Building an Ensemble Seismic Hazard Model for the Magnitude Distribution by Using Alternative Bayesian Implementations
Abstract
In this work we show how we built an ensemble seismic hazard model for the magnitude distribution for the TSUMAPS-NEAM EU project (http://www.tsumaps-neam.eu/). The considered source area includes the whole NEAM region (North East Atlantic, Mediterranean and connected seas). We build our models by using the catalogs (EMEC and ISC), their completeness and the regionalization provided by the project. We developed four alternative implementations of a Bayesian model, considering tapered or truncated Gutenberg-Richter distributions, and fixed or variable b-value. The frequency size distribution is based on the Weichert formulation. This allows for simultaneously assessing all the frequency-size distribution parameters (a-value, b-value, and corner magnitude), using multiple completeness periods for the different magnitudes. With respect to previous studies, we introduce the tapered Pareto distribution (in addition to the classical truncated Pareto), and we build a novel approach to quantify the prior distribution. For each alternative implementation, we set the prior distributions using the global seismic data grouped according to the different types of tectonic setting, and assigned them to the related regions. The estimation is based on the complete (not declustered) local catalog in each region. Using the complete catalog also allows us to consider foreshocks and aftershocks in the seismic rate computation: the Poissonicity of the tsunami events (and similarly the exceedances of the PGA) will be insured by the Le Cam's theorem. This Bayesian approach provides robust estimations also in the zones where few events are available, but also leaves us the possibility to explore the uncertainty associated with the estimation of the magnitude distribution parameters (e.g. with the classical Metropolis-Hastings Monte Carlo method). Finally we merge all the models with their uncertainty to create the ensemble model that represents our knowledge of the seismicity in the studied region.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2017
- Bibcode:
- 2017AGUFMNH21A0166T
- Keywords:
-
- 4302 Geological;
- NATURAL HAZARDS;
- 4307 Methods;
- NATURAL HAZARDS;
- 7212 Earthquake ground motions and engineering seismology;
- SEISMOLOGY