Efficient treatment and quantification of uncertainty in probabilistic seismic hazard and risk analysis

The main goals of this thesis are the development of a computationally efficient framework for stochastic treatment of various important uncertainties in probabilistic seismic hazard and risk assessment, its application to a newly created seismic risk model of Indonesia, and the analysis and quantification of the impact of these uncertainties on the distribution of estimated seismic losses for a large number of synthetic portfolios modeled after real-world counterparts. The treatment and quantification of uncertainty in probabilistic seismic hazard and risk analysis has already been identified as an area that could benefit from increased research attention. Furthermore, it has become evident that the lack of research considering the development and application of suitable sampling schemes to increase the computational efficiency of the stochastic simulation represents a bottleneck for applications where model runtime is an important factor. In this research study, the development and state of the art of probabilistic seismic hazard and risk analysis is first reviewed and opportunities for improved treatment of uncertainties are identified. A newly developed framework for the stochastic treatment of portfolio location uncertainty as well as ground motion and damage uncertainty is presented. The framework is then optimized with respect to computational efficiency. Amongst other techniques, a novel variance reduction scheme for portfolio location uncertainty is developed. Furthermore, in this thesis, some well-known variance reduction schemes such as Quasi Monte Carlo, Latin Hypercube Sampling and MISER (locally adaptive recursive stratified sampling) are applied for the first time to seismic hazard and risk assessment. The effectiveness and applicability of all used schemes is analyzed. Several chapters of this monograph describe the theory, implementation and some exemplary applications of the framework. To conduct these exemplary applications, a seismic hazard model for Indonesia was developed and used for the analysis and quantification of loss uncertainty for a large collection of synthetic portfolios. As part of this work, the new framework was integrated into a probabilistic seismic hazard and risk assessment software suite developed and used by Munich Reinsurance Group. Furthermore, those parts of the framework that deal with location and damage uncertainties are also used by the flood and storm natural catastrophe model development groups at Munich Reinsurance for their risk models

Efficient treatment and quantification of uncertainty in probabilistic seismic hazard and risk analysis

The main goals of this thesis are the development of a computationally efficient framework for stochastic treatment of various important uncertainties in probabilistic seismic hazard and risk assessment, its application to a newly created seismic risk model of Indonesia, and the analysis and quantification of the impact of these uncertainties on the distribution of estimated seismic losses for a large number of synthetic portfolios modeled after real-world counterparts. The treatment and quantification of uncertainty in probabilistic seismic hazard and risk analysis has already been identified as an area that could benefit from increased research attention. Furthermore, it has become evident that the lack of research considering the development and application of suitable sampling schemes to increase the computational efficiency of the stochastic simulation represents a bottleneck for applications where model runtime is an important factor. In this research study, the development and state of the art of probabilistic seismic hazard and risk analysis is first reviewed and opportunities for improved treatment of uncertainties are identified. A newly developed framework for the stochastic treatment of portfolio location uncertainty as well as ground motion and damage uncertainty is presented. The framework is then optimized with respect to computational efficiency. Amongst other techniques, a novel variance reduction scheme for portfolio location uncertainty is developed. Furthermore, in this thesis, some well-known variance reduction schemes such as Quasi Monte Carlo, Latin Hypercube Sampling and MISER (locally adaptive recursive stratified sampling) are applied for the first time to seismic hazard and risk assessment. The effectiveness and applicability of all used schemes is analyzed. Several chapters of this monograph describe the theory, implementation and some exemplary applications of the framework. To conduct these exemplary applications, a seismic hazard model for Indonesia was developed and used for the analysis and quantification of loss uncertainty for a large collection of synthetic portfolios. As part of this work, the new framework was integrated into a probabilistic seismic hazard and risk assessment software suite developed and used by Munich Reinsurance Group. Furthermore, those parts of the framework that deal with location and damage uncertainties are also used by the flood and storm natural catastrophe model development groups at Munich Reinsurance for their risk models

Spatial seismic hazard variation and adaptive sampling of portfolio location uncertainty in probabilistic seismic risk analysis

Probabilistic seismic risk analysis is widely used in the insurance industry to model the likelihood and severity of losses to insured portfolios by earthquake events. The available ground motion data - especially for strong and infrequent earthquakes - are often limited to a few decades, resulting in incomplete earthquake catalogues and related uncertainties and assumptions. The situation is further aggravated by the sometimes poor data quality with regard to insured portfolios. For example, due to geocoding issues of address information, risk items are often only known to be located within an administrative geographical zone, but precise coordinates remain unknown to the modeler. We analyze spatial seismic hazard and loss rate variation inside administrative geographical zones in western Indonesia. We find that the variation in hazard can vary strongly between different zones. The spatial variation in loss rate displays a similar pattern as the variation in hazard, without depending on the return period. In a recent work, we introduced a framework for stochastic treatment of portfolio location uncertainty. This results in the necessity to simulate ground motion on a high number of sampled geographical coordinates, which typically dominates the computational effort in probabilistic seismic risk analysis. We therefore propose a novel sampling scheme to improve the efficiency of stochastic portfolio location uncertainty treatment. Depending on risk item properties and measures of spatial loss rate variation, the scheme dynamically adapts the location sample size individually for insured risk items. We analyze the convergence and variance reduction of the scheme empirically. The results show that the scheme can improve the efficiency of the estimation of loss frequency curves and may thereby help to spread the treatment and communication of uncertainty in probabilistic seismic risk analysis