[1]张小艳,王萌娟.基于克里金算法的井田煤层三维建模方法研究[J].计算机技术与发展,2022,32(04):164-170.[doi:10. 3969 / j. issn. 1673-629X. 2022. 04. 028]
　ZHANG Xiao-yan,WANG Meng-juan.Research on 3D Modeling Method of Coal Seam in Mine Field Based on Kriging Algorithm[J].,2022,32(04):164-170.[doi:10. 3969 / j. issn. 1673-629X. 2022. 04. 028]
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基于克里金算法的井田煤层三维建模方法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年04期
页码:
164-170
栏目:
应用前沿与综合
出版日期:
2022-04-10

文章信息/Info

Title:
Research on 3D Modeling Method of Coal Seam in Mine Field Based on Kriging Algorithm
文章编号:
1673-629X(2022)04--0164-07
作者:
张小艳; 王萌娟
西安科技大学 计算机科学与技术学院,陕西 西安 710600
Author(s):
ZHANG Xiao-yan; WANG Meng-juan
School of Computer Science and Technology,Xi’an University of Science and Technology,Xi’an 710600,China
关键词:
三维建模; 克里金法; 井田; 支持向量回归; 自适应差分进化算法
Keywords:
3D modeling; Kriging; mine; support vector regression; adaptive differential evolution algorithm
分类号:
TP301. 6
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 04. 028
摘要:
为了增加实现井田煤层三维模型的数据点,得到更为真实的井田煤层图形,提出了基于改进的克里金插值算法的煤层高程估算模型。 利用钻孔数据和高程数据计算实验变差函数值和步长,根据步长分组计算得到组内平均半方差,采用支持向量回归对变差函数进行拟合,避免了常规克里金对变差函数选择的局限性;在支持向量回归参数的求解中,采用可修改变异方向的差分进化算法对其进行优化,求出支持向量回归最优参数,得出最优理论变差函数;构建了基于支持向量回归的克里金高程估算模型,并与其他优化算法构建的克里金估算模型在同一数据集上进行实验对比,实验结果表明,基于改进支持向量回归优化的克里金估算模型的估算精度更高,建立的某井田煤层三维图形能够更为真实地反映井田煤层的分布情况。
Abstract:
In order to increase the number of data points for realizing the three-dimensional model of coal seams in coal mines and draw more realistic? ? coal seam graphics in coal mines,a coal seam elevation estimation model based on improved Kriging interpolation algorithm is proposed. The borehole data and elevation data are used to calculate the experimental variogram value and step length,the averagesemivariance within the group is calculated according to the step length grouping, and support vector regression is used to fit thevariogram,avoiding the limitations of conventional Kriging for the selection of the variogram. In the solution of support vector regression parameters,the differential evolution algorithm with modified variation direction is used to optimize them,and the optimal support vector regression parameters are obtained,and the optimal theoretical variation function is obtained. A Kriging elevation estimation model based on support vector regression is constructed and compared with the Kriging estimation model constructed by other optimization algorithms on the same data set. The experimental results show that Kriging estimation model based on improved support vector regression optimization has higher estimation accuracy,and the established three-dimensional graphs of coal seams in a certain mine field can more truly reflect the distribution of coal? seams in the mine field.

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更新日期/Last Update: 2022-04-10