Talk about 3D GPS interpolation at the AOGS 15th Annual Meeting
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
code
data
images
.gitignore
README.md
aogs2018-gps.odp
aogs2018-gps.pdf
environment.yml

README.md

Joint Interpolation of 3-component GPS Velocities Constrained by Elasticity

Leonardo Uieda, David Sandwell, and Paul Wessel

Talk given at the Asia Oceania Geosciences Society (AOGS) 2018 15th Annual Meeting at Honolulu, HI, USA.

Info
Abstract ID SE21-A023
Time Tuesday 2018-06-05 8:30 - 10:30
Room 321A
Slides doi:10.6084/m9.figshare.6387467

Abstract

Vertical ground motion at fault systems can be difficult to detect due to their small amplitude and contamination from non-tectonic sources, such as ground water loading. However, it may play an important role in our understanding of the earthquake cycle and the associated seismic hazards. Ground motion measurements from GPS are often sparse and must be interpolated onto a regular grid (e.g., for computing strain rate), ideally taking into account the varying degrees of uncertainty of the data. Traditionally, each vector component is interpolated separately using minimum curvature or biharmonic spline methods. Recently, a joint interpolation of the two horizontal components has been developed using the Green's functions for a point force deforming a thin elastic sheet. The elasticity constraints provide a coupling between the two vector components and lead to improved results because the underlying physics of the method approximately matches that of the GPS observations. We propose an expansion of this method into 3D in order to incorporate vertical GPS velocity measurements. To smooth the model and avoid singularities, we formulate the interpolation as a weighted least-squares inverse problem with damping regularization. Optimal values of the regularization parameter and the Poisson's ratio of the elastic medium are determined through K-fold cross-validation, a technique often used in machine learning for model selection. Additionally, the cross-validation provides a measure of the accuracy of model predictions and eliminates the need for manual configuration. The computational load of the inversion is lessened by imposing a cutoff distance to the Green's functions computations, which makes the sensitivity matrix sparse. We will present preliminary results from an application to EarthScope GPS data from the San Andreas Fault system. In the future, we aim to develop a joint inversion of 3D GPS and InSAR line-of-sight velocities to improve data coverage.

License

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

All source code is distributed under the BSD 3-clause License.