闪电的分形特征研究及其在自动识别中的应用

火元莲; 张广庶; 吕世华; 袁萍

doi:10.7498/aps.62.059201

摘要

利用2009年夏季青海地区的快电场变化测量仪的野外观测资料, 对120例地闪和77例云闪辐射场信号的分形特征进行了深入研究, 结果表明地闪辐射场信号的分形维数与云闪辐射场信号的分形维数有明显的差别, 再利用闪电的分形维特征, 构造了5个特征值, 将其作为支持向量机的输入进行地闪和云闪不同放电类型的识别, 有效识别率达到95%以上; 通过构造地闪辐射场时间序列信号的分形维数轨迹图表明分形维数最低点对应于原时间序列的回击位置, 利用分形维数轨迹中的最低点的位置能够快速准确地对地闪辐射场信号的回击点进行检测, 检测率可达到100%. 分形维是闪电的一种具有鉴别性的特征, 可用于闪电的智能分析与自动化处理.

关键词:

Abstract

By analyzing the fractal feature of 120 cloud-to-ground lightning signals and 77 intracloud lightning signals obtained by the fast antenna system in Qinghai area during the summer of 2009, the results show fractal dimension of cloud-to-ground lightning signal is obviously different from that of intracloud lightning signal. Then 5 characteristic values of fractal dimension are used to recognize the discharge types of lightning signal via support vector machine, and the recognition rate is higher than 95%. The construction of cloud-to-ground lightning time series signal fractal dimension trajectory map shows that the fractal dimension minimum value corresponds to the return stroke of the original time series signal, which can be used to quickly and accurately detect the return stroke of lightning signal, and the detection rate can reach 100%.The fractal dimension is a discriminatively physical property which can be used for intelligently analyzing and automatically processing the lightning signal.

Keywords:

作者及机构信息

1.
中国科学院寒区旱区环境与工程研究所, 兰州 730000;

2.
西北师范大学物理与电子工程学院, 兰州 730070

基金项目: 国家自然科学基金 (批准号: 41075002, 40775004)、国家自然科学基金重点项目 (批准号: 41030960) 和公益性行业科研专项基金 (批准号: GYHY201006005-03) 资助的课题.

Authors and contacts

1.
Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China;

2.
College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China

Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 41075002, 40775004), the Key Program of the National Natural Science Foundation of China (Grant No. 41030960), and the Special Scientific Research Fund of Meteorological Public Welfare Profession of China (Grant No. GYHY201006005-03).

参考文献

[1]	Cao D J, Qie X S, Duan S, Xuan Y J, Wang D F 2012 Acta Phy. Sin. 61 069202 (in Chinese) [曹冬杰, 郄秀书, 段树 ,宣越建, 王东方 2012 61 069202]
[2]	Zhao X Y, Yuan P, Wang J, Shen X Z, Guo Y X, Qiao H Z 2009 Acta Phy. Sin. 58 3243 (in Chinese) [赵学燕, 袁萍, 王杰, 申晓志, 郭逸潇, 乔红贞 2009 58 3243]
[3]	Zhao Y, Qie X S, Kong X Z, Zhang G S, Zhang T, Yang J, Feng G L, Zhang Q L, Wang D F 2009 Acta Phy. Sin. 58 6616 (in Chinese) [赵阳, 郄秀书, 孔祥贞, 张广庶, 张彤, 杨静, 冯桂力, 张其林, 王东方 2009 58 6616]
[4]	Mandelbrot B B 1982 The Fractal Geometry of Nature (New York:Freeman) p28
[5]	Deng Y, Shi W K, Liu Q 2002 Acta Phys. Sin. 51 759 (in Chinese) [邓勇, 施文康, 刘棋 2002 51 759]
[6]	Tsonis A A, Elsner J B 1987 Beitr Phys. Atmos. 60 187
[7]	Vecchi G, Labate D, Canavero F 1994 Radio Sci. 29 694
[8]	Ren S P, Chi J P, Zhang H C, Liu L C 1999 Power System Technology 23 11 (in Chinese) [任顺平, 迟建平, 庄洪春, 刘来存 1999 电网技术 23 11]
[9]	Gou X Q, Zhang Y J, Dong W S 2006 Acta Phys.Sin. 55 957 (in Chinese) [苟学强, 张义军, 董万胜 2006 55 957]
[10]	Gou X Q, Zhang Y J, Dong W S, Qie X S 2007 Chinese J. Geophys. 50 101 (in Chinese) [苟学强, 张义军, 董万胜, 郄秀书 2007 地球 50 101]
[11]	Gou X Q, Chen M L, Zhang Y J, Dong W S, Qie X S 2008 Journal of Lanzhou University (Natural Sciences) 44 24 (in Chinese) [苟学强, 陈明理, 张义军, 董万胜, 郄秀书 2008 兰州大学学报(自然科学版) 44 24]
[12]	Krider E P, Noggle R C, Uman M A 1976 Appl. Metoer 15 301
[13]	Weidman C D, Krider E P 1979 J. Geophys. Res. 84 3159
[14]	Massey R S, Eack K B, Eberle M H, Shao X M,Smith D A 1999 the 11th international conference on atmospheric electricity Guntersville, AL (United States), Jun 7-11 1999 p684
[15]	Smith D A, Eack K B, Harlin J 2002 J. Geophys. Res. 13 4183
[16]	Shao X M, Mark Stanley, Amy Regan 2006 J. Atmos. Ocean. Tech. 23 1273
[17]	Murphy M J, Cummins K L, Alburt E P 2003 United States Patent, pub. No: US 2003/0151397 A1
[18]	Liu H Y, Dong W S, Wang T, Qiu S 2009 Meteorological Monthly 35 49 (in Chinese) [刘恒毅, 董万胜, 王涛, 邱实 2009 气象 35 49]
[19]	Li P, Zheng Y, Zhang Y J 2007 High Power Laser and Particle Beams 19 1512 (in Chinese) [李鹏, 郑毅, 张义军 2007 强激光与粒子束 19 1512]
[20]	Xiang Z, Liu M, Li P, Zheng Y, Fan J B 2011 Opto-Electronic Engineering 38 28 (in Chinese) [项震, 刘明, 李鹏, 郑毅, 范江兵 2011 光电工程 38 28]
[21]	Falconer K J 1990 Fractal Geometry: Mathematical Foundation and Application (New York: John Wiley and Sons) p86
[22]	Vapnik V N 1995 The nature of statistical learning (New York: Springer-Verlag) p235
[23]	Cristianini N, Taylor J An Introduction to Support Vector Machines and Other Kernel-based Learning Methods (New York:Cambridge University Press) p78

施引文献

[1]	Cao D J, Qie X S, Duan S, Xuan Y J, Wang D F 2012 Acta Phy. Sin. 61 069202 (in Chinese) [曹冬杰, 郄秀书, 段树 ,宣越建, 王东方 2012 61 069202]
[2]	Zhao X Y, Yuan P, Wang J, Shen X Z, Guo Y X, Qiao H Z 2009 Acta Phy. Sin. 58 3243 (in Chinese) [赵学燕, 袁萍, 王杰, 申晓志, 郭逸潇, 乔红贞 2009 58 3243]
[3]	Zhao Y, Qie X S, Kong X Z, Zhang G S, Zhang T, Yang J, Feng G L, Zhang Q L, Wang D F 2009 Acta Phy. Sin. 58 6616 (in Chinese) [赵阳, 郄秀书, 孔祥贞, 张广庶, 张彤, 杨静, 冯桂力, 张其林, 王东方 2009 58 6616]
[4]	Mandelbrot B B 1982 The Fractal Geometry of Nature (New York:Freeman) p28
[5]	Deng Y, Shi W K, Liu Q 2002 Acta Phys. Sin. 51 759 (in Chinese) [邓勇, 施文康, 刘棋 2002 51 759]
[6]	Tsonis A A, Elsner J B 1987 Beitr Phys. Atmos. 60 187
[7]	Vecchi G, Labate D, Canavero F 1994 Radio Sci. 29 694
[8]	Ren S P, Chi J P, Zhang H C, Liu L C 1999 Power System Technology 23 11 (in Chinese) [任顺平, 迟建平, 庄洪春, 刘来存 1999 电网技术 23 11]
[9]	Gou X Q, Zhang Y J, Dong W S 2006 Acta Phys.Sin. 55 957 (in Chinese) [苟学强, 张义军, 董万胜 2006 55 957]
[10]	Gou X Q, Zhang Y J, Dong W S, Qie X S 2007 Chinese J. Geophys. 50 101 (in Chinese) [苟学强, 张义军, 董万胜, 郄秀书 2007 地球 50 101]
[11]	Gou X Q, Chen M L, Zhang Y J, Dong W S, Qie X S 2008 Journal of Lanzhou University (Natural Sciences) 44 24 (in Chinese) [苟学强, 陈明理, 张义军, 董万胜, 郄秀书 2008 兰州大学学报(自然科学版) 44 24]
[12]	Krider E P, Noggle R C, Uman M A 1976 Appl. Metoer 15 301
[13]	Weidman C D, Krider E P 1979 J. Geophys. Res. 84 3159
[14]	Massey R S, Eack K B, Eberle M H, Shao X M,Smith D A 1999 the 11th international conference on atmospheric electricity Guntersville, AL (United States), Jun 7-11 1999 p684
[15]	Smith D A, Eack K B, Harlin J 2002 J. Geophys. Res. 13 4183
[16]	Shao X M, Mark Stanley, Amy Regan 2006 J. Atmos. Ocean. Tech. 23 1273
[17]	Murphy M J, Cummins K L, Alburt E P 2003 United States Patent, pub. No: US 2003/0151397 A1
[18]	Liu H Y, Dong W S, Wang T, Qiu S 2009 Meteorological Monthly 35 49 (in Chinese) [刘恒毅, 董万胜, 王涛, 邱实 2009 气象 35 49]
[19]	Li P, Zheng Y, Zhang Y J 2007 High Power Laser and Particle Beams 19 1512 (in Chinese) [李鹏, 郑毅, 张义军 2007 强激光与粒子束 19 1512]
[20]	Xiang Z, Liu M, Li P, Zheng Y, Fan J B 2011 Opto-Electronic Engineering 38 28 (in Chinese) [项震, 刘明, 李鹏, 郑毅, 范江兵 2011 光电工程 38 28]
[21]	Falconer K J 1990 Fractal Geometry: Mathematical Foundation and Application (New York: John Wiley and Sons) p86
[22]	Vapnik V N 1995 The nature of statistical learning (New York: Springer-Verlag) p235
[23]	Cristianini N, Taylor J An Introduction to Support Vector Machines and Other Kernel-based Learning Methods (New York:Cambridge University Press) p78

[1]	梁可达, 刘滕飞, 常哲, 张猛, 李志鑫, 黄松松, 王晶. 基于最小二乘法和支持向量机的海洋内孤立波传播速度反演模型. , 2023, 72(2): 028301. doi: 10.7498/aps.72.20221633
[2]	张毅军, 慕晓冬, 郭乐勐, 张朋, 赵导, 白文华. 一种基于量子线路的支持向量机训练方案. , 2023, 72(7): 070302. doi: 10.7498/aps.72.20222003
[3]	张涛, 陈万忠, 李明阳. 基于频率切片小波变换和支持向量机的癫痫脑电信号自动检测. , 2016, 65(3): 038703. doi: 10.7498/aps.65.038703
[4]	宋堃, 高太长, 刘西川, 印敏, 薛杨. 基于支持向量机的微波链路雨强反演方法. , 2015, 64(24): 244301. doi: 10.7498/aps.64.244301
[5]	赵志刚, 张纯杰, 苟向锋, 桑虎堂. 基于粒子群优化支持向量机的太阳电池温度预测. , 2015, 64(8): 088801. doi: 10.7498/aps.64.088801
[6]	孟庆芳, 陈珊珊, 陈月辉, 冯志全. 基于递归量化分析与支持向量机的癫痫脑电自动检测方法. , 2014, 63(5): 050506. doi: 10.7498/aps.63.050506
[7]	赵永平, 张丽艳, 李德才, 王立峰, 蒋洪章. 过滤窗最小二乘支持向量机的混沌时间序列预测. , 2013, 62(12): 120511. doi: 10.7498/aps.62.120511
[8]	于洋, 郝中骐, 李常茂, 郭连波, 李阔湖, 曾庆栋, 李祥友, 任昭, 曾晓雁. 支持向量机算法在激光诱导击穿光谱技术塑料识别中的应用研究. , 2013, 62(21): 215201. doi: 10.7498/aps.62.215201
[9]	王芳芳, 张业荣. 基于支持向量机的电磁逆散射方法. , 2012, 61(8): 084101. doi: 10.7498/aps.61.084101
[10]	行鸿彦, 祁峥东, 徐伟. 基于选择性支持向量机集成的海杂波背景中的微弱信号检测. , 2012, 61(24): 240504. doi: 10.7498/aps.61.240504
[11]	阎晓妹, 刘丁. 基于最小二乘支持向量机的分数阶混沌系统控制. , 2010, 59(5): 3043-3048. doi: 10.7498/aps.59.3043
[12]	王革丽, 杨培才, 毛宇清. 基于支持向量机方法对非平稳时间序列的预测. , 2008, 57(2): 714-719. doi: 10.7498/aps.57.714
[13]	蔡俊伟, 胡寿松, 陶洪峰. 基于选择性支持向量机集成的混沌时间序列预测. , 2007, 56(12): 6820-6827. doi: 10.7498/aps.56.6820
[14]	张家树, 党建亮, 李恒超. 时空混沌序列的局域支持向量机预测. , 2007, 56(1): 67-77. doi: 10.7498/aps.56.67
[15]	叶美盈. 基于最小二乘支持向量机建模的混沌系统控制. , 2005, 54(1): 30-34. doi: 10.7498/aps.54.30
[16]	叶美盈, 汪晓东, 张浩然. 基于在线最小二乘支持向量机回归的混沌时间序列预测. , 2005, 54(6): 2568-2573. doi: 10.7498/aps.54.2568
[17]	盛永刚, 徐耀, 李志宏, 吴东, 孙予罕, 吴中华. 气体吸附法测定二氧化硅干凝胶的分形维数. , 2005, 54(1): 221-227. doi: 10.7498/aps.54.221
[18]	崔万照, 朱长纯, 保文星, 刘君华. 基于模糊模型支持向量机的混沌时间序列预测. , 2005, 54(7): 3009-3018. doi: 10.7498/aps.54.3009
[19]	刘涵, 刘丁, 任海鹏. 基于最小二乘支持向量机的混沌控制. , 2005, 54(9): 4019-4025. doi: 10.7498/aps.54.4019
[20]	崔万照, 朱长纯, 保文星, 刘君华. 混沌时间序列的支持向量机预测. , 2004, 53(10): 3303-3310. doi: 10.7498/aps.53.3303

计量

文章访问数: 8231
PDF下载量: 997
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

搜索

留言板