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SPIN ESR 1.3: Wavefield gradient methods to monitor the Earth’s crust


Host institution: University of Grenoble Alpes (UGA) drawing drawing

Supervisors:

main supervisor: Michel Campillo (ISTerre, UGA, F)
co-supervisors: Helle Pedersen (ISTerre, UGA, F)
  Florent Brenguier (ISTerre, UGA, F)
external collaboration: Heiner Igel (LMU, D)

Application deadline: application closed Earliest possible starting date: September 1st, 2021

General information

This PhD position is one of the 15 Early Stage Researcher (ESR) positions within the SPIN project. SPIN is an Innovative Training Network (ITN) funded by the European Commission under the Horizon 2020 Marie Sklodowska-Curie Action (MSCA).

SPIN will focus on training 15 PhD candidates in emerging measurement technologies in seismology. We will research the design of monitoring systems for precursory changes in material properties, all while optimizing observation strategies. The unique interdisciplinary and inter-sectoral network will enable PhDs to gain international expertise at excellent research institutions, with a meaningful exposure of each PhD to other disciplines and sectors, thus going far beyond the education at a single PhD programme.

Project description

This PhD project capitalises on the progress that has allowed the emergence of new observables in seismology (rotation sensors, advanced array processing, Distributed Acoustic Sensing,…). The goal is to evaluate the possibilities offered by wave field gradient measurements in addition to traditional local measurements for monitoring temporal variations of elastic properties (mean velocities, structural changes) in the Earth’s crust. The field of application will focus on a volcanic area where such changes are already reported but not precisely characterized. The study will start with a test phase to adapt the method on realistic numerical simulations in media with high scattering and attenuation. Different strategies and observables will be tested to evaluate their ability to image changes in the environment associated with for example localized velocity changes or appearance of filled cracks. The method will next be applied to field data with a noise based monitoring approach. The processing of the field data will include a stage of characterization and classification of the ambient wavefield. The subsequent imaging will rely on inversion kernels for scattered waves based on a model of coupling of surface and body waves. The work is based at ISTerre, Grenoble (France), with collaboration with LMU, Munich (Germany).

The project will be carried out in a highly collaborative environment, and within one of the leading organisations in the world in the field of seismic imaging and monitoring using seismic noise.