爆破、塌陷识别研究进展综述

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...

A Review on Research Progress in Recognition of Blasting and Collapse

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.