Simulation of landslides and the associated water waves by a 3D code
Until now, tsunamis generated by underwater landslides were modelled at the CEA using a 2D long wave code (Avalanche) that was adapted to the computing resources available at the time but now seems obsolete in the literature. An initial post-doctoral study (2023-2025) showed that the 3D OpenFoam tool could accurately simulate a landslide and the associated waves in the generation zone. During this post-doctoral fellowship, a coupling between the CEA's ‘2D’ propagation code (Taitoko) and the 3D code was developed in order to propagate waves over long distances. The work carried out will be continued. The first objective will be to familiarise with the tools developed and to publish the work carried out on the 80 Mm3 collapse that occurred in Mururoa in 1979. The main objective is then to carry out simulations of potential collapses in the northern zone, bearing in mind that the main difficulty lies in defining the geometry of these potential collapses. The propagation of waves over long distances is simulated by a ‘2D’ tsunami code coupled with the OpenFoam code.
detection of multiplets and application to turkey-Syria seismic crisis of february 2023
The correlation technique, or template matching, applied to the detection and analysis of seismic events has demonstrated its performance and usefulness in the processing chain of the CEA/DAM National Data Center. Unfortunately, this method suffers from limitations which limit its effectiveness and its use in the operational environment, linked on the one hand to the computational cost of massive data processing, and on the other hand to the rate of false detections that could generate low-level processing. The use of denoising methods upstream of processing (example: deepDenoiser, by Zhu et al., 2020), could also increase the number of erroneous detections. The first part of the research project consists of providing a methodology aimed at improving the processing time performance of the multiplets detector, in particular by using information indexing techniques developed in collaboration with LIPADE (L-MESSI method , Botao Peng, Panagiota Fatourou, Themis Palpanas. Fast Data Series Indexing for In-Memory Data. International Journal on Very Large Data Bases (VLDBJ) 2021). The second part of the project concerns the development of an auto-encoder type “filtering” tool for false detections built using machine learning. The Syria-Turkey seismic crisis of February 2023, dominated by two earthquakes of magnitude greater than 7.0, will serve as a learning database for this study.