云计算环境下震前震源异常次声波自动识别方法
Automatic Identification of Anomalous Infrasonic Waves prior to Earthquake in Cloud Computing Environment

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1.3 min，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-infrasonic-wave 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 abnormal infrasonic 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 1.3 min.