We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Bayesian earthquake forecasting approach based on the epidemic type aftershock sequence model.
- Authors
Petrillo, Giuseppe; Zhuang, Jiancang
- Abstract
The epidemic type aftershock sequence (ETAS) model is used as a baseline model both for earthquake clustering and earthquake prediction. In most forecast experiments, the ETAS parameters are estimated based on a short and local catalog, therefore the model parameter optimization carried out by means of a maximum likelihood estimation may be not as robust as expected. We use Bayesian forecast techniques to solve this problem, where non-informative flat prior distributions of the parameters is adopted to perform forecast experiments on 3 mainshocks occurred in Southern California. A Metropolis–Hastings algorithm is employed to sample the model parameters and earthquake events. We also show, through forecast experiments, how the Bayesian inference allows to obtain a probabilistic forecast, differently from one obtained via MLE.
- Subjects
SOUTHERN California; EARTHQUAKE aftershocks; MAXIMUM likelihood statistics; EARTHQUAKES; EARTHQUAKE prediction; EPIDEMICS; BAYESIAN field theory
- Publication
Earth, Planets & Space, 2024, Vol 76, Issue 1, p1
- ISSN
1343-8832
- Publication type
Article
- DOI
10.1186/s40623-024-02021-8