© 2017 by Luis Ceferino

Description

Earthquake Rupture Modelling

Innovative probabilistic model for assessing earthquake rupture occurrence. The model accounts for the complexity of time and space interactions of ruptures. The rupture occurrence is modeled as a time-varying Multivariate Bernoulli according to the last rupture at different locations of the fault. The likelihood of rupture propagation is accounted by a spatial correlation model. This model resolves the inconsistency problem of current methodologies noticed in the time-dependent earthquake hazard estimation of California. The model was tested on the subduction zone of Peru, and it closely matched the average release of energy and the histogram of the earthquake magnitudes on the fault (see figure at the right).

Contributors
  • Luis Ceferino

  • Prof. Anne Kiremidjian

  • Prof. Greg Deierlein

Part 1: Probabilistic model for earthquake rupture occurrence

  • Luis Ceferino

  • Prof. Anne Kiremidjian

  • Prof. Greg Deierlein

Contributors

Innovative probabilistic model for assessing earthquake rupture occurrence. The model accounts for the complexity of time and space interactions of ruptures. The rupture occurrence is modeled as a time-varying Multivariate Bernoulli according to the last rupture at different locations of the fault. The likelihood of rupture propagation is accounted by a spatial correlation model. This model resolves the inconsistency problem of current methodologies noticed in the time-dependent earthquake hazard estimation of California. The model was tested on the subduction zone of Peru, and it closely matched the average release of energy and the histogram of the earthquake magnitudes on the fault (see figure at the right).

Description

Part 2: Physics-based simulation of earthquake rupture cycles

Description

Dynamic modelling of earthquake rupture cycles in the subduction zone along the Coast of Peru using QDYN software (Ongoing project).

Contributors
  • Luis Ceferino

  • Prof. Anne Kiremidjian

  • Prof. Greg Deierlein

Contributors

Markov Chain Monte Carlo (MCMC) applied for Bayesian parameter learning from two data sources: physics-based simulated earthquake rupture catalog and historic earthquake catalog (Ongoing project).

Description

Part 3: Bayesian updating for parameter learning in earthquake rupture model