Loss Estimates for a Puente Hills Blind-Thrust Earthquake in Los Angeles, California

2005 ◽  
Vol 21 (2) ◽  
pp. 329-338 ◽  
Author(s):  
Edward H. Field ◽  
Hope A. Seligson ◽  
Nitin Gupta ◽  
Vipin Gupta ◽  
Thomas H. Jordan ◽  
...  
Keyword(s):  
Los Angeles ◽  
Ground Motion ◽  
Epistemic Uncertainty ◽  
Rare Event ◽  
Significant Risk ◽  
Economic Loss ◽  
Fault Rupture ◽  
Motion Models ◽  
Ground Motion Model ◽  
Blind Thrust

Based on OpenSHA and HAZUS-MH, we present loss estimates for an earthquake rupture on the recently identified Puente Hills blind-thrust fault beneath Los Angeles. Given a range of possible magnitudes and ground motion models, and presuming a full fault rupture, we estimate the total economic loss to be between $82 and $252 billion. This range is not only considerably higher than a previous estimate of $69 billion, but also implies the event would be the costliest disaster in U.S. history. The analysis has also provided the following predictions: 3,000–18,000 fatalities, 142,000–735,000 displaced households, 42,000–211,000 in need of short-term public shelter, and 30,000–99,000 tons of debris generated. Finally, we show that the choice of ground motion model can be more influential than the earthquake magnitude, and that reducing this epistemic uncertainty (e.g., via model improvement and/or rejection) could reduce the uncertainty of the loss estimates by up to a factor of two. We note that a full Puente Hills fault rupture is a rare event (once every ∼3,000 years), and that other seismic sources pose significant risk as well.

scholarly journals Local and Moment Magnitude Analysis in the Ridgecrest Region, California: Impact on Interevent Ground-Motion Variability

2020 ◽  
Author(s):  
Dino Bindi ◽  
Riccardo Zaccarelli ◽  
Sreeram Reddy Kotha
Keyword(s):  
Ground Motion ◽  
Stress Drop ◽  
Ad Hoc ◽  
Epistemic Uncertainty ◽  
Earthquake Catalog ◽  
Motion Models ◽  
Ground Motion Model ◽  
Ground Motion Variability ◽  
Source Models ◽  
The Impact

ABSTRACT We investigate the dependence of event-specific ground-motion residuals in the Ridgecrest region, California. We focus on the impact of using either local (ML) or moment (Mw) magnitude, for describing the source scaling of a regional ground-motion model. To analyze homogeneous Mw, we compute the source spectra of about 2000 earthquakes in the magnitude range 2.5–7.1, by performing a nonparametric spectral decomposition. Seismic moments and corner frequencies are derived from the best-fit ω−2 source models, and stress drop is computed assuming standard circular rupture model. The Brune stress drop varies between 0.62 and 24.63 MPa (with median equal to 3.0 MPa), and values for Mw>5 are mostly distributed above the 90th percentile. The median scaled energy for Mw<5 is −4.57, and the low values obtained for the Mw 6.4 and 7.1 mainshocks (−5 and −5.2, respectively) agree with previous studies. We calibrate an ad hoc nonparametric ML scale for the Ridgecrest region. The main differences with the standard ML scale for California are observed at distances between 30 and 100 km, in which differences up to 0.4 magnitude units are obtained. Finally, we calibrate ground-motion models for the Fourier amplitude spectra, considering the ML and Mw scales derived in this study and the magnitudes extracted from Comprehensive Earthquake Catalog. The analysis of the residuals shows that ML better describes the interevent variability above 2 Hz. At intermediate frequencies (between about 3 and 8 Hz), the interevent residuals for the model based on Mw show a correlation with stress drop: this correlation disappears, when ML is used. The choice of the magnitude scale has an impact also on the statistical uncertainty of the median model: for any fixed magnitude value, the epistemic uncertainty is larger for ML below 1.5 Hz and larger for Mw above 1.5 Hz.

Framework for a Ground-Motion Model for Induced Seismic Hazard and Risk Analysis in the Groningen Gas Field, The Netherlands

2017 ◽  
Vol 33 (2) ◽  
pp. 481-498 ◽  
Author(s):  
Julian J. Bommer ◽  
Peter J. Stafford ◽  
Benjamin Edwards ◽  
Bernard Dost ◽  
Ewoud van Dedem ◽  
...  
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Quantitative Estimation ◽  
Epistemic Uncertainty ◽  
Induced Earthquakes ◽  
Gas Field ◽  
Small Magnitude ◽  
Ground Motion Model ◽  
Hazard And Risk ◽  
Groningen Gas Field

The potential for building damage and personal injury due to induced earthquakes in the Groningen gas field is being modeled in order to inform risk management decisions. To facilitate the quantitative estimation of the induced seismic hazard and risk, a ground motion prediction model has been developed for response spectral accelerations and duration due to these earthquakes that originate within the reservoir at 3 km depth. The model is consistent with the motions recorded from small-magnitude events and captures the epistemic uncertainty associated with extrapolation to larger magnitudes. In order to reflect the conditions in the field, the model first predicts accelerations at a rock horizon some 800 m below the surface and then convolves these motions with frequency-dependent nonlinear amplification factors assigned to zones across the study area. The variability of the ground motions is modeled in all of its constituent parts at the rock and surface levels.

A Suite of Alternative Ground-Motion Models (GMMs) for Israel

2021 ◽  
Author(s):  
Soumya Kanti Maiti ◽  
Gony Yagoda-Biran ◽  
Ronnie Kamai
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Empirical Data ◽  
Epistemic Uncertainty ◽  
Ground Motions ◽  
Plate Boundary ◽  
Strong Motion ◽  
Engineering Applications ◽  
Motion Models ◽  
Ground Motion Models

ABSTRACT Models for estimating earthquake ground motions are a key component in seismic hazard analysis. In data-rich regions, these models are mostly empirical, relying on the ever-increasing ground-motion databases. However, in areas in which strong-motion data are scarce, other approaches for ground-motion estimates are sought, including, but not limited to, the use of simulations to replace empirical data. In Israel, despite a clear seismic hazard posed by the active plate boundary on its eastern border, the instrumental record is sparse and poor, leading to the use of global models for hazard estimation in the building code and all other engineering applications. In this study, we develop a suite of alternative ground-motion models for Israel, based on an empirical database from Israel as well as on four data-calibrated synthetic databases. Two host models are used to constrain model behavior, such that the epistemic uncertainty is captured and characterized. Despite the lack of empirical data at large magnitudes and short distances, constraints based on the host models or on the physical grounds provided by simulations ensure these models are appropriate for engineering applications. The models presented herein are cast in terms of the Fourier amplitude spectra, which is a linear, physical representation of ground motions. The models are suitable for shallow crustal earthquakes; they include an estimate of the median and the aleatory variability, and are applicable in the magnitude range of 3–8 and distance range of 1–300 km.

Relational database used for ground-motion model development in the NGA-sub project

2021 ◽  
pp. 875529302110552
Author(s):  
Silvia Mazzoni ◽  
Tadahiro Kishida ◽  
Jonathan P Stewart ◽  
Victor Contreras ◽  
Robert B Darragh ◽  
...  
Keyword(s):  
Subduction Zone ◽  
Ground Motion ◽  
Data Storage ◽  
Relational Database ◽  
Model Development ◽  
Intensity Measures ◽  
Motion Models ◽  
Ground Motion Model ◽  
Ground Motion Models ◽  
Pseudo Spectral

The Next-Generation Attenuation for subduction zone regions project (NGA-Sub) has developed data resources and ground motion models for global subduction zone regions. Here we describe the NGA-Sub database. To optimize the efficiency of data storage, access, and updating, data resources for the NGA-Sub project are organized into a relational database consisting of 20 tables containing data, metadata, and computed quantities (e.g. intensity measures, distances). A database schema relates fields in tables to each other through a series of primary and foreign keys. Model developers and other users mostly interact with the data through a flatfile generated as a time-stamped output of the database. We describe the structure of the relational database, the ground motions compiled for the project, and the means by which the data can be accessed. The database contains 71,340 three-component records from 1880 earthquakes from seven global subduction zone regions: Alaska, Central America and Mexico, Cascadia, Japan, New Zealand, South America, and Taiwan. These data were processed on a component-specific basis to minimize noise effects in the data and remove baseline drifts. Provided ground motion intensity measures include peak acceleration, peak velocity, and 5%-damped pseudo-spectral accelerations for a range of oscillator periods.

scholarly journals A Non-Ergodic Effective Amplitude Ground-Motion Model for California

2021 ◽  
Author(s):  
Grigorios Lavrentiadis ◽  
Norman A. Abrahamson ◽  
Nicolas M. Kuehn
Keyword(s):  
Standard Deviation ◽  
Ground Motion ◽  
Epistemic Uncertainty ◽  
Amplitude Spectrum ◽  
Time History ◽  
Motion Model ◽  
Small Magnitude ◽  
Varying Coefficients ◽  
Ground Motion Model ◽  
Systematic Effects

Abstract A new non-ergodic ground-motion model (GMM) for effective amplitude spectral (EAS) values for California is presented in this study. EAS, which is defined in Goulet et al. (2018), is a smoothed rotation-independent Fourier amplitude spectrum of the two horizontal components of an acceleration time history. The main motivation for developing a non-ergodic EAS GMM, rather than a spectral acceleration GMM, is that the scaling of EAS does not depend on spectral shape, and therefore, the more frequent small magnitude events can be used in the estimation of the non-ergodic terms. The model is developed using the California subset of the NGAWest2 dataset Ancheta et al. (2013). The Bayless and Abrahamson (2019b) (BA18) ergodic EAS GMM was used as backbone to constrain the average source, path, and site scaling. The non-ergodic GMM is formulated as a Bayesian hierarchical model: the non-ergodic source and site terms are modeled as spatially varying coefficients following the approach of Landwehr et al. (2016), and the non-ergodic path effects are captured by the cell-specific anelastic attenuation attenuation following the approach of Dawood and Rodriguez-Marek (2013). Close to stations and past events, the mean values of the non-ergodic terms deviate from zero to capture the systematic effects and their epistemic uncertainty is small. In areas with sparse data, the epistemic uncertainty of the non-ergodic terms is large, as the systematic effects cannot be determined. The non-ergodic total aleatory standard deviation is approximately 30 to 40% smaller than the total aleatory standard deviation of BA18. This reduction in the aleatory variability has a significant impact on hazard calculations at large return periods. The epistemic uncertainty of the ground motion predictions is small in areas close to stations and past event.

NGA-Subduction research program

2021 ◽  
pp. 875529302110560
Author(s):  
Yousef Bozorgnia ◽  
Norman A Abrahamson ◽  
Sean K Ahdi ◽  
Timothy D Ancheta ◽  
Linda Al Atik ◽  
...  
Keyword(s):  
Ground Motion ◽  
Research Program ◽  
Epistemic Uncertainty ◽  
Specific Model ◽  
Peak Ground Velocity ◽  
Ground Acceleration ◽  
Motion Data ◽  
Motion Models ◽  
Aleatory Variability ◽  
Adjustment Factors

This article summarizes the Next Generation Attenuation (NGA) Subduction (NGA-Sub) project, a major research program to develop a database and ground motion models (GMMs) for subduction regions. A comprehensive database of subduction earthquakes recorded worldwide was developed. The database includes a total of 214,020 individual records from 1,880 subduction events, which is by far the largest database of all the NGA programs. As part of the NGA-Sub program, four GMMs were developed. Three of them are global subduction GMMs with adjustment factors for up to seven worldwide regions: Alaska, Cascadia, Central America and Mexico, Japan, New Zealand, South America, and Taiwan. The fourth GMM is a new Japan-specific model. The GMMs provide median predictions, and the associated aleatory variability, of RotD50 horizontal components of peak ground acceleration, peak ground velocity, and 5%-damped pseudo-spectral acceleration (PSA) at oscillator periods ranging from 0.01 to 10 s. Three GMMs also quantified “within-model” epistemic uncertainty of the median prediction, which is important in regions with sparse ground motion data, such as Cascadia. In addition, a damping scaling model was developed to scale the predicted 5%-damped PSA of horizontal components to other damping ratios ranging from 0.5% to 30%. The NGA-Sub flatfile, which was used for the development of the NGA-Sub GMMs, and the NGA-Sub GMMs coded on various software platforms, have been posted for public use.

Efficient Propagation of Epistemic Uncertainty in the Median Ground‐Motion Model in Probabilistic Hazard Calculations

2019 ◽  
Vol 109 (5) ◽  
pp. 2063-2072 ◽  
Author(s):  
Maxime Lacour ◽  
Norman A. Abrahamson
Keyword(s):  
Ground Motion ◽  
Traditional Approach ◽  
Epistemic Uncertainty ◽  
Hazard Analysis ◽  
Full Range ◽  
Motion Model ◽  
Uncertainty Distribution ◽  
Earthquake Scenarios ◽  
Ground Motion Model ◽  
Pc Method

Abstract A computationally efficient methodology for propagating the epistemic uncertainty in the median ground motion in probabilistic seismic hazard analysis is developed using the polynomial chaos (PC) approach. For this application, the epistemic uncertainty in the median ground motion for a specific scenario is assumed to be lognormally distributed and fully correlated across earthquake scenarios. In the hazard calculation, a single central ground‐motion model (GMM) is used for the median along with the epistemic standard error of the median for each scenario. A set of PC coefficients is computed for each scenario and each test ground‐motion level. The additional computation burden in computing these PC coefficients depends on the order of the approximation but is less than computing the median ground motion from one additional GMM. With the PC method, the mean and fractiles of the hazard due to the epistemic uncertainty distribution of the median ground motion are computed as a postprocess that is very fast computationally. For typical values of the standard deviation of epistemic uncertainty in the median ground motion (<0.2 natural log units), the methodology accurately estimates the epistemic uncertainty distribution of the hazard over the 1%–99% range. This full epistemic range is not well modeled with just a small number of GMM branches uses in the traditional logic‐tree approach. The PC method provides more accuracy, faster computation, and reduced memory requirements than the traditional approach. For large values of the epistemic uncertainty in the median ground motion, a higher order of the PC expansion may be needed to be included to capture the full range of the epistemic uncertainty.

NESS2.0: An Updated Version of the Worldwide Dataset for Calibrating and Adjusting Ground-Motion Models in Near Source

2021 ◽  
Author(s):  
Sara Sgobba ◽  
Chiara Felicetta ◽  
Giovanni Lanzano ◽  
Fadel Ramadan ◽  
Maria D’Amico ◽  
...  
Keyword(s):  
Ground Motion ◽  
Reference Model ◽  
Hanging Wall ◽  
Strong Motion ◽  
Flat File ◽  
Intensity Measures ◽  
Processing Scheme ◽  
Epicentral Area ◽  
Motion Models ◽  
Ground Motion Model

ABSTRACT We present an extended and updated version of the worldwide NEar-Source Strong-motion (NESS) flat file, which includes an increased number of moderate-to-strong earthquakes recorded in epicentral area, new source metadata and intensity measures, comprising spectral displacements and fling-step amplitudes retrieved from the extended baseline correction processing of velocity time series. The resulting dataset consists of 81 events with moment magnitude≥5.5 and hypocentral depth shallower than 40 km, corresponding to 1189 three-component waveforms, which are selected to have a maximum source-to-site distance within one fault length. Details on the flat files, metadata, and ground-motion parameters, processing scheme, and statistical findings are presented and discussed. The analysis of these data allows recognizing the presence of distinctive features (such as pulse-like waveforms, large vertical components, and hanging-wall effects) that can be exploited to assess their impact on near-source seismic motion. As an example, we use the NESS2.0 dataset for calibrating an empirical correction factor of a regional ground-motion model (GMM) mainly based on far-field records. In this way, we can adjust the median predictions to account for near-source effects not fully captured by the reference model. The final goal of this work is to promote the use of the NESS2 flat file as a tool to disseminate qualified and referenced near-source data and metadata in the light of improving the constraints of GMMs (both empirical and physics-based) close to the source.

Residual analysis of large strong-motion flatfiles as a tool for detecting data error and anomalies

2021 ◽  
Author(s):  
Claudia Mascandola ◽  
Giovanni Lanzano ◽  
Francesca Pacor
Keyword(s):  
Ground Motion ◽  
Site Response ◽  
Epistemic Uncertainty ◽  
Strong Motion ◽  
Consistency Check ◽  
Mixed Effect ◽  
Motion Models ◽  
Tree Classifier ◽  
The Common ◽  
Ground Motion Models

&lt;p&gt;The rapid increase of seismic waveforms, due to the increment of seismic stations and continuous real-time streaming to data centres, leads to the need for automatic procedures aimed at supporting data processing and data quality control. In this study, we propose a semi-automatic procedure for the consistency check of large strong-motion datasets, classifying the anomalies observed on the residuals analysis and identifying the possible causes.&lt;/p&gt;&lt;p&gt;The data collected in the strong-motion databases are usually arranged as parametric tables (called flatfiles), used to disseminate the Intensity Measures (IMs) and the associated metadata of the processed waveforms. This is the current practice for the ITalian ACcelerometric Archive (ITACA, D&amp;#8217;Amico et al., 2020) and Engineering Strong Motion (ESM; Lanzano et al. 2019a) databases. The adopted criteria for flatfile compilation are designed to collect IMs and related metadata in a uniform, updated, and traceable way, with the aim of providing datasets useful to develop Ground Motion Models (GMMs) for Probabilistic Seismic Hazard Assessment (PSHA) and engineering applications. Therefore, the consistency check of the flatfiles is a crucial task to improve the quality of the products provided by the waveform services.&lt;/p&gt;&lt;p&gt;The proposed procedure is based on the residual distributions obtained from ad-hoc ground motion prediction equations for the ordinates of the 5% damped acceleration response spectra. In this study, we focus on the active shallow crust events in ITACA, considering the ITA18 ground motion model (Lanzano et al., 2019b) as a reference for Italy. The total residuals, computed as logarithm difference between observations and predictions, are decomposed in between-event, between-station and event-and-station corrected residuals by applying a mixed-effect regression (Bates et al., 2015). This is the common practice for the (partial) removal of the ergodic assumption in empirical GMMs (e.g., Stafford 2014), where the contribution of the systematic corrective effects of event and station on aleatory variability are identified and shifted to the epistemic uncertainty. Afterward, the proposed procedure is applied to raise a warning in case of anomalous residual values. Warnings are provided when the normalized residuals exceed a certain threshold, in three ranges of periods (i.e., 0.01-0.15 s, 0.15-1 s, 1-5 s). The causes of warnings may be several and may concern the event, the site, the waveform, or a combination of them. Among the possible sources of anomalous trends, the more common are: preliminary or inaccurate event localization or magnitude, wrong soil category assigned based on proxies, misleading tectonic regime assigned to the earthquake, and fault directivity that may cause strong-ground motion amplification in certain directions. Warnings may also raise for peculiarities in the site-response (e.g., large amplifications/de-amplifications at certain frequency-bands) and to the occurrence of near-source effects in the waveforms (see Pacor et al., 2018). Based on the raised warnings, a decision tree classifier is developed to identify the common anomaly sources and to support the consistency check of the semi-automatic procedure.&lt;/p&gt;&lt;p&gt;This study may help to enhance the waveform services and related products, besides reducing the variability of ground motion models and guiding decisions for site characterization studies and network maintenance.&lt;/p&gt;

Evaluation of Ground-Motion Models for USGS Seismic Hazard Models Using Near-Source Instrumental Ground-Motion Recordings of the Ridgecrest, California, Earthquake Sequence

2020 ◽  
Vol 110 (4) ◽  
pp. 1517-1529
Author(s):  
Daniel E. McNamara ◽  
Emily L. G. Wolin ◽  
Morgan P. Moschetti ◽  
Eric M. Thompson ◽  
Peter M. Powers ◽  
...  
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Southern California ◽  
Epistemic Uncertainty ◽  
Site Amplification ◽  
Earthquake Sequence ◽  
Scoring Method ◽  
Motion Models ◽  
Tectonically Active ◽  
Ground Motion Models

ABSTRACT We evaluated the performance of 12 ground-motion models (GMMs) for earthquakes in the tectonically active shallow crustal region of southern California using instrumental ground-motion observations from the 2019 Ridgecrest, California, earthquake sequence (Mw 4.0–7.1). The sequence was well recorded by the Southern California Seismic Network and rapid response portable aftershock monitoring stations. Ground-motion recordings of this size and proximity are rare, valuable, and independent of GMM development, allowing us to evaluate the predictive powers of GMMs. We first compute total residuals and compare the probability density functions, means, and standard deviations of the observed and predicted ground motions. Next we use the total residuals as inputs to the probabilistic scoring method (log-likelihood [LLH]). The LLH method provides a single score that can be used to weight GMMs in the U.S. Geological Survey (USGS) National Seismic Hazard Model (NSHM) logic trees. We also explore GMM performance for a range of earthquake magnitudes, wave propagation distances, and site characteristics. We find that the Next Generation Attenuation West-2 (NGAW2) active crust GMMs perform well for the 2019 Ridgecrest, California, earthquake sequence and thus validate their use in the 2018 USGS NSHM. However, significant ground-motion residual scatter remains unmodeled by NGAW2 GMMs due to complexities such as local site amplification and source directivity. Results from this study will inform logic-tree weights for updates to the USGS National NSHM. Results from this study support the use of nonergodic GMMs that can account for regional attenuation and site variations to minimize epistemic uncertainty in USGS NSHMs.

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