HR: 08:45h
AN: G41D-04    [Abstracts]
TI: Detecting Deformation in Heavily-Vegetated Areas Using InSAR Persistent Scatterers
AU: * Hooper, A
EM: ahooper@stanford.edu
AF: Stanford University, Department Geophysics Mitchell Building, Stanford, CA 94305 United States
AU: Segall, P
EM: segall@stanford.edu
AF: Stanford University, Department Geophysics Mitchell Building, Stanford, CA 94305 United States
AU: Zebker, H
EM: zebker@stanford.edu
AF: Stanford University, Department Geophysics Mitchell Building, Stanford, CA 94305 United States
AB: In Hooper et al. (GRL, 2004) we presented a new InSAR persistent scatterer (PS) method for analyzing episodic crustal deformation in non-urban environments. We have since successfully applied it in various settings, including those where conventional interferometry has proven difficult, while making significant improvements to the method. The PS method provides a means to remove atmospheric noise and produces time series that are convenient for deformation modeling. A further potential advantage comes in being able to tease out a signal in vegetated areas where conventional InSAR does not work well. One such area is Mt. St. Helens, which began erupting in September 2004 after a hiatus of 13 years. It is not known, however, when the lava currently erupting entered shallow depths. One potential time period is during the earthquake swarms in the first half of 1998. GPS data covering this period suggest little deformation, but are noisy. Using our PS method we analyzed SAR data acquired over this time period and also concluded that no significant deformation occurred. The lusher parts of the Galapagos are another region where conventional InSAR fails. Deformation of the Sierra Negra crater and northwest flank is imaged well by conventional InSAR, but the vegetated southeast flank is not. We applied our PS method to Sierra Negra and were able to extract a time series of inflation/deflation from 1998 to 2000 for the entire volcano, including the southeast flank. Our results show the inflation rate was constant and spatially invariant for the period November 1998 to March 1999. Our algorithm for detecting PS depends on spatial correlation with other PS. In our original algorithm we assumed a fixed length-scale for the spatial correlation over the entire region of interest. However, the actual distance over which PS are correlated, which is primarily dependent on atmospheric and deformation gradients, will likely vary over the region. We have modified the algorithm to allow for a spatially-variable length-scale that is determined from the data itself using a modified adaptive phase filter. We validated the method in the Lost Hills region of California where the deformation gradient due to oilfield-related subsidence varies greatly across the region. We have also developed three-dimensional unwrapping algorithms to incorporate the time dimension characteristic of PS datasets, in addition to the two usual spatial dimensions. These algorithms enable us to unwrap the PS more reliably than using existing two-dimensional algorithms, as demonstrated on our Long Valley dataset.
UR: http://pangea.stanford.edu/~ahooper/AGU2005
DE: 1240 Satellite geodesy: results (6929, 7215, 7230, 7240)
DE: 1241 Satellite geodesy: technical issues (6994, 7969)
DE: 8485 Remote sensing of volcanoes
DE: 8494 Instruments and techniques
SC: Geodesy [G]
MN: Fall Meeting 2005