DARE: Dense ARray for seismic site effect Estimation

• Applicants: Professor Dr. Fabrice Cotton; Dr. Matthias Ohrnberger
• Subject Area: Geophysics ; Palaeontology
• Term: from 2019 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 431362334

• Final Report Year: 2023

Final Report Abstract

The DARE project aims to propose approaches for the estimation of seismic site effects associated with a complex geological structure located in a low-to-moderate seismicity region (French Rhône valley). In particular, the interest of using seismic ambient noise in such context is explored. The project is based on the acquisition and analysis of dense and complementary in situ datasets. The approach adopted enables us to propose and compare alternative methods, to aggregate the information provided by each and to assess their interest and limitations. This extensive study provides an unprecedented characterization of the geological medium in the study area and the associated site effects. The results of this project will help future studies of site effects in terms of understanding the phenomena, the approaches to be considered and the interpretation of results.

Publications

• Complementary Dense Datasets Acquired in a Low-to-Moderate Seismicity Area for Characterizing Site Effects: Application in the French Rhône Valley. Seismological Research Letters, 94(1), 531-547.
  Froment, Bérénice; Olivar-Castaño, Andrés; Ohrnberger, Matthias; Gisselbrecht, Loic; Hannemann, Katrin; Cushing, Edward Marc; Boué, Pierre; Gélis, Céline; Haendel, Annabel; Pilz, Marco; Hillmann, Laura; Barbaux, Occitane; Beauprêtre, Sophie; Bouzat, Gilbert; Chaljub, Emmanuel; Cotton, Fabrice; Lavoué, François; Stehly, Laurent; Zhu, Chuanbin ... & Tourette, Alexandre
  
• First 3D characterization of the Rhône Messinian Canyon in the Tricastin area from complementary geophysical approaches. ECEES 2022-3rd European Conference on Earthquake Engineering and Seismology
  Bérénice Froment et al.
  
• How well can we predict earthquake site response so far? Machine learning vs physics‐based modeling. Earthquake Spectra, 39(1), 478-504.
  Zhu, Chuanbin; Cotton, Fabrice; Kawase, Hiroshi & Nakano, Kenichi
  
• Insights into the conditions of application of noise-based spectral ratios in a highly industrialized area: a case study in the French Rhone Valley. Geophysical Journal International, 234(2), 985-997.
  Gisselbrecht, Loïc; Froment, Bérénice; Boué, Pierre & Gélis, Céline