Geospatial AI for Liquefaction Hazard and Impact Forecasting: A Demonstrative Study in the U.S. Pacific Northwest
This work provides high-resolution liquefaction hazard forecasts for regional-scale planning and disaster management in a seismically vulnerable region, though it is an incremental application of existing methods to a new geographic area.
The study uses a machine learning-driven geospatial model to predict liquefaction hazard probabilities for 85 earthquake scenarios in the U.S. Pacific Northwest, achieving improved performance over prior regional models by incorporating mechanics, more geospatial data, and geostatistical anchoring to subsurface conditions. The predictions are demonstrated for asset and network vulnerability assessments and made publicly available.
Recent large-magnitude earthquakes have demonstrated the damaging consequences of soil liquefaction and reinforced the need to understand and plan for liquefaction hazards at a regional scale. In the United States, the Pacific Northwest is uniquely vulnerable to such consequences given the potential for crustal, intraslab, and subduction zone earthquakes. In this study, the liquefaction hazard is predicted geospatially at high resolution and across regional scales for 85 scenario earthquakes in the states of Washington and Oregon. This is accomplished using an emergent geospatial model that is driven by machine learning, and which predicts the probability of damaging ground deformation by surrogating state-of-practice geotechnical models. The adopted model shows improved performance and has conceptual advantages over prior regional-scale modeling approaches in that predictions (i) are informed by mechanics, (ii) employ more geospatial information using machine learning, and (iii) are geostatistically anchored to known subsurface conditions. The utility of the resulting predictions for the 85 scenarios is then demonstrated via asset and network infrastructure vulnerability assessments. The liquefaction hazard forecasts are published in a GIS-ready, public repository and are suitable for disaster simulations, evacuation route planning, network vulnerability analysis, land-use planning, insurance loss modeling, hazard communication, public investment prioritization, and other regional-scale applications.