Inferring earthquake ground motion fields with Bayesian Networks

Research output: Contribution to journalArticle

Bayesian Networks (BNs) have the ability to perform inference on uncertain variables given evidence on observed quantities, which makes them relevant mathematical tools for the updating of ground-motion fields based on strong-motion records or macroseismic observations. Therefore the present article investigates the use of BN models of spatially correlated Gaussian random fields as an accurate and scalable method for the generation of ground-motion maps. The proposed BN model is based on continuous Gaussian variables, as opposed to discrete variables as in previous formulations, and it is built to account for cross-correlated ground-motion parameters as well as macroseismic observations. This approach is validated with respect to the analytical solution (i.e., conditional multivariate normal distributions) and it is also compared to the USGS ShakeMap method, thus demonstrating a better ability to model jointly the inter- and intra-event error terms of ground-motion models. The scalability of the approach, i.e. its capacity to be applied to large grids, is ensured by a grid sub-division strategy, which appears to be computationally efficient and accurate within an error rate of a fraction of percent. Finally, the BN implementation is demonstrated on a real-world example (the Mw 6.2 Kumamoto, Japan, 2016 foreshock), where vector-valued shake-maps of cross-correlated intensity measures are generated, along with the integration of macroseismic observations.
Original languageEnglish
Number of pages44
JournalBulletin of the Seismological Society of America
StateAccepted/In press - 22 Jul 2017

    Research areas

  • Bayesian network, earthquake, strong ground motion, ShakeMap, ground-motion prediction, engineering seismology

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