Templeta generale

2.2.WP 1240 Data selection for Phase 2 - inter- and post-seismic deformation

Between PM2 and PM3 acquisitions were ongoing for datasets TD3.1 and TD3.5. The current status is reported in Table and detailed further in Table and Table . While TD3.5 was completed, TD3.1 was interrupted before the end of the acquisition requested time. This almost certainly compromises the planned activities for this site, as explained below.

Id	Site	Location	Deformation	Data source	Status
TD3.1	North Anatolian Fault	Turkey	Interseismic	COSMO	Acquisition interrupted
				PALSAR	Archived
TD3.2	Istanbul	Turkey	Interseismic	COSMO	Archived
TD3.3	Aquila	Italy	Post-seismic	COSMO	Archived
TD3.4	South Rigan	Iran	Post-seismic	COSMO	Archived
				PALSAR	Archived
TD3.5	Mansion	Costa Rica	Post-seismic	COSMO	Acquisition completed

Table Phase 2 datasets.

2.2.1.TD3.1 (North Anatolian Fault)

The end of the requested acquisition timespan at this site was March 2014, i.e. 1.5 years total acquisition period. This was considered the minimum time necessary to be able to measure the inter-seismic crustal movements along the North Anatolian Fault (expected range 1-2 cm/yr ). For unknown reasons the COSMO-SkyMed coverage was instead interrupted one year after its beginning; it has been restarted in April 2014, but unfortunately this implies that the site will very likely not provide good results if the available 1-year dataset is processed.

We suggest to discuss a possible back-up solution at the RA3 meeting.

Id	Site	Location	Start date [YYYYMMDD]	Stop date [YYYYMMDD]	Number of frames
TD3.1	North Anatolian Fault	Turkey	20120919	20131005	1582

Table Status of TD 3.1 acquisitions.

2.2.2.TD3.5 (Mansion)

Id	Site	Location	Start date [YYYYMMDD]	Stop date [YYYYMMDD]	Number of frames
TD3.5	Mansion	Costa Rica	20121028	20140204	104

Table Status of TD3.5 acquisitions.

2.3.WP 1250 Atmospheric correction method development and testing

Two correction methods were implemented for the effects of temporal fluctuations in tropospheric stratification. These were tested by comparison against GPS deformation time-series on the L'Aquila post-seismic defomation datset TD3.3 dataset in Table . The GPS time-series were instead provided by R. Devoti, INGV. The results of the tests are discussed in section 2.7, whereas the methods are discussed in the following sub-sections.

2.3.1.SAR-based approach

Application of this method within the StaMPS work flow is shown in Figure . The rationale ([Pinel et al., 2011], [Doin et al., 2009]) is to estimate the parameters of a correction model from each unwrapped interferogram in a least-squares sense and to apply the corrections to the wrapped interferometric phases. In a second iteration phase unwrapping is repeated and the multi-temporal analysis for the separation of DEM and atmospheric propagation errors is carried out as in the original StaMPS algorithm formulation [Hooper et al., 2007].

The steps required to estimate the corrections are shown in Figure .

The main problem lies in the fact that the observed interferometric phase does not only contain contributions from tropospheric delay, so that in general a mask will have to be used to select the Persistent Scatterers or Small Baseline points used in the estimation. There is no general purpose way of generating such a mask, although typically an operator is able to define one using knowledge of the areas most likely to undergo deformation and quality parameters such as multitemporal coherence (_x, in [Hooper et al., 2007]).

In the second step, points are binned in height intervals, and an equal number of points per height bin is retained.

The linear system of equations (1) is then solved, in which (x_i, y_i, z_i) represent the longitude, latitude and height of the i-th point and _i the corresponding unwrapped phase. Model parameters [k₀ k_x k_y k_z k_zz] are estimated solving the system in weighted least squares sense, with weights provided by the _x values (equation 20 in [Hooper et al., 2007]).

(1)
The correction model is then applied to the wrapped phase of all the PS and/or SB pixels and the phase unwrapping step is repeated. From this stage onwards the StaMPS algorithm is applied without modifications to derive the deformation time-series and mean deformation rate.

Figure SAR-based correction approach interface with the multi-temporal DInSAR processor

Figure SAR-based correction approach block diagram

2.3.2.Global meteorological model approach

Application of this method within the StaMPS work flow is shown in Figure . The rationale ([Jolivet et al., 2011], [Pinel et al., 2011], [Doin et al., 2009]) is to "flatten" the wrapped interferometric phases with synthetic delay maps obtained by integrating profiles of the tropospheric refractivity, computed from numerical weather model parameters.

In this project ERA-Interim data was used, i.e. the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) [Dee et al., 2011]. ERA-Interim provides a wealth of surface and pressure level parameters daily at 4 time intervals (00, 06, 12, 18 UTC) since Jan. 1, 1989. Spatially, data have global coverage and an approximately 0.75° x 0.75° horizontal resolution (about 80 km x 80 km at mid latitudes). Vertically, pressure-level data is provided at 37 levels from 100 Pa (about 40 km) down to 100000 Pa (sea-level). Data is easily accessible through a simple registration procedure.

The steps required to estimate the corrections are shown in Figure .

Three pressure-level products are extracted for the time closest to the radar acqisition, namely geopotential height, temperature and specific humidity. Spatially the extent of the area of interest is chosen to cover the radar footprints with a margin sufficient to make horizontal spatial interpolation of meteorological quantities meaningful.

Pressure-level data are first resampled to a common height grid using a spline interpolation. In fact the same pressure level at two different horizontal positions corresponds to different heights. The height of each pressure level is represented (approximately) by the spatially varying geopotental height.

A zenith propagation delay lookup table is then constructed on the (coarse) horizontal grid of the metorological products. Delays are computed by numerically integrated refractivity profiles computed from the equations provided in [Hanssen, 1998], using however the refractivity coefficients of [Rüeger, 2002].

For all points on the (fine) horizontal grid of an external Digital Elevation Model (DEM), zenith delays are computed by 3D bilinear interpolation of surrounding lookup table grid points. This step yields Zenith Total Delay (ZTD) maps at each acquisition time in the radar time-series and in a map projection (the same one as the input DEM).

For every interferogram, the ZTD maps are differenced and converted to 2-way slant-range propagation delay maps. Values at the (x,y) coordinates of each point considered for multitemporal analysis are extracted.

At this stage the wrapped interferograms can be corrected and the phase unwrapping repeated, as for the SAR-based method described in section 2.3.1.

Figure Numerical weather model correction approach interface with the multi-temporal DInSAR processor

Figure Numerical Weather Model correction approach block diagram