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Seismic signal processing for near-field source localization.

机译：用于近场源定位的地震信号处理。

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摘要

The goal of the research performed in this dissertation is to correctly estimate the location of a seismic source from acceleration records collected via a network of seismic stations. Specifically, multi-axial seismic accelerometer stations coupled to the surface of the ground collect accelerations generated by seismic sources; the sensor network consists of many such accelerometer stations spaced less than 50 meters apart from each other; and, the seismic sources are located in the near-field of sensor arrays. Near-field scenarios refer to cases where the distances between sensors and the source are less than a few wavelengths of the signal in question. Typical seismic signals comprise wavelengths ranging between 0.05-15 km. Thus, near-field scenarios are pertinent to sensor-source distances less than 30 to 50 km. Near-field seismic signals exhibit characteristics different from the well understood and frequently utilized far-field signals, and pose unique challenges for source localization.; Two main approaches for seismic source localization are developed in this dissertation. The first approach involves estimating the Direction-Of-Arrival (DOA) of the source from collected sensor data first, and subsequently locating the source using the estimated DOA's. Three DOA estimation techniques, namely, the Covariance Matrix Analysis (CMA), Surface Wave Analysis (SWA), and Modified Kirlin Method (MKM) were investigated. CMA is an existing method developed for far-field data. Here, the effectiveness of CMA for near-field data is evaluated. The other two techniques---i.e., SWA and MKM---are novel, and were developed based on the unique characteristics of near-field seismic signals. SWA exploits the characteristics of Rayleigh waves; whereas MKM is based on a noise equalization estimate of the frequency domain signal-part covariance matrix of the near-field signal.; The second approach is a maximum likelihood optimization method that was developed based on a theoretical model of the near-field seismic signal. This, seismic Approximate Maximum Likelihood, algorithm is also a two-step process in which the experiment site parameters are calculated first, then source location is estimated directly from site parameters and collected sensor data.; All of the techniques developed for seismic source localization are validated using data collected in a number of field experiments. These experiments involve stationary impact sources, as well as a moving vehicle.
机译：本文的研究目的是从通过地震台站网络收集的加速度记录中正确估算出地震源的位置。具体而言，耦合到地面的多轴地震加速度计台站收集地震源产生的加速度；传感器网络由许多这样的加速度计站组成，它们彼此之间的距离小于50米；并且，地震源位于传感器阵列的近场中。近场方案是指传感器与光源之间的距离小于所讨论信号的几个波长的情况。典型的地震信号的波长范围在0.05-15 km之间。因此，近场场景与传感器源距离小于30至50 km有关。近场地震信号表现出与众所周知和经常使用的远场信号不同的特征，并且对震源定位提出了独特的挑战。本文提出了两种主要的地震源定位方法。第一种方法包括首先根据收集的传感器数据估算源的到达方向（DOA），然后使用估算的DOA定位源。研究了三种DOA估计技术，即协方差矩阵分析（CMA），表面波分析（SWA）和改进的Kirlin方法（MKM）。 CMA是为远场数据开发的现有方法。在此，评估了CMA对于近场数据的有效性。其他两种技术-即SWA和MKM-是新颖的，并且是根据近场地震信号的独特特性开发的。 SWA利用瑞利波的特性。而MKM基于近场信号的频域信号部分协方差矩阵的噪声均衡估计。第二种方法是基于近场地震信号的理论模型开发的最大似然优化方法。地震近似最大似然算法也是一个两步过程，首先计算实验现场参数，然后直接从现场参数和收集的传感器数据中估算出震源位置。使用在许多现场实验中收集到的数据验证了为地震源定位开发的所有技术。这些实验涉及固定冲击源以及移动的车辆。

著录项

  • 作者

    Zhao, Jing.;

  • 作者单位

    University of California, Los Angeles.;

  • 授予单位 University of California, Los Angeles.;
  • 学科 Geophysics.; Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 160 p.
  • 总页数 160
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 地球物理学;无线电电子学、电信技术;
  • 关键词

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