2016年5月22日(日) 15:30 〜 17:00 201B (2F)

コンビーナ:*Schorlemmer Danijel(GFZ German Research Centre for Geosciences)、Gerstenberger Matt(GNS Science)、はお 憲生(防災科学技術研究所 防災システムセンター)、Pagani Marco(Global Earthquake Model)、座長:Gerstenberger Matt(GNS Science)、はお 憲生(防災科学技術研究所 防災システムセンター)、Danijel Schorlemmer(GFZ German Research Centre for Geosciences)

The core methods behind probabilistic seismic hazard analysis (PSHA) were first formalized by Cornell in 1968. Since that time, the fundamental components have largely remained unchanged in most applications: 1) a source model, often made up of zones of expected activity, or an active fault model coupled with a smoothed seismicity model based on catalog data, and; 2) empirically based ground motion prediction equations (GMPE) that are based on several basic parameters, such as moment magnitude and distance. The development of the individual components has become increasingly complex in recent years, however the basic structure has largely remain unchanged. In this session we invite presentations that explore some of the key assumptions currently used in PSHA and their implications for hazard, or alternative PSHA methods that might provide different insight into the hazard. Some examples might be the improved quantification of uncertainty in the source modeling, and moving beyond the typical Poisson-based formulations. The development of PSH models is challenged by the independence of fault and catalog datasets; Can hybrid models be used to improve the forecasting skill of PSHA? How can time dependence of earthquake activity be best built into PSHA? How can fault segmentation be overcome? Can earthquake simulators contribute to PSHA? How can we best incorporate GMPEs into PSHA when the models are becoming increasingly complex, and all parameters need to be specified in advance? Are there viable modeling alternatives for PSHA (e.g., an integrated source model) that can improve current best-practice? Finally, given the uncertainties in source modeling, are the current outputs of PSHA the most effective way of communicating our understanding to end-users in the risk and decision making communities?