Some thoughts on the use of InSAR data to constrain models of surface deformation: Noise structure and data downsampling

被引:352
作者
Lohman, RB
Simons, M
机构
[1] Woods Hole Oceanog Inst, Woods Hole, MA 02543 USA
[2] CALTECH, Seismol Lab, Pasadena, CA 91125 USA
来源
GEOCHEMISTRY GEOPHYSICS GEOSYSTEMS | 2005年 / 6卷
关键词
correlated noise; InSAR; Little Skull Mountain;
D O I
10.1029/2004GC000841
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
[1] Repeat-pass Interferometric Synthetic Aperture Radar (InSAR) provides spatially dense maps of surface deformation with potentially tens of millions of data points. Here we estimate the actual covariance structure of noise in InSAR data. We compare the results for several independent interferograms with a large ensemble of GPS observations of tropospheric delay and discuss how the common approaches used during processing of InSAR data affects the inferred covariance structure. Motivated by computational concerns associated with numerical modeling of deformation sources, we then combine the data-covariance information with the inherent resolution of an assumed source model to develop an efficient algorithm for spatially variable data resampling ( or averaging). We illustrate these technical developments with two earthquake scenarios at different ends of the earthquake magnitude spectrum. For the larger events, our goal is to invert for the coseismic fault slip distribution. For smaller events, we infer the hypocenter location and moment. We compare the results of inversions using several different resampling algorithms, and we assess the importance of using the full noise covariance matrix.
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页数:12
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