JpGU Meeting 2020
Session outline| Solid Earth Sciences(S) | ||
|---|---|---|
| Session Sub-category | Seismology (SS) | |
| Session ID | S-SS10 | |
| Title | Rethinking PSHA | |
| Short Title | Rethinking PSHA | |
| Main Convener | Name | Naoshi Hirata |
| Affiliation | Earthquake Research Institute, the University of Tokyo | |
| Co-Convener 1 | Name | Danijel Schorlemmer |
| Affiliation | GFZ German Research Centre for Geosciences | |
| Co-Convener 2 | Name | Matt Gerstenberger |
| Affiliation | GNS Science | |
| Co-Convener 3 | Name | Kuo-Fong Ma |
| Affiliation | Institute of Geophysics, National Central University, Taiwan, ROC | |
| Session Language | E | |
| Scope | 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. 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. How are uncertainties propagated through the model and can they correctly reflect the knowledge. How can non-Poissonian dynamics be best built into time-independent PSHA? How to quantify and use uncertainties in fault and earthquake-catalog source models as well as those in ground-motion prediction? How can fault segmentation be overcome? New types of models (with increasing complexity) are being developed and they will be integrated into PSHA. How can hybrid models be used to improve the forecasting skill of PSHA? Can earthquake simulators contribute to PSHA? What are improvements in GMPEs but also their limits? Is their increasing complexity justified? Or are there viable modeling alternatives for PSHA that can improve current best practice?How can all these potential improvements being tested before they contribute to societal relevant decisions? | |
| Presentation Format | Oral and Poster presentation | |