D3-ID32431: Seismic Detection from InSAR
InSAR monitoring of ground motion in response to climatic or tectonic forcing : from the exploitation of the Envisat archive to the processing of the recent Sentinel-1 database over the tibetan plateau
1ISTerre, CNRS, Université Grenoble-Alpes, Grenoble, France; 2UCLA, Los Angeles, USA; 3Laboratoire de Géologie de Lyon, Terre, Planètes et Environnement, Lyon, France; 4Institute of Geology, China Earthquake Administration, Beijing, China; 5Pekin University, Beijing, China
Multitemporal interferometric synthetic aperture radar (InSAR) observations have been widely used to characterize spatial and temporal variations of ground deformation of tectonic, climatic or anthropic origin. An accuracy in the order of a millimeter per year on velocity measurements can be achieved in areas where a dense archive of InSAR data exists, and when methodological developments are applied to enhance coherence and improve the signal to noise ratio. With systematic acquisitions and a revisit time of 24 days over Tibet, the new Sentinel-1 radar data offer new perspectives to retrieve small deformation signal and better separate various sources of deformation. We review here our most recent studies over the tibetan plateau, emphasing improvments in InSAR processing of both the Envisat archive and newly acquired Sentinel-1 data.
We characterize the ground motion induced by the response of the permafrost active layer to climatic forcing, over a 60000m2 area in the northwestern part of the tibetan plateau, during 8 years spanned by the Envisat archive. This phenomena is limited to Cenozoic sedimentary basins and is spatially variable in both its seasonal amplitude (2.5–12 mm) and multiannual trend (−2 to 3 mm/yr). A degree-day integrated model adjusted to the data indicates that subsidence occurs when the surface temperature exceeds zero (May to October) over areas where seasonal movements are large (>8 mm). The period of subsidence is delayed by 1–2 months over areas where smaller seasonal movements are observed, suggesting an unsaturated soil where water occurs in the deeper part of the active layer. We use the same Envisat archive to characterize the interseismic strain pattern across the Altyn Tagh fault system. The previous permafrost study helps better unwrapping interferograms and referencing velocity maps on adjacent tracks, which leads to a better quantification of the tectonic signal. Here we also test various strategies to correct tropospheric delays, improving as well the differentiation between secular tectonic deformation and seasonal signals. A linear feature of strain localization (1-2 mm/yr of velocity change in the line of sight -LOS-) is identified north of the Altyn Tagh strike-slip Fault (ATF) within the Tarim basin, parallel to the ATF. We suggest that the ATF is connected at depth with a transpressive ramp emerging north of it in the basin, both structures accommodating by slip partitioning an oblique (N52°E) convergence rate of 14.2 mm/yr. South of the ATF, we also observe strain localization along a continuous feature, possibly the Jinsha suture, corresponding to 3 mm/yr of velocity change in the LOS. Finally, taking advantage of our experience in InSAR processing of long-tracks, small deformation and difficult terrain conditions areas, we adapt our chain to the processing of Sentinel-1 (S1) data, focusing on the eastern-southeastern border of the tibetan plateau, from the Haiyuan fault to the north to the Xian Shui He fault to the south.
Retrieve near-field deformation of large earthquakes from Sentinel-1 radar interferometry data
1Institute of Geology, China Earthquake Administration; 2School of Earth and Space Sciences, Peking University, China
It is challenging to effectively extract the near-field deformation of large earthquakes using InSAR approach, especially from the short-wavelength radar sensors, such as the C-, X- band systems. The issue leads to incomplete deformation field of large earthquakes in the vital regions, which are critical for earthquake studies. Some of the well-known decorrelation effects could lead to signal loss and prevent successful phase unwrapping, such as the geometric (or spatial), temporal, and Doppler decorrelation, etc. The decorrelation effects may reduce the received energy of satellite radar sensors from the back-scattering of ground targets. Hence, the incoherence (or partial of it) of radar signals will greatly reduce the signal-to-noise ratio of InSAR phase and leads to information loss in the deformation field or serious phase jumping issues in the final products. In the near-field of large earthquakes, high phase gradient may be irresolvable by traditional unwrapping methods. In extreme situations, the complete incoherence of radar returns could occur when the phase difference of neighbor pixels exceedingπradians. Except for this physical limitation of the radar systems, it is possible to extract useful information from InSAR data. Moreover, severe DEM errors (depending on baselines and topography) will also greatly influence the phase unwrapping process and introduce heavy phase jumping errors, leading to unreliability of InSAR observations.
To overcome the limitations described above, we developed a simple strategy for phase unwrapping. Due to strong phase gradient following large earthquakes, we first multi-look the InSAR phase to a lower resolution and implement conventional Goldstein filtering and unwrapping. After the step, we use a 2-D spatial filter to estimate the first order deformation signals and remove this component from the unwrapped phase. We do the same procedure in an iterative way until it is impossible to unwrap the residual phase. The final residual phase plus the filtered-out phase using Goldstein filter are deemed as the total residuals for the wrapped phase. We then resample the total residuals to the original spatial resolution of InSAR phase and removed it from the observations. Then the conventional unwrapping procedures will be able to used for extracting InSAR phase covering even the near-field of earthquake deformation area. The total residuals could also include useful information excluded from earthquake deformation, such as localized deformation of landslides, small areas of uplift or subsidence, besides the DEM error phase.
We had successfully applied the method to some large earthquakes, such as the 2015 Illapel, Chile earthquake, the 2016 Ecuador earthquake, the 2016 Central Itlay earthquake sequence, and the 2016 New Zeland earthquake, etc. So far, we only consider the Sentinel-1 data here, due to its small temporal decorrelation effects, but it would be much easier to work with L-band data, such as ALOS-1/2. Also, in some mountainous regions, it is also valuable to use the method to overcome the phase unwrapping issues for interseismic or postseismic InSAR phase retrieval.
The Ganzi segment of the Xian Shui He fault system : present-day behavior constrained by time serie analysis of Sentinel-1 InSAR data
1ISTerre, CNRS, Université Grenoble-Alpes, Grenoble, France; 2Laboratoire de Géologie de Lyon, Terre, Planètes et Environnement, Lyon, France; 3Institute of Geology, China Earthquake Administration, Beijing, China; 4Institute of Geology, Chinese Academy of Geological Sciences, Beijing, China
In eastern Tibet, the left-lateral, ~1400 km-long, Xianshuihe fault system (XFS) is oneof the most tectonically active intra-continental fault system in China. More than 20 M>6.5 earthquakes broke this fault since 1700, including the recent 2010 Mw6.9 Yushu earthquake. We focus here on the northwestern segment of the XFS, the Ganzi fault, east of the Yushu rupture, identified as a seismic gap capable of producing Mw7.6 earthquakes. The Quaternary slip-rate of this Ganzi segment is estimated to 6-8 mm/yr, from offsets measurements and cosmogenic dating of moraine crests and alluvial fan edges (Chevalier et al., 2017). To characterize the present-day behavior of this segment, we process the first two-years of Sentinel-1 InSAR data to produce an average velocity map across the Ganzi fault, using both descending and ascending data. We adapt our InSAR processing chain (NSBAS) to the specificities of Sentinel-1 data and produce a simple elastic model of the InSAR-derived velocity field. We discuss our first results in comparison with the existing GPS data and the long-term tectonic setting.