基于UPSO-Kriging的综采工作面三维建模研究

doi:10.11720/wtyht.2021.0087

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摘要

基于传统地质统计学的综采工作面煤层赋存形态三维建模的基础是Kriging插值算法,但其选择并拟合的变差函数模型并不能较好地反映实际的地质特征与空间数据的变化趋势。对此,本文提出UPSO-Kriging插值法:针对PSO算法存在的收敛速度慢、易陷入局部解等问题,优化算法,将优化的PSO算法(UPSO)引入Kriging插值中求解变差参数,拟合出变差函数模型,实现工作面煤层结构中各层的高程值预测;在此基础上,基于规则网格法建立DEM数字高程模型,运用Three.js实现了综采工作面煤层赋存形态三维可视化,可为煤炭企业的透明、智能、分质开采提供科学依据。

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	张小艳
	许慧
	姜水军

关键词 ： Kriging插值, 变差函数, 粒子群算法, 规则网格, 三维可视化

Abstract：

Kriging interpolation algorithm is the basis of 3D modeling of coal seam occurrence form in fully mechanized mining face based on traditional geostatistics. However, the variation function model selected and fitted by Kriging interpolation algorithm cannot reflect the actual geological characteristics and the variation trend of spatial data. In this paper, UPSO-Kriging interpolation method is thus proposed: PSO algorithm is optimized to solve the problems of slow convergence and easily falling into local solution, and the optimized algorithm UPSO is then introduced into Kriging interpolation to solve the variation parameters and fit the variation function model, thus realizing the height prediction of each layer in the coal seam structure of working face. In addition, with DEM model established on regular grid method, the three-dimensional visualization of the occurrence form of coal seam in fully mechanized mining face is realized by using Three.js, which provides scientific basis for transparent mining, intelligent mining and quality mining of coal enterprises.

Key words： Kriging interpolation variation function PSO regular grid 3D visualization

收稿日期: 2021-02-23 修回日期: 2021-03-18 出版日期: 2021-08-20

ZTFLH:

TP391

基金资助:神东集团煤质预测现场管理(合作项目)(20199154803)

通讯作者: 许慧

作者简介: 张小艳1967-),女,汉族,陕西省西安市,硕士,教授,研究方向为人工智能及应用。Email: 1161880978@qq.com

引用本文:

张小艳, 许慧, 姜水军. 基于UPSO-Kriging的综采工作面三维建模研究[J]. 物探与化探, 2021, 45(4): 1071-1076.
ZHANG Xiao-Yan, XU Hui, JIANG Shui-Jun. Research on 3D modeling of fully mechanized mining face based on UPSO-Kriging. Geophysical and Geochemical Exploration, 2021, 45(4): 1071-1076.

链接本文:

https://www.wutanyuhuatan.com/CN/10.11720/wtyht.2021.0087 或 https://www.wutanyuhuatan.com/CN/Y2021/V45/I4/1071

Fig.1 Sphere函数收敛测试

Fig.2 Schwefel函数收敛测试

Fig.4 Rastrigrin函数收敛测试

Table 1 采样点数据

Table 2 PSO算法的参数设置

Table 3 变差参数的边界设置

Table 4 估算精度比较

Fig.5 采样点高程分布对比实验

Fig.6 单层DEM模型

Fig.7 多层DEM模型

Fig.8 人机交互

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