{"id":287,"date":"2021-01-13T11:24:12","date_gmt":"2021-01-13T11:24:12","guid":{"rendered":"http:\/\/lptms.u-psud.fr\/alberto_rosso\/?page_id=287"},"modified":"2021-01-13T16:37:54","modified_gmt":"2021-01-13T16:37:54","slug":"80-prime","status":"publish","type":"page","link":"http:\/\/www.lptms.universite-paris-saclay.fr\/alberto_rosso\/80-prime\/","title":{"rendered":"80 PRIME"},"content":{"rendered":"

Temporal spatial correlations in earthquakes dynamics: physical modelling and data anlysis<\/strong><\/p>\n

R\u00e9sum\u00e9 du projet<\/strong><\/p>\n

One of the most distinctive and poorly understood feature of earthquakes is the significant increase of the<\/p>\n

seismic rate observed after large events. Well established empirical laws of aftershocks occurrence<\/p>\n

demand for a physical explanation. Foreshocks are also observed before a large event but their statistical<\/p>\n

fingerprints, potentially important for human security, are much more elusive. In this project, using the<\/p>\n

methods developed in the Statistical Physics we will design a model of the fault able to reproduce complex<\/p>\n

spatio-temporal patterns with foreshocks, mainshocks and aftershocks. Using Machine Learning we will<\/p>\n

understand the statistical properties of the short sequence of foreshocks. First, using our synthetic<\/p>\n

sequences, we determine how much information is needed to predict the following events. Then we will<\/p>\n

use actual data: on one side to calibrate the model on the real fault activity, on the other side to predict<\/p>\n

how dangerous is a real sequence of foreshocks.<\/p>\n

 <\/p>\n

Team:<\/strong><\/p>\n