Exploring the Proximity of Ground-Motion Models Using High-Dimensional Visualization Techniques

Abstract

Logic trees have become a popular tool to capture epistemic uncertainties in seismic hazard analysis. They are commonly used by assigning weights to models on a purely descriptive basis (nominal scale). This invites the creation of unintended inconsistencies regarding the weights on the corresponding hazard curves. On the other hand, for human experts it is difficult to confidently express degrees-of-beliefs in particular numerical values. Here we demonstrate for ground-motion models how the model and the value-based perspectives can be partially reconciled by using high-dimensional information-visualization techniques. For this purpose we use Sammon's (1969) mapping and self-organizing mapping to project ground-motion models onto a two-dimensional map (an ordered metric set). Here they can be evaluated jointly according to their proximity in predicting similar ground motions, potentially making the assignment of logic tree weights consistent with their ground motion characteristics without having to abandon the model-based perspective.