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Published January 25, 2005 | public
Journal Article Open

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


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.

Additional Information

Copyright 2005 by the American Geophysical Union. Received: 11 September 2004; Revised: 24 November 2004; Accepted: 16 December 2004; Published: 25 January 2005. We acknowledge the helpful comments of the editor and two anonymous reviewers. R. Lohman is partially supported by a NASA New Investigator Program grant award to M. Simons. Contribution 9097, Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, California.


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