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爆破、塌陷识别研究进展综述
周少辉1, 蒋海昆2, 曲均浩1, 李健3, 郭宗斌1, 郑旭1
1.山东省地震局, 济南 250014;2.中国地震台网中心, 北京 100045;3.海南省地震局, 海口 570203
摘要:
简要介绍当前国内外关于天然地震与爆破、塌陷等非天然地震特征研究及事件类型识别的进展。对各类事件的定义及主要波形特征进行简要综述,重点介绍了事件类型判定的各类识别方法。与自然界天然地震不同,非天然地震由人工干预或人类活动间接引发。爆破是炸药在爆炸瞬间能量迅速释放,部分能量以地震波形式向外传播,引起地表振动而产生破坏效应的一种地震;塌陷是由于岩层崩塌陷落而形成的地震。虽然在地震台网记录到的天然地震与爆破、塌陷的波形存在一定的共性特征,但由于震源类型、波的传播路径、震源深度等不同,各类事件的波形记录在P波初动、P波与S波最大振幅比、持续时间、震相、短周期面波发育情况、发震时刻、空间位置分布以及频谱特征等方面差异明显。目前主要有两类方法来识别地震与爆破、塌陷等非天然事件。一类为直接基于波形在信号、数据方面的特征,通过定性分析来进行事件类型判定,如波形时频分析对比法、小波变换、相关系数等;另一类为统计学领域诸如模式识别等算法,利用统计算法综合考虑多个事件特征判据的定量判定阀值来实现地震与爆破、塌陷事件类型的识别,如最小距离法、改进的连续亨明方法、Fisher方法、逐步代价最小决策法、支持向量机、前馈神经网络等。两类方法本质上均为提取有效特征判据,即对数据进行降维使用,未将事件记录的全部信息用于事件判定。因此,有必要使用一种可从全部事件记录中自动提取各类信息并可组合底层特征的算法来对各类事件进行判断识别。
关键词:  爆破  塌陷  事件类型识别
DOI:
分类号:
基金项目:山东省自然科学基金重点项目(ZR2020KF003)、国家重点研发专项课题(2018YFC1503305)和山东省地震局成果推广转化项目(CGZH2001)共同资助
A Review on Research Progress in Recognition of Blasting and Collapse
Zhou Shaohui1, Jiang Haikun2, Qu Junhao1, Li Jian3, Guo Zongbin1, Zheng Xu1
1.Shandong Earthquake Agency, Jinan 250014, China;2.China Earthquake Networks Center, Beijing 100045, China;3.Hainan Earthquake Agency, Haikou 570203, China
Abstract:
This paper briefly summarizes the current development in the seismic characteristics and event type identification of earthquakes,blasting and collapses at home and abroad. Briefly with the definitions and main features of various types of events we focus on various identification methods for event type recognition. Unlike earthquakes formed by nature itself,non-natural earthquake is an event that is indirectly caused by artificial intervention or human activities. Blasting is a kind of event in which the explosive energy is rapidly released at the moment of explosion and propagates outward in the form of seismic waves,causing surface vibrations and destructive effects. The collapse is a kind of event caused by the collapse of the rock formation. Although the earthquakes recorded in the seismic network have certain common characteristics with the waveforms of blasting and collapse,there are significant differences in the P-wave first motion,the maximum amplitude ratio of P-wave and S-wave,duration time,phase,the development of short-period surface wave,seismogenic time,the distribution of spatial position and spectral characteristics due to the different types of sources,wave propagation paths,and focal depths,and etc. At present,there are two main methods to identify non-natural events such as earthquakes and blasting and collapse. A class of criteria based on waveform in terms of signal and data or clear geophysical significance is directly identified;such as waveform time-frequency analysis contrast method,wavelet transform,and etc. A class of algorithms based on the field of statistical pattern recognition;such as the minimum distance method,ICHAM,the Fisher,SAMC,SVM,and the feedforward neural network. In essence,the two types of methods first extract the effective feature criterion,that is,use the data for dimensionality reduction,and do not use all the information of the event record for event determination. Therefore,it is necessary to use an algorithm that can automatically extract various types of information from all event records and combine the underlying features to judge and identify various events.
Key words:  Blasting  Collapse  Event type recognition