Research on microseismic source localization based on optimized leading wolfpack algorithm
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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。Abstract: In order to analyze the impact of different heuristic methods on the precision of microseismic source localization, an optimized Dominant Wolf Pack Algorithm(DWPA)is proposed.This algorithm builds upon the Dominant Wolf Pack Algorithm and introduces adjustments to two parameters, namely the search step size and the siege step size, enhancing its ability to escape local optima during the search process.The effectiveness of the optimized DWPA is validated through theoretical model inversion and engineering numerical analysis.A comparative study with commonly used heuristic algorithms, Particle Swarm Optimization(PSO)and Simulated Annealing(SA), reveals that the optimized DWPA exhibits faster convergence, higher accuracy, and reduced sensitivity to P-wave velocity errors.This research provides new insights for the application of intelligent heuristic algorithms in microseismic source localization.
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图 1 微震源定位原理示意图
Figure 1. The schematic diagram of the microseismic source localization principle
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 
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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
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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
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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
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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
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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
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[1] 张航. 基于深度学习的隧道微震信号处理及岩爆智能预警研究[D]. 成都: 成都理工大学, 2020: 1-4.ZHANG Hang. Research on microseismic signal processing of tunnel and intelligent early warning of rockburst based on deep learning[D]. Chengdu: Chengdu University of Technology, 2020: 1-4. [2] 赵洪宝, 刘瑞, 刘一洪, 等. 基于深度学习方法的矿山微震信号分类识别研究[J]. 矿业科学学报, 2022, 7(2): 166-174. doi: 10.19606/j.cnki.jmst.2022.02.003ZHAO Hongbao, LIU Rui, LIU Yihong, et al. Research on classification and identification of mine microseismic signals based on deep learning method[J]. Journal of Mining Science and Technology, 2022, 7(2): 166-174. doi: 10.19606/j.cnki.jmst.2022.02.003 [3] SHAN K, ZHENG Y H, ZHANG Y J, et al. Effects of different fracture parameters on microseisms induced by hydraulic fracturing[J]. Bulletin of Engineering Geology and the Environment, 2023, 82(6): 231. doi: 10.1007/s10064-023-03240-1 [4] HE S Q, SONG D Z, MITRI H, et al. Integrated rockburst early warning model based on fuzzy comprehensive evaluation method[J]. International Journal of Rock Mechanics and Mining Sciences, 2021, 142: 104767. doi: 10.1016/j.ijrmms.2021.104767 [5] 任政, 姜耀东. 采动影响下逆断层冲击地压矿震时空分布规律分析[J]. 矿业科学学报, 2020, 5(5): 482-489. doi: 10.19606/j.cnki.jmst.2020.05.002REN Zheng, JIANG Yaodong. Analysis of spatial and temporal distribution laws of mine earthquake induced by thrust fault coal bumps under mining disturbance[J]. Journal of Mining Science and Technology, 2020, 5(5): 482-489. doi: 10.19606/j.cnki.jmst.2020.05.002 [6] 史金朋. 改进MFO-SOA算法的微震定位和成像研究[D]. 沈阳: 辽宁大学, 2022: 5-7.SHI Jinpeng. Research on microseismic positioning and imaging based on improved MFO-SOA algorithm[D]. Shenyang: Liaoning University, 2022: 5-7. [7] GEIGER L. Probability method for the determination of earthquake epicenters from arrivaltime only[J]. Bulletin of Saint Louis University, 8(1): 60-71. [8] 沈统. 微地震事件定位精度优化关键技术研究[D]. 成都: 成都理工大学, 2019: 4-5.SHEN Tong. Research on key technologies for the optimization of microseismic events location accuracy[D]. Chengdu: Chengdu University of Technology, 2019: 4-5. [9] 李会义, 姜福兴, 杨淑华. 基于Matlab的岩层微地震破裂定位求解及其应用[J]. 煤炭学报, 2006, 31(2): 154-158. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB200602004.htmLI Huiyi, JIANG Fuxing, YANG Shuhua. Research and application of microseismic monitoring location of strata fracturing based on Matlab[J]. Journal of China Coal Society, 2006, 31(2): 154-158. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB200602004.htm [10] 李楠, 王恩元, 孙珍玉, 等. 基于L1范数统计的单纯形微震震源定位方法[J]. 煤炭学报, 2014, 39(12): 2431-2438. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB201412015.htmLI Nan, WANG Enyuan, SUN Zhenyu, et al. Simplex microseismic source location method based on L1 norm statistical standard[J]. Journal of China Coal Society, 2014, 39(12): 2431-2438. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB201412015.htm [11] WALDHAUSER F, ELLSWORTH W. A double-difference earthquake location algorithm: method and application to the northern Hayward fault, California[J]. Bulletin of the Seismological Society of America, 2000, 90: 1353-1368. doi: 10.1785/0120000006 [12] 李峰. 微震信号双震相自动识别及震源定位方法研究[D]. 阜新: 辽宁工程技术大学, 2022: 4-13.LI Feng. Study on automatic identification and source location method of double seismic phases in microseismic signal[D]. Fuxin: Liaoning Technical University, 2022: 4-13. [13] 吕进国, 姜耀东, 赵毅鑫, 等. 基于稳健模拟退火-单纯形混合算法的微震定位研究[J]. 岩土力学, 2013, 34(8): 2195-2203. https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX201308016.htmLÜ Jinguo, JIANG Yaodong, ZHAO Yixin, et al. Study of microseismic positioning based on steady simulated annealing-simplex hybrid algorithm[J]. Rock and Soil Mechanics, 2013, 34(8): 2195-2203. https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX201308016.htm [14] 姜天琪, 裴烁瑾. 基于网格搜索-牛顿迭代法的微震震源定位算法[J]. 矿业科学学报, 2019, 4(6): 480-488. http://kykxxb.cumtb.edu.cn/article/id/249JIANG Tianqi, PEI Shuojin. Micro-seismic event location based on Newton iteration method and grid-search method[J]. Journal of Mining Science and Technology, 2019, 4(6): 480-488. http://kykxxb.cumtb.edu.cn/article/id/249 [15] 郭一楠, 崔宁, 程健. 基于MOPSO-SA混合算法的矿山微震震源定位方法[J]. 煤炭科学技术, 2020, 48(3): 126-132. https://www.cnki.com.cn/Article/CJFDTOTAL-MTKJ202003014.htmGUO Yinan, CUI Ning, CHENG Jian. Microeismic source localization method based on hybrid algorithm of MOPSO-SA[J]. Coal Science and Technology, 2020, 48(3): 126-132. https://www.cnki.com.cn/Article/CJFDTOTAL-MTKJ202003014.htm [16] 陈炳瑞, 冯夏庭, 李庶林, 等. 基于粒子群算法的岩体微震源分层定位方法[J]. 岩石力学与工程学报, 2009, 28(4): 740-749. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX200904015.htmCHEN Bingrui, FENG Xiating, LI Shulin, et al. Microseism source location with hierarchical strategy based on particle swarm optimization[J]. Chinese Journal of Rock Mechanics and Engineering, 2009, 28(4): 740-749. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX200904015.htm [17] LIAO Z, FENG T, YU W J, et al. Microseismic source location method and application based on NM-PSO algorithm[J]. Applied Sciences, 2022, 12(17): 8796. doi: 10.3390/app12178796 [18] WANG K K, TANG C A, MA K, et al. Research on the design of coal mine microseismic monitoring network based on improved particle swarm optimization[J]. Applied Sciences, 2022, 12(17): 8439. doi: 10.3390/app12178439 [19] 庞聪, 马武刚, 李查玮, 等. 利用新型群体智能优化算法研究微震震源定位[J]. 大地测量与地球动力学, 2023, 43(7): 708-714. https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB202307009.htmPANG Cong, MA Wugang, LI Chawei, et al. Research on micro-seismic source localization using novel swarm intelligence optimization algorithms[J]. Journal of Geodesy and Geodynamics, 2023, 43(7): 708-714. https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB202307009.htm [20] YANG C G, TU X Y, CHEN J. Algorithm of marriage in honey bees optimization based on the wolf pack search[C]//The 2007 International Conference on Intelligent Pervasive Computing(IPC 2007). Jeju, Korea(South). IEEE, 2007: 462-467. [21] 吴虎胜, 张凤鸣, 吴庐山. 一种新的群体智能算法: 狼群算法[J]. 系统工程与电子技术, 2013, 35(11): 2430-2438. https://www.cnki.com.cn/Article/CJFDTOTAL-XTYD201311034.htmWU Husheng, ZHANG Fengming, WU Lushan. New swarm intelligence algorithm—wolf pack algorithm[J]. Systems Engineering and Electronics, 2013, 35(11): 2430-2438. https://www.cnki.com.cn/Article/CJFDTOTAL-XTYD201311034.htm [22] WANG D X, WANG H B, BAN X J, et al. An adaptive, discrete space oriented wolf pack optimization algorithm for a movable wireless sensor network[J]. Sensors, 2019, 19(19): 4320. doi: 10.3390/s19194320 [23] FENG X, HU K Q, LOU X C. Infrared and visible image fusion based on the total variational model and adaptive wolf pack algorithm[J]. IEEE Access, 2019, 8: 2348-2361. [24] 周强, 周永权. 一种基于领导者策略的狼群搜索算法[J]. 计算机应用研究, 2013, 30(9): 2629-2632. https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ201309017.htmZHOU Qiang, ZHOU Yongquan. Wolf colony search algorithm based on leader strategy[J]. Application Research of Computers, 2013, 30(9): 2629-2632. https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ201309017.htm [25] ZHU Y, JIANG W L, KONG X D, et al. A chaos wolf optimization algorithm with self-adaptive variable step-size[J]. AIP Advances, 2017, 7(10): 1-17. [26] 张院生, 高永涛, 王喆, 等. 基于SA-PSO混合算法的微震定位研究[J]. 现代隧道技术, 2016, 53(3): 137-145. https://www.cnki.com.cn/Article/CJFDTOTAL-XDSD201603020.htmZHANG Yuansheng, GAO Yongtao, WANG Zhe, et al. Microseism positioning based on an SA-PSOhybrid algorithm[J]. Modern Tunnelling Technology, 2016, 53(3): 137-145. https://www.cnki.com.cn/Article/CJFDTOTAL-XDSD201603020.htm [27] 林峰, 李庶林, 薛云亮, 等. 基于不同初值的微震源定位方法[J]. 岩石力学与工程学报, 2010, 29(5): 996-1002. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201005019.htmLIN Feng, LI Shulin, XUE Yunliang, et al. Microseismic sources location methods based on different initial values[J]. Chinese Journal of Rock Mechanics and Engineering, 2010, 29(5): 996-1002. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201005019.htm -
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