Job advertisement PhD in geophysics, signal processing, learning methods

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European Geosciences Union

PhD in geophysics, signal processing, learning methods

PhD in geophysics, signal processing, learning methods


Commissariat à l'Energie Atomique

The CEA’s Military applications division (Direction des applications militaires, DAM) is involved in the fight against nuclear proliferation and terrorism, which includes contributing its expertise to the International Atomic Energy Agency (IAEA) and the Comprehensive nuclear Test-Ban Treaty Organization (CTBTO). This contribution to strategic programs is based on the expertise of the environmental assessment and monitoring Department (Département analyse, surveillance, environnement, DASE) in monitoring seismic events, detecting radionuclides, measuring atmospheric phenomena and designing sensors and related networks.


Arpajon, 91680, France


Relevant division
Geosciences Instrumentation and Data Systems (GI)


Student / Graduate / Internship


Preferred education

Application deadline
Open until the position is filled

16 July 2020

Job description

Thesis subject: Detection, separation and localization of infrasound sources

Context: The CEA DAM is a research center which exploits and analyses the infrasound data in view of the development of the International Monitoring System (IMS) being set up by the Comprehensive Test-Ban Treaty (CTBT). So far, correlation-based method is used by the CTBT organization to analyze the infrasound signals recorded within the different stations of the IMS. In the frequency bandwidth of interest, the real data analysis within the current operational system has shown the existence of several interfering (non-desired) signals which have to be taken into account. The measurement of the angle of arrival and propagation velocity of coherent infrasound sources is achieved via propagation delays estimation (between different sensors) and by using an appropriate time frequency grid. It is observed that such interference signals lead to erroneous detections and inaccurate estimations. The used detection method has been essentially developed to detect and localize a single coherent source signal within a given time-frequency cell. In order, to overcome this limitation, one needs to consider a kind of ‘source separation’ processing to get rid or mitigate the impact of the interfering signals.

Thesis objectives: The main objectives of this thesis would be to elaborate new detection and localization methods and validate and test them on both (controlled) real as well as simulated infrasound data. More precisely we will investigate the following items:

  • The use of high resolution methods like MUSIC (Multiple SIgnal Classification) to localize several narrow-band sources within the same time frequency cell.
  • Develop detection and localization methods for sources that are relatively wideband (i.e. may exist in several time frequency cells).
  • Develop new methods for the mitigation of spatially distributed (wide spread) interference sources.
  • Consider statistical criteria for the estimation of the number of sources via a penalized maximum likelihood approach.
  • Eventually, consider the use of learning methods for a better mitigation of certain ‘known’ ambient interference sources.

For the performance assessment and validation of the different methods under investigation, we will develop and enrich databases of controlled real-life data or synthetic data that would be representative of the genuine conditions and different scenarios for infrasound source detection. In particular, these data should well represent the diversity and variability of the infrasound signals and noise/interference sources as well as the different array configurations we might have in the current base stations.

The outcomes for the CEA/DASE, would be the improvement of the existing operational tools for the detection, localization and characterization of the infrasound sources of interest from noisy measurements and in the presence of interfering signals.