108 Multi-sensor InSAR for ground deformation analysis in southwest Taiwan

Job title:

108 Multi-sensor InSAR for ground deformation analysis in southwest Taiwan

Company:

Centre National d’Etudes Spatiales

Job description

25-108 Multi-sensor InSAR for ground deformation analysis in southwest TaiwanPostuler25-108 Multi-sensor InSAR for ground deformation analysis in southwest Taiwan

• Doctorat, 36 mois
• Temps plein
• Indifférent
• Maitrise, IEP, IUP, Bac+4
• Internal geophysics, geodynamics and geodesy

PostulerMissionThe dual aim of this PhD subject is to develop new InSAR processing method for multi-sensor time series and to answer fundamental questions about seismic hazard assessment, which requires analysis based on different sources of geodetic, geophysical and hydrological data. This work will be based on the case study of southwest Taiwan.Quantifying the accumulation of elastic energy within tectonic plates, which is then released in the form of seismic energy during earthquakes, is fundamental to estimate seismic hazard. This quantification is essentially based on seismological and geodetic methods. Geodetic techniques such as InSAR (which has the potential to measure ground deformation with an accuracy of better than a centimeter and a spatial resolution of better than a hundred meters) or GNSS measure surface displacements resulting in part from the elastic deformation of rocks. However, some of the displacements may have other origins, such as inelastic soil compaction, fault creep, or other plastic deformations, which are essentially aseismic processes. Being able to distinguish between these elastic and inelastic deformations, and understanding the physical processes that control their relative contribution, is a major challenge for seismic hazard assessment.In southwestern Taiwan, geodesy records exceptionally high strain rates (µstrain/y), but at the same time very low seismicity in the first eight km of the crust, making this area a rare and highly relevant case study, as it is well instrumented by GNSS and seismological networks and, above all, represents an extreme case where aseismic deformation could dominate over elastic deformation. However, the 3D complexity of geological structures, requiring fine spatial resolution (100 m), and the presence of transient deformations, notably caused by regional earthquakes, justify the methodological effort to be done in InSAR processing to improve the spatial density and precision of InSAR displacement time series.Indeed, despite the hopes raised by the arrival in 2014 of the Sentinel-1 (C-band, 6 cm wavelength) and ALOS-2 (L-band 24 cm wavelength) satellites, it remains difficult to obtain spatially continuous coverage of INSAR time series in tropical areas such as southwestern Taiwan, due to current limitations related to heavy vegetation cover and strong topographical gradients.We want to take advantage of the availability of several satellites. The continuity of the Sentinel-1 mission (up to 2030), combined with the arrival of new L-band sensors such as ALOS-4 (July 2024), the successor to ALOS-2, and NISAR (launch scheduled for February 2025), or in a few years’ time the ESA’s ROSE-L mission, including ionospheric correction modes, offers new opportunities to improve our ability to track ground deformation. Rather than a classical combination of displacement time series after InSAR processing carried out independently on each type of data, we envisage here an iterative process integrating the results of several processing carried out in parallel (Sentinel-1, NISAR ALOS-4 in ascending and descending tracks). For example: using a decomposition of a coseismic displacement into East-West and Vertical components obtained at iteration N-1 from descending and ascending passes of several InSAR satellites, to correct at iteration N, interferograms in order to help the phase unwrapping step. Such a principle could be applied to various stages of InSAR processing: coregistration, topographic correction, adaptive spatial and/or temporal filtering, time series inversion… The idea is that information obtained at iteration N-1, albeit with the possibility of significant error, would be better than no a-priori information at iteration N, and that over the course of iterations we could gain in terms of accuracy, coverage and spatial resolution. One difficulty will be to optimize the combination of information acquired in C-band and L-band and with different viewing angles. Such a cross-analysis should also enable a better understanding and mitigation of the phase biases observed in C-band and L-band, particularly in vegetated areas.In addition, we will try to take into account land cover classification information with possible indication of temporal changes, or information on the presence of displacement discontinuities (e.g. creep on a fault reaching the surface). We are also considering the use of AI methods, for example to improve spatial interpolation of masked data in noisy areas, and for the unwrapping step.Regarding the second axis of the thesis, which addresses the issue of quantifying accumulated elastic deformation, INSAR time series analysis will be cross-referenced with other data sources to reconstruct a 3D displacement field, in order to better separate the sources of deformation (hydrological processes, volume deformation, creep on faults etc.) and model them.For more Information about the topics and the co-financial partner (found by the lab !);contact Directeur de thèse –Then, prepare a resume, a recent transcript and a reference letter from your M2 supervisor/ engineering school director and you will be ready to apply online before March 14th, 2025 Midnight Paris time !

Expected salary

Location

Gières, Isère

Job date

Wed, 05 Feb 2025 06:10:23 GMT

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