Poster about my work on gridding GPS data presented at AGU 2018
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.

Coupled interpolation of three-component GPS velocities

Leonardo Uieda1, Xiaohua Xu2, Paul Wessel1, David T. Sandwell2

1Department of Geology and Geophysics, SOEST, University of Hawai'i at Mānoa, Honolulu, Hawaii, USA
2Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California, USA

Abstract submitted to the AGU 2018 Fall Meeting.

Session G009: Geodetic Imaging and Interpretation of the Seismic Cycle
Abstract G23B-0587
Time Tuesday, 11 December 2018 / 13:40 - 18:00
Room Convention Ctr - Hall A-C (Poster Hall)
Poster doi:10.6084/m9.figshare.7440683

A low resolution preview of the poster


GPS/GNSS measurements of deformation have high accuracy and temporal resolution but are spatially sparse. Conversely, InSAR provides great spatial resolution but is limited by the satellite look angle, atmospheric noise, and the delay between repeat passes. The sparse GPS data often need to be interpolated on regular grids to be used as constraints during InSAR processing or to calculate strain rates. The interpolation is routinely done separately for each component of the velocity field using minimum curvature or specialized geostatistical algorithms. Recently, a joint interpolation of the horizontal components has been proposed. It estimates forces on a thin elastic sheet that fit the observed data and subsequently uses the estimated model to predict data on regular grids or arbitrary points. The Green’s functions for the physical model serve as a coupling between the two vector components through elasticity theory. We propose an extension of this method to 3D, using the elastic Green’s functions to couple the horizontal and vertical components. This enables the inclusion of vector data projected in arbitrary directions, such as InSAR line-of-sight velocities. The degree of coupling can be controlled through the Poisson’s ratio of the medium. We apply damping regularization to smooth the model and avoid instabilities in the inverse problem. Furthermore, we automatically select optimal values for the Poisson’s ratio and regularization parameter through cross-validation, which is common in machine learning applications. We compare the performance of the coupled model with uncoupled alternatives to grid 2- and 3-component GPS velocities and calculate derivatives through finite-differences approximations. We will present preliminary results from applications to GPS data from the Himalayas and the calibration of InSAR data products. A future goal is to integrate InSAR line-of-sight velocities in a joint interpolation with GPS velocities.


The poster was made entirely on Inkscape. The font is Barlow.

The code that implements the method is based on the Verde library and will be integrated into it in the near future.


Creative Commons License
This content is licensed under a Creative Commons Attribution 4.0 International License.

This project is funded by grant number 1829371 from the US National Science Foundation.