Identification of characteristics of earthquake and explosion: A research review
-
摘要: 近年来，天然地震与人工爆破特征识别作为地震事件分类识别的关键技术之一，随受关注度的不断提升获得了长足的发展。文中针对该研究方向出现的识别特征和特征提取方法进行了整体综述，以期为进一步深入研究天然地震与人工爆破特征识别以及拓展其应用领域奠定一定的基础。Abstract: In recent year, the identification of characteristics of earthquake and explosion is one of the key technologies for earthquake event classification and recognition, and has made to a considerable development and progress. The overall review of existing identification and characteristic extraction method is carried out, in order to establish a platform for identification of characteristics of earthquake and explosion future researches.
-
Key words:
- earthquake /
- explosion /
- identification charateristics /
- characteristic extraction
-
表 1 天然地震与人工爆破常用识别特征
Table 1. The common characteristics for earthquake and explosion identification
特征参数 具体特征 数据处理方法 人工爆破 天然地震 优点 缺点 参考文献 事
件
波
形
直
观
特
征发震时间、地点规律 ①时间一般通常具有规律性；
②部分人工爆破地点固定，如采石场发震时间、
地点随机直观明了 适用范围局限于爆破时间、地点固定地区 [9-11] 短周期面波
（瑞利型Rg波）在S波之后往往有较大周期的瑞利面波，呈正弦波状态的波 瑞利面波不发育 直观明了 ①近场爆破产生的面波不是完全的瑞利波
②辅助判据，无法定量[12-13] 水
平
分
量
振
幅
比P波初动振幅和S波最大振幅比（Pc/Sm） 为了减弱震源大小、传播路径、台站方位角以及震中距的影响，P波与S波幅值比一般采用经过震中距衰减校正后的各台记录的算术平均值
${{\rm{P}}{\text{波与} }{\rm{S}}{\text{波幅值比} }=}$
${\dfrac{ { \sum {\text{衰减校正后的各台记录的幅值比} } } }{\text{台数} }}$
Pc/Sm：人工爆破＞天然地震
Pm/Sm：人工爆破＞天然地震
可根据样本数据计算得到最优阈值
减小震级、地震仪的放大倍数、频率特性影响①主要利用相应波段上孤立点的测量信息，具有一定的局限性
②需要通过克里金方法进行路径校正，减弱传播路径差异影响[14-20] P波最大振幅和S波最大振幅比（Pm/Sm） P波初动方向 P波初动判据准则：
①${\dfrac{ { {\text{初动向上} } } }{ { {\text{清晰初动} } } } }$≥80%，
记为1，偏向爆破；
②${\dfrac{ { {\text{初动向下} } } }{ { {\text{清晰初动} } } } }$≥50%，
记为−1，偏向地震；
③处于中间的记为0，无法通过初动判断事件类型
近台（△≤100 km）P波垂直分量初动方向向上
有象限分布，P 波垂直分量初动方向可能向上，也可能向下
直观明了受震级、地球介质结构等因素影响，部分波形记录初动不清晰，部分人工爆破P波初动向下 [18, 20] 尾波持续时间（从最大S波振幅开始到接近噪声水平的时间段） 每一事件各记录台站尾波持续时间的
平均尾波持续时间短 尾波持续时间长 尾波持续时间量取直观方便 只适用于天然地震与人工爆破震级相等的情况 [11, 18] 体波震级相同的天然地震与人工爆破：
天然地震＞人工爆破倒谱参数 倒谱分析 ①倒谱图单调下降，无非零峰值
②倒谱参量C＜1①多峰值分布
②倒谱参量C＞1使用全波例信号，对滤波带宽不敏感 依赖合理有效的传播路径介质衰减模式 [2-4, 21] 震
源
参
数
特
征拐角频率 波谱分析方法 体波震级相同的天然地震与人工爆破同类波相比：天然地震＞人工爆破 可根据样本数据计算得到最优阈值，定量分析 与震级存在一定相关性，具有一定局限性 [22-23] 最大振幅
卓越周期波谱分析方法 平均卓越周期约0.7 s 平均卓越周期约0.3 s 受震中距影响 [22, 24] 优势频率 ①短时傅里叶变换
②Hilbert-Huang变换
③非线性赵-阿特拉斯-马克斯（Zhao-Atlas-
Marks）时频分析方法一般处于低频段，能量衰减快 一般处于高频区，能量衰减慢 [6-8, 25-28] 顶峰频率平均值：天然地震＞人工爆破 能量比 小波包变换 人工爆破＞天然地震 小波变换选取基函数需要通过实验方法验证，工作量较大 [18-19,
29-30]P波瞬时频率 非稳态WD理论 瞬时频率平稳 瞬时频率不稳定 计算复杂 [9, 31] 瞬时频率复杂度公式
$IFC={ \sum\limits _{ {i}=\text{1} }^{ {n} }({ {\displaystyle {g} } }_{{i}+\text{1} } }-{ {g} }_{{i} }{)}^{\text{2} }$
$g_i $：瞬时频率曲线上各拐点的值天然地震＞人工爆破 Hilbert-Huang变换 自相关系数 三分向的自相关系数计算结果求和 人工爆破＞天然地震 [32-35] 波形复杂度C $C = \displaystyle\int\limits_A^B {X{ {(t)}^2} } {\text{d} }t/\displaystyle\int\limits_B^C {X{ {(t)}^2} } {\text{d} }t$
X（t）为t时刻的波形幅值，分母取为从第A时刻到第B时刻的波形样本幅值和，分子取为从B时刻到第C时刻的波形样本幅值和人工爆破＞天然地震 [32,
34-35]频谱比 $SR = \dfrac{ {\displaystyle\int_{ {L_{\text{1} } } }^{ {H_1} } {\left| {x(f)} \right|{\rm{d}}f} } }{ {\displaystyle\int_{ {L_{\text{2} } } }^{ {H_2} } {\left| {x(f)} \right|{\rm{d}}f} } }$
x（f）：傅里叶频谱中频率f处幅值的模；
H1，L1：低频段的上下限；
H2，L2：高频段的上下限天然地震＞人工爆破 受震源到台站间的射线传播路径和地球介质的非均匀性影响 [32,
34-35]
下载: 导出CSV
-
[1] Bogert B P, Healy M J R, Tukey J W. The quefrency analysis of time serics for echoes: Cepstrum, pseudo-autocovariance, cross-cepstrum and saphe cracking[J]. Proceeding of the Symposium on Time, 1963, 15: 209-243 [2] 魏富胜,黎明. 震源性质的倒谱分析[J]. 地震学报,2003,25(1):47-54 doi: 10.3321/j.issn:0253-3782.2003.01.006Wei Fusheng,Li Ming. Cepstrum anlysis of source character[J]. Acta Seismologica Sinica,2003,25(1):47-54 doi: 10.3321/j.issn:0253-3782.2003.01.006 [3] 郭祥云,王淑辉,魏富胜. 小震级地震事件的倒谱差异[J]. 地震地磁观测与研究,2010,31(1):12-16 doi: 10.3969/j.issn.1003-3246.2010.01.003Guo Xiangyun,Wang Shuhui,Wei Fusheng. Difference cepstrum of smaller magtutide earthquake[J]. Seismological and Geomagnetic Observation and Research,2010,31(1):12-16 doi: 10.3969/j.issn.1003-3246.2010.01.003 [4] 陈银燕. 基于HMM和GMM天然地震与人工爆破识别算法研究[D]. 桂林: 广西师范大学, 2011Chen Yinyan. A research on algorithms for discriminating earthquake and explosion based upon HMM and GMM[D]. Guilin: Guangxi Normal University, 2011 [5] Gabor D. Theory of communication[J]. J. Inst. Electr. Eng.,1946,93:429-457 [6] 崔鑫,许力生,许忠淮,等. 小地震与人工爆破记录的时频分析[J]. 地震工程学报,2016,38(1):71-78 doi: 10.3969/j.issn.1000-0844.2016.01.0071Cui Xin,Xu Lisheng,Xu Zhonghuai,et al. Time-frequency analysis of records of small earthquakes and explosions[J]. China Earthquake Engineering Journal,2016,38(1):71-78 doi: 10.3969/j.issn.1000-0844.2016.01.0071 [7] 荣伟健,赵建明,董祎玮,等. 曹妃甸地震台网天然地震与人工爆破时频分析[J]. 地震地磁观测与研究,2017,38(5):39-43 doi: 10.3969/j.issn.1003-3246.2017.05.007Rong Weijian,Zhao Jianming,Dong Yiwei,et al. Time frequency analysis of earthquakes and explosions recorded by Caofeidian seismic network[J]. Seismological and Geomagnetic Observation and Research,2017,38(5):39-43 doi: 10.3969/j.issn.1003-3246.2017.05.007 [8] 高家乙,刘晓峰,闫睿. 河南平顶山平煤矿区天然地震、爆破、塌陷时频特征分析[J]. 地震地磁观测与研究,2020,41(3):67-74Gao Jiayi,Liu Xiaofeng,Yan Rui. Time-frequency analysis of seismic records generated by natural earthquakes,blasts,and collapses near Pingmei mine based on STFT[J]. Seismological and Geomagnetic Observation and Research,2020,41(3):67-74 [9] 张博. 爆炸和地震的识别研究[D]. 北京: 中国地震局地球物理研究所, 2013Zhang Bo. Research on earthquakes and explosions identification[D]. Beijing: Institute of Geophysics, China Earthquake Administration, 2013 [10] 杨芳,朱嘉健,刘智,等. 广东地区地震与爆破事件识别方法研究[J]. 华南地震,2016,36(3):110-I15Yang Fang,Zhu Jiajian,Liu Zhi,et al. Study on identification methods between earthquakes and explosions occurred in Guangdong region[J]. South China Journal of Seismology,2016,36(3):110-I15 [11] 王惠琳,李志雄,徐晓枫,等. 琼北确定性人工爆破与天然地震识别判据[J]. 地震地磁观测与研究,2017,38(4):75-80Wang Huilin,Li Zhixiong,Xu Xiaofeng,et al. Discrimination criterions of doubtless explosions and earthquakes in northern Hainan area[J]. Seismological and Geomagnetic Observation and Research,2017,38(4):75-80 [12] 库尔哈奈克. 地震图解析[M]. 刘启元, 吴宁远, 修济刚, 译. 北京: 地震出版社, 1992Kulhanek Ota. Anatomy of Seismograms[M]. Translated by Liu Qiyuan, Wu Ningyuan, Xiu Jigang. Beijing: Seismological Press, 1992 [13] 包宝小,贾宝金,刘芳,等. 乌兰浩特地震台典型地震事件震相特征分析[J]. 地震地磁观测与研究,2020,41(1):33-39 doi: 10.3969/j.issn.1003-3246.2020.01.006Bao Baoxiao,Jia Baojin,Liu Fang,et al. Analysis of seismic characteristics of Wulanhot seismic event[J]. Seismological and Geomagnetic Observation and Research,2020,41(1):33-39 doi: 10.3969/j.issn.1003-3246.2020.01.006 [14] Pomeroy P W,Best W J,McEvilly T V. Test ban treaty verification with regional data:A review[J]. Bull. Seismol. Soc. Amer.,1982,72(6B):S89-S129 doi: 10.1785/BSSA07206B0089 [15] 潘常周,靳平,王红春. P/S震相幅值比判据对低震级地震事件的适用性检验[J]. 地震学报,2007,29(5):521-528 doi: 10.3321/j.issn:0253-3782.2007.05.009Pan Changzhou,Jin Ping,Wang Hongchun. Applicability of P/S amplitude ratios for the discrimination of low magnitude seismic events[J]. Acta Seismologica Sinica,2007,29(5):521-528 doi: 10.3321/j.issn:0253-3782.2007.05.009 [16] 潘常周,靳平,肖卫国. 利用克里金技术标定新疆及附近地区P/S震相幅值比及其在地震事件识别中的应用[J]. 地震学报,2007,29(6):625-634 doi: 10.3321/j.issn:0253-3782.2007.06.007Pan Changzhou,Jin Ping,XiaoWeiguo. Calibration of P/S amplitude ratios for seismic events in Xinjiang and adjacent areas based on a bayesian kriging method[J]. Acta Seismologica Sinica,2007,29(6):625-634 doi: 10.3321/j.issn:0253-3782.2007.06.007 [17] 边银菊,黄汉明,郭永霞. 明灯一号及邻近地区地震与爆炸的识别[J]. 地震地磁观测与研究,2010,31(5):49-55 doi: 10.3969/j.issn.1003-3246.2010.05.009Bian Yinju,HuangHanming,GuoYongxia. Identification of earthquakes and explosions in Bright Lamp No. 1 and its nearby area[J]. Seismological and Geomagnetic Observation and Research,2010,31(5):49-55 doi: 10.3969/j.issn.1003-3246.2010.05.009 [18] 王婷婷. 地震和爆破识别判据及识别方法研究[D]. 北京: 中国地震局地球物理研究所, 2012Wang Tingting. The recognized criteria and methods research on earthquakes and explosions identification[D]. Beijing: Institute of Geophysics, China Earthquake Administration, 2012 [19] 蔡杏辉,张燕明,陈惠芳,等. 基于小波特征和神经网络的天然地震与人工爆破自动识别[J]. 大地测量与地球动力学,2020,40(6):634-639Cai Xinghui,Zhang Yanming,Chen Huifang,et al. Automatic identification of earthquake and explosion based on wavelet transform and neural network[J]. Journal of Geodesy and Geodynamics,2020,40(6):634-639 [20] 马丽. 青海东部地区非天然地震识别与波形特征分析[J]. 高原地震,2020,32(4):40-46 doi: 10.3969/j.issn.1005-586X.2020.04.007Ma Li. Identification and waveform analysis of non-natural earthquakes in eastern Qinghai Province[J]. Plateau Earthquake Research,2020,32(4):40-46 doi: 10.3969/j.issn.1005-586X.2020.04.007 [21] Baumgardt D R,Ziegler K A. Spectral evidence for source multiplicity in explosions:Application to regional discrimination of earthquakes and explosions[J]. Bull. Seismol. Soc. Amer.,1988,78(5):1773-1795 [22] 郑秀芬,傅瑀,许绍燮. 地震记录中小爆破的识别与判据研究[J]. 地震地磁观测与研究,2006,27(5):29-33 doi: 10.3969/j.issn.1003-3246.2006.05.006Zheng Xiufen,Fu Yu,Xu Shaoxie. The discrimination and criteria study between blasts and small earthquakes[J]. Seismological and Geomagnetic Observation and Research,2006,27(5):29-33 doi: 10.3969/j.issn.1003-3246.2006.05.006 [23] 张萍,魏富胜,潘科,等. 爆破与地震的拐角频率比较[J]. 地震地磁观测与研究,2009,30(5):20-25Zhang Ping,Wei Fusheng,Pan Ke,et al. Comparison of corner frequency between explosion and earthquake[J]. Seismological and Geomagnetic Observation and Research,2009,30(5):20-25 [24] 李发,张佑龙,汪贵章,等. 安徽及周边地区小爆破的识别与判据研究[J]. 华北地震科学,2012,30(2):43-47 doi: 10.3969/j.issn.1003-1375.2012.02.009Li Fa,Zhang Youlong,Wang Guizhang,et al. Research on Identification of small explosions in Anhui and its surrounding area[J]. North China Earthquake Sciences,2012,30(2):43-47 doi: 10.3969/j.issn.1003-1375.2012.02.009 [25] 张帆. 地震波时频谱分析及其在爆破识别中的应用[D]. 合肥: 中国科学技术大学, 2006Zhang Fan. Seismic signal time-frequency analysis and its application to blast distinguishing[J]. Hefei: University of Science and Technology of China, 2006 [26] 张丽芬,廖武林,曾夏生,等. 三峡重点监视区构造地震与矿震时频分析谱特征分析[J]. 地震地质,2009,31(4):699-706 doi: 10.3969/j.issn.0253-4967.2009.04.013Zhang Lifen,Liao Wulin,Zeng Xiasheng,et al. Analysis of time-frequency characteristics of wave spectrum between tectonic earthquake and mine earthquake[J]. Seismology and Geology,2009,31(4):699-706 doi: 10.3969/j.issn.0253-4967.2009.04.013 [27] 王玥琪. 不同类型事件宽频带地震仪记录的频谱特征研究[D]. 兰州: 中国地震局兰州地震研究所, 2015Wang Yueqi. Study on frequency spectrum characteristics of different type events record by broadband seismograph[D]. Lanzhou: Lanzhou Institute of Seismology, China Earthquake Administration, 2015 [28] 陈晓龙,陈继锋,蒲举,等. 兰州市红古区非天然地震记录特征分析[J]. 地震工程学报,2021,43(3):545-550 doi: 10.3969/j.issn.1000-0844.2021.03.545Chen Xiaolong,Chen Jifeng,Pu Ju,et al. Characteristics of non-natural earthquake records in Honggu district,Lanzhou[J]. China Earthquake Engineering Journal,2021,43(3):545-550 doi: 10.3969/j.issn.1000-0844.2021.03.545 [29] 曾宪伟,赵卫明,盛菊琴,等. 应用小波包识别宁夏及邻区的地震和爆破[J]. 地震研究,2008,31(2):142-148 doi: 10.3969/j.issn.1000-0666.2008.02.009Zeng Xianwei,Zhao Weiming,Sheng Juqin,et al. Using wavelet packet to discriminate earthquakes and explosions in Ningxia and Neighboring areas[J]. Journal of Seismological Research,2008,31(2):142-148 doi: 10.3969/j.issn.1000-0666.2008.02.009 [30] 李锐. 天然地震与人工爆破的特征提取及识别算法研究[D]. 桂林: 广西师范大学, 2010Li Rui. Research of features extraction and recognition algorithm of earthquake and explosion[D]. Guilin: Guangxi Normal University, 2010 [31] 薛思敏. 地震波形希尔伯特-黄变换特征提取与震源类型识别研究[D]. 桂林: 广西师范大学, 2020Xue Simin. Research on seismic wave’s hilbert-huang transform feature extraction and seismic event source type discrimination[D]. Guilin: Guangxi Normal University, 2020 [32] 邱宏茂,刘俊民,范万春. 基于BP神经网络的地震信号识别分类[J]. 计算机应用与软件,2005,22(7):74-76 doi: 10.3969/j.issn.1000-386X.2005.07.032Qiu Hongmao,Liu Junmin,Fan Wanchun. Discrimination and classification of seismic signals based on BP neural network[J]. Computer Applications and Software,2005,22(7):74-76 doi: 10.3969/j.issn.1000-386X.2005.07.032 [33] Kuyuk H S,Motosaka M. Real-time ground motion forecasting using front-site waveform data based on Artifical neural network[J]. Journal of Disaster Research,2009,4(4):260-266 [34] 赵静. 天然地震与人工爆破波形特征提取与识别算法研究[D]. 桂林: 广西师范大学, 2011Zhao Jing. Research of seismic wave feature extraction and recognition algorithm of earthquake and explosion[D]. Guilin: Guangxi Normal University, 2011 [35] 任涛,林梦楠,陈宏峰,等. 基于Bagging集成学习算法的地震事件性质识别分类[J]. 地球物理学报,2019,62(1):383-392 doi: 10.6038/cjg2019M0380Ren Tao,Lin Mengnan,Chen Hongfeng,et al. Seismic event classification based on bagging ensemble learning algorithm[J]. Chinese Journal of Geophysics,2019,62(1):383-392 doi: 10.6038/cjg2019M0380 [36] 刘希强,沈萍,张玲,等. 用小波变换能量线度方法识别天然地震与爆破或塌方[J]. 西北地震学报,2003,25(3):204-209Liu Xiqiang,Shen Ping,Zhang Ling,et al. Using method of energy linearity in wavelet transform to distinguish explosion or collapse from nature earthquake[J]. Northwestern Seismological Journal,2003,25(3):204-209 [37] 和雪松,李世愚,沈萍,等. 用小波包识别地震和矿震[J]. 中国地震,2006,22(3):425-434He Xuesong,Li Shiyu,Shen Ping,et al. A wavelet packet approach to wave classification of earthquakes and mining shocks[J]. Earthquake Research in China,2006,22(3):425-434 [38] 黄明汉,边银菊,卢世军,等. v-SVC算法在地震与爆破识别及窗长度选取中的应用[J]. 地震地磁观测与研究,2010,31(3):24-31 doi: 10.3969/j.issn.1003-3246.2010.03.005Huang Minghan,Bian Yinju,Lu Shijun,et al. v-SVC algorithm applied in earthquake and explosion recognition and the choice of window length[J]. Seismological and Geomagnetic Observation and Research,2010,31(3):24-31 doi: 10.3969/j.issn.1003-3246.2010.03.005 [39] Huang N E,Shen Z,Long S R,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc. R. Soc. Lond.,1998,454:903-995 doi: 10.1098/rspa.1998.0193 [40] 陈奇. 时间窗法提取地震波形特征的算法研究[D]. 桂林: 广西师范大学, 2018Chen Qi. A research on algorithms for seismic wave feature extraction by the time window method[D]. Guilin: Guangxi Normal University, 2018 [41] Perol T,Gharbi M,Denolle M. Convolutional neural network for earthquake detection and location[J]. Science Advances,2018,4(2):e1700578 [42] 陈润航,黄明汉,柴慧敏. 地震和爆破事件源波形信号的卷积神经网络分类研究[J]. 地球物理学进展,2018,33(4):1331-1338 doi: 10.6038/pg2018BB0326Chen Runhang,Huang Minghan,Chai Huimin. Study on the discrimination of seismic waveform signals between earthquake and explosion events by convolutional neural network[J]. Progress in Geophysics,2018,33(4):1331-1338 doi: 10.6038/pg2018BB0326 [43] 周少辉,蒋海昆,李健,等. 基于深度学习的地震事件分类识别−以山东地震台网记录为例[J]. 地震地质,2021,43(3):663-676 doi: 10.3969/j.issn.0253-4967.2021.03.012Zhou Shaohui,Jiang Haikun,Li Jian,et al. Research on identification of seismic events based on deep learning:Taking the records of Shandong seismic network as an example[J]. Seismology and Geology,2021,43(3):663-676 doi: 10.3969/j.issn.0253-4967.2021.03.012 [44] 隗永刚,杨千里,王婷婷,等. 基于深度学习残差网络模型的地震和爆破识别[J]. 地震学报,2019,41(5):646-657 doi: 10.11939/jass.20190030Wei Yonggang,Yang Qianli,Wang Tingting,et al. Earthquake and explosion identification based on deep learning residual network mode[J]. Acta Seismologica Sinica,2019,41(5):646-657 doi: 10.11939/jass.20190030 [45] Mousavi S M,Ellsworth W L,Zhu W Q,et al. Earthquake transformer— an attentive deep-learning model for simultaneous earthquake detection and phase picking[J]. Nature Communications,2020,11(1):3952 doi: 10.1038/s41467-020-17591-w [46] 高中强. 基于深度学习的震源类型识别方法[D]. 河南: 信阳师范学院, 2020Gao Zhongqiang. Research on source type identification methods based on deep learning[D]. Henan: Xinyang Normal University, 2020 [47] 李进. 基于深度神经网络模型的地震信号识别研究[D]. 石家庄: 河北地质大学, 2021Li Jin. Seismic signal recognition based on deep neural network model[D]. Shijiazhuang: Hebei GEO University, 2021 -
点击查看大图
图(4) / 表(1)
计量
- 文章访问数: 269
- HTML全文浏览量: 151
- PDF下载量: 63
