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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.

In their analysis of the U.S. Geological Survey’s (USGS) “Did You Feel It?” (DYFI) data Hough and Martin (2021) claim, among other assertions, that the following: Socioeconomic and geopolitical factors can introduce biases in the USGS’ characterization of earthquakes and their effects, especially if online data collection systems are not designed to be broadly accessible;These biases can, in turn, potentially cascade in myriad ways, potentially shaping our understanding of an earthquake’s impact and the characterization of seismic hazard; andCaution should be urged when relying on data from the DYFI system to characterize the distribution of shaking from large earthquakes in India and other parts of the world (outside of the United States). Claims of inequity in access, systematic data biases, or urging caution in the usage of data from critical governmental earthquake information systems should not be made, nor taken, lightly. Several assertions made by Hough and Martin (hereafter, H&M) about the nature of DYFI contributors—and the data they provide—leave a false narrative concerning DYFI system accessibility and quality that H&M have not adequately substantiated. I describe several shortcomings of H&M’s demographic statistics and methodology, focusing on four main concerns. First, DYFI has revolutionized and greatly facilitated access to reporting intensities, in contrast to H&M claims to the contrary. Second, because DYFI does not directly collect demographic data other than the observer’s location, any demographic analyses require extraordinary inferences, well outside the normal bounds of sociodemographic analyses. Third, independent of accessibility and the geographic distribution of contributions from the public, the macroseismic data collected are nonetheless representative of the shaking and impact at each location, of quality, rapid, and thus extremely useful. Lastly, H&M fail to cite critical and pertinent prior, highly relevant scholarly studies, and as such, they misrepresent the novelty of their own work as well as miss key practical matters detailed in those prior studies. Prior to rebutting what H&M claim DYFI does not do, I will remind the reader the ways in which DYFI excels.

This study assesses existing intensity prediction equations (IPEs) for small unspecified magnitude (M ≤3.5) earthquakes at short hypocentral distances (Dh) and explores such earthquakes’ contribution to the felt shaking hazard. In particular, we consider IPEs by Atkinson and Wald (2007) and Atkinson et al. (2014), and evaluate their performance based on “Did You Feel It” (DYFI) reports and recorded peak ground velocities (PGVs) in the central United States. Both IPEs were developed based on DYFI reports in the central and eastern United States with moment magnitudes above Mw 3.0. DYFI reports are often used as the ground truth when evaluating and developing IPEs, but they could be less reliable when there are limited responses for small-magnitude earthquakes. We first compare the DYFI reports with intensities interpolated from recorded PGVs. Results suggest a minimal discrepancy between the two when the intensity is large enough to be felt (i.e., M >2 and Dh

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.

Large earthquakes, such as Wenchuan in 2008, Mw 7.9, Sichuan, China, provide an opportunity for earthquake early warning (EEW), as many heavily shaken areas are far (∼50 km) from the epicenter and warning times could be sufficient (≥5 s) to take preventive action. On the other hand, earthquakes with magnitudes larger than ∼M 6.5 are challenging for EEW because source dimensions need to be defined to adequately estimate shaking. Finite-fault rupture detector (FinDer) is an approach to identify fault rupture extents from real-time seismic records. In this study, we playback local and regional onscale strong-motion waveforms of the 2008 Mw 7.9 Wenchuan, 2013 Mw 6.6 Lushan, and 2017 Mw 6.5 Jiuzhaigou earthquakes to study the performance of FinDer for the current layout of the China Strong Motion Network. Overall, the FinDer line-source models agree well with the observed spatial distribution of aftershocks and models determined from waveform inversion. However, because FinDer models are constructed to characterize seismic ground motions (as needed for EEW) instead of source parameters, the rupture length can be overestimated for events radiating high levels of high-frequency motions. If the strong-motion data used had been available in real time, 50%–80% of sites experiencing intensity modified Mercalli intensity IV–VII (light to very strong) and 30% experiencing VIII–IX (severe to violent) could have been issued a warning with 10 and 5 s, respectively, before the arrival of the S wave. We also show that loss estimates based on the FinDer line source are more accurate compared to point-source models. For the Wenchuan earthquake, for example, they predict a four to six times larger number of fatalities and injured, which is consistent with official reports. These losses could be provided 1/2∼3 hr faster than if they were based on more complex inversion rupture models.

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.

In the minutes to hours after a major earthquake, such as the recent 2018 Mw 7.1 Anchorage event, the U.S. Geological Survey (USGS) produces a suite of interconnected earthquake products that provides diverse information ranging from basic earthquake source parameters to loss estimates. The 2018 Anchorage earthquake is the first major domestic earthquake to occur since several new USGS products have been developed, thus providing an opportunity to discuss the newly expanded USGS earthquake product suite, its timeliness, performance, and reception. Overall, the products were relatively timely, accurate, well received, and widely used, including by the media, who used information and visualizations from many products to frame their early reporting. One downside of the codependence of multiple products is that reasonable updates to upstream products (e.g., magnitude and source characterization) can result in significant changes to downstream products; this was the case for the Anchorage earthquake. However, the coverage of strong‐motion stations and felt reports was so dense that the ShakeMap and downstream products were relatively insensitive to changes in magnitude or fault‐plane orientation once the ground‐motion data were available. Shaking and loss indicators initially fluctuated in the first hour or two after the earthquake, but they stabilized quickly. To understand how the products are being used and how effectively they are being communicated, we analyze the media coverage of USGS earthquake products. Most references to USGS products occurred within the first 48 hr after the event. The lack of coverage after 48 hr could indicate that longer‐term products addressing what actions the USGS is taking or what early reconnaissance has revealed might be useful for those people wanting additional information about the earthquake.

On August 24, 2016, a magnitude-6.2 earthquake in Central Italy resulted in at least 290 deaths, significant ground failure (including landslides and liquefaction), and building damage. After the event, the NASA Advanced Rapid Imaging and Analysis team produced Damage Proxy Maps (DPM) that reflect earthquake-induced surficial changes using synthetic aperture data from the COSMO-SkyMed satellite. However, exact causes of these surface changes, e.g., ground failure, building damage, or other environmental changes, are difficult to directly differentiate from the satellite images alone. For example, changes could reflect building damage, landslides, the co-occurrence of both, or numerous other processes that are not related to the earthquake. Alternatively, existing ground failures models are useful in locating areas of higher likelihoods but suffer from high false alarm rates due to inaccurate or incomplete geospatial proxies and complex physical interdependencies between shaking and specific sites of ground failure. In this work, we present a joint Bayesian updating framework using a causal graph strategy. The Bayesian causal graph models physical interdependencies among ground shaking, ground failures, building damage, and remote sensing observations. Based on the graph, a variational inference approach is designed to jointly update the estimates of ground failure and building damage through fusing traditional geospatial models and the remotely sensed data. As a case study, the DPMs in Central Italy are input to the model for jointly calibrating and updating the probability of ground failure estimations as well as for estimating building damage probabilities. The results showed that by incorporating high-resolution imagery, our model significantly reduces the false alarm rate of ground failure estimates and improves the spatial accuracy and resolution of ground failure and building damage inferences.

Ham radio networks gear up to provide real-time, on-the-ground information about earthquake shaking and damage when other communication pathways are knocked out of commission.

Human behavioral response to earthquake ground motion has long been a subject of multidisciplinary interest and research. In most versions of seismic intensity scales, human perceptions and behavior are one component of the assignment of intensity. Public health research has shown that actions taken during earthquakes have a significant impact on the incidence of injury or the maintenance of safety. Based on this research, emergency managers and organizations promoting emergency preparedness have advocated strategies such as drop, cover, and hold on (DCHO) and promoted this safety measure through public education and annual drills. The “Did You Feel It?” (DYFI) mapping system (see Data and Resources) based on an online questionnaire developed and maintained by the U.S. Geological Survey has provided opportunities for those who have experienced an earthquake to report this experience worldwide since 2004. The DYFI questionnaire, although designed to assign intensity, also contains questions regarding the behavior in which one has engaged during the earthquake. The questionnaire includes other important information that may elucidate behavioral response to earthquakes, including assigned intensity, emotional reaction, and whether damage occurred at the location where the earthquake was experienced. The very large number of people who completed DYFI questionnaires following the July 2019 Ridgecrest, California, earthquakes provides a robust dataset for analysis and suggests that as intensity and levels of fear increase, behavior becomes more active in terms of physical movement to locations of presumed safety. Among active responses including DCHO, going to a doorway, and running outside, DCHO was the least likely to be implemented. The study provides possible explanations for low participation in DCHO despite active campaigns to promote this strategy.

The U.S. Geological Survey (USGS) “Did You Feel It?” (DYFI) system is an automatic method for rapidly collecting macroseismic intensity data from Internet users’ shaking and damage reports and for generating intensity maps immediately following felt earthquakes. DYFI has been in operation for nearly two decades (1999-2019) in the United States, and for nearly 15 years globally. During that period the amount of data collected is astounding: Over 5 million individual DYFI intensity reports—spanning all magnitude and distance ranges—have been amassed and archived. DYFI allows for macroseismic data collection at rates and quantities never before imagined, and thus high-quality macroseismic intensity maps can be made almost immediately, and with more complete coverage at higher resolution than in the past. DYFI also allows for valuable positive interactions of the citizenry with a Federal science agency. In essence, the widespread adoption of DYFI—along with ShakeMap—has facilitated the general acceptance of the very concept of shaking intensity, fundamentally improving our agency’s ability to communicate both hazard and risk to the population. DYFI effectively confirms the importance of reporting and inculcating the public’s understanding of intensity—in addition to magnitude—for a proper perspective of earthquake risk-related decision-making. Furthermore, the vast amount of DYFI data allow for data-rich analyses of otherwise intractable seismological, sociological, and earthquake impact studies that range from quantifying the shaking due to induced earthquakes, to human response and risk perception, to relating recorded shaking metrics to macroseismic effects, and the attenuation of intensity with magnitude and distance. Naturally, web-based data collection also poses challenges. After two decades of experience acquiring data with the DYFI system, we address some of these challenges by documenting refinements to our algorithmic and operational procedures that have evolved over that time. Lastly, we outline new opportune research and development directions for our DYFI approach to citizen seismology.

The Inter-American Development Bank (IDB) has developed financial risk management strategies for natural disasters focusing primarily on the emergency phase of the catastrophes where financial support is more cost-efficient and certainly most needed. The main IDB financial instrument to provide liquidity in the aftermath of catastrophic events is the Contingent Credit Facility (CCF). The CCF is a parametric financial insurance product that makes payments upon the occurrence of events of specific characteristics previously defined with the country. Specifically, in the case of earthquake coverage, the USGS and IDB have been collaborating together in order to improve the trigger design of the loans. CCF is now based on parametric triggers that correlate the magnitude, intensity, and population exposure of the event with the payments. This chapter presents the IDB journey to develop this state-of-the-art parametric index for CCF earthquakes pay offs.

Earthquake‐triggered landslides are a significant hazard in seismically active regions, but our ability to assess the hazard they pose in near real‐time is limited. In this study, we present a new globally applicable model for seismically induced landslides based on the most comprehensive global dataset available; we use 23 landslide inventories that span a range of earthquake magnitudes and climatic and tectonic settings. We use logistic regression to relate the presence and distribution of earthquake‐triggered landslides with spatially distributed estimates of ground shaking, topographic slope, lithology, land cover type, and a topographic index designed to estimate variability in soil wetness to provide an empirical model of landslide distribution. We tested over 100 combinations of independent predictor variables to find the best‐fitting model, using a diverse set of statistical tests. Blind validation tests show the model accurately estimates the distribution of available landslide inventories. The results indicate that the model is reliable and stable, with high “balanced accuracy” (correctly vs. incorrectly classified pixels) for the majority of test events. A cross validation analysis shows high balanced accuracy for a majority of events as well. By combining near‐real time estimates of ground shaking with globally available landslide susceptibility data, this model provides a tool to estimate the distribution of coseismic landslide hazard within minutes of the occurrence of any earthquake worldwide for which a U.S. Geological Survey ShakeMap is available.

The impressive number of stream gauges in Chile, combined with a suite of past and recent large earthquakes, makes Chile a unique natural laboratory to study several streams that recorded responses to multiple seismic events. We document changes in discharge in eight streams in Chile following two or more large earthquakes. In all cases, discharge increases. Changes in discharge occur for peak ground velocities greater than about 7-11cm/s. Above that threshold, the magnitude of both the increase in discharge and the total excess water do not increase with increasing peak ground velocities. While these observations are consistent with previous work in California, they conflict with lab experiments that show that the magnitude of permeability changes increases with increasing amplitude of ground motion. Instead, our study suggests that streamflow responses are binary. Plain Language Summary Earthquakes deform and shake the surface and the ground below. These changes may affect groundwater flows by increasing the permeability along newly formed cracks and/or clearing clogged pores. As a result, groundwater flow may substantially increase after earthquakes and remain elevated for several months. Here we document streamflow anomalies following multiple high magnitude earthquakes in multiple streams in one of the most earthquake prone regions worldwide, Chile. We take advantage of the dense monitoring network in Chile that recorded streamflow since the 1940s. We show that once a critical ground motion is exceeded, streamflow responses to earthquakes can be expected.

In Switzerland, nearly all historical Mw ~ 6 earthquakes have induced damaging landslides, rockslides and snow avalanches that, in some cases, also resulted in damage to infrastructure and loss of lives. We describe the customisation to Swiss conditions of a globally calibrated statistical approach originally developed to rapidly assess earthquake-induced landslide likelihoods worldwide. The probability of occurrence of such earthquake-induced effects is modelled through a set of geospatial susceptibility proxies and peak ground acceleration. The predictive model is tuned to capture the observations from past events and optimised for near-real-time estimates based on USGS-style ShakeMaps routinely produced by the Swiss Seismological Service. Our emphasis is on the use of high-resolution geospatial datasets along with additional local information on ground failure susceptibility. Even if calibrated on historic events with moderate magnitudes, the methodology presented in this paper yields sensible results also for low-magnitude recent events. The model is integrated in the Swiss ShakeMap framework. This study has a high practical relevance to many Swiss ShakeMap stakeholders, especially those managing lifeline systems, and to other global users interested in conducting a similar customisation for their region of interest.

We make and analyze structural damage observations from within the Kathmandu valley following the 2015 M7.8 Gorkha, Nepal earthquake to derive macroseismic intensities at several locations including some located near ground motion recording sites. The macroseismic intensity estimates supplement the limited strong ground motion data in order to characterize the damage statistics. This augmentation allows for direct comparisons between ground motion amplitudes and structural damage characteristics and ultimately produces a more constrained ground shaking hazard map for the Gorkha earthquake. For systematic assessments, we focused on damage to three specific building categories: (a) low/mid-rise reinforced concrete frames with infill brick walls, (b) unreinforced brick masonry bearing walls with reinforced concrete slabs, and (c) unreinforced brick masonry bearing walls with partial timber framing. Evaluating dozens of photos of each construction type, assigning each building in the study sample to a European Macroseismic Scale (EMS)-98 Vulnerability Class based upon its structural characteristics, and then individually assigning an EMS-98 Damage Grade to each building allows a statistically derived estimate of macroseismic intensity for each of nine study areas in and around the Kathmandu valley. This analysis concludes that EMS-98 macroseismic intensities for the study areas from the Gorkha mainshock typically were in the VII–IX range. The intensity assignment process described is more rigorous than the informal approach of assigning intensities based upon anecdotal media or first-person accounts of felt-reports, shaking, and their interpretation of damage. Detailed EMS-98 macroseismic assessments in urban areas are critical for quantifying relations between shaking and damage as well as for calibrating loss estimates. We show that the macroseismic assignments made herein result in fatality estimates consistent with the overall and district-wide reported values.

Modeling the spatial correlation of ground motion residuals, caused by coherent contributions from source, path, and site, can provide valuable loss and hazard information, as well as a more realistic depiction of ground motion intensities. The U.S. Geological Survey (USGS) software package, ShakeMap, utilizes a deterministic empirical approach to estimate median ground shaking in conjunction with observed seismic data. ShakeMap-based shaking estimates are used in concert with loss estimation algorithms to estimate fatalities and economic losses after significant seismic events around the globe. Incorporating the spatial correlation of ground motion residuals has been shown to improve seismic loss estimates. In particular, Park, Bazzuro, and Baker (Applications of Statistics and Probability in Civil Engineering, 2007) investigated computing spatially correlated random fields of residuals. However, for large scale ShakeMap grids, computational requirements of the method are prohibitive. In this work, a memory efficient algorithm is developed to compute the random fields and implemented using the ShakeMap framework. This new, iterative parallel algorithm is based on decay properties of an associated ground motion correlation function and is shown to significantly reduce computational requirements associated with adding spatial variability to the ShakeMap ground motion estimates. Further, we demonstrate and quantify the impact of adding peak ground motion spatial variability on resulting earthquake loss estimates. HighlightsSuccessive computations increase the efficiency of computing correlated random fields.The use of parallel processes significantly reduces CPU time.Method reduces memory constraints and computes realizations with near-linear speedup.Method is suitable for large ShakeMap grids and can condition upon station data.Realizations are used to compute valuable seismic loss distributions.

Methods that account for site response range in complexity from simple linear categorical adjustment factors to sophisticated nonlinear constitutive models. Seismic‐hazard analysis usually relies on ground‐motion prediction equations (GMPEs); within this framework site response is modeled statistically with simplified site parameters that include the time‐averaged shear‐wave velocity to 30 m ( V S 30 ) and basin depth parameters. Because V S 30 is not known in most locations, it must be interpolated or inferred through secondary information such as geology or topography. In this article, we analyze a subset of stations for which V S 30 has been measured to address effects of V S 30 proxies on the uncertainty in the ground motions as modeled by GMPEs. The stations we analyze also include multiple recordings, which allow us to compute the repeatable site effects (or empirical amplification factors [EAFs]) from the ground motions. Although all methods exhibit similar bias, the proxy methods only reduce the ground‐motion standard deviations at long periods when compared to GMPEs without a site term, whereas measured V S 30 values reduce the standard deviations at all periods. The standard deviation of the ground motions are much lower when the EAFs are used, indicating that future refinements of the site term in GMPEs have the potential to substantially reduce the overall uncertainty in the prediction of ground motions by GMPEs.

Earthquake response and related information products are important for placing recent seismic events into context and particularly for understanding the impact earthquakes can have on the regional community and its infrastructure. These tools are even more useful if they are available quickly, ahead of detailed information from the areas affected by such earthquakes. Here we provide an overview of the response activities and related information products generated and provided by the U.S. Geological Survey National Earthquake Information Center in association with the 2015 M 7.8 Gorkha, Nepal, earthquake. This group monitors global earthquakes 24 hrs/day and 7 days/week to provide rapid information on the location and size of recent events and to characterize the source properties, tectonic setting, and potential fatalities and economic losses associated with significant earthquakes. We present the timeline over which these products became available, discuss what they tell us about the seismotectonics of the Gorkha earthquake and its aftershocks, and examine how their information is used today, and might be used in the future, to help mitigate the impact of such natural disasters.

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 analyzed the regional dependence of ground motion to intensity conversion equations and derived a new global relationship to improve ground motion and intensity estimates for earthquake hazard applications, including those related to the ShakeMap system. For this purpose, we merged several databases collected by other authors in different geographical regions to highlight any systematic regional effects in the relationship between macroseismic intensities and both peak ground velocity and peak ground acceleration. Our database contains macroseismic intensities derived from expert assignments or from the “Did You Feel It?” database, paired with peak ground motions (PGM) from seismic stations. We constrain our intensity–ground‐motion pairs to those with a maximum 2 km separation. For each region, we derived invertible relationships between intensities and ground motion using an orthogonal regression. We also derived a global relationship to quantify the regional differences. We investigated the dependence of intensity on predictor variables such as PGM, magnitude, and hypocentral distance. Our analyses indicate that PGM is the most robust predictor variable of intensity. Within one standard deviation, our regional and global results are in agreement with the relations of Worden et al. (2012) for California, Faenza and Michelini (2010) for Italy, Tselentis and Danciu (2008) for Greece, and Atkinson and Kaka (2007) for central–eastern United States. The earthquakes in the study ranged in magnitude from 2.5 to 7.3, and the distances ranged from less than a kilometer to about 200 km from the epicenter. Online Material: Table summarizing published relationships and figures showing the relationship between peak ground acceleration and intensity.

The M w 6.0 South Napa earthquake, which occurred at 10:20 UTC 24 August 2014 was the largest earthquake to strike the greater San Francisco Bay area since the M w 6.9 1989 Loma Prieta earthquake. The rupture from this right‐lateral earthquake propagated mostly unilaterally to the north and up‐dip, directing the strongest shaking toward the city of Napa, where peak ground accelerations (PGAs) between 45% g and 61% g were recorded and modified Mercalli intensities (MMIs) of VII–VIII were reported. Tectonic surface rupture with dextral slip of up to 46 cm was observed on a 12.5 km long segment, some of which was along a previously mapped strand of the West Napa fault system, although the rupture extended to the north of the mapped Quaternary strand. Modeling of seismic and geodetic data suggests an average coseismic slip of 50 cm, with a maximum slip of about 1 m at depths of 10–11 km. We observed up to 35 cm of afterslip along the surface trace in the week following the mainshock, primarily along the southern half of the surface rupture that experienced relatively little coseismic offset. Relocation of the sparse aftershock sequence suggests en echelon southwest‐ and northeast‐dipping fault planes, reflective of the complex fault geometry in this region. The Napa basin and historic and late Holocene alluvial flood deposits in downtown Napa amplified the ground motions there. Few ground failures were mapped, reflecting the dry season (as well as a persistent drought that had lowered the groundwater table) and the short duration of strong shaking in the epicentral area. The South Napa fault rupture lies within an 80 km wide set of major north‐northwest‐trending faults of the San Andreas fault system, forming the boundary between the Pacific and North American tectonic …

Equations that predict intensity as a function of magnitude and distance are useful tools for hazard and risk assessment, and in interpretation of both contemporary and historical earthquake information. The intensity prediction equations of Atkinson and Wald (2007; hereafter AW07) have been remarkably successful in describing the level and intensity of motions reported under the “Did You Feel It?” (DYFI) program over the last several years. Examination of the performance of AW07 for North American earthquakes, evaluated using an extensive compiled database of DYFI observations from 2000 to 2013, suggests that there is little statistical basis for revising these equations. However, a problem with the AW07 equations is that they predict unrealistically large median intensities for large events ( M >6) at close distances. In this study, we revise AW07 to improve the intensity scaling at large magnitudes and close distances, by reconciling intensity equations with ground‐motion prediction equations.

For many earthquake engineering applications, site response is estimated through empirical correlations with the time‐averaged shear‐wave velocity to 30 m depth ( V S 30 ). These applications therefore depend on the availability of either site‐specific V S 30 measurements or V S 30 maps at local, regional, and global scales. Because V S 30 measurements are sparse, a proxy frequently is needed to estimate V S 30 at unsampled locations. We present a new V S 30 map for California, which accounts for observational constraints from multiple sources and spatial scales, such as geology, topography, and site‐specific V S 30 measurements. We apply the geostatistical approach of regression kriging (RK) to combine these constraints for predicting V S 30 . For the V S 30 trend, we start with geology‐based V S 30 values and identify two distinct trends between topographic gradient and the residuals from the geology V S 30 model. One trend applies to deep and fine Quaternary alluvium, whereas the second trend is slightly stronger and applies to Pleistocene sedimentary units. The RK framework ensures that the resulting map of California is locally refined to reflect the rapidly expanding database of V S 30 measurements throughout California. We compare the accuracy of the new mapping method to a previously developed map of V S 30 for California. We also illustrate the sensitivity of ground motions to the new V S 30 map by comparing real and scenario ShakeMaps with V S 30 values from our new map to those for existing V S 30 maps. Online Material: California statewide V S 30 and ShakeMaps for Loma Prieta.