A Caltech Library Service

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

Jolivet, R. and Simons, M. (2018) A Multipixel Time Series Analysis Method Accounting for Ground Motion, Atmospheric Noise, and Orbital Errors. Geophysical Research Letters, 45 (4). pp. 1814-1824. ISSN 0094-8276. doi:10.1002/2017GL076533.

[img] PDF - Published Version
See Usage Policy.

[img] PDF - Accepted Version
See Usage Policy.

[img] PDF - Supplemental Material
See Usage Policy.


Use this Persistent URL to link to this item:


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.

Item Type:Article
Related URLs:
URLURL TypeDescription
Jolivet, R.0000-0002-9896-3651
Simons, M.0000-0003-1412-6395
Alternate Title:A multi-pixel time series analysis method accounting for ground motion, atmospheric noise and orbital errors
Additional Information:© 2018 American Geophysical Union. Received 24 NOV 2017; Accepted 5 FEB 2018; Accepted article online 9 FEB 2018; Published online 22 FEB 2018. We thank Angelyn Moore and Susan Owen for making the GPS time series available through the Aria project. We would like to thank N. Brantut for the fruitful discussions about inverse problems in general. M. S. was partially supported by NASA grant NNX16AK58G. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement 758210). This work was granted access to the HPC resources of MesoPSL financed by the Region Ile de France and the project Equip@Meso (reference ANR‐10‐EQPX‐29‐01) of the program Investissements d'Avenir supervised by the Agence Nationale pour la Recherche. Codes are available on our website with instructions for installation ( along with examples. Envisat raw data have been obtained upon request via the EOLISA tool. We thank the European Space Agency for the acquisition and the distribution of these data. ERA‐Interim products are directly available for download at ECMWF (
Group:Seismological Laboratory
Funding AgencyGrant Number
European Research Council (ERC)758210
Agence Nationale pour la Recherche (ANR)ANR-10-EQPX-29-01
Subject Keywords:InSAR; Time Series
Issue or Number:4
Record Number:CaltechAUTHORS:20180212-090658890
Persistent URL:
Official Citation:Jolivet, R., & Simons, M. (2018). A multipixel time series analysis method accounting for ground motion, atmospheric noise, and orbital errors. Geophysical Research Letters, 45, 1814–1824.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:84783
Deposited By: Tony Diaz
Deposited On:13 Feb 2018 18:26
Last Modified:15 Nov 2021 20:22

Repository Staff Only: item control page