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Earthquake-induced secondary ground failure hazards, such as liquefaction andlandslides, result in catastrophic building and infrastructure damage as wellas human fatalities. To facilitate emergency responses and mitigate losses, theU.S. Geological Survey provides a rapid hazard estimation system forearthquake-triggered landslides and liquefaction using geospatialsusceptibility proxies and ShakeMap ground motion estimates. In this study, wedevelop a generalized causal graph-based Bayesian network that models thephysical interdependencies between geospatial features, seismic groundfailures, and building damage, as well as DPMs. Geospatial features providephysical insights for estimating ground failure occurrence while DPMs containevent-specific surface change observations. This physics-informed causal graphincorporates these variables with complex physical relationships in oneholistic Bayesian updating scheme to effectively fuse information from bothgeospatial models and remote sensing data. This framework is scalable andflexible enough to deal with highly complex multi-hazard combinations. We thendevelop a stochastic variational inference algorithm to jointly update theintractable posterior probabilities of unobserved landslides, liquefaction, andbuilding damage at different locations efficiently. In addition, a localgraphical model pruning algorithm is presented to reduce the computational costof large-scale seismic ground failure estimation. We apply this framework tothe September 2018 Hokkaido Iburi-Tobu, Japan (M6.6) earthquake and January2020 Southwest Puerto Rico (M6.4) earthquake to evaluate the performance of ouralgorithm.

Bayesian Updating of Seismic Ground Failure Estimates via Causal Graphical Models and Satellite Imagery