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.



  • A. Rosso & V. M. Schimmenti (LPTMS)
  • F. Landes & M. Schoenauer (LISN)

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