科研之友
微信
新浪微博
Facebook
分享链接
摘要
We present a new method to derive 3-D surface deformation from an integration of interferometric synthetic aperture radar (InSAR) images and Global Navigation Satellite System (GNSS) observations based on Akaike's Bayesian Information Criterion (ABIC), considering relationship between deformations of neighbouring locations. This method avoids interpolated errors by excluding the interpolation of GNSS into the same spatial resolution as InSAR images and harnesses the data sets and the prior smooth constraints of surface deformation objectively and simultaneously by using ABIC, which were inherently unresolved in previous studies. In particular, we define surface roughness measuring smoothing degree to evaluate the performance of the prior constraints and deduce the formula of the covariance for the estimation errors to estimate the uncertainty of modelled solution. We validate this method using synthetic tests and the 2008 M-w 7.9 Wenchuan earthquake. @@@ We find that the optimal weights associated with ABIC minimum are generally at trade-off locations that balance contributions from InSAR, GNSS data sets and the prior constraints. We use this method to evaluate the influence of the interpolated errors from the Ordinary Kriging algorithm on the derivation of surface deformation. Tests show that the interpolated errors may contribute to biasing very large weights imposed on Kriged GNSS data, suggesting that fixing the relative weights is required in this case. We also make a comparison with SISTEM method, indicating that our method allows obtaining better estimations even with sparse GNSS observations. In addition, this method can be generalized to provide a solution for situations where some types of data sets are lacking and can be exploited further to account for data sets such as the integration of displacements along radar lines and offsets along satellite tracks.
- 出版日期2016-1
- 单位武汉大学
