Missions

The recruited post-doctoral researcher will work within the framework of the ANR PrESENCE project, which presents and tests a new paradigm of collaborative monitoring of geohazards in urban environments : seismological observations using a large number of low cost internet-connected equipment (DSSN - Dense Semi-permanent Seismic Networks) together with strong involvement of local public authorities and citizens. The PrESENCE project focuses on seismic hazards induced by deep geothermal operations and their associated societal perception. This topic is becoming a major issue in the development of renewable energies that involve the subsurface as seismic hazards are of significant public concern and can have major socio-economic impacts. The PrESENCE project is based on the rich experience from the recent seismic crisis in Strasbourg, which culminated in the paroxysmal Dec 4, 2020 M3.6 earthquake that led to the closure of the deep geothermal energy Geoven site (Fonroche-Geothermie company). The PrESENCE project will characterize the impacts of this new network concept on operational seismic monitoring, subsurface imaging, temporal monitoring of subsurface properties, warning systems, and the estimation of sociological impacts on the perception of seismic risk.

Activités

The recruited post-doctoral researcher will work at demonstrating that DSSNs based on low cost stations can contribute significantly to improving our knowledge of deep reservoirs or targeted regional structures (e.g. large-scale faults) prior to and during operational phases. To quantitatively assess the potential of these emerging seismic monitoring technologies under real-world conditions, two DSSNs have been deployed in two sensitive areas : the Eurometropole and Soultz-sous-forêts - Rittershoffen areas where deep geothermal projects have been targeted. The post-doc activity is to analyze these data sets in three steps : 1) test how critical the DSSN present at that time of the 2019-2022 seismic crisis of Strasbourg was for capturing the evolution of the earthquake sequence knowing that they were installed inside buildings where weak seismic events are strongly noise contaminated; 2) process the continuous seismic data using new generation deep learning picking algorithms (e.g. Zhu and Beroza 2018) and draw a link with public hydraulic data associated with the Geoven project using a trained convolutional neural network similar to the one proposed by Rouet-Leduc et al., 2019 ; 3) monitor Vp/Vs ratios as proposed in Lin & Shearer, 2007 to assess the presence of fluid and the possibility of tracking the migration of water within the reservoir at depth.

Compétences

The recruited post-doctoral researcher must have:
• PhD in seismology (at time of employment)
• Experience working in observational seismology
• Expertise in several of data science / machine learning / artificial intelligence / big data analysis / parametric time series analysis / statistics
• Programming skills (AI/ML programming frameworks, e.g. pytorch or tensorflow, Python, MATLAB, SQL)
• Experience in communicating, writing, publishing scientific works
• Proficiency in English level C2 (according to the European Framework for languages)

Contexte de travail

The recruited post-doctoral researcher will work on induced seismicity for 18 months at ITES (Strasbourg Institute of Earth & Environment) in Strasbourg (France), under the responsibility of Olivier Lengliné and Jean Schmittbuhl (members of the Seismology team at ITES). ITES (UMR7063) is a research unit under the supervision of the CNRS (National Centre for Scientific Research) and the University of Strasbourg. The project is hosted in the framework of the Interdisciplinary Thematic Institute for the energy system transition (ITI GeoT), a program part or the excellence initiative at Strasbourg University which explores geoscience solutions to the energy transition, with a particular focus on deep groundwater: exploration, characterization and optimization of crustal reservoirs, risk management and of public perceptions.

Contraintes et risques

Not applicable