A Multipixel Time Series Analysis Method Accounting for Ground Motion, Atmospheric Noise, and Orbital Errors

A Multipixel Time Series Analysis Method Accounting for Ground Motion, Atmospheric Noise, and... Interferometric synthetic aperture radar time series methods aim to reconstruct time‐dependent ground displacements over large areas from sets of interferograms in order to detect transient, periodic, or small‐amplitude deformation. Because of computational limitations, most existing methods consider each pixel independently, ignoring important spatial covariances between observations. We describe a framework to reconstruct time series of ground deformation while considering all pixels simultaneously, allowing us to account for spatial covariances, imprecise orbits, and residual atmospheric perturbations. We describe spatial covariances by an exponential decay function dependent of pixel‐to‐pixel distance. We approximate the impact of imprecise orbit information and residual long‐wavelength atmosphere as a low‐order polynomial function. Tests on synthetic data illustrate the importance of incorporating full covariances between pixels in order to avoid biased parameter reconstruction. An example of application to the northern Chilean subduction zone highlights the potential of this method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Geophysical Research Letters Wiley

A Multipixel Time Series Analysis Method Accounting for Ground Motion, Atmospheric Noise, and Orbital Errors

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
©2018. American Geophysical Union. All Rights Reserved.
ISSN
0094-8276
eISSN
1944-8007
D.O.I.
10.1002/2017GL076533
Publisher site
See Article on Publisher Site

Abstract

Interferometric synthetic aperture radar time series methods aim to reconstruct time‐dependent ground displacements over large areas from sets of interferograms in order to detect transient, periodic, or small‐amplitude deformation. Because of computational limitations, most existing methods consider each pixel independently, ignoring important spatial covariances between observations. We describe a framework to reconstruct time series of ground deformation while considering all pixels simultaneously, allowing us to account for spatial covariances, imprecise orbits, and residual atmospheric perturbations. We describe spatial covariances by an exponential decay function dependent of pixel‐to‐pixel distance. We approximate the impact of imprecise orbit information and residual long‐wavelength atmosphere as a low‐order polynomial function. Tests on synthetic data illustrate the importance of incorporating full covariances between pixels in order to avoid biased parameter reconstruction. An example of application to the northern Chilean subduction zone highlights the potential of this method.

Journal

Geophysical Research LettersWiley

Published: Jan 28, 2018

Keywords: ;

References

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