Resolve 3-Dimensional dimensional Crustal Deformation Field using GNSS and InSAR data

Zheng-Kang Shen, & Zhen Liu

Submitted August 15, 2020, SCEC Contribution #10696, 2020 SCEC Annual Meeting Poster #110

Three-dimensional (3-D) deformation field is essential in studying crustal deformation processes and natural hazards. We have recently developed an algorithm to integrate spatially limited GNSS and dimensionally limited InSAR data for a 3-D crustal deformation field at the Earth’s surface (Shen and Liu, 2020). In the algorithm discrete GPS data points are interpolated to a 3-D continuous velocity field, which is then combined with the InSAR line-of-sight (LOS) velocity data pixel by pixel using a least-squares method. Our method has the following advantages: 1) The GPS data points are optimally interpolated through balancing a trade-off between spatial resolution and solution stability. 2) Realistic uncertainties for the interpolated GNSS velocity field are estimated using a newly developed algorithm and used as weights for GNSS data in GNSS-InSAR combination. 3) Realistic uncertainties for the InSAR LOS rate data are estimated and used as weights for InSAR data in the combination. 4) The algorithm has the flexibility in integrating InSAR data from multiple SAR sensors with different viewing geometries. 5) 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. We have made initial efforts in applying this method to real data to restore 3-D velocity field in southern California. The GNSS velocity data we use is from the MEaSUREs project (http://geoapp03.ucsd.edu/gridsphere/gridsphere). We consider the InSAR data from different satellites such as the ERS-1,2 and Envisat from the 1990s-2000s, and more recent Sentinel-1A and 1B and L-band ALOS-2 ScanSAR data since 2015. Both Sentinel-1 and ALOS-2 data provide broad spatial coverage with good temporal coherence. The deformation field we obtained so far reveals water withdrawal induced subsidence and drought caused uplift at various regions in southern California. We are in the process of extending this approach to incorporate more data from different satellite sensors.

Reference
Shen, Z.‐K., and Liu, Z. (2020). Integration of GPS and InSAR data for resolving 3‐dimensional crustal deformation. Earth and Space Science, 7, e2019EA001036. https://doi.org/10.1029/2019EA001036.

Key Words
GNSS, InSAR, Combination, California

Citation
Shen, Z., & Liu, Z. (2020, 08). Resolve 3-Dimensional dimensional Crustal Deformation Field using GNSS and InSAR data. Poster Presentation at 2020 SCEC Annual Meeting.


Related Projects & Working Groups
Tectonic Geodesy