SCEC Award Number 13045 View PDF
Proposal Category Individual Proposal (Integration and Theory)
Proposal Title Integrating InSAR and GPS data into time-varying deformation maps
Name Organization
Rowena Lohman Cornell University
Other Participants Chelsea Scott, graduate student
SCEC Priorities 1, 1, 5 SCEC Groups Geodesy, SDOT, FARM
Report Due Date 03/15/2014 Date Report Submitted N/A
Project Abstract
In recent years, the steadily increasing volume of continuous (and campaign) GPS observations and freely-available SAR imagery has prompted calls from the community for a model of ground deformation that is consistent with both of these data types. Such products have been produced in the past, such as the Crustal Motion Map, which included contributions from campaign and continuous GPS data, as well as models of coseismic offsets. The Plate Boundary Observatory, SCRIPPS Orbit and Permanent Array Center (SOPAC) and JPL all have versions of a secular model of motion at existing sites that can be easily accessed through web portals or through ftp. However, these products all are GPS-only and do not explicitly include a characterization of seasonal signals, which may be quite large for some stations. In this proposal, we explored two aspects of the generation of such a model – how we can best characterize the seasonal deformation fields, which are often large compared to the secular rate and are not always fit well by the standard annual and biannual terms, and the performance of some simple approaches for combining the InSAR and GPS into a single product.
Intellectual Merit This proposal contributes to the generation of a time-variable deformation field that is consistent with all available geodetic data. Such models are used in the characterization of tectonic settings and assessment of seismic hazard.
Broader Impacts This project supported a female graduate student and an early-career female faculty member. It also is contributing to broader use of geodetic imagery (InSAR) by communities who have previously focused primarily on GPS observations.
Exemplary Figure Figure 2: Top row: Synthetic InSAR (a,c) and GPS (b,d) data sets with Gaussian signals with a range of spatial scales (left vs. right). Data used in inversion (middle row) also contains white noise with a quadratic ramp added to the InSAR.. GPS data places stonger constraints on the long wavelength component of the inferred displacement, while InSAR data has a larger impact on the short wavelength scale. Bottom row: Inferred signal. Note that the GPS data performs better at longer wavelengths (l vs. k) and InSAR at shorter scales (i vs. j).