SPIN ESR 3.4: Ambient signals as a tool to characterize material properties

Host institution: University of Hamburg (UHH) drawing


main supervisor: Dr. Céline Hadziioannou (University of Hamburg, D)
co-supervisors: Dr. Sven Schippkus (University of Hamburg, D)
  Dr. Eleonore Stutzmann (Institut de Physique du Globe de Paris, F)

This position is filled

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

Many of the recent observations of transient changes of subsurface material properties are based on extracting deterministic signals from the seismic background noise field. Since seismologists have started using the continuously available ambient noise for monitoring applications, the interest in the location and behaviour of ambient noise sources has increased.
Using data already available from pilot projects with new sensors (rotations, DAS, large-N arrays), we will characterize the time-dependent distribution of noise sources at different frequencies and scales (e.g. urban noise, local environmental noise, ocean noise on the global scale). What can the inclusion of additional ground motion observables tell us about how the noise sources work?
Once noise generation mechanisms are better understood, it will become possible to accurately distinguish effects that are due to changing noise source characteristics from actual changes in the subsurface. How do the noise field characteristics affect the accuracy of noise cross-correlation signals? What about seismic noise interferometry with new observables?
The project will benefit from interactions with other PhD projects within the SPIN network. For example, coherence based analysis, developed by a different PhD candidate, will be incorporated into the ambient noise signal characterization approach. Land-based array observations will be compared to ocean floor observations studied by another research group in SPIN. The outcome of this PhD project will directly influence several projects that focus on applications on volcano, permafrost and structural health monitoring.