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Seismological data acquisition and signal processing using wavelets

机译:利用小波进行地震数据采集和信号处理

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摘要

This work deals with two main fields: a) The design, built, installation, test, evaluation, deployment and maintenance of Seismological Network of Crete (SNC) of the Laboratory of Geophysics and Seismology (LGS) at Technological Educational Institute (TEI) at Chania. b) The use of Wavelet Transform (WT) in several applications during the operation of the aforementioned network. SNC began its operation in 2003. It is designed and built in order to provide denser network coverage, real time data transmission to CRC, real time telemetry, use of wired ADSL lines and dedicated private satellite links, real time data processing and estimation of source parameters as well as rapid dissemination of results. All the above are implemented using commercial hardware and software which is modified and where is necessary, author designs and deploy additional software modules. Up to now (July 2008) SNC has recorded 5500 identified events (around 970 more than those reported by national bulletin the same period) and its seismic catalogue is complete for magnitudes over 3.2, instead national catalogue which was complete for magnitudes over 3.7 before the operation of SNC. During its operation, several applications at SNC used WT as a signal processing tool. These applications benefited from the adaptation of WT to non-stationary signals such as the seismic signals. These applications are: HVSR method. WT used to reveal undetectable non-stationarities in order to eliminate errors in site’s fundamental frequency estimation. Denoising. Several wavelet denoising schemes compared with the widely used in seismology band-pass filtering in order to prove the superiority of wavelet denoising and to choose the most appropriate scheme for different signal to noise ratios of seismograms. EEWS. WT used for producing magnitude prediction equations and epicentral estimations from the first 5 secs of P wave arrival. As an alternative analysis tool for detection of significant indicators in temporal patterns of seismicity. Multiresolution wavelet analysis of seismicity used to estimate (in a several years time period) the time where the maximum emitted earthquake energy was observed.
机译:这项工作涉及两个主要领域:a)美国技术教育学院(TEI)的地球物理和地震学实验室(LGS)的克里特岛地震网络(SNC)的设计,建造,安装,测试,评估,部署和维护哈尼亚。 b)在上述网络的运行过程中,小波变换(WT)在几种应用中的使用。 SNC于2003年开始运营。其设计和构建旨在提供更密集的网络覆盖范围,向CRC的实时数据传输,实时遥测,有线ADSL线路和专用专用卫星链路的使用,实时数据处理和源估计参数以及结果的快速传播。以上所有内容都是使用商业硬件和软件实现的,这些软件和软件经过修改,必要时,作者可以设计并部署其他软件模块。截至目前(2008年7月),SNC记录了5500个已识别的事件(比同期国家公告所报告的事件多970个),其地震目录的震级超过3.2,而国家目录的震级超过3.7。 SNC的操作。在其运行期间,SNC的一些应用程序将WT用作信号处理工具。这些应用受益于WT对非平稳信号(例如地震信号)的适应。这些应用是:HVSR方法。为了消除站点基本频率估计中的错误,WT曾经揭示出无法检测到的非平稳性。去噪。为了证明小波去噪的优越性,并针对不同的地震图信噪比选择最合适的方案,与在地震学带通滤波中广泛使用的几种小波去噪方案进行了比较。 EEWS。 WT用于从P波到达的前5秒生成震级预测方程和震中估计。作为检测地震时空模式中重要指标的替代分析工具。用于地震活动的多分辨率小波分析用于估计(在几年的时间段内)观测到的最大发射地震能量的时间。

著录项

  • 作者

    Stonham T J; Hloupis Georgios;

  • 作者单位
  • 年度 2009
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  • 原文格式 PDF
  • 正文语种 English
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