Post-Event GM Estimation Using GNNs - Data

Relevant data for the journal paper "Post-Event Ground Motion Estimation Using Graph Neural Networks"
Paper Abstract
Accurate ground motion estimates are essential for forensic analysis of structural damage following major earthquakes when direct recordings at the location(s) of interest are unavailable. Contemporary post-event ground motion estimation methods often leverage nearby observations to constrain estimates of intensity measures (IMs); however, existing approaches rely on empirical ground-motion models with well-known limitations in capturing spatial dependencies. This study introduces a graph neural network (GNN) approach for estimating ground-motion IMs, leveraging a graph-based representation to naturally encode spatial dependencies and allow for different observation types. Applied to a New Zealand case study, the GNN achieves performance comparable to the established multivariate normal conditional IM method, while learning spatial correlations directly from the data. Athough the proof-of-concept illustration does not yet surpass existing methods, the results demonstrate the viability of GNNs for post-event GM estimation. Continued improvements in model architecture and increased data availability are expected to further enhance performance and applicability.