固体地球科学(S)
セッション小記号地震学(SS)
セッションIDS-SS10
タイトル和文Rethinking PSHA
英文Rethinking PSHA
タイトル短縮名和文Rethinking PSHA
英文Rethinking PSHA
代表コンビーナ氏名和文平田 直
英文Naoshi Hirata
所属和文東京大学地震研究所
英文Earthquake Research Institute, the University of Tokyo
共同コンビーナ 1氏名和文Schorlemmer Danijel
英文Danijel Schorlemmer
所属和文GFZ German Research Centre for Geosciences
英文GFZ German Research Centre for Geosciences
共同コンビーナ 2氏名和文Matt Gerstenberger
英文Matt Gerstenberger
所属和文GNS Science
英文GNS Science
共同コンビーナ 3氏名和文Ma Kuo-Fong
英文Kuo-Fong Ma
所属和文Institute of Geophysics, National Central University, Taiwan, ROC
英文Institute of Geophysics, National Central University, Taiwan, ROC
発表言語E
スコープ和文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?
英文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?
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