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本文提出一种适用于地震加速度仪观测台站的实时地震信号检测方法。该方法的运用基于Z~-变换的冲激不变和阶跃不变法,对地震加速度仪运动方程所表示的连续系统的传递函数进行数字模拟,导出加速度仪对于地震地面运动加速度的位移反应和速度反应递推公式。根据地震反应谱理论,进一步导出计算地震信号能量谱的快速数值方法。通过对地震台站背景噪声的长期观测和统计分析,建立了特定台站背景噪声模型;又根据特定台站已获得的真实地震记录的统计频谱特性,建立了该台站的地震动预期谱,从而建立起事件检测的标准。最后,以信号和背景噪声的能量谱为比较对象,应用所谓最适信号检测滤波器,对采样率为100点/秒的加速度时间信号进行检测,可以得到理想的结果。 相似文献
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《地球物理学进展》2016,(5)
由于微地震信号能量微弱、信噪比低,需要对采集到的微地震数据进行去噪处理,从而提高微地震记录的信噪比,提高震源定位的精度.目前存在许多基于模型的先进滤波方法,如基于机理模型的卡尔曼滤波已经成功应用在微地震信号去噪中.为了建立微地震信号的数学模型,改善卡尔曼滤波效果,本文通过数据辨识方法,对微地震信号建立了ARMA模型,并进一步转化为适用于卡尔曼滤波算法的状态空间模型.在此基础上研究了卡尔曼滤波方法,设计了适用于微地震去噪的卡尔曼滤波实现算法.理论模型和实际微地震监测数据处理结果表明,基于辨识模型的卡尔曼滤波算法能够有效抑制微地震信号中的随机噪声,显著提高微地震监测信号的信噪比,从而验证了该辨识模型的准确性和滤波算法的可行性. 相似文献
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为了提高我国微小地震监测能力,"一带一路"项目拟在黑龙江鹤岗地区建设地震台阵。在2019年8月4日至9月4日,鹤岗地震台阵监测到部分近震及远震,与黑龙江省鹤岗地震台观测数据进行对比分析,得到以下结果：与鹤岗地震台相比,鹤岗地震台阵对近震及区域性地震的监测能力较高,对远震和极远震的监测范围较大,记录完整的地震个数更多,整体背景噪声更小,地震波形质量更高,定位更精确,提高了区域地震监测能力。 相似文献
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2019年黑龙江省完成"一带一路"地震科学台阵项目中台址勘选工作,基于科学台阵中136个台址的地面运动噪声数据,通过计算不同频段范围内背景噪声记录的加速度功率谱密度,研究不同环境噪声下科学台阵记录数据的地噪声特征及其台基响应。结果表明：黑龙江西北和东南部地区地面运动噪声水平低,观测环境较好;中部和东北部地区噪声水平较高,大庆地区尤为严重。勘选结果真实反映了黑龙江区域内的背景噪声分布,使我们对本区域地噪声水平和干扰因素有了新的认识。 相似文献
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目前针对强震临震前震动信号的拾取和分析基本都是在有限的频带内开展,且分析的数据量时空范围有限。本文提出一种基于大数据的地震背景噪声计算和分析方法,对海量地震观测数据进行分布式噪声功率谱计算,数据采用2013年3—4月的四川地震台网记录的连续波形数据。通过对2013年四川芦山7.0级地震前四川地震台网记录的50Hz～200s范围内的多个频点时序变化进行分析,发现芦山地震距震中50km范围内的MDS台和BAX台在震前水平方向30～150s周期上噪声出现增强10～20dB且持续3～5天的情况,垂直向未发现明显变化,同时超出50km范围内的台站也未有明显变化。这种长周期异常变化很难通过传统的地震动信号拾取来发现,表明本文提出的强震临震异常分析方法可以用于发现和分析震前的长周期异常信号。 相似文献
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分析了相邻井下、地表地震计记录的背景噪声及近震、远震波形特征,以及相关的振幅谱及近震、远震接收函数波形特征。结果表明,井下地震计记录的背景噪声比地表地震计小1个数量级,其近震、远震波形记录较清晰、简洁;井下、地表地震计记录的远震振幅谱和径向接收函数具有较好的一致性,但背景噪声、近震振幅谱、近震接收函数、远震切向接收函数波形存在一定差异;由地表地震计得到的近震震级比井下地震计的大0.12,地表地震计到井下地震计之间的P波速度约为2 km/s。 相似文献
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本文基于信号指纹技术,通过研究短时（<10 min）地磁异常数据识别算法,完成了对地磁干扰信号类型的识别。本文所用信号指纹技术结合了短时傅里叶变换、小波变换、信号二值化、文本相似性哈希等多种数据和文本处理方法,将一定时间内的波形数据转换为一个32位的整型数字,极大地压缩了信号的特征信息,因此在很大程度上减少了后续查找与分类过程中所需处理的数据。利用该算法对河北红山地磁台2016年5月1—3日两套GM4磁通门磁力仪的原始秒数据的计算结果表明,本文算法可以快速准确地识别干扰信号类型,为实现地磁相对观测数据中异常信号的自动提取提供技术支撑。 相似文献
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采用双差定位方法, 对朝鲜2006年、2009年和2013年的3次核试验进行相对定位.根据19个区域地震台和8个远震台的垂直分量波形资料, 计算了多种震相(P波、S波和LR波等)的互相关时差, 并由测量时差反演两两核爆的相对空间位置, 通过随机生成部分台站组合进行扰动实验以估算定位的不确定度.结果表明, 相对于朝鲜2009年核试验, 2013年核试验位于其南257 m、西385 m, 2006年核试验位于其南503 m、东2589 m.当扰动实验中随机选取的台站数目为20个时, 在95%置信度下, 朝鲜2013年和2006年核试验相对于2009年核试验的地表定位结果的不确定度约为150 m. 相似文献
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Jean-Luc Got Vadim Monteiller Jean Virieux Stéphane Operto 《Geophysical Prospecting》2008,56(4):477-491
Double-difference tomographic methods use directly accurate time delays computed between similar signals. Such methods are designed to image very heterogeneous media, such as volcanoes or fault zones. In seismological applications, similar signals are recorded at a given station from earthquakes sharing similar and close-by sources. In seismic exploration experiments, similar signals are often recorded at neighbouring receivers. After a brief presentation of the tomographic algorithm used, a seismological application is summarized. The potential and limits of double-difference tomographic methods are explored using various numerical experiments. They show that two effects are competing in double-difference tomography: (i) the degradation of the stability of the inversion due to the geometrical proximity of the rays used in the differentiation and (ii) the decrease in modelling error, which allows improving the stability of the inversion and using smaller quantities of a priori information when data are sufficiently accurate. The best resolution is obtained for an optimal value of the inter-source or inter-receiver distance. For optimal values of these distances and a priori information, tomography using traveltime differences provides significantly better resolved results than using traveltimes. 相似文献
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芦山MS7.0地震前的电磁异常信号 总被引：1，自引：0，他引：1
对南北地震带18个地电场及电磁扰动观测台站进行了跟踪与研究, 2013年4月20日四川芦山MS7.0地震前只有其中5个台站观测到了异常电磁信号. 研究表明, 地震前的这些异常信号时空分布非常不均匀. 在时间上表现出震前电磁异常信号在出现的台站上不是同步的, 且随着时间的推移有向震中方向迁移的趋势; 在空间上体现在成都以北的台站都没有记录到震前电磁异常信号, 主要出现在成都及其以南的一些台站. 芦山MS7.0地震前地震电磁信号出现的最早时间是震前21天, 其次是震前16天; 临震时间则为震前5天、 4天和1天. 通过接地极注入地下的大电流以及以往的震例分析, 认为仙女山台是又一个比较突出的电磁信号观测的敏感点. 相似文献
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IntroductionThe present-day digital seismic observation technique provides us a large amount of accurate seismic records of high-precision, wide-band, and large dynamic range and conditions for higher-solution and higher-speed discrimination of earthquakes. But the problem we face is how to use the modern theory and method for digital signal processing in fast and precise extraction of the characteristic parameters from the large amount of digital seismic records. A basis or the important prob… 相似文献
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一种稳定的波场延拓反Q滤波算法 总被引：1，自引：0，他引：1
反Q滤波算法中稳定性是关键。针对地下介质为层状Q模型结构,本文提出一种稳定的反Q滤波算法,基于波场延拓原理,对于每个常Q层反Q滤波算法分两步完成:(1)利用一种稳定的波场延拓算法,将地表波场记录直接延拓到当前层上;(2)在当前层内进行常Q反滤波。在步骤(1)中,利用稳定的波场延拓算法并结合稳定因子,将地表波场记录直接延拓到当前层上,避免了上覆层反Q滤波误差的累积。在步骤(2)中,对于当前层内的反Q滤波,通过对该层地震信号的Gabor谱分析,拾取时变的限幅频率,并确定相应于该频率值的限幅增益值来实现反Q滤波算法的稳定性。最后通过理论模型与实际数据验证了算法的优越性。 相似文献
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Gloria Curilem Jorge Vergara Gustavo Fuentealba Gonzalo Acuña Max Chacón 《Journal of Volcanology and Geothermal Research》2009
Each volcano has its own unique seismic activity. The aim of this work is to construct a system able to classify seismic signals for the Villarrica volcano, one of the most active volcanoes in South America. Since seismic signals are the result of particular processes inside the volcano's structure, they can be used to forecast volcanic activity. This paper describes the different kinds of seismic signals recorded at the Villarrica volcano and their significance. Three kind of signals were considered as most representative of this volcano's activity: the long-period, the tremor, and the energetic tremor signals. A classifier is implemented to read the seismic registers at 30-second intervals, extract the most relevant features of each interval, and classify them into one of the three kinds of signals considered as most representative of this particular volcano. To do so, 1033 different kinds of 30-s signals were extracted and classified by a human expert. A feature extraction process was applied to obtain the main characteristics of each of them. This process was developed using criteria which have been shown by others to effectively classify seismic signals, based on the experience of a human expert. The classifier was implemented with a Multi-Layer Perceptron (MLP) artificial neural network whose architecture and training process were optimized by means of a genetic algorithm. This technique searched for the most adequate MLP configuration to improve the classification performance, optimizing the number of hidden neurons, the transfer functions of the neurons, and the training algorithm. The optimization process also performed a feature selection to reduce the number of signal features, optimizing the number of network inputs. The results show that the optimized classifier reaches more than 93% exactitude. identifying the signals of each kind. The amplitude of the signals is the most important feature for its classification, followed by its frequency content. The described methodology can be used to classify more seismic signals to improve the study of the activity of this volcano or to extend the study to other active volcanoes of the region. 相似文献
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In order to study the excitation of large-volume airgun source with different combinations in Hutubi, Xinjiang,China,we conducted a targeted experiment. The method of time-frequency analysis is used to study the signals recorded by a seismometer on the shore of the excited pool, and it is concluded that different gun combinations will lead to different frequency of bubble pulse signals. Besides, linear combination method is used to analyze the signal-to-noise ratios of signals excited by different gun combinations which was recorded by seismic stations around the airgun source. In order to improve the signal-to-noise ratios, it is more effective to increase the activation energy (the number of excited guns at the same time) than to stack the excited signals with smaller energy repeatedly. 相似文献