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Summary of Proposal LAN1799

TitleMonitoring human-induced surface deformation with TerraSAR-X data stacks
Investigator Liao, Mingsheng - Wuhan University, LIESMARS
Team Member
Dr. Zhang, Lu - Wuhan University, LIESMARS
Dr.-Ing. Balz, Timo - Wuhan University, LIESMARS
Summary

Three AOIs have been selected for the proposed study in the Los Angeles Basin, including two oil fields in Santa Fe Springs and Orange County as well as the Santa Ana aquifers in Pomona. From the available GPS data, we know that there is a distinctive seasonal deformation in the study areas. Therefore, we need to model the periodical deformation function in the PS-InSAR processing to get more precise deformation estimations.

A similar strategy can be used for Taiyuan. There are three minor differences. First, the available Taiyuan data stack is much larger. Second, another external data (leveling data) other than GPS will be available for result validation. Third, the available ENVISAT ASAR PS-InSAR results cover a long time span, which can help us to derive the relationship between time and deformation in case of limited GPS/leveling measurements.

In general, five main steps should be carried out:

  • PS-InSAR techniques will be applied to generate the surface motion velocity map.
  • Detecting and modeling the seasonal oscillations in these regions from GPS/leveling measurements using periodical function. PS-InSAR results from other bands of SAR data can also be applied in modeling when there is not enough GPS/leveling data available in some sites, because the leveling and GPS network is not very dense.
  • Searching for the match-points near the GPS/leveling sites to connect the GPS/leveling and time-series InSAR results and also to calibrate the InSAR time-series results.
  • Correcting the InSAR time series with an annual periodical trend to investigate the dynamics of the geothermal fields and hydrologic systems
  • InSAR measurement from TerraSAR-X data will be evaluated by comparison with results from C-band ENVISAT ASAR and L-band ALOS PALSAR data.

The project is funded by the National Key Basic Research Program of China (Grant No. 2013CB733205) and the National Natural Science Foundation of China (Grant No. 41174120, 41271457 and 41021061).

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