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开采沉陷监测数据处理的分段卡尔曼滤波模型

郝登程 王国瑞 李培现 沈佳琦 曹禹锡 杨中辉

郝登程, 王国瑞, 李培现, 沈佳琦, 曹禹锡, 杨中辉. 开采沉陷监测数据处理的分段卡尔曼滤波模型[J]. 矿业科学学报, 2021, 6(4): 371-378. doi: 10.19606/j.cnki.jmst.2021.04.001
引用本文: 郝登程, 王国瑞, 李培现, 沈佳琦, 曹禹锡, 杨中辉. 开采沉陷监测数据处理的分段卡尔曼滤波模型[J]. 矿业科学学报, 2021, 6(4): 371-378. doi: 10.19606/j.cnki.jmst.2021.04.001
Hao Dengcheng, Wang Guorui, Li Peixian, Shen Jiaqi, Cao Yuxi, Yang Zhonghui. Subsection Kalman filter model for mining subsidence monitoring data processing[J]. Journal of Mining Science and Technology, 2021, 6(4): 371-378. doi: 10.19606/j.cnki.jmst.2021.04.001
Citation: Hao Dengcheng, Wang Guorui, Li Peixian, Shen Jiaqi, Cao Yuxi, Yang Zhonghui. Subsection Kalman filter model for mining subsidence monitoring data processing[J]. Journal of Mining Science and Technology, 2021, 6(4): 371-378. doi: 10.19606/j.cnki.jmst.2021.04.001

开采沉陷监测数据处理的分段卡尔曼滤波模型

doi: 10.19606/j.cnki.jmst.2021.04.001
基金项目: 

中央高校基本科研业务费专项资金 2020YQDC05

河北省自然科学基金生态智慧矿山联合基金 E2020402086

详细信息
    作者简介:

    郝登程(1997—),男,山西平遥人,硕士研究生,主要从事矿山开采沉陷及岩层移动的多源监测方向的研究工作。Tel:18586042726,E-mail:hdc@student.cumtb.edu.cn

    通讯作者:

    李培现(1983—),男,山东巨野人,博士,副教授,主要从事矿山开采沉陷及岩层移动方面的研究工作。Tel:18310258116,E-mail:lipx@cumtb.edu.cn

  • 中图分类号: P228

Subsection Kalman filter model for mining subsidence monitoring data processing

  • 摘要: 为解决长周期、高频率的GNSS煤矿开采沉陷监测数据受外界环境影响大、干扰噪声高、可靠性低的问题,采用卡尔曼滤波对开采沉陷GNSS监测数据进行分段滤波处理。首先采用回归分析方法将监测数据分割为开始阶段-活跃阶段-衰退阶段三部分。开始阶段、衰退阶段下沉较为稳定,以标准卡尔曼滤波模型对沉陷数据进行滤波处理;活跃阶段沉陷变化快,通过构建加入修正数的卡尔曼滤波模型对沉陷数据进行处理。采用Python语言建立滤波程序对宁夏某矿5年沉降监测数据计算分析结果显示,对不同阶段采用不同的卡尔曼滤波结果与实测过程曲线吻合,滤波效果良好。加入修正数的卡尔曼滤波模型可以有效地处理沉陷变化大的矿区监测数据,减少数据波动对沉陷结果的影响,提高监测数据的可靠性。研究成果为长周期、高频率的沉陷监测数据处理提供了科学依据。
  • 图  1  卡尔曼滤波流程

    Figure  1.  Kalman filtering flow chart

    图  2  沉陷监测数据处理流程

    Figure  2.  Flow chart of settlement monitoring data processing

    图  3  原始数据下沉过程曲线

    Figure  3.  Original data subsidence process curve

    图  4  K值计算结果

    Figure  4.  Calculation result of K value

    图  5  分割后第一部分数据下沉过程曲线

    Figure  5.  Data subsidence process curve of the first part after segmentation

    图  6  分割后第二部分数据下沉过程曲线

    Figure  6.  Data subsidence process curve of the second part after segmentation

    图  7  分割后第三部分数据下沉过程曲线

    Figure  7.  Data subsidence process curve of the third part after segmentation

    图  8  F检验图

    Figure  8.  F test diagram

    图  9  第一部分数据卡尔尔曼滤波图

    Figure  9.  Kalman filter graph for the first part of data after segmentation

    图  10  第二部分数据差分值卡尔曼滤波图

    Figure  10.  Kalman filter graph of data difference value in the second part after segmentation

    图  11  第二部分数据卡尔尔曼滤波图

    Figure  11.  Kalman filter data in the second part after segmentation

    图  12  第二部分数据卡尔尔曼滤波图细节

    Figure  12.  Details of the second part of Kalman filter data after segmentation

    图  13  分割后第三部分数据卡尔曼滤波图

    Figure  13.  Kalman filter data in the third part after segmentation

    图  14  卡尔曼滤波数据处理结果

    Figure  14.  Kalman filter data processing result diagram

    图  15  卡尔曼滤波数据处理结果对比

    Figure  15.  Comparison of data processing results of Kalman filter

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  • 收稿日期:  2020-12-29
  • 修回日期:  2021-03-02
  • 刊出日期:  2021-08-01

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