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A Multipixel Time Series Analysis Method Accounting for Ground Motion, Atmospheric Noise, and Orbital Errors
Author(s) -
Jolivet R.,
Simons M.
Publication year - 2018
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/2017gl076533
Subject(s) - pixel , residual , series (stratigraphy) , synthetic aperture radar , algorithm , computer science , function (biology) , exponential function , noise (video) , remote sensing , mathematics , artificial intelligence , geology , mathematical analysis , image (mathematics) , paleontology , evolutionary biology , biology
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.