Observation of Local Infrasound Coupled by Seismic Wave on Wide Spread Infrasound Network
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摘要: 通过提出应用于广域次声传感器阵列的最小方差法信号源定位模型, 分析了阵列信号相关系数特征和本地次声波实时大气传播特性, 对阵列阵元数量、阵元组成结构引起的定位误差以及本地次声波的真实大气传播射线进行仿真, 并利用中国境内布置的广域次声传感器网络监测到了2013年4月20日四川芦山(雅安)地震的瑞利波激发的本地次声波, 验证了上述模型和仿真, 结合中国地震台网的国内的地震监测台站数据, 从信号走时、信号互相关系数、小波时频图、质点运动轨迹等方面进行了分析与对比, 并使用广域最小方差法搜索的算法对次声波和地震波进行定位, 结果显示: 各次声站点接收到由地震瑞利波引起次声站附近地表震动产生并垂直地表向上传播的次声波, 在地震瑞利波之后到达, 而且相关系数都达到0.6~0.9, 计算得到次声波源方位角为230°(以北京为原点), 距离震中小于150 km, 而且本地次声波受大气传播影响较小, 能够较容易的被广域次声阵列探测到, 因此地震本地次声波监测能够作为地震监测、研究地面起伏运动与大气波动关系的有效手段.Abstract: A kind of least-square-error localization algorithm applied on wide spread infrasound network is proposed in this article. Models of cross correlation between distant sensors and atmosphere infrasound propagation are analyzed. The localization error caused by quantity and distribution structure of network and ray tracing of local infrasound in real atmosphere are also calculated. Infrasound coupled by local seismic Rayleigh wave of Lushan (Ya'an) earthquake on April 20th, 2013 is detected by infrasound network and could prove the algorithm and analysis above. Comparing infrasound signals with seismic recording of IRIS global network, we find that they ware well correlated for the corresponding time period in signal travel time, signal correlation (0.6-0.9), particle motion trajectory analysis, etc.. The zone of infrasound source calculated by the least-square-error localizing algorithm is not compact but its center (minimum value determined by least-square-error method) is less than 150 km distant from the epicenter. Due to the less absorption and refraction in atmosphere propagation, local infrasound is easily detected and recognized and could be a possible and feasible way to monitor earthquake and relationship between ground motion and pressure perturbation in atmosphere.
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图 1 阵元数量对最小方差法方位角估计精度影响
Fig. 1. Accuracy of azimuth estimation affected by number of array spot
图 2 次声阵列分布对方位角估计产生的归一化误差
Fig. 2. Normalized error of azimuth error caused by distribution of infrasound array
图 3 枝江(a, b)与北京(c, d)本地次声波大气传播剖面
声线仰角为-90°~+90°，仰角变化间隔为2°，azi为方位角，蓝色为从平流层顶逸出的声线，红色代表能够反射回地面的声线
Fig. 3. Atmosphere propagation profile of local infrasound in Zhijiang (a, b) and Beijing (c, d)
图 4 芦山地震国内地震台站垂直分量监测信息(起始时间2013-04-20T08∶02∶00 UTC+8，发震时间为信号开始后的46 s)
Fig. 4. Vertical-component of Lushan earthquake monitored by seismic station
图 5 芦山地震各次声站本地次声波波形(起始时间2013-04-20T08∶02∶00 UTC+8，发震时间为信号开始后的46 s)
Fig. 5. Local infrasound waveform monitored by infrasound stations
图 6 北京次声台站三点阵次声相关系数(a)和闭合时延和与各阵元时延量(b)
Fig. 6. Correlation coefficient of Beijing tripartite infrasound array (a) and consistence of time-delay between each element of array (b)
图 7 芦山地震(恩施ENH)垂直分量信号峰值时刻起始50 s内的质点运动轨迹
Fig. 7. Particle motion trajectory analysis (ENH) of 100 s from the time of peak amplitude in Lushan earthquake
图 10 最小方差法计算地震源与次声波源
Fig. 10. Location of seismic source and infrasound source calculated by least-squared-error method
图 8 次声与地震台站信号小波分析结果
a.北京地震台站；b.北京次声台站.起始时间2013-04-20T08∶02∶00 UTC+8，发震时间为信号开始后的46 s
Fig. 8. Wavelet analysis of infrasound and seismic signal of Beijing seismic infrasound
图 9 芦山地震后北京地震台站(BJT)与北京次声台站信号互相关分析
Fig. 9. Cross-correlation of signals between infrasound and seismic station (BJT) of Beijing
表 1 各地震台站地震走时
Table 1. Seismic travel time of each station
站点 走时(s) 震中距离(km) 速度(km/s) 地理坐标 KMI(昆明) 184 539 2.93 25.12°N, 102.74°E ENH(恩施) 220 625 2.84 30.28°N, 109.49°E XAN(西安) 234 717 3.06 34.03°N, 108.92°E LSA(拉萨) 384 1 142 2.97 29.70°N, 91.13°E QIZ(琼中) 484 1 398 2.89 19.03°N, 109.84°E TIA(泰安) 505 1 485 2.94 36.21°N, 117.12°E BJT(北京) 543 1 635 3.01 40.02°N, 116.17°E SSE(上海) 614 1 744 2.84 31.09°N, 121.19°E WMQ(乌鲁木齐) 664 2 044 3.07 43.81°N, 87.70°E HIA(海拉尔) 844 2 566 3.04 49.27°N, 119.74°E MDJ(牡丹江) 1 084 2 838 2.62 44.62°N, 129.59°E 
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表 2 本地次声波信号走时
Table 2. Local infrasound travel time
站点 信号传播时间(s) 与震中距离(km) 估计速度(km/s) 北京 565 1 635 2.89 济南 470 1 400 2.97 襄阳 310 910 2.92 枝江 280 821 2.93
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