天然地震波形与工业化爆破波形振幅比对比的研究

选取乌鲁木齐遥测地震台记录的部分天然地震与新疆工业化爆破项目中的部分爆破资料,通过波形振幅比AS/AP以及记录在P波垂直向的初动符号的对比,总结出了识别爆破与地震的依据。工业化爆破振幅比的比值比较稳定,且小于天然地震振幅的比值,而天然地震受多种因素的影响,振幅比的比值变化范围大。工业化爆破波形记录的初动向上为" ",而天然地震产生的地震波的初动符号具有四象限分布的明显规律。     

天然地震与人工爆破的波形小波特征研究

研究了如何从天然地震和人工爆破事件的波形记录中提取出有效、适用的波形特征,以用于对爆破事件的识别.首先对波形记录进行了4层小波包变换;然后对变换得到的最后一层小波包系数提取3种波形特征:能量比特征、香农熵特征及对数能量熵特征;最后利用v-SVC支持向量分类机对这3种特征的分类能力进行了外推检验.通过选用不同地区、不同台站、不同震级的天然地震与人工爆破的波形记录,力求提取的特征量能尽可能地反映天然地震与人工爆破波形的本质区别,尽量弱化震中距、震级等因素对识别效果的影响.结果表明,上述3种特征中以香农熵特征的识别效果最好,能反映天然地震与人工爆破的本质区别,可作为识别天然地震与人工爆破的一个有效判据.     

大厂矿区地震与爆破记录波形特征分析

运用小波理论和分形理论方法对大厂矿区典型爆破和显著地震事件波形进行定量处理和特征分析。先用小波理论对爆破和地震波形进行消噪处理,然后再运用分形维对爆破和地震波形进行定量处理和特征分析。结果表明:一般情况下取分维值为1.2可分辨该区的天然地震和爆破或其他干扰波动。     

朝鲜地区地震与爆破波形识别研究

由于爆破、地震的震源机制不同,在低频和高频段中,存在的特征也不同。基于这一思路,本文对丹东台网4个地震台记录的爆破与地震资料开展波谱对比研究,试图找出识别爆破、核爆与地震的一些新判据。     

重庆数字地震记录人工爆破波形特征

分析了2016年1月至6月重庆地震台网数字记录的巫山机场人工爆破波形资料,认为爆破事件的时空强具有明显规律性,其P波初动震相、A_S/A_P振幅比、频率谱等特征与天然地震具有明显区别,为正确识别重庆地区人工爆破和天然地震具有参考价值。     

地震波形记录特征分析

测震图分析是地震预报的一项基础工作,近年来随着地震预报工作的发展,对地震图的研究越来越深入.本文总结了小浪底水库数字地震台网记录的波形资料,用实例说明了不同距离、不同方位、不同类型的地震震相的特征,以及地震、爆破和塌陷波形的区别,发现小浪底水库地震台网波形记录有其特定的记录特征.     

曹妃甸地震台网天然地震与爆破波形记录特征

统计曹妃甸地震台网成立以来记录的地震波形资料,对比天然地震和化学爆炸、矿爆的波形,发现天然地震与爆破震相特征在P波初动、周期、波形衰减及振幅等方面有很大不同,并对波形进行频谱分析,进一步识别天然地震及爆破。     

天然地震和人工爆破波形特征对比分析

从反射波、面波、瑞利波等不同的角度,从波形上分析了人工爆破和天然地震的区别,从而在地震定位的过程中能够快速有效地识别出人工地震和天然地震,并把此方法应用在北京台网的实际工作中.     

地震和爆破事件源波形信号的卷积神经网络分类研究

本文首先从震源波形中提取梅尔频率倒谱系数(MFCC)图,然后采用卷积神经网络(CNN)进行地震波形信号的震源类型—天然地震和爆破事件—分类识别.事件为首都圈及其附近的72个天然地震和101个人工爆破事件,用于提取梅尔频率倒谱系数图的波形信号为各观测台站波形3分量中的垂直分量波形.在各个事件的所有观测台站的垂直分量波形中,通过滑动窗口按同一准则去除被噪声淹没的部分台站波形,只选择留下未被噪声淹没的台站波形.每一个事件有107个观测台站,故有107份垂直分量波形,而不同事件被留下未被噪声淹没的波形则有几份至几十份不等.然后提取被留下未被噪声淹没的波形的梅尔频率倒谱系数图,以梅尔频率倒谱系数图作为CNN的输入,CNN的输出则为波形的震源类型(天然地震事件或爆破事件).若以单份波形为识别单元,采用五折交叉验证法进行测试,得到的平均准确率为95.78%.使用训练集中单份波形为识别单元,提取梅尔频率倒谱系数图,采用CNN训练出了天然地震事件与爆破事件波形分类器,一个事件在测试集中的多份波形信号通常不会都被正确识别,很可能有些波形被识别为天然地震事件,另一些波形被识别为爆破事件;这时,若识别单元改为事件,一个事件各台站的有效垂直分量波形中,超过一半的波形被识别为某一事件类型,则这个事件被归类为该事件类型,得到的正确识别率为97.1%.实验结果表明:卷积神经网络在天然地震事件与爆破事件的识别方面表现出色.这说明MFCC与卷积神经网络可以用于识别天然地震和爆破事件,尤其是深度学习更值得在地震信号处理方面做进一步的研究.     

天然地震与人工爆破波形信号HHT特征提取和SVM识别研究

天然地震和人工爆破信号属于非线性非平稳信号,而传统信号分析方法是针对线性系统平稳信号的,本文采用希尔伯特—黄变换(Hilbert-Huang Transform,简称HHT)试图提取可明确区分天然地震和人工爆破事件的波形特征.通过经验模态分解(Empirical Mode Decomposition,简称EMD)把原信...     

安徽及周边地区小爆破的识别与判据研究

根据安徽测震台网记录的爆破与地震的数字资料,采用波谱分析方法,对该区域记录的小爆破与小震级地震事件计算纵横波拐角频率和卓越周期,试图获得爆破和地震识别的定量指标。对比研究波谱特征,发现爆破与地震的纵横波拐角频率和卓越周期等存在明显差异,为该地区小爆破的识别,提供了新的判据。最后,对爆破与地震其他方面的判据特征进行了总结。     

Discrimination Between Nuclear Explosions and Earthquakes: An Application of Wigner Distribution

In this paper,the nonstationary theory of Wigner Distribution is used to discriminate between underground nuclear explosions and natural earthquakes.Five underground explosions in Kazakhstan region and seven regional earthquakes in its adjacent areas have been analyzed.The result shows that the transient spectra of underground nuclear explosions are concentrated in the frequency range of 5-10 Hz,while the transient spectra of natural earthquakes are distributed widely from lower frequency to higher frequency.The transient frequency of nuclear explosions shows linearity in the first stage(0相似文献   

太原基准地震台核爆震相识别

对太原基准地震台记录的核爆及天然地震波形进行对比分析,结果显示：①与天然地震相比,核爆震相特征相对独特;②地震优势频率较窄,而核爆优势频率则较宽,即对于震中距相近、当量不同的核爆波形,太原台记录的时频变化特征相似;③对于震中距相近的天然地震与核爆波形,太原台记录的时频特征差异明显。     

不同地区人工爆炸与天然地震记录特征及识别研究

本文分析了河北怀来多次爆炸、河北三河采石场多次爆炸和低震级天然地震事件的记录特征和时频差异。结果显示:河北怀来爆炸的P波能量强、衰减快、S波发育弱;河北三河采石场爆炸的P波、S波主频均低于怀来爆炸,S波与面波混淆,不同震中距的台站记录低频发育明显;而天然地震的有效频带更宽,频率成分更为复杂。将Pg/Sg谱比判据应用于小震级地震与爆炸的识别中,探索交叉频带谱比对不同地区爆炸的识别。结果表明:高频（＞5 Hz）Pg/Sg谱比判据可将研究数据中的爆炸与小震级地震完全区分;与Sg低频（0—2 Hz）有关的交叉频带谱比可对两个不同地区的爆炸进行识别,交叉频带的谱比判据较传统的单一频带谱比判据能够更好地反映出不同类型事件的特征差异。      

琼北确定性人工爆破与天然地震识别判据

对琼北地区确定性井下人工爆破和天然地震事件波形特征进行梳理,分析人工爆破与天然地震波不同判据特征。结果表明:P波初动方向、振幅比是识别人工爆破和天然地震的2个主要判据;尾波持续时间、S波最大振幅与持续时间比可作为识别人工爆破和天然地震的一般判据;发震时间可根据事件的强度、规律性,并结合其他判据,仅作为识别过程中的参考因素。     

Dynamics of Fine Particle Emissions in Open Pit Mines

Izvestiya, Physics of the Solid Earth - Abstract—Mass industrial explosions on extended benches of open pits are the most effective way to crush rock. Such explosions are accompanied by the...     

基于倒谱的2015年天津港“8·12”爆炸分析

本文通过对记录到2015年天津港“8·12”爆炸的32个台站的三分量数字地震波形进行倒谱分析,得到了以下结论:①2个主要爆炸的发生时间间隔约为32.3s;②爆炸-2发生在爆炸-1的西北侧约353°处;③依据这些记录的倒谱,无法判定在爆炸-1前是否还存在微小的爆炸。以上结论均与前人研究成果吻合。由于倒谱叠加采用的是全波形,对滤波频带不敏感,因此,在检测发生在同一地点的多次爆炸或其它类似事件（如核爆）上有一定的优势。     

Analysis of Russian Hydroacoustic Data for CTBT Monitoring

—?As part of a collaborative research program for the purpose of monitoring the Comprehensive Nuclear-Test-Ban Treaty (CTBT), we are in the process of examining and analyzing hydroacoustic data from underwater explosions conducted in the former Soviet Union. We are using these data as constraints on modeling the hydroacoustic source as a function of depth below the water surface. This is of interest to the CTBT because although even small explosions at depth generate signals easily observable at large distances, the hydroacoustic source amplitude decreases as the source approaches the surface. Consequently, explosions in the ocean will be more difficult to identify if they are on or near the ocean surface. We are particularly interested in records featuring various combinations of depths of explosion, and distances and depths of recording.¶Unique historical Russian data sets have now become available from test explosions of 100-kg TNT cast spherical charges in a shallow reservoir (87?m length, 25?m to 55?m width, and 3?m depth) with a low-velocity air-saturated layer of sand on the bottom. A number of tests were conducted with varying water level and charge depths. Pressure measurements were taken at varying depths and horizontal distances in the water. The available data include measurements of peak pressures from all explosions and digitized pressure-time histories from some of them. A reduction of peak pressure by about 60–70% is observed in these measurements for half-immersed charges as compared with deeper explosions. In addition, several peak-pressure measurements are also available from a 1957 underwater nuclear explosion (yield <10?kt and depth 30?m) in the Bay of Chernaya (Novaya Zemlya).¶The 100-kg TNT data were compared with model predictions. Shockwave modeling is based on spherical wave propagation and finite element calculations, constrained by empirical data from US underwater chemical and nuclear tests. Modeling was performed for digitized pressure-time histories from two fully-immersed explosions and one explosion of a half-immersed charge, as well as for the peak-pressure measurements from all explosions carried out in the reservoir with water level at its maximum (3?m). We found that the model predictions match the Russian data well.¶Peak-pressure measurements and pressure-time histories were simulated at 10?km distance from hypothetical 1-kt and 10-kt nuclear explosions conducted at various depths in the ocean. The ocean water was characterized by a realistic sound velocity profile featuring a velocity minimum at 700?m depth. Simulated measurements at that same depth predict at least a tenfold increase in peak pressures from explosions in the SOFAR channel as compared with very shallow explosions (e.g., ～3?m depth).¶ The observations and the modeling results were also compared with predictions calculated at the Lawrence Livermore National Laboratory using a different modeling approach. All results suggest that although the coupling is reduced for very shallow explosions, a shallow 1-kt explosion should be detectable by the IMS hydroacoustic network.     

Earthquake-explosion discrimination using waveform cross-correlation technique for mines in southeast of Tehran

The presence of man-made explosions in a seismic catalogue leads to errors in statistical analyses of seismicity. Recently, the need to monitor man-made explosions used for mining, road excavating, and other constructional applications has been become a demanding challenge for the seismologists. In this way, we gain new insight into the cross-correlation technique and conduct this approach to discriminate explosions from seismic datasets. Following this, improved P-wave arrival times are used for more precise relocation. In this study, the waveform cross-correlation technique provides a reliable means for discriminating explosions which have cross-correlation coefficients (CC) of 0.6 or greater with their own corresponding stacked waveforms. The results illustrate that approximately 80 % of seismicity of southeast of Tehran, recorded by the Iranian Seismological Center (IRSC), includes events which have cross-correlation coefficients of ≥0.6 with their corresponding stacked waveforms. Furthermore, with improved P-wave arrival time, there is a better chance to relocate explosions precisely in the region under study.