Integration of GPS and InSAR Data for Resolving 3-Dimensional Crustal
Deformation
Abstract
We develop an algorithm to integrate GPS and InSAR data for a
3-dimensional crustal deformation field at the Earth’s surface. In the
algorithm discrete GPS data points are interpolated to obtain a
3-dimensional continuous velocity field, which is then combined with the
InSAR line-of-sight (LOS) velocity data pixel by pixel using the
least-squares method. Advantages of our method over previous ones are
that: 1) The GPS data points are optimally interpolated by balancing a
trade-off between spatial resolution and solution stability. 2) A new
algorithm is developed to estimate realistic uncertainties for the
interpolated GPS velocities, to be used as weights for GPS data in
GPS-InSAR combination. 3) Realistic uncertainties for the InSAR LOS rate
data are estimated and used as weights for InSAR data in GPS-InSAR
combination. 4) The ramps and/or offsets of the InSAR data are globally
estimated for all the images to minimize data misfit, particularly at
regions where the data overlaps. Application of this method to real data
from southern California shows its capability of successfully restoring
3-dimensional continuous deformation field from spatially limited GPS
and dimensionally limited InSAR data. The deformation field reveals
water withdrawal induced subsidence and drought caused uplift at various
regions in southern California.