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基于优化领先狼群算法的微震源定位研究

李晓燕 张明伟 宋雷 庞迎春 张结如

李晓燕, 张明伟, 宋雷, 庞迎春, 张结如. 基于优化领先狼群算法的微震源定位研究[J]. 矿业科学学报, 2024, 9(2): 233-242. doi: 10.19606/j.cnki.jmst.2024.02.010
引用本文: 李晓燕, 张明伟, 宋雷, 庞迎春, 张结如. 基于优化领先狼群算法的微震源定位研究[J]. 矿业科学学报, 2024, 9(2): 233-242. doi: 10.19606/j.cnki.jmst.2024.02.010
LI Xiaoyan, ZHANG Mingwei, SONG Lei, PANG Yingchun, ZHANG Jieru. Research on microseismic source localization based on optimized leading wolfpack algorithm[J]. Journal of Mining Science and Technology, 2024, 9(2): 233-242. doi: 10.19606/j.cnki.jmst.2024.02.010
Citation: LI Xiaoyan, ZHANG Mingwei, SONG Lei, PANG Yingchun, ZHANG Jieru. Research on microseismic source localization based on optimized leading wolfpack algorithm[J]. Journal of Mining Science and Technology, 2024, 9(2): 233-242. doi: 10.19606/j.cnki.jmst.2024.02.010

基于优化领先狼群算法的微震源定位研究

doi: 10.19606/j.cnki.jmst.2024.02.010
详细信息
    作者简介:

    李晓燕(2000—),女,硕士研究生,山东潍坊人,主要从事微震定位方面的研究工作。Tel:18706558383,E-mail:1165040112@qq.com

    通讯作者:

    张明伟(1984—),男,山东昌乐人,博士,副研究员,硕士生导师,主要从事煤矿灾害监测与防治研究工作。Tel:13615138879,E-mail:mingweizhang@cumt.edu.cn

  • 中图分类号: TD76

Research on microseismic source localization based on optimized leading wolfpack algorithm

  • 摘要: 为分析不同启发式方法对求解微震源定位精度问题的影响,提出一种优化领先狼群算法。该算法在领先狼群算法的基础上,调整搜索步长和围攻步长两个参数,提高了在搜索过程中跳出局部最优解的能力。通过理论模型反演和工程数据分析,验证了优化领先狼群算法的有效性。与常用的粒子群算法和模拟退火算法两种启发式算法相比,优化领先狼群算法收敛更快,精度更高,受P波波速误差影响更小。该算法为智能启发式算法应用于微震源定位提供了新思路。
  • 图  1  微震源定位原理示意图

    Figure  1.  The schematic diagram of the microseismic source localization principle

    图  2  领先狼群算法基本流程

    Figure  2.  The basic flowchart of the Dominant Wolf Pack Algorithm

    图  3  传感器及震源的空间位置

    Figure  3.  The spatial configuration of sensors and the microseismic sources

    图  4  震源1某次迭代中首领狼位置的移动图示

    Figure  4.  The movement of the alpha wolf's position during a certain iteration for Source 1

    图  5  对波速添加扰动后3种算法定位误差对比

    Figure  5.  The comparison of localization errors among the three algorithms after introducing perturbations to the wave velocity

    表  1  模型中传感器及震源坐标

    Table  1.   The coordinates of sensors and microseismic sources in the model

    传感器以及震源序号 坐标值/m
    X Y Z
    A 0 0 0
    B 0 1 000 0
    C 1 000 1 000 0
    D 1 000 0 0
    E 0 0 1 000
    F 0 1 000 1 000
    G 1 000 1 000 1 000
    H 1 000 0 1 000
    1 562 680 95
    2 790 514 453
    3 52 1320 130
    4 365 279 509
    5 102 225 664
    6 1 404 830 905
    下载: 导出CSV

    表  2  模型传感器坐标接收到的初至到时

    Table  2.   The first arrival times recorded by sensors in the model  ms

    传感器序号 P波初至到时
    1 2 3 4 5 6
    A 197.17 145.26 122.38 180.98 280.85 247.18
    B 232.38 229.39 154.84 159.24 242.16 239.29
    C 294.98 077.62 224.21 362.30 351.50 206.32
    D 152.37 212.24 241.61 191.18 149.43 210.13
    E 157.44 227.92 302.09 253.17 92.680 189.08
    F 414.50 373.12 223.46 287.27 363.06 314.99
    G 197.17 145.26 122.38 180.98 280.85 247.18
    H 232.38 229.39 154.84 159.24 242.16 239.29
    下载: 导出CSV

    表  3  算法优化前后定位结果

    Table  3.   Positioning results before and after optimisation

    算法 震源序号 绝对误差/m 运行时间/s
    优化前 1 3.55 1.12
    2 2.03 1.20
    3 4.25 1.04
    4 1.47 1.36
    5 3.52 1.08
    6 2.18 1.22
    优化后 1 2.03 1.43
    2 1.10 1.52
    3 0.91 1.42
    4 0.79 1.34
    5 1.01 1.59
    6 1.13 1.61
    下载: 导出CSV

    表  4  多震源下3种定位算法结果

    Table  4.   The results of the three localization algorithms under multi-source conditions

    算法名称 震源序号 算法定位结果/m 与真实值误差/m 绝对误差/m 目标函数值F 运行时间/s
    X Y Z X Y Z
    优化领先狼群算法 1 562.48 681.72 95.96 -0.48 -1.72 -0.96 2.03 2.55×10-6 1.43
    2 789.95 512.91 453.12 0.05 1.09 -0.13 1.10 2.04×10-6 1.52
    3 52.26 1 319.31 130.54 -0.26 0.69 -0.54 0.91 1.39×10-6 1.42
    4 365.15 279.52 509.57 -0.15 -0.52 -0.57 0.79 1.09×10-6 1.34
    5 101.44 224.90 664.83 0.56 0.10 -0.83 1.01 2.24×10-6 1.59
    6 1 403.37 829.16 904.58 0.63 0.84 0.42 1.13 2.47×10-6 1.61
    粒子群算法 1 562.98 676.12 105.68 -0.98 3.88 -10.68 11.41 7.72×10-4 3.23
    2 787.05 511.47 446.89 2.95 2.53 6.11 7.24 8.46×10-4 3.13
    3 49.31 1 312.08 123.26 2.69 7.92 6.74 10.74 3.05×10-4 3.86
    4 365.33 271.69 493.21 -0.33 7.31 15.79 17.40 9.50×10-4 2.73
    5 97.74 215.04 660.16 4.26 9.96 3.84 11.49 7.41×10-4 2.94
    6 1 350.71 818.19 872.77 53.29 11.81 32.23 63.39 4.61×10-4 3.31
    模拟退火算法 1 561.25 679.88 90.97 0.75 0.12 4.03 4.10 5.79×10-6 2.18
    2 790.24 508.64 459.71 -0.24 5.36 -6.71 8.59 1.33×10-5 2.10
    3 54.10 1 313.10 132.84 -2..01 6.90 -2.84 7.75 5.95×10-6 2.11
    4 364.27 282.67 500.52 0.73 -3.67 8.48 9.27 1.38×10-5 2.10
    5 101.34 224.57 659.86 0.66 0.43 4.14 4.21 2.44×10-5 2.08
    6 1 415.70 831.83 914.63 -11.70 -1.83 -9.63 15.26 1.90×10-5 2.20
    下载: 导出CSV

    表  5  工程数据

    Table  5.   Engineering Data

    传感器编号 X/m Y/m Z/m 观测到时/ms
    9 8 761.00 6 614.00 522.00 34.90
    21 8 737.00 6 609.00 565.00 36.60
    5 8 666.00 6 600.00 520.00 39.30
    17 8 668.00 6 599.00 565.00 41.10
    4 8 641.00 6 515.00 520.00 42.30
    8 8 691.00 6 684.00 522.00 44.50
    2 8 721.00 6 449.00 522.00 47.80
    26 8 702.00 6 604.00 647.00 50.00
    下载: 导出CSV

    表  6  工程数据下3种算法定位结果

    Table  6.   the localization results of the three algorithms using the engineering data

    算法名称 算法定位结果 与真实值误差 绝对误差/m 目标函数值F 运行时间/s
    X/m Y/m Z/m X/m Y/m Z/m
    优化领先狼群算法 8 730.26 6 573.98 516.90 2.44 -3.38 -5.60 6.98 2.33×10-5 1.41
    粒子群算法 8 756.67 6 578.76 511.35 -23.97 -8.16 -0.05 25.32 1.12×10-4 2.57
    模拟退火算法 8 736.46 6 575.77 519.82 -3.76 -5.17 -8.52 10.65 5.08×10-5 2.09
    下载: 导出CSV
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  • 收稿日期:  2023-11-10
  • 修回日期:  2023-12-15
  • 刊出日期:  2024-04-30

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