HR: 08:15h
AN: G41D-02    [Abstracts]
TI: Some Lessons to be Learned After a Decade of InSAR
AU: * Jonsson, S
EM: sj@erdw.ethz.ch
AF: ETH Zurich, Institute of Geophysics, Zurich, 8093 Switzerland
AB: Our understanding of many geophysical phenomena has increased through model parameter estimations using Interferometric Synthetic Aperture Radar (InSAR) data. Good examples include InSAR observations of volcanic uplift that have in many cases allowed us to move beyond simple Mogi point-source representations of magma accumulation. Earthquake fault parameter estimations have also improved as InSAR data can provide constraints on fault geometry and location, as well as spatial variations of fault slip. The large number of published studies may seem to indicate that model parameter estimations using InSAR have become a simple routine task. However, there are still many problems in the way InSAR data are post-processed and treated in these estimations. These problems include data error characterization, phase unwrapping, data sub-sampling, estimation of model-parameter uncertainties, etc. Here we discuss some of these problems and possible methods to improve model parameter estimations. Unwanted signals or errors in InSAR deformation data come from various sources, such as from inaccurate orbit information, poor DEMs, unwrapping mistakes, interferometric decorrelation, and water vapor inhomogeneities in the atmosphere. The effect of some errors can be reduced by careful post-processing analysis while other errors remain in the data. Atmospheric errors are particularly difficult as they vary between interferograms and also within individual interferograms. These errors are spatially correlated with increasing noise power at larger spatial scales, but most researchers simply ignore these correlations, leading to biased model-parameter estimates, especially when the signal-to-noise ratio is low. We present results on under what circumstances the inclusion of the full data covariance matrix is important, as well as describing some practical methods on characterizing atmospheric errors. Another important issue is InSAR data sub-sampling. Interferograms contain millions of data points that make inversion calculations impractical. Several sub-sampling methods have been tried, such as regular sub-sampling, circular, quadtree, resolution based sub-sampling, etc. Here we discuss several schemes and compare them in earthquake model parameter estimations. In conclusion, it is clear that our understanding of how to treat InSAR data in source inversions has improved during the last few years, although there are still many lessons to be learned on identifying the methods that lead to the most reliable results.
DE: 1242 Seismic cycle related deformations (6924, 7209, 7223, 7230)
DE: 1243 Space geodetic surveys
SC: Geodesy [G]
MN: Fall Meeting 2005