Temporal spatial correlations in earthquakes dynamics: physical modelling and data anlysis
Résumé du projet
One of the most distinctive and poorly understood feature of earthquakes is the significant increase of the
seismic rate observed after large events. Well established empirical laws of aftershocks occurrence
demand for a physical explanation. Foreshocks are also observed before a large event but their statistical
fingerprints, potentially important for human security, are much more elusive. In this project, using the
methods developed in the Statistical Physics we will design a model of the fault able to reproduce complex
spatio-temporal patterns with foreshocks, mainshocks and aftershocks. Using Machine Learning we will
understand the statistical properties of the short sequence of foreshocks. First, using our synthetic
sequences, we determine how much information is needed to predict the following events. Then we will
use actual data: on one side to calibrate the model on the real fault activity, on the other side to predict
how dangerous is a real sequence of foreshocks.
Team:
- A. Rosso & V. M. Schimmenti (LPTMS)
- F. Landes & M. Schoenauer (LISN)