During 2017–2018, the National Seismic Hazard Model for the conterminous United States was updated as follows: (1) an updated seismicity catalog was incorporated, which includes new earthquakes that occurred from 2013 to 2017; (2) in the central and eastern United States (CEUS), new ground motion models were updated that incorporate updated median estimates, modified assessments of the associated epistemic uncertainties and aleatory variabilities, and new soil amplification factors; (3) in the western United States (WUS), amplified shaking estimates of long-period ground motions at sites overlying deep sedimentary basins in the Los Angeles, San Francisco, Seattle, and Salt Lake City areas were incorporated; and (4) in the conterminous United States, seismic hazard is calculated for 22 periods (from 0.01 to 10 s) and 8 uniform VS30 maps (ranging from 1500 to 150 m/s). We also include a description of updated computer codes and modeling details. Results show increased ground shaking in many (but not all) locations across the CEUS (up to ~30%), as well as near the four urban areas overlying deep sedimentary basins in the WUS (up to ~50%). Due to population growth and these increased hazard estimates, more people live or work in areas of high or moderate seismic hazard than ever before, leading to higher risk of undesirable consequences from forecasted future ground shaking.

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Since 2015, the Global Earthquake Model (GEM) Foundation and its partners have been supporting regional programs and bilateral collaborations to develop an open global earthquake risk model. These efforts led to the development of a repository of probabilistic seismic hazard models, a global exposure dataset comprising structural and occupancy information regarding the residential, commercial and industrial buildings, and a comprehensive set of fragility and vulnerability functions for the most common building classes. These components were used to estimate probabilistic earthquake risk globally using the OpenQuake-engine, an open-source software for seismic hazard and risk analysis. This model allows estimating a number of risk metrics such as annualized average losses or aggregated losses for particular return periods, which are fundamental to the development and implementation of earthquake risk mitigation measures.

We develop a global database of building inventories using taxonomy of global building types for use in near-real-time post-earthquake loss estimation and pre-earthquake risk analysis, for the U.S. Geological Survey's Prompt Assessment of Global Earthquakes for Response (PAGER) program. The database is available for public use, subject to peer review, scrutiny, and open enhancement. On a country-by-country level, it contains estimates of the distribution of building types categorized by material, lateral force resisting system, and occupancy type (residential or nonresidential, urban or rural). The database draws on and harmonizes numerous sources: (1) UN statistics, (2) UN Habitat's demographic and health survey (DHS) database, (3) national housing censuses, (4) the World Housing Encyclopedia and (5) other literature.

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Building collapse is the dominant cause of casualties during earthquakes. In order to better predict human fatalities, the U.S. Geological Survey's Prompt Assessment of Global Earthquakes for Response (PAGER) program requires collapse fragility functions for global building types. The collapse fragility is expressed as the probability of collapse at discrete levels of the input hazard defined in terms of macroseismic intensity. This article provides a simple procedure for quantifying collapse fragility using vulnerability criteria based on the European Macroseismic Scale (1998) for selected European building types. In addition, the collapse fragility functions are developed for global building types by fitting the beta distribution to the multiple experts’ estimates for the same building type (obtained from EERI's World Housing Encyclopedia (WHE)-PAGER survey). Finally, using the collapse probability distributions at each shaking intensity level as a prior and field-based collapse-rate observations as likelihood, it is possible to update the collapse fragility functions for global building types using the Bayesian procedure.

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