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Earthquake early warning (EEW) systems are relatively new technologies having first emerged as regional systems in the 1990s. Japan was the first nation to develop and implement a nationwide system in October 2007, and in the United States, ShakeAlert® became available on the entire length of the US West Coast in May 2021. Assessing how EEW is perceived and utilized by alert recipients is considered essential. Such assessments are necessary to evaluate whether alert recipients are taking advantage of alert messages to initiate protective actions upon receipt of an alert, how they regard the usefulness of alerts, desirable thresholds for issuing alerts, and other aspects of these systems. Having information from users will also facilitate assessments of the success of earthquake preparedness educational programs such as the ShakeOut and whether annual drills which include information on EEW systems are resulting in behavioral response consistent with the content of these programs. Finally, information on EEW utilization will provide data useful to social scientists who study hazards to advance our understanding of behavioral response to warnings. Survey research in the aftermath of a significant earthquake in which an EEW has been issued is one obvious method of achieving these objectives and there already exist a number of survey instruments for this purpose. A related strategy and the goal of the present research is to develop a brief questionnaire, consistent with those already developed, as a supplement to the United States Geological Survey’s “Did You Feel It?” questionnaire that has provided earthquake intensities and information on behavioral response in earthquakes, both domestic and international, since 2004. Having the intensity level at each respondent’s location is essential for relating their perspectives and actions to the shaking they experienced.

Since late 2018, the US Geological Survey (USGS) ground failure (GF) earthquake product has provided publicly available spatial estimates of earthquake-triggered landslide and liquefaction hazards, along with the qualitative hazard and population exposure-based alerts for M > 6 earthquakes worldwide and in near real time (within ∼30 min). Earthquake losses are oftentimes greatly aggravated by the impacts due to ground failure, yet those particular events with dramatic additional losses have not, heretofore, been rapidly identifiable. The GF product now provides situational awareness about the potential extent and severity of ground failure in the crucial time period before direct observations are available. We describe our implementation of the GF product and the lessons learned from the earthquakes that have occurred since the GF product was released. We describe the product design process, the underlying GF models, the methods we have developed for modeling uncertainty, and the development of the alert levels. The GF product has been produced in near real time for 320 events over the 2-year period since its public implementation in late 2018 through early 2021. The majority of these events yielded the lowest level (green) alerts for all ground-failure types, with 25 resulting in elevated hazard or exposure to landslides and 47 for liquefaction. In a qualitative comparison between the GF product alerts and GF occurrence information, we found that the product succeeds at assigning appropriate alert levels in the majority of cases. Based on our experience with the product, we have identified the following priorities for future improvements: (1) refinements of the underlying probabilistic models to incorporate severity and explicitly model the type of landslide/liquefaction; (2) development of models for fatalities and economic losses due to ground failure; and (3) estimation of the impacts of ground failure on infrastructure.

The US Geological Survey’s ShakeMap is used domestically and globally for post-earthquake emergency management and response, engineering analyses, financial instruments, and other decision-making activities. Recent developments in the insurance, reinsurance, and catastrophe bond sectors link payouts of potentially hundreds of millions of dollars to ShakeMap products. Similarly, building codes, post-earthquake building damage forensic evaluations, and geotechnical evaluations often rely on estimated peak response-spectral values for site-specific evaluations that may lead to costly analyses, retrofits, or other expenditures. Given such activities, financial, engineering, and other technical users demand processing specifications and a metadata trail for actuarial, escrow, and forensic purposes for each significant earthquake. Recent inquiries include how and why maps change with time, how to interpret metadata, and how to obtain the creation and update history of various map layers. Similarly, the collection of ShakeMap scenarios and historical ShakeMaps—either created in earlier versions or rerun as part of the latest version of the ShakeMap Atlas—warrant a full explanation of the inputs, processing, and archiving given their contribution to fragility curve development and loss model calibration. For these reasons, in addition to event-specific ShakeMap metadata and a comprehensive online ShakeMap Manual, we have crafted this practice paper to answer several of the most frequently asked technical questions. We also describe an application programming interface (API) for accessing site-specific shaking metrics and their uncertainties for earthquake forensic purposes in a consistent fashion. In all, we describe the advantages of employing ShakeMaps for these critical purposes as well as describe their limitations and uncertainties, offering an extensive set of instructions and disclaimers that can be referenced by ShakeMap users.

We introduce a Bayesian framework for incorporating time-varying noisy reported data on damage and loss information to update near real-time loss estimates/alerts for the U.S. Geological Survey’s Prompt Assessment of Global Earthquakes for Response (PAGER) system. Initial loss estimation by PAGER immediately following an earthquake includes several uncertainties. Historically, the PAGER’s alerting on fatality and economic losses has not incorporated location-specific reported data on physical damage or casualties for a given earthquake. The proposed framework provides the ability to include early reports on fatalities at any given time and improve the overall impact forecast for the earthquake. The reported data on fatalities or damage are generally incomplete and noisy, especially in the early hours of the disaster. To address these challenges, we develop a recursive Bayesian updating framework that takes into account the loss projection model and the measurement and model uncertainties. The framework is applied to loss data for three example earthquakes, and the results show that the proposed updating improves the loss estimates and alert level to the correct level within the first day of the earthquake.

Time-averaged shear wave velocity over the upper 30 m of the earth’s surface ( V S30) is a key parameter for estimating ground motion amplification as both a predictive and a diagnostic tool for earthquake hazards. The first-order approximation of V S30 is commonly obtained through a topographic slope–based or terrain proxy due to the widely available nature of digital elevation models. However, better-constrained V S30 maps have been developed in many regions. Such maps preferentially employ various combinations of V S30 measurements, higher-resolution elevation models, lithologic, geologic, geomorphic, and other proxies and often utilize refined interpolation schemes. We develop a new hybrid global V S30 map database that defaults to the global slope-based V S30 map, but smoothly inserts regional V S30 maps where available. In addition, we present comparisons of the default slope-based proxy maps against the new hybrid version in terms of V S30 and amplification ratio maps, and uncertainties in assigned V S30 values.

Earthquake early warning (EEW) entails detection of initial earthquake shaking and rapid estimation and notification to users prior to imminent, stronger shaking. EEW (ShakeAlert Phase 1, version 2.0) went operational in California in October 2019 and is coming to the rest of the U.S. West Coast. But what are the technical and social challenges to delivering actionable information on earthquake shaking before it arrives? Although there will be tangible benefits, there are also limitations. Basic seismological principles, alert communication challenges, and potential response actions, as well as substantial lessons learned from the use of EEW in Japan, point to more limited opportunities to warn and protect than perhaps many expect. This is in part because potential warning times vary by region and are influenced by tectonic environment, hypocentral depth, and the fault’s proximity to the alert user. For the U.S. West Coast, particularly for crustal earthquakes, warning times are shorter—and possible mitigation actions are likely to be less effective—than often maintained. Nevertheless, EEW is an additional arrow in the quiver of earthquake information tools available in the service of earthquake risk reduction. What is called for, then, is transparency and balance in the EEW discussion: along with its potential, the acknowledgment of EEW’s inherent and practical limitations is needed. Recognizing these limitations could, in fact, make EEW implementation more successful as part of a holistic earthquake mitigation strategy, where its role among other earthquake information tools is quite natural.

The United States Geological Survey’s Prompt Assessment of Global Earthquakes for Response (PAGER) alert system provides rapid (10–20 min) but general loss estimates of ranges of fatalities and economic impact for significant global earthquakes. FEMA’s Hazus software, in contrast, provides time-consuming (2–5 h) but more detailed loss information quantified in terms of structural, social, and economic consequences estimated at a much higher spatial resolution for large domestic earthquakes. We developed a rapid hybrid post-earthquake product that takes advantage of the best of both loss models. First, though, we conducted a systematic comparison of loss estimates from PAGER with Hazus for all significant, relatively recent, domestic earthquakes for which adequate loss data exist—augmented by a dozen ShakeMap scenarios. The systematic comparison of Hazus and PAGER losses provided the basis for selecting the specific loss metrics to present from each system. The signature product will serve as a supplement to the widely deployed PAGER alert product for significant domestic earthquakes.

We describe an approach to model liquefaction extent that focuses on identifying broadly available geospatial variables (e.g., derived from digital elevation models) and earthquake-specific parameters (e.g., peak ground acceleration, PGA). A key step is database development: We focus on the 1995 Kobe and 2010–2011 Christchurch earthquakes because the presence/absence of liquefaction has been mapped so that the database is unbiased with respect to the areal extent of liquefaction. We derive two liquefaction models with explanatory variables that include PGA, shear-wave velocity, compound topographic index, and a newly defined normalized distance parameter (distance to coast divided by the sum of distance to coast and distance to the basin inland edge). To check the portability/reliability of these models, we apply them to the 2010 Haiti earthquake. We conclude that these models provide first-order approximations of the extent of liquefaction, appropriate for use in rapid response, loss estimation, and simulations.

We extended the U.S. Geological Survey's Prompt Assessment of Global Earthquakes for Response (PAGER) empirical fatality estimation methodology proposed by Jaiswal et al. (2009) to rapidly estimate economic losses after significant earthquakes worldwide. The requisite model inputs are shaking intensity estimates made by the ShakeMap system, the spatial distribution of population available from the LandScan database, modern and historic country or sub-country population and Gross Domestic Product (GDP) data, and economic loss data from Munich Re's historical earthquakes catalog. We developed a strategy to approximately scale GDP-based economic exposure for historical and recent earthquakes in order to estimate economic losses. The process consists of using a country-specific multiplicative factor to accommodate the disparity between economic exposure and the annual per capita GDP, and it has proven successful in hindcast-ing past losses. Although loss, population, shaking estimates, and economic data used in the calibration process are uncertain, approximate ranges of losses can be estimated for the primary purpose of gauging the overall scope of the disaster and coordinating response. The proposed methodology is both indirect and approximate and is thus best suited as a rapid loss estimation model for applications like the PAGER system.

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. [DOI: 10.1193/1.3606398]

We analyzed mortality rates of earthquakes worldwide and developed a country/region-specific empirical model for earthquake fatality estimation within the U.S. Geological Survey's Prompt Assessment of Global Earthquakes for Response (PAGER) system. The earthquake fatality rate is defined as total killed divided by total population exposed at specific shaking intensity level. The total fatalities for a given earthquake are estimated by multiplying the number of people exposed at each shaking intensity level by the fatality rates for that level and then summing them at all relevant shaking intensities. The fatality rate is expressed in terms of a two-parameter lognormal cumulative distribution function of shaking intensity. The parameters are obtained for each country or a region by minimizing the residual error in hindcasting the total shaking-related deaths from earthquakes recorded between 1973 and 2007. A new global regionalization scheme is used to combine the fatality data across different countries with similar vulnerability traits.

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.

When a potentially damaging earthquake occurs, utility and other lifeline managers, emergency responders, and other critical users have an urgent need for information about the impact on their particular facilities so they can make appropriate decisions and take quick actions to ensure safety and restore system functionality. ShakeMap, a tool used to portray the extent of potentially damaging shaking following an earthquake, on its own can be useful for emergency response, loss estimation, and public information. However, to take full advantage of the potential of ShakeMap, we introduce ShakeCast. ShakeCast facilitates the complicated assessment of potential damage to a user's widely distributed facilities by comparing the complex shaking distribution with the potentially highly variable damageability of their inventory to provide a simple, hierarchical list and maps of structures or facilities most likely impacted. ShakeCast is a freely available, post-earthquake situational awareness application that automatically retrieves earthquake shaking data from ShakeMap, compares intensity measures against users’ facilities, sends notifications of potential damage to responsible parties, and generates facility damage maps and other Web-based products for both public and private emergency managers and responders.

Slip histories for the 2002 M7.9 Denali fault, Alaska, earthquake are derived rapidly from global teleseismic waveform data. In phases, three models improve matching waveform data and recovery of rupture details. In the first model (Phase I), analogous to an automated solution, a simple fault plane is fixed based on the preliminary Harvard Centroid Moment Tensor mechanism and the epicenter provided by the Preliminary Determination of Epicenters. This model is then updated (Phase II) by implementing a more realistic fault geometry inferred from Digital Elevation Model topography and further (Phase III) by using the calibrated P-wave and SH-wave arrival times derived from modeling of the nearby 2002 M6.7 Nenana Mountain earthquake. These models are used to predict the peak ground velocity and the shaking intensity field in the fault vicinity. The procedure to estimate local strong motion could be automated and used for global real-time earthquake shaking and damage assessment. [DOI: 10.1193/1.1778388]

We describe a new probabilistic method that uses observations of modified Mercalli intensity (MMI) from past earthquakes to make quantitative estimates of ground shaking parameters (i.e., peak ground acceleration, peak ground velocity, 5% damped spectral acceleration values, etc.). The method uses a Bayesian approach to make quantitative estimates of the probabilities of different levels of ground motions from intensity data given an earthquake of known location and magnitude. The method utilizes probability distributions from an intensity/ground motion data set along with a ground motion attenuation relation to estimate the ground motion from intensity. The ground motions with the highest probabilities are the ones most likely experienced at the site of the MMI observation. We test the method using MMI/ground motion data from California and published ground motion attenuation relations to estimate the ground motions for several earthquakes: 1999 Hector Mine, California (M7.1); 1988 Saguenay, Quebec (M5.9); and 1982 Gaza, New Hampshire (M4.4). In an example where the method is applied to a historic earthquake, we estimate that the peak ground accelerations associated with the 1727 (M;5.2) earthquake at Newbury, Massachusetts, ranged from 0.23 g at Newbury to 0.06 g at Boston. [DOI: 10.1193/1.1596549]

We have developed regression relationships between Modified Mercalli Intensity ( Imm) and peak ground acceleration (PGA) and velocity (PGV) by comparing horizontal peak ground motions to observed intensities for eight significant California earthquakes. For the limited range of Modified Mercalli intensities ( Imm), we find that for peak acceleration with V ≤ Imm ≤ VIII, Imm = 3.66 log( PGA) − 1.66, and for peak velocity with V ≤ Imm ≤ IX, Imm = 3.47 log( PGV) + 2.35. From comparison with observed intensity maps, we find that a combined regression based on peak velocity for intensity > VII and on peak acceleration for intensity < VII is most suitable for reproducing observed Imm patterns, consistent with high intensities being related to damage (proportional to ground velocity) and with lower intensities determined by felt accounts (most sensitive to higher-frequency ground acceleration). These new Imm relationships are significantly different from the Trifunac and Brady (1975) correlations, which have been used extensively in loss estimation.

Rapid (3-5 minutes) generation of maps of instrumental ground-motion and shaking intensity is accomplished through advances in real-time seismographic data acquisition combined with newly developed relationships between recorded ground-motion parameters and expected shaking intensity values. Estimation of shaking over the entire regional extent of southern California is obtained by the spatial interpolation of the measured ground motions with geologically based frequency and amplitude-dependent site corrections. Production of the maps is automatic, triggered by any significant earthquake in southern California. Maps are now made available within several minutes of the earthquake for public and scientific consumption via the World Wide Web; they will be made available with dedicated communications for emergency response agencies and critical users.