Abstract. 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. 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. In the first part of this paper, we analyze spatial seismic hazard and loss rate variation inside administrative geographical zones in western Indonesia. We find that the variation of hazard can vary strongly not only between different zones, but also between different return periods for a fixed zone. However, the spatial variation of loss rate displays a similar pattern as the variation of hazard, without depending on the return period. We build upon these results in the second part of this paper. 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.
Review of “Spatial Seismic Hazard Variation and Adaptive Sampling of Portfolio Location Uncertainty in Probabilistic Seismic Risk Analysis” by Christoph Scheingraber and Martin Käser
