is provided for quantitative evaluation work of hidden disaster-causing factors of a coal mine, and the method is suitable for large-scale industrial use and popularization.

Description

Technical Field

[0001] The invention belongs to the application field of three-dimensional geological models of coal mines, and in particular relates to a three-dimensional reconstruction method of coal mine karst collapse columns based on morphological feature analysis. Background Art

[0002] When analyzing hidden disaster-causing factors in a well field, collapse column is one of the most influential factors among all types of disaster-causing bodies. However, the current evaluation of collapse column mostly remains at the level of qualitative and partially quantitative description and analysis, and lacks a quantitative evaluation method in three-dimensional space. The reason is that there are few three-dimensional descriptions of collapse column, which greatly reduces the availability of evaluation results. With the continuous development of current three-dimensional geological modeling, making full use of various drilling and geophysical data to construct three-dimensional geological models can provide a reliable model basis for the quantitative evaluation of hidden disaster-causing factors.

[0003] At the same time, the three-dimensional modeling of collapse columns in the industry is mainly carried out through inversion using geophysical methods such as three-dimensional seismic to obtain the three-dimensional model. Its advantage is that it can define the development range of collapse columns, but its specific detailed features such as development height, collapse angle and plane section shape cannot be accurately expressed, resulting in the inability to provide calculable objective indicators for the quantitative evaluation of collapse columns. Summary of the invention

[0004] The purpose of the present invention is to provide a three-dimensional reconstruction method of coal mine karst collapse column based on morphological feature analysis, so as to solve the problem that the methods in the prior art cannot accurately express the specific detailed features of the collapse column.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions to achieve the above problems:

[0006] A three-dimensional reconstruction method of coal mine karst collapse column based on morphological feature analysis includes the following steps:

[0007] Step 1: Collect the data of coal mine karst collapse columns mined underground, convert the shape characteristics of all coal mine karst collapse columns into quantitative characteristics, calculate the collapse angles of all coal mine karst collapse columns, and obtain all quantitative characteristics of all coal mine karst collapse columns;

[0008] Step 2, determine the comprehensive weight, and fuse all the quantitative features of all coal mine karst collapse columns according to the comprehensive weight to obtain the comprehensive evaluation index of each coal mine karst collapse column;

[0009] Step 3, calculating the height of all coal mine karst collapse columns according to the comprehensive evaluation index of each coal mine karst collapse column;

[0010] Step 4, completing the three-dimensional reconstruction of the coal mine karst collapse columns according to the heights of all coal mine karst collapse columns.

[0011] The present invention also has the following features:

[0012] Furthermore, in the data of the exposed coal mine karst collapse columns in the underground excavation in step 1, all the quantitative characteristics of the coal mine karst collapse columns further include the major axis, minor axis, perimeter, and area of the coal mine karst collapse columns.

[0013] Furthermore, in step 1, when converting the shape characteristics of all the coal mine karst collapse columns into quantitative characteristics, specifically, the shape of the coal mine karst collapse columns is mapped according to the relationship between the area and perimeter of the coal mine karst collapse columns, and the mapping relational formula is as follows:

[0014]

[0015] where Geometry represents the shape of the selected coal mine karst collapse column;

[0016] Area represents the area of the selected coal mine karst collapse column;

[0017] Round represents the perimeter of the selected coal mine karst collapse column;

[0018] The collapse angle of the coal mine karst collapse column is calculated using the following formula:

[0019]

[0020] where Angel represents the collapse angle of the selected coal mine karst collapse column;

[0021] h represents the elevation difference at the selected coal mine karst collapse column exposed by the upper and lower coal seams;

[0022] r1 and r2 respectively represent the radii of the equivalent circles of the areas of the selected coal mine karst collapse column exposed by the upper and lower coal seams.

[0023] Furthermore, step 2 includes the following sub-steps:

[0024] Step 21, calculating the subjective weight value of each quantitative characteristic of the coal mine karst collapse column using the AHP (Analytic Hierarchy Process) method; obtaining the objective weight value of each quantitative characteristic using the EWM (Entropy Weight Method);

[0025] Step 22, according to the judgment matrix, linearly weighting the subjective weight and objective weight of each quantitative characteristic using the following formula to obtain the comprehensive weight value corresponding to each quantitative characteristic:

[0026] Weight = AHP_Weight * q + EWM_Weight * (1 - q)

[0027] where Weight represents the comprehensive weight value;

[0028] q represents the weight of AHP confirmed according to the judgment matrix;

[0029] The weight value after linear weighting of AHP is represented by AHP_Weight*q;

[0030] The weight value after linear weighting of EWM is represented by EWM_Weight*(1 - q);

[0031] Step 23: Perform feature fusion on all quantitative features of all coal mine karst collapse columns according to the comprehensive weight value to obtain the comprehensive evaluation index index of each coal mine karst collapse column.

[0032] Furthermore, the judgment matrix in Step 2 is as follows:

[0033]

[0034] Furthermore, in Step 3, use the following formula to obtain the height H of the coal mine karst collapse column:

[0035] H = b*index + a

[0036] where H represents the height of the coal mine karst collapse column;

[0037] b represents the slope of the linear regression equation;

[0038] a represents the intercept of the linear regression equation;

[0039] index represents the comprehensive evaluation index of the coal mine karst collapse column.

[0040] Furthermore, the slope of the linear regression equation and the intercept of the linear regression equation in Step 3 are calculated using the following formulas respectively:

[0041]

[0042] where represents the average value of the heights of all coal mine karst collapse columns;

[0043] represents the average value of the height evaluation indexes of all coal mine karst collapse columns;

[0044] n represents the number of all coal mine karst collapse columns;

[0045] index i represents the evaluation index of the i-th coal mine karst collapse column;

[0046] H i represents the height of the i-th coal mine karst collapse column.

[0047] Further, step 4 specifically includes the following operations: Combining the subsidence angle of the corresponding coal mine karst collapse column obtained in step 1, using the interpolation algorithm to interpolate the data of the collapse column exposed by mining in the vertical direction to obtain point cloud data, and then based on the Delaunay triangulation network, performing three-dimensional reconstruction on the coal mine karst collapse column;

[0048] Repeat the above operations until the three-dimensional reconstruction of all coal mine karst collapse columns is completed.

[0049] Compared with the prior art, the present invention has the following technical effects:

[0050] The three-dimensional reconstruction method of coal mine karst collapse columns based on morphological feature analysis of the present invention, on the basis of the data of the collapse columns exposed by underground mining, uses the method of combining the analytic hierarchy process and the entropy weight method to perform data fusion on the morphological features of the exposed collapse columns, then uses regression analysis to fit the height of the collapse columns, and finally uses the interpolation algorithm and the Delaunay triangulation network to perform three-dimensional reconstruction of the collapse columns, which can accurately express the specific detail features of the collapse columns, provides an important collapse column model basis for the quantitative evaluation of hidden disaster-causing factors in coal mines, and is suitable for large-scale industrial use and promotion. Description of the Drawings

[0051] Figure 1 is the flowchart of the three-dimensional reconstruction method of coal mine karst collapse columns based on morphological feature analysis of the present invention. Detailed Embodiments

[0052] It should be noted that all the methods in the present invention, unless otherwise specified, all adopt the methods known in the prior art.

[0053] The following gives specific embodiments of the present invention. It should be noted that the present invention is not limited to the following specific embodiments, and all equivalent transformations made on the basis of the technical solutions of the present application fall within the protection scope of the present invention.

[0054] As Figure 1 shown, a three-dimensional reconstruction method of coal mine karst collapse columns based on morphological feature analysis includes the following steps:

[0055] Step 1, collect the data of the coal mine karst collapse columns exposed by underground mining, convert the shape features of the coal mine karst collapse columns into quantitative features and calculate the subsidence angle of the coal mine karst collapse columns to obtain all the quantitative features of the coal mine karst collapse columns;

[0056] Step 2, perform feature fusion on all the quantitative features of the coal mine karst collapse columns to obtain the comprehensive evaluation index matrix of the coal mine karst collapse columns;

[0057] Step 3, calculate the height of the coal mine karst collapse columns according to the comprehensive evaluation index matrix of the coal mine karst collapse columns;

[0058] Step 4: Complete the three-dimensional reconstruction of the coal mine karst collapse columns according to the heights of all coal mine karst collapse columns.

[0059] Furthermore, the sinkhole columns identified by inversion using geophysical exploration methods such as three-dimensional seismic can only delineate the development range of the sinkhole columns, but their specific details such as development height, sinkhole angle, and plane section shape cannot be accurately expressed, and the identified sinkhole column model is not a solid model.

[0060] Therefore, it is also necessary to use the data of exposed collapse columns from underground mining to extract and analyze the morphological characteristics of coal mine karst collapse columns, which mainly include two categories: one is quantitative description as shown in Table 1, including major axis, minor axis, collapse angle, perimeter, and area;

[0061] The other type is qualitative description, which mainly describes the shape, such as elliptical, nearly elliptical, and nearly circular.

[0062]

[0063] Table 1. Comparison table of quantitative description of collapse column

[0064] Specifically, in step 1, the shape of the coal mine karst collapse column is mapped according to the relationship between the area and the perimeter of the coal mine karst collapse column, and the mapping relationship is as follows:

[0065]

[0066] Among them, Geometry represents the shape of the coal mine karst collapse column;

[0067] Area represents the area of ​​the coal mine karst collapse column;

[0068] Round represents the perimeter of the coal mine karst collapse column;

[0069] The following formula is used to calculate the collapse angle of the coal mine karst collapse column:

[0070]

[0071] Among them, Angel represents the collapse angle of the coal mine karst collapse column;

[0072] h represents the elevation difference at the coal mine karst collapse column exposed by the upper and lower coal seams;

[0073] r1 and r2 represent the radii of the equivalent circles of the coal mine karst collapse columns exposed by the upper and lower coal seams, respectively.

[0074] This embodiment is aimed at a coal mining area, and the method of this embodiment is used to perform characteristic statistics on the collapse column, and the results are shown in Table 2;

[0075]

[0076]

[0077]

[0078]

[0079] Table 2 Statistical Table of Characteristics of Collapse Columns

[0080] Specifically, step 2 includes the following sub-steps:

[0081] Step 2 includes the following sub-steps:

[0082] Step 21, use the AHP (Analytic Hierarchy Process) to calculate the subjective weight value of each quantitative characteristic of the coal mine karst collapse column; use the EWM (Entropy Weight Method) to obtain the objective weight value of each quantitative characteristic;

[0083] Step 22, according to the judgment matrix, use the following formula to linearly weight the subjective weight and objective weight of each quantitative characteristic to obtain the comprehensive weight value corresponding to each quantitative characteristic:

[0084] Weight = AHP_Weight * q + EWM_Weight * (1 - q)

[0085] Where, Weight represents the comprehensive weight value;

[0086] q represents the weight of AHP confirmed according to the judgment matrix;

[0087] AHP_Weight * q represents the weight value after linear weighting of AHP;

[0088] EWM_Weight * (1 - q) represents the weight value after linear weighting of EWM;

[0089] Step 23, after fusing the quantitative characteristics of the coal mine karst collapse column according to the comprehensive weight value, obtain the comprehensive evaluation index matrix of the coal mine karst collapse column.

[0090] Furthermore, the judgment matrix in step 2 is as follows:

[0091]

[0092] The finally obtained weights are shown in Table 3:

[0093]

[0094]

[0095] Table 3 Weight Table

[0096] Next, use Weight to fuse the characteristics of the karst collapse columns in Table 2. In this embodiment, Weight is a 6*1 matrix. Take the data in Table 2 as a 65*6 Statics matrix, and comprehensively evaluate the karst collapse columns through the following formula to obtain a 65*1 comprehensive evaluation index matrix Index.

[0097] Index = Statics · Weight

[0098] Specifically, in step 3, use the following formula to obtain the height H of the unknown karst collapse column:

[0099] H = b * index + a

[0100] Among them, H represents the height of the coal mine karst collapse column;

[0101] b represents the slope of the linear regression equation;

[0102] a represents the intercept of the linear regression equation;

[0103] index represents the comprehensive evaluation index of the coal mine karst collapse column.

[0104] Specifically, in the regression analysis fitting equation of step 3, a and b are calculated using the following formulas respectively:

[0105]

[0106] Among them, represents the average value of the height of the coal mine karst collapse column;

[0107] represents the average value of the evaluation index of the height of the coal mine karst collapse column;

[0108] n represents the number of all coal mine karst collapse columns;

[0109] index i represents the evaluation index of the i-th coal mine karst collapse column to be evaluated;

[0110] H i represents the height of the i-th coal mine karst collapse column.

[0111] Specifically, after fitting the height h of the coal mine karst collapse column, combine the collapse angle of the coal mine karst collapse column, use the interpolation algorithm to interpolate the data of the collapse column exposed by mining in the longitudinal direction to obtain point cloud data, and then perform three-dimensional reconstruction based on the Delaunay triangulation network.

[0112] When analyzing the concealed disaster-causing factors in the mine field, the collapse column is one of the factors with greater influence among all types of disaster-causing bodies. Based on the data of the exposed collapse columns in underground mining and excavation, this invention combines the analytic hierarchy process and the entropy weight method to conduct data fusion on the morphological characteristics of the exposed collapse columns, then uses regression analysis to fit the height of the collapse columns, combines the collapse angle parameters, and conducts three-dimensional reconstruction of the collapse columns based on the interpolation algorithm and the Delaunay triangulation network. It provides an important collapse column model basis for the quantitative evaluation of concealed disaster-causing factors in coal mines.

Claims

1. A three-dimensional reconstruction method of coal mine karst collapse columns based on morphological feature analysis, characterized in that It includes the following steps: Step 1: Collect the data of coal mine karst collapse columns mined underground, convert the shape features of all coal mine karst collapse columns into quantitative features, calculate the collapse angles of all coal mine karst collapse columns, and obtain all the quantitative features of all coal mine karst collapse columns; Step 2: Determine the comprehensive weight, perform feature fusion on all the quantitative features of all coal mine karst collapse columns according to the comprehensive weight, and obtain the comprehensive evaluation index of each coal mine karst collapse column; Step 3: Calculate the heights of all coal mine karst collapse columns according to the comprehensive evaluation index of each coal mine karst collapse column; Step 4: Complete the three-dimensional reconstruction of coal mine karst collapse columns according to the heights of all coal mine karst collapse columns.

2. The three-dimensional reconstruction method of coal mine karst collapse columns based on morphological feature analysis according to claim 1, wherein Among the data of the exposed coal mine karst collapse columns mined underground in Step 1, all the quantitative features of the coal mine karst collapse columns also include the major axis, minor axis, perimeter, and area of the coal mine karst collapse columns.

3. The three-dimensional reconstruction method of coal mine karst collapse columns based on morphological feature analysis according to claim 2, wherein, In Step 1, when converting the shape features of all coal mine karst collapse columns into quantitative features, specifically, the shape of the coal mine karst collapse column is mapped according to the relationship between the area and perimeter of the coal mine karst collapse column, and the mapping relation formula is as follows: Among them, Geometry represents the shape of the selected coal mine karst collapse column; Area represents the area of the selected coal mine karst collapse column; Round represents the perimeter of the selected coal mine karst collapse column; The collapse angle of the coal mine karst collapse column is calculated using the following formula: Among them, Angel represents the collapse angle of the selected coal mine karst collapse column; h represents the elevation difference at the location of the selected coal mine karst collapse column exposed by the upper and lower coal seams; r1 and r2 respectively represent the radii of the equivalent circles of the areas of the selected coal mine karst collapse column exposed by the upper and lower coal seams.

4. The three-dimensional reconstruction method of coal mine karst collapse columns based on morphological feature analysis according to claim 1, characterized in that Step 2 includes the following sub-steps: Step 21: Calculate the subjective weight values of each quantitative feature of the coal mine karst collapse column using the AHP (Analytic Hierarchy Process) method; obtain the objective weight values of each quantitative feature using the EWM (Entropy Weight Method); Step 22: According to the judgment matrix, linearly weight the subjective weight and objective weight of each quantitative feature using the following formula to obtain the comprehensive weight value corresponding to each quantitative feature: Weight = AHP_Weight * q + EWM_Weight * (1 - q) Among them, Weight represents the comprehensive weight value; q represents the weight occupied by AHP confirmed according to the judgment matrix; AHP_Weight * q represents the weight value after AHP linear weighting; EWM_Weight * (1 - q) represents the weight value after EWM linear weighting; Step 23: Perform feature fusion on all the quantitative features of all coal mine karst collapse columns according to the comprehensive weight value, and obtain the comprehensive evaluation index index of each coal mine karst collapse column.

5. The three-dimensional reconstruction method of coal mine karst collapse columns based on morphological feature analysis according to claim 4, wherein The judgment matrix in Step 2 is as follows:

6. The three-dimensional reconstruction method of coal mine karst collapse columns based on morphological feature analysis according to claim 1, characterized in that In Step 3, use the following formula to obtain the height H of the coal mine karst collapse column: H = b * index + a Among them, H represents the height of the coal mine karst collapse column; b represents the slope of the linear regression equation; a represents the intercept of the linear regression equation; index represents the comprehensive evaluation index of the coal mine karst collapse column.

7. The three-dimensional reconstruction method of coal mine karst collapse columns based on morphological feature analysis according to claim 6, wherein, The slope of the linear regression equation and the intercept of the linear regression equation in Step 3 are calculated using the following formulas respectively: Among them, represents the average height of all karst collapse columns in coal mines; represents the average value of the height evaluation indices of all karst collapse columns in coal mines; n represents the number of all coal mine karst collapse columns; index i Indicates the evaluation index of the i-th karst collapse column in coal mines; H i represents the height of the i-th karst collapse column in the coal mine.

8. The three-dimensional reconstruction method of coal mine karst collapse columns based on morphological feature analysis according to claim 1, characterized in that, Step 4 specifically includes the following operations: Combining the subsidence angle of the corresponding coal mine karst collapse column obtained in Step 1, using the interpolation algorithm to interpolate the collapse column data revealed by mining in the vertical direction to obtain point cloud data, and then based on the Delaunay triangulation network, performing 3D reconstruction on the coal mine karst collapse column; Repeat the above operations until the 3D reconstruction of all coal mine karst collapse columns is completed.

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