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.

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.

scholarly journals The new Italian seismic hazard model (MPS19)

2021 ◽  
Vol 64 (1) ◽  
Author(s):  
Carlo Meletti ◽  
Warner Marzocchi ◽  
Vera D'Amico ◽  
Giovanni Lanzano ◽  
Lucia Luzi ◽  
...  
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Epistemic Uncertainty ◽  
Hazard Model ◽  
Ground Acceleration ◽  
Motion Models ◽  
Novel Approach ◽  
Wide Range ◽  
Macroseismic Intensities ◽  
Ground Motion Models

We describe the main structure and outcomes of the new probabilistic seismic hazard model for Italy, MPS19 [Modello di Pericolosità Sismica, 2019]. Besides to outline the probabilistic framework adopted, the multitude of new data that have been made available after the preparation of the previous MPS04, and the set of earthquake rate and ground motion models used, we give particular emphasis to the main novelties of the modeling and the MPS19 outcomes. Specifically, we (i) introduce a novel approach to estimate and to visualize the epistemic uncertainty over the whole country; (ii) assign weights to each model components (earthquake rate and ground motion models) according to a quantitative testing phase and structured experts’ elicitation sessions; (iii) test (retrospectively) the MPS19 outcomes with the horizontal peak ground acceleration observed in the last decades, and the macroseismic intensities of the last centuries; (iv) introduce a pioneering approach to build MPS19_cluster, which accounts for the effect of earthquakes that have been removed by declustering. Finally, to make the interpretation of MPS19 outcomes easier for a wide range of possible stakeholders, we represent the final result also in terms of probability to exceed 0.15 g in 50 years.

Evaluation of Ground‐Motion Models for U.S. Geological Survey Seismic Hazard Models: 2018 Anchorage, Alaska, Mw 7.1 Subduction Zone Earthquake Sequence

2019 ◽  
Vol 91 (1) ◽  
pp. 183-194 ◽  
Author(s):  
Daniel E. McNamara ◽  
Emily Wolin ◽  
Peter M. Powers ◽  
Alison M. Shumway ◽  
Morgan P. Moschetti ◽  
...  
Keyword(s):  
Seismic Hazard ◽  
Subduction Zone ◽  
Ground Motion ◽  
Hazard Model ◽  
Earthquake Sequence ◽  
Geological Survey ◽  
Hazard Models ◽  
Data Set ◽  
Motion Models ◽  
Ground Motion Models

Abstract Instrumental ground‐motion recordings from the 2018 Anchorage, Alaska (Mw 7.1), earthquake sequence provide an independent data set allowing us to evaluate the predictive power of ground‐motion models (GMMs) for intraslab earthquakes associated with the Alaska subduction zone. In this study, we evaluate 15 candidate GMMs using instrumental ground‐motion observations of peak ground acceleration and 5% damped pseudospectral acceleration (0.02–10 s) to inform logic‐tree weights for the update of the U.S. Geological Survey seismic hazard model for Alaska. GMMs are evaluated using two methods. The first is a total residual visualization approach that compares the probability density function, mean, and standard deviations σ of the observed and predicted ground motion. The second GMM evaluation method we use is the common total residual probabilistic scoring method (log likelihood [LLH]). The LLH method provides a single score that can be used to weight GMMs in the Alaska seismic hazard model logic trees. To test logic branches in previous seismic hazard models, we evaluate GMM performance as a function of depth and we demonstrate that some GMMs show improved performance for earthquakes with focal depths greater than 50 km. Ten of the initial 15 candidate GMMs fit the observed ground motions and meet established criteria for inclusion in the next update of the Alaska seismic hazard model.

Testing non-linear amplification factors used in ground motion models

2021 ◽  
Author(s):  
Karina Loviknes ◽  
Danijel Schorlemmer ◽  
Fabrice Cotton ◽  
Sreeram Reddy Kotha
Keyword(s):  
Ground Motion ◽  
Site Effects ◽  
Ground Motions ◽  
Site Amplification ◽  
Building Codes ◽  
Linear Amplification ◽  
Motion Models ◽  
Earthquake Predictability ◽  
Non Linear ◽  
Ground Motion Models

<p>Non-linear site effects are mainly expected for strong ground motions and sites with soft soils and more recent ground-motion models (GMM) have started to include such effects. Observations in this range are, however, sparse, and most non-linear site amplification models are therefore partly or fully based on numerical simulations. We develop a framework for testing of non-linear site amplification models using data from the comprehensive Kiban-Kyoshin network in Japan. The test is reproducible, following the vision of the Collaboratory for the Study of Earthquake Predictability (CSEP), and takes advantage of new large datasets to evaluate <span>whether or not</span> non-linear site effects predicted by site-amplification models are supported by empirical data. The site amplification models are tested using residuals between the observations and predictions from a GMM based only on magnitude and distance. When the GMM is derived without any site term, the site-specific variability extracted from the residuals is expected to capture the site response of a site. The non-linear site amplification models are tested against a linear amplification model on individual well-record<span>ing</span> stations. Finally, the result is compared to building codes where non-linearity is included. The test shows that for most of the sites selected as having sufficient records, the non-linear site-amplification models do not score better than the linear amplification model. This suggests that including non-linear site amplification in GMMs and building codes may not yet be justified, at least not in the range of ground motions considered in the test (peak ground acceleration < 0.2 g).</p>

scholarly journals Seismic hazard evaluation

2000 ◽  
Vol 33 (3) ◽  
pp. 371-386 ◽  
Author(s):  
Paul Somerville
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Ground Motions ◽  
Strong Motion ◽  
Earthquake Source ◽  
Hazard Evaluation ◽  
Data Set ◽  
Motion Models ◽  
The Past ◽  
Ground Motion Models

This paper reviews concepts and trends in seismic hazard characterization that have emerged in the past decade, and identifies trends and concepts that are anticipated during the coming decade. New methods have been developed for characterizing potential earthquake sources that use geological and geodetic data in conjunction with historical seismicity data. Scaling relationships among earthquake source parameters have been developed to provide a more detailed representation of the earthquake source for ground motion prediction. Improved empirical ground motion models have been derived from a strong motion data set that has grown markedly over the past decade. However, these empirical models have a large degree of uncertainty because the magnitude - distance - soil category parameterization of these models often oversimplifies reality. This reflects the fact that other conditions that are known to have an important influence on strong ground motions, such as near- fault rupture directivity effects, crustal waveguide effects, and basin response effects, are not treated as parameters of these simple models. Numerical ground motion models based on seismological theory that include these additional effects have been developed and extensively validated against recorded ground motions, and used to estimate the ground motions of past earthquakes and predict the ground motions of future scenario earthquakes. The probabilistic approach to characterizing the ground motion that a given site will experience in the future is very compatible with current trends in earthquake engineering and the development of building codes. Performance based design requires a more comprehensive representation of ground motions than has conventionally been used. Ground motions estimates are needed at multiple annual probability levels, and may need to be specified not only by response spectra but also by suites of strong motion time histories for input into time-domain non-linear analyses of structures.

Ranking of Ground-Motion Models (GMMs) for Use in Probabilistic Seismic Hazard Analysis for Iran Based on an Independent Data Set

2020 ◽  
Author(s):  
Zoya Farajpour ◽  
Milad Kowsari ◽  
Shahram Pezeshk ◽  
Benedikt Halldorsson
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Selection Process ◽  
Hazard Analysis ◽  
Probabilistic Seismic Hazard Analysis ◽  
Seismic Hazard Analysis ◽  
Probabilistic Seismic Hazard ◽  
Data Set ◽  
Motion Models ◽  
Ground Motion Models

ABSTRACT We apply three data-driven selection methods, log-likelihood (LLH), Euclidean distance-based ranking (EDR), and deviance information criterion (DIC), to objectively evaluate the predictive capability of 10 ground-motion models (GMMs) developed from Iranian and worldwide data sets against a new and independent Iranian strong-motion data set. The data set includes, for example, the 12 November 2017 Mw 7.3 Ezgaleh earthquake and the 25 November 2018 Mw 6.3 Sarpol-e Zahab earthquake and includes a total of 201 records from 29 recent events with moment magnitudes 4.5≤Mw≤7.3 with distances up to 275 km. The results of this study show that the prior sigma of the GMMs acts as the key measure used by the LLH and EDR methods in the ranking against the data set. In some cases, this leads to the resulting model bias being ignored. In contrast, the DIC method is free from such ambiguity as it uses the posterior sigma as the basis for the ranking. Thus, the DIC method offers a clear advantage of partially removing the ergodic assumption from the GMM selection process and allows a more objective representation of the expected ground motion at a specific site when the ground-motion recordings are homogeneously distributed in terms of magnitudes and distances. The ranking results thus show that the local models that were exclusively developed from Iranian strong motions perform better than GMMs from other regions for use in probabilistic seismic hazard analysis in Iran. Among the Next Generation Attenuation-West2 models, the GMMs by Boore et al. (2014) and Abrahamson et al. (2014) perform better. The GMMs proposed by Darzi et al. (2019) and Farajpour et al. (2019) fit the recorded data well at short periods (peak ground acceleration and pseudoacceleration spectra at T=0.2  s). However, at long periods, the models developed by Zafarani et al. (2018), Sedaghati and Pezeshk (2017), and Kale et al. (2015) are preferable.

scholarly journals A New Procedure for Evaluating Ground-Motion Models, with Application to Hydraulic-Fracture-Induced Seismicity in the United Kingdom

2020 ◽  
Vol 110 (5) ◽  
pp. 2380-2397 ◽  
Author(s):  
Gemma Cremen ◽  
Maximilian J. Werner ◽  
Brian Baptie
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Shale Gas ◽  
Hydraulic Fracture ◽  
Induced Seismicity ◽  
Ground Motions ◽  
Induced Earthquakes ◽  
Statistical Tool ◽  
Motion Models ◽  
Ground Motion Models

ABSTRACT An essential component of seismic hazard analysis is the prediction of ground shaking (and its uncertainty), using ground-motion models (GMMs). This article proposes a new method to evaluate (i.e., rank) the suitability of GMMs for modeling ground motions in a given region. The method leverages a statistical tool from sensitivity analysis to quantitatively compare predictions of a GMM with underlying observations. We demonstrate the performance of the proposed method relative to several other popular GMM ranking procedures and highlight its advantages, which include its intuitive scoring system and its ability to account for the hierarchical structure of GMMs. We use the proposed method to evaluate the applicability of several GMMs for modeling ground motions from induced earthquakes due to U.K. shale gas development. The data consist of 195 recordings at hypocentral distances (R) less than 10 km for 29 events with local magnitude (ML) greater than 0 that relate to 2018/2019 hydraulic-fracture operations at the Preston New Road shale gas site in Lancashire and 192 R<10  km recordings for 48 ML>0 events induced—within the same geologic formation—by coal mining near New Ollerton, North Nottinghamshire. We examine: (1) the Akkar, Sandikkaya, and Bommer (2014) models for European seismicity; (2) the Douglas et al. (2013) model for geothermal-induced seismicity; and (3) the Atkinson (2015) model for central and eastern North America induced seismicity. We find the Douglas et al. (2013) model to be the most suitable for almost all of the considered ground-motion intensity measures. We modify this model by recomputing its coefficients in line with the observed data, to further improve its accuracy for future analyses of the seismic hazard of interest. This study both advances the state of the art in GMM evaluation and enhances understanding of the seismic hazard related to U.K. shale gas development.

Probabilistic seismic hazard assessments for Northern Southeast Asia (Indochina): Smooth seismicity approach

2020 ◽  
Vol 36 (1_suppl) ◽  
pp. 69-90 ◽  
Author(s):  
Teraphan Ornthammarath ◽  
Pennung Warnitchai ◽  
Chung-Han Chan ◽  
Yu Wang ◽  
Xuhua Shi ◽  
...  
Keyword(s):  
Seismic Hazard ◽  
Southeast Asia ◽  
Epistemic Uncertainty ◽  
Slip Rate ◽  
Earthquake Catalog ◽  
Maximum Magnitude ◽  
Ground Acceleration ◽  
Motion Models ◽  
Smoothed Seismicity ◽  
Ground Motion Models

We present an evaluation of the 2018 Northern Southeast Asia Seismic Hazard Model (NSAHM18) based on a combination of smoothed seismicity, subduction zone, and fault models. The smoothed seismicity is used to model observed distributed seismicity from largely unknown sources in the current study area. In addition, due to a short instrumental earthquake catalog, slip rate and characteristic earthquake magnitudes are incorporated through the fault model. To achieve this objective, the compiled earthquake catalogs and updated active fault databases in this region were reexamined with consistent use of these input parameters. To take into account epistemic uncertainty, logic tree analysis has been implemented incorporating basic quantities such as ground-motion models (GMMs) for three different tectonic regions (shallow active, subduction interface, and subduction intraslab), maximum magnitude, and earthquake magnitude frequency relationships. The seismic hazard results are presented in peak ground acceleration maps at 475- and 2475-year return periods.

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

<p>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.</p><p>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’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.</p><p>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.</p><p>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.</p>

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