Post-Event GM Estimation Using GNNs - Data

Relevant data for the journal paper "Post-Event Ground Motion Estimation Using Graph Neural Networks"<b>Paper Abstract</b>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.