Conventional probabilistic seismic hazard analysis (PSHA) is performed at a specific location by considering all important earthquake events, their locations and sizes, and their likelihood of occurring in combination with ground motion models to evaluate ground motion exceedance rates. Conventional PSHA can be repeated for many locations to develop hazard maps. Such maps are not suitable for assessing risk to spatially distributed infrastructure. A robust, but computationally expensive approach is to analyze the spatially distributed infrastructure system for every single event considered in the PSHA. However, PSHA often involves hundreds of thousands of events, and this approach is generally not practical. This paper presents a procedure for selecting a manageable event subset that, in aggregate, matches the hazard across a spatially distributed system. Scenario events and their adjusted rates are selected based on disaggregation in an optimization approach that aims to match the hazard at selected points throughout the system while preserving the relative contributions of events with different magnitudes and distances. We introduce here an efficient regression-based method for event selection that meets these requirements.

Regression-Based Event Selection for Hazard-Consistent Seismic Risk Assessment

Zimmaro P.;
2022

Abstract

Conventional probabilistic seismic hazard analysis (PSHA) is performed at a specific location by considering all important earthquake events, their locations and sizes, and their likelihood of occurring in combination with ground motion models to evaluate ground motion exceedance rates. Conventional PSHA can be repeated for many locations to develop hazard maps. Such maps are not suitable for assessing risk to spatially distributed infrastructure. A robust, but computationally expensive approach is to analyze the spatially distributed infrastructure system for every single event considered in the PSHA. However, PSHA often involves hundreds of thousands of events, and this approach is generally not practical. This paper presents a procedure for selecting a manageable event subset that, in aggregate, matches the hazard across a spatially distributed system. Scenario events and their adjusted rates are selected based on disaggregation in an optimization approach that aims to match the hazard at selected points throughout the system while preserving the relative contributions of events with different magnitudes and distances. We introduce here an efficient regression-based method for event selection that meets these requirements.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/335691
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