The inversion of geodetic data for earthquake parameters
The inversion of geodetic data for earthquake parameters
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DOI：
10.7907/9fyh-hd84
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发表时间：
2004-12
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影响因子：
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通讯作者：
R. Lohman
中科院分区：
文献类型：
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作者：
R. Lohman
诸如干涉合成孔径雷达（干涉合成孔径雷达）和全球定位系统（GPS）等大地测量数据集的空间和时间覆盖范围不断扩大，我们已经能够限制与地震和火山爆发有关的变形的许多方面。随着我们对变形运动学的理解的提高，我们可以开始探索驱动世界各地构造活动区地震和火山变形的动力学过程。在这篇论文中，我使用干涉合成孔径雷达数据反演的震源参数为两个小（4.0 7）地震。对于小震，我着重于约束震源位置和地震矩。我检查数据的小地震在盆地和山脉省的美国西部，并在伊朗南部的扎格罗斯山脉。对于大地震，我把同震滑动分布的限制，为一个预先确定的断层面的几何形状，并探讨如何敏感的反演是在断层面参数化的不足。我对1999年南加州赫克托7.1级地震和1995年智利安托法加斯塔8.1级地震进行了反演。我还介绍了一些进步的技术细节，使用干涉合成孔径雷达观测反演变形源参数。我使用全噪声协方差矩阵在我的反演和比较推断的噪声协方差的几个干涉图覆盖莫哈韦沙漠，南加州，与GPS观测对流层结构功能。我提供了一个算法的resstrive（或平均）干涉合成孔径雷达数据，以尽量减少计算负担，通过减少数据点的数量作为输入反演。我还探讨了技术规范确定不好的反演大地测量数据同震断层滑动。
The spatial and temporal coverage of geodetic data sets such as Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System (GPS) is increasing to the point where we can constrain many aspects of the deformation associated with earthquakes and volcanic eruptions. As our understanding of the kinematics of deformation improves, we can begin to explore the dynamic processes that drive seismic and volcanic deformation in tectonically active regions around the world. In this thesis, I use InSAR data in inversions for earthquake source parameters for both small (4.0 7) earthquakes. For small earthquakes, I focus on constraining the hypocenter location and seismic moment. I examine data for small earthquakes in the Basin and Range province of the Western United States, and in the Zagros mountains of Southern Iran. For large earthquakes, I place constraints on the coseismic slip distribution for a pre-determined fault plane geometry and explore how sensitive the inversion is to inadequacies in the fault plane parameterization. I perform inversions for both the 1999 Mw 7.1 Hector Mine earthquake in Southern California and the 1995 Mw 8.1 Antofagasta earthquake in Chile. I also describe some advances in the technical details of using InSAR observations in inversions for deformation source parameters. I use the full noise covariance matrix in my inversions and compare inferred noise covariances for several interferograms covering the Mojave desert, Southern California, with GPS observations of tropospheric structure functions. I provide an algorithm for resampling (or averaging) InSAR data to minimize the computational burden by reducing the number of data points used as input to inversions. I also explore techniques for regularizing poorly determined inversions of geodetic data for coseismic fault slip.