云计算环境下震前震源异常次声波自动识别方法
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摘要
云计算下采用三点阵次声源定位方法,在自动识别震前震源次声波过程中不能自动筛选识别大量的异常次声波数据,导致震前监测准确度不高且效率低下。因此提出一种云计算环境下震前震源异常次声波自动识别方法,构建JNS异常次声波数据采集筛查模组,全天候实时扫描访问端口,快速反馈异常次声波数据,采用NDS异常次声波数据序列异常检测算法快速识别错误序阵,准确回查、定位和锁定异常次声波数据;利用震前震源异常次声波自动识别方法分类识别异常次声波信号,判断该信号是否是地震可疑信号。实验结果表明,所提方法可有效自动识别震前震源异常次声波信号类型,信号分类准确率最大值达到99.99%;多次识别耗时最大均值仅为1.3min,具有准确率高和效率快的优势。
In the process of automatically identifying infrasonic waves in a focal region prior to the occurrence of an earthquake,tripartite array arithmetic has difficulty locating the source of infrasonic waves as it cannot automatically screen and identify a large amount of abnormal-infrasonicwave data.This leads to low monitoring accuracy and efficiency prior to an earthquake.In this work,we present an automatic method for identifying anomalous infrasonic waves in the cloud computing environment.We constructed a JNS abnormal-infrasonic-wave data acquisition and screening module to scan the access port in real time,and quickly provide feedback regarding abnormal infrasonic data.We use an NDS abnormal-infrasonic-data-sequence detection algorithm to quickly identify a wrong sequence matrix,and accurately retrieve,locate,and lock the abnormalinfrasonic wave data.This automatic recognition method can be used to classify anomalous infrasonic waves and determine whether the seismic signal is suspicious.The experimental results show that the proposed method can efficiently and automatically identify abnormal infrasonic signals prior to an earthquake,with a maximum signal classification accuracy of 99.99%,and a maximum average multiple recognition time of only 1.3min.
In the process of automatically identifying infrasonic waves in a focal region prior to the occurrence of an earthquake,tripartite array arithmetic has difficulty locating the source of infrasonic waves as it cannot automatically screen and identify a large amount of abnormal-infrasonicwave data.This leads to low monitoring accuracy and efficiency prior to an earthquake.In this work,we present an automatic method for identifying anomalous infrasonic waves in the cloud computing environment.We constructed a JNS abnormal-infrasonic-wave data acquisition and screening module to scan the access port in real time,and quickly provide feedback regarding abnormal infrasonic data.We use an NDS abnormal-infrasonic-data-sequence detection algorithm to quickly identify a wrong sequence matrix,and accurately retrieve,locate,and lock the abnormalinfrasonic wave data.This automatic recognition method can be used to classify anomalous infrasonic waves and determine whether the seismic signal is suspicious.The experimental results show that the proposed method can efficiently and automatically identify abnormal infrasonic signals prior to an earthquake,with a maximum signal classification accuracy of 99.99%,and a maximum average multiple recognition time of only 1.3min.
引文
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[13]沈琰辉,刘华文,徐晓丹,等.基于邻域离散度的异常点检测算法[J].计算机科学与探索,2016,10(12):1763-1772.SHEN Yanhui,LIU Huawen,XU Xiaodan,et al.Outlier Detection Algorithm Based on Dispersion of Neighbors[J].Journal of Frontiers of Computer Science&Technology,2016,10(12):1763-1772.
[14]王端理.网络数据库中异常数据检测优化仿真[J].计算机仿真,2017,34(5):410-413.WANG Duanli.Network to Detect Abnormal Data in the Database Optimization Simulation[J].Computer Simulation,2017,34(5):410-413.
[15]周继磊,杨迪雄,陈国海.近断层脉冲型地震动功率谱特性分析[J].世界地震工程,2017,33(1):18-26.ZHOU Jilei,YANG Dixiong,CHEN Guohai.Analysis of Power Spectrum Characteristics of Near-fault Pulsed Ground Motion[J].World Earthquake Engineering,2017,33(1):18-26.
[2]徐洪,陈正华,周廷强,等.基于小波分解的岩石破坏次声信息特征研究[J].应用声学,2016,35(3):231-238.XU Hong,CHEN Zhenghua,ZHOU Tingqiang,et al.Characteristics Analysis of Infrasound before Rock Failure Applying the Wavelet Decomposition[J].Applied Acoustics,2016,35(3):231-238.
[3]江朝晖,张静,饶元,等.基于聚类和异常检测的作物监测数据预处理研究[J].浙江农业学报,2016,28(5):885-892.JIANG Zhaohui,ZHANG Jing,RAO Yuan,et al.Study on Crop Monitoring Data Preprocess Based on Cluster Analysis and Anomaly Detection[J].Acta Agriculturae Zhejiangensis,2016,28(5):885-892.
[4]惠少兴,严川,王平,等.陕西地区小微震震源机制研究[J].地震工程学报,2018,40(2):288-293.HUI Shaoxing,YAN Chuan,WANG Ping,et al.Research on Focal Mechanisms of the Small and Micro Earthquakes in Shaanxi Area[J].China Earthquake Engineering Journal,2018,40(2):288-293.
[5]李琴,赵仕英,黄志强,等.可控震源振动器系统不确定性分析[J].中国安全生产科学技术,2016,12(8):158-164.LI Qin,ZHAO Shiying,HUANG Zhiqiang,et al.Uncertainty Analysis of Vibroseis System[J].China Science and Technology for Safe Production,2016,12(8):158-164.
[6]许卫晓,杨伟松,孙景江,等.震中烈度与震级和震源深度经验关系的统计回归分析[J].自然灾害学报,2016,25(2):139-145.XU Weisong,YANG Weisong,SUN Jingjiang,et al.Statistical Regression Analysis of the Empirical Relationship between Epicenter Intensity and Magnitude and Focal Depth[J].Journal of Natural Disasters,2016,25(2):139-145.
[7]王欣,周晓梅.云计算环境下大数据合理分流技术研究与仿真[J].计算机仿真,2016,33(3):292-295.WANG Xin,ZHOU Xiaomei.Research and Simulation on Big Data Reasonable Splitting Technology in Cloud Computing Environment[J].Computer Simulation,2016,33(3):292-295.
[8]ZIMMER V L,SITAR N.Detection and Location of Rock Falls Using Seismic and Infrasound Sensors[J].Engineering Geology,2015,193:49-60.
[9]费欢,李光辉.基于K-means聚类的WSN异常数据检测算法[J].计算机工程,2015,41(7):124-128.FEI Huan,LI Guanghui.Abnormal Data Detection Algorithm for WSN Based on K-means Clustering[J].Computer Engineering,2015,41(7):124-128.
[10]曹科研,栾方军,孙焕良,等.不确定数据基于密度的局部异常点检测[J].计算机学报,2017,40(10):2231-2244.CAO Keyan,LUAN Fangjun,SUN Huanliang,et al.DensityBased Local Outlier Detection on Uncertain Data[J].Chinese Journal of Computers,2017,40(10):2231-2244.
[11]MARUYAMA T,YUSUPOV K,AKCHURIN A.Interpretation of Deformed Ionograms Induced by Vertical Ground Motion of Seismic Rayleigh Waves and Infrasound in the Thermosphere[J].Annales Geophysicae,2016,34(2):271-278.
[12]许海清,黄敏.浅谈电力大数据对信息运行的影响[J].电力工程技术,2015,34(2):62-64.XU Haiqing,HUANG Min.A Discussion on the Influence of Power Big Data on Information Operation[J].Jiangsu Electrical Engineering,2015,34(2):62-64.
[13]沈琰辉,刘华文,徐晓丹,等.基于邻域离散度的异常点检测算法[J].计算机科学与探索,2016,10(12):1763-1772.SHEN Yanhui,LIU Huawen,XU Xiaodan,et al.Outlier Detection Algorithm Based on Dispersion of Neighbors[J].Journal of Frontiers of Computer Science&Technology,2016,10(12):1763-1772.
[14]王端理.网络数据库中异常数据检测优化仿真[J].计算机仿真,2017,34(5):410-413.WANG Duanli.Network to Detect Abnormal Data in the Database Optimization Simulation[J].Computer Simulation,2017,34(5):410-413.
[15]周继磊,杨迪雄,陈国海.近断层脉冲型地震动功率谱特性分析[J].世界地震工程,2017,33(1):18-26.ZHOU Jilei,YANG Dixiong,CHEN Guohai.Analysis of Power Spectrum Characteristics of Near-fault Pulsed Ground Motion[J].World Earthquake Engineering,2017,33(1):18-26.