Estimating impacts due to earthquakes - whether rapidly for emerging disasters or planning for future scenarios - entails the direct interface of seismological and civil engineering expertise and tools. Both endeavors require considering uncertain models and data since the main components of loss estimation - namely shaking, exposure, and vulnerabilities - entail inherent uncertainties. Since actionable response or planning requires confidence in our results, improvements in our loss calculations require continued collaboration. Fortunately, advancements in remote sensing, rapid in-situ monitoring and impact reporting, and machine learning allow for innovative data-fusion strategies that integrate with existing models and should significantly improve the accuracy and spatial resolution of rapid shaking and loss estimates. Some key contributing datasets, when integrated, could radically improve our loss estimate capabilities include better ShakeMap macroseismic constraints, global building footprints and inventories, Bayesian fatality updating based on early reporting, Structural Health Monitoring (SHM), and several other emerging technologies. Some of these same tools and strategies are also applicable for long-term loss and risk assessments. Wald’s William B. Joyner lecture features a combined seismological and earthquake engineering view of future earthquake response and recovery, where the initial impact estimates - as well as secondary hazards - are rapidly supplemented with crowd-sourced and remotely sensed observations that are integrated holistically for more a more accurate view of the consequences.
Speaker: David Wald, USGS
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