Geology topics
David J. Wald
Publications and source records attributed to David J. Wald.
Integrated strategies for enhanced rapid earthquake shaking, ground failure, and impact estimation employing remotely sensed and ground truth constraints
Estimating earthquake impacts using physical or empirical models is challenging because the three components of loss estimation-shaking, exposure, and vulnerabilities-entail inherent uncertainties. Loss modeling in near-real-time adds additional uncertainties, yet expectations for actionable information with a reasonable level of confidence in the results are real. The modeling approaches described herein augment inherently uncertain prior hazard and loss models with an integrated strategy for updating these priors with ground-truth observations, thereby greatly reducing their uncertainties. Two strategies are employed. Early reports of casualties are used in a Bayesian updating fashion to constrain the possible range of fatalities and to lower the prior models' uncertainties. Additionally, remotely sensed satellite radar data, in the form of a Damage Proxy Map (or DPM), are used in a Bayesian causal graph framework combined with machine learning to optimize the mapping among the physical processes that cause shaking-based building damage, landslides, and liquefaction to prior expectation models. The casual graph framework also affords the potential for removing anthropogenetic noise contained in the imagery. Ultimately, our two-fold model updating strategy will accommodate key ground-truth observations such as fatality reports, locations of building damage, and ground failure reports to converge on actual losses more rapidly.
Applying consequence-driven scenario selection to lifelines
We present a new consequence-driven framework for earthquake scenario selection. For emergency managers, utility operators, policy makers, and other stakeholders, a scenario-based seismic risk assessment is often necessary for the purpose of emergency management and planning. In developing a scientifically defensible scenario, stakeholders can simulate a realistic event in order to pre-identify vulnerabilities in the system and support action to address these vulnerabilities. Selecting scenarios is particularly challenging for important population centers and critical infrastructure in stable tectonic environments, such as in the central and eastern United States, where uncertain long-term seismicity and unknown faults offer inadequate constraints. Notably, significant events in these so-called stable regions do occur (e.g., Nahanni, Canada, 1985, M6.9; Tennant Creek, Australia, 1998, M6.7). In regions of low seismicity, even moderate events can be consequential due to the higher vulnerability of buildings typical of such regions when compared to regions of higher seismicity. Furthermore, communicating seismic risk to stakeholders and the general public in these regions can be especially challenging due to the complexities of characterizing the hazard level. This framework has been developed to address these challenges for scenario selection in low seismic hazard regions. In this new approach, the analysis begins instead with the explicit definition of a consequence of concern to the specific stakeholder. This can range from a definition of loss (in lives, dollars, or another metric of interest), or a performance metric for critical infrastructure. The framework leverages United States Geological Survey software to run the hazard and consequence analysis. Driven by this stakeholder-defined consequence, an inversion analysis generates a complete event set of candidate scenarios that could breach this consequence. The final selection of a scenario, or family of scenarios, is then scientifically informed, but not limited by our lack of constraints in characterizing the hazard.
Comment on “Which earthquake accounts matter” by Susan E. Hough and Stacey S. Martin
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; and Caution 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.
Evaluation of intensity prediction equations (IPEs) for small-magnitude earthquakes
This study assesses existing intensity prediction equations (IPEs) for small unspecified magnitude ( M ≤3.5) earthquakes at short hypocentral distances ( D h ) 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 M w 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 D h < 15 km ). We then compare intensities from 31,617 DYFI reports of 3049 earthquakes with the two IPEs. Results suggest that both the IPEs match well with observed intensities for 2.0< M <3.0 and D h < 10 km , but the IPE by Atkinson et al. (2014) matches better for larger distances. We also observe that intensities from DYFI reports attenuate faster compared with the two IPEs, especially for distances greater than 10 km. We then group DYFI reports by inferred V S 30 as a proxy for site amplification effects. We observe that intensities at sites with V S 30 around 300 m/s are consistently higher than at sites with V S 30 around 700 m/s and are also closer to the two IPEs. Finally, we conduct hazard disaggregation for earthquakes at close distances ( D h = 7.5 km ) using the observed records. Results suggest that earthquakes with magnitudes below M 3.0 contribute more than 40% to the occurrence of felt shaking.
The US Geological Survey ground failure product: Near-real-time estimates of earthquake-triggered landslides and liquefaction
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.
ShakeMap operations, policies, and procedures
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.
Quantifying nuisance ground motion thresholds for induced earthquakes
Hazards from induced earthquakes are a growing concern with a need for effective management. One aspect of that concern is the “nuisance” from unexpected ground motions, which have the potential to cause public alarm and discontent. In this article, we borrow earthquake engineering concepts to quantify the chance of building damage states and adapt them to quantify felt thresholds for induced earthquakes in the Central and Eastern United States. We compare binary data of felt or not-felt reports from the “Did You Feel It” database with ShakeMap ground motion intensity measures (IM) for ∼360 earthquakes. We use a Monte Carlo logistic regression to discern the likelihood of perceiving various degrees of felt intensity, given a particular IM. These best-fit nuisance functions are reported in this article and are readily transferable. Of the shaking types considered, we find that peak ground velocity tends to be the best predictor of a felt earthquake. We also find that felt thresholds tended to decrease with increasing earthquake magnitude, after M ∼3.9. We interpret this effect as related to the duration of the event, where events smaller than M 3.9 are perceived as “impulsive” to the human senses. Improved quantification of the nuisance from induced earthquake ground motions could be utilized in management of the public perception of their causal operations. Although aimed at anthropogenic earthquakes, thresholds we derive could be useful in other realms, such as establishing best practices and protocols for earthquake early warning.
Amateur radio operators help fill earthquake donut holes
If you’ve ever seen tall antennas rising from everyday residences in your community and wondered what they are for, it could be that those homes belong to ham radio enthusiasts who enjoy communicating with each other over the airwaves. In addition to having fun with their radios and finding camaraderie, many ham radio operators are also prepared to help neighbors and authorities communicate during disasters. One such group of radio enthusiasts is poised now to serve yet another important role: They will be contributing to a more robust delivery mechanism for critical seismic intensity reports after major earthquakes through the U.S. Geological Survey’s (USGS) Did You Feel It? (DYFI) system.
Near real-time updating of pager loss estimates
Initial alerts by PAGER (Prompt Assessment of Global Earthquakes for Response) within minutes following an earthquake include several uncertainties, mainly due to potential inaccuracies in location, depth, fault delineation, and shaking estimates. We enhance an updating framework by incorporating early reports of fatalities within the first 24 hours, or so, of an earthquake to update PAGER’s overall fatality estimates and its resulting alert level. Though initial loss reports by officials or the media are uncertain and often undercount the eventual reported impacts, their temporal evolution provides predictive constraints for the PAGER model. The proposed framework helps capture these in a systematic way to minimize potential large fluctuations in PAGER alerts as ShakeMap (the USGS product which estimates how an area is affected by an earthquake) gets updated in the early hours after an earthquake. The new framework also accounts for uncertainties associated with early fatality reports as well as PAGER model-related uncertainties in order to improve the overall impact forecast. This updating framework improves the loss estimate and alert level to the correct level within the first 24 hours even when the initial estimation from PAGER is assumed to be off by two levels of alert, which is plausible due to potential over- or under-estimation of the PAGER model. While test results are very encouraging, our future work aims at implementation of operational PAGER model updating, which entails additional challenges in acquiring useful data, estimating their credibility, and developing rigorously tested operational code and protocols
Bayesian updating of seismic ground failure estimates via causal graphical models and satellite imagery
Earthquake-induced secondary ground failure hazards, such as liquefaction and landslides, result in catastrophic building and infrastructure damage as well as human fatalities. To facilitate emergency responses and mitigate losses, the U.S. Geological Survey provides a rapid hazard estimation system for earthquake-triggered landslides and liquefaction using geospatial susceptibility proxies and ShakeMap ground motion estimates. However, the resolution and accuracy of these models are often limited by coarse-granularity and large uncertainties of available geospatial features provided at a regional scale. Recently, with the advancement of remote sensing technologies, synthetic aperture radar (SAR) images are captured and analyzed to obtain a rapid estimate of earthquake-induced correlation changes between pre- and post-event images. These correlation changes indicate ground failures and building damage t, showing the potential to provide supplementary information for rapid hazard and loss estimation. However, the exact causes of changes in satellite images are not directly ascertained by the DPM alone. For example, changes could be due to building damage, landslides, liquefaction, noise or any combination thereof. More importantly, the occurrence and intensity of landslides, liquefaction, and building damages are spatially correlated, which makes it yet more challenging to distinguish the sources of any such changes. In this study, we develop a generalized causal graph-based Bayesian Network that models the physical interdependencies between geospatial features, seismic ground failures and building damage, as well as DPMs. Geospatial features provide physical insights for estimating ground failure occurrence while DPMs contain event-specific surface change observations. This physics-informed causal graph incorporate these variables with complex physical relationships in one holistic Bayesian updating scheme to effectively fuse information from both geospatial models and remote sensing data. This framework is scalable and flexible enough to deal with highly complex multi-hazard combinations. We then develop a stochastic variational inference algorithm to jointly update the intractable posterior probabilities of unobserved landslides, liquefaction, and building damage at different locations efficiently. In addition, a local graphical model pruning algorithm is presented to reduce the computational cost of large-scale seismic ground failure estimation. We apply this framework to September 2018 Hokkaido Iburi-Tobu, Japan (M6.6) earthquake and January 2020 Southwest Puerto Rico (M6.4) earthquake to evaluate the performance of our algorithm
An efficient Bayesian framework for updating PAGER loss estimates
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.
Financial risk innovation: Development of earthquake parametric triggers for contingent credit instruments
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.
Human behavioral response in the Ridgecrest earthquakes: Assessing immediate actions based on data from “Did You Feel It?”
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.
A global hybrid VS30 map with a topographic slope–based default and regional map insets
Time-averaged shear wave velocity over the upper 30 m of the earth’s surface ( V S 30 ) 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 S 30 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 S 30 maps have been developed in many regions. Such maps preferentially employ various combinations of V S 30 measurements, higher-resolution elevation models, lithologic, geologic, geomorphic, and other proxies and often utilize refined interpolation schemes. We develop a new hybrid global V S 30 map database that defaults to the global slope-based V S 30 map, but smoothly inserts regional V S 30 maps where available. In addition, we present comparisons of the default slope-based proxy maps against the new hybrid version in terms of V S 30 and amplification ratio maps, and uncertainties in assigned V S 30 values.
USGS “Did You Feel It?” — Science and lessons from twenty years of citizen science-based macroseismology
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 hallenges 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.
Practical limitations of Earthquake Early Warning
Earthquake Early Earning (EEW) entails detection of initial earthquake shaking and rapid estimation and notification to users prior to imminent, stronger shaking. EEW is coming to 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 less impressive—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 acknowledgement 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.
Earthquakes, ShakeCast
ShakeCast® – short for ShakeMap Broadcast – is a fully automated software system for delivering specific ShakeMap products to critical users and for triggering established post-earthquake response protocols. ShakeCast is a freely available, postearthquake situational awareness software application that automatically retrieves earthquake shaking data from ShakeMap to compare ground shaking intensity measures against users’ facilities (Lin and Wald 2008). ShakeCast then generates potential damage assessment and inspection priority notifications, maps, and web-based products for critical users, emergency managers, and those on a need-to-know basis.