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.

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.

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.

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.

Ground motion models for the new seismic hazard model of Italy (MPS19): selection for active shallow crustal regions and subduction zones

2020 ◽  
Vol 18 (8) ◽  
pp. 3487-3516
Author(s):  
Giovanni Lanzano ◽  
Lucia Luzi ◽  
Vera D’Amico ◽  
Francesca Pacor ◽  
Carlo Meletti ◽  
...  
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Subduction Zones ◽  
Hazard Model ◽  
Motion Models ◽  
Selection For ◽  
Ground Motion Models

Evaluation of Ground-Motion Models for U.S. Geological Survey Seismic Hazard Forecasts: Hawaii Tectonic Earthquakes and Volcanic Eruptions

2020 ◽  
Vol 110 (2) ◽  
pp. 666-688 ◽  
Author(s):  
Daniel E. McNamara ◽  
Emily Wolin ◽  
Peter M. Powers ◽  
Allison M. Shumway ◽  
Morgan P. Moschetti ◽  
...  
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Ground Motions ◽  
Volcanic Eruptions ◽  
Hazard Model ◽  
Geological Survey ◽  
Motion Models ◽  
Short Period ◽  
Tectonic Earthquakes ◽  
Ground Motion Models

ABSTRACT The selection and weighting of ground-motion models (GMMs) introduces a significant source of uncertainty in U.S. Geological Survey (USGS) National Seismic Hazard Modeling Project (NSHMP) forecasts. In this study, we evaluate 18 candidate GMMs using instrumental ground-motion observations of horizontal peak ground acceleration (PGA) and 5%-damped pseudospectral acceleration (0.02–10 s) for tectonic earthquakes and volcanic eruptions, to inform logic-tree weights for the update of the USGS seismic hazard model for Hawaii. GMMs are evaluated using two methods. The first is a total residual visualization approach that compares the probability density function (PDF), 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 Hawaii seismic hazard model logic trees. The total residual PDF approach provides additional information by preserving GMM over- and underprediction across a broad spectrum of periods that is not available from a single value LLH score. We apply these GMM evaluation methods to two different data sets: (1) a database of instrumental ground motions from historic earthquakes in Hawaii from 1973 to 2007 (Mw 4–7.3) and (2) available ground motions from recent earthquakes (Mw 4–6.9) associated with 2018 Kilauea eruptions. The 2018 Kilauea sequence contains both volcanic eruptions and tectonic earthquakes allowing for statistically significant GMM comparisons of the two event classes. The Kilauea ground observations provide an independent data set allowing us to evaluate the predictive power of GMMs implemented in the new USGS nshmp-haz software system. We evaluate GMM performance as a function of earthquake depth and we demonstrate that short-period volcanic eruption ground motions are not well predicted by any candidate GMMs. Nine of the initial 18 candidate GMMs fit the observed ground motions and meet established criteria for inclusion in the update of the Hawaii seismic hazard model. A weighted mean of four top performing GMMs in this study (NGAsubslab, NGAsubinter, ASK14, A10) is 50% lower for PGA than for GMMS used in the previous USGS seismic hazard model for Hawaii.

Ground Motion Models Used in the 2014 U.S. National Seismic Hazard Maps

2015 ◽  
Vol 31 (1_suppl) ◽  
pp. S59-S84 ◽  
Author(s):  
Sanaz Rezaeian ◽  
Mark D. Petersen ◽  
Morgan P. Moschetti
Keyword(s):  
United States ◽  
Seismic Hazard ◽  
Ground Motion ◽  
The United States ◽  
Hazard Maps ◽  
Source Characterization ◽  
Ground Acceleration ◽  
Seismic Hazard Maps ◽  
Motion Models ◽  
Ground Motion Models

The National Seismic Hazard Maps (NSHMs) are an important component of seismic design regulations in the United States. This paper compares hazard using the new suite of ground motion models (GMMs) relative to hazard using the suite of GMMs applied in the previous version of the maps. The new source characterization models are used for both cases. A previous paper ( Rezaeian et al. 2014 ) discussed the five NGA-West2 GMMs used for shallow crustal earthquakes in the Western United States (WUS), which are also summarized here. Our focus in this paper is on GMMs for earthquakes in stable continental regions in the Central and Eastern United States (CEUS), as well as subduction interface and deep intraslab earthquakes. We consider building code hazard levels for peak ground acceleration (PGA), 0.2-s, and 1.0-s spectral accelerations (SAs) on uniform firm-rock site conditions. The GMM modifications in the updated version of the maps created changes in hazard within 5% to 20% in WUS; decreases within 5% to 20% in CEUS; changes within 5% to 15% for subduction interface earthquakes; and changes involving decreases of up to 50% and increases of up to 30% for deep intraslab earthquakes for most U.S. sites. These modifications were combined with changes resulting from modifications in the source characterization models to obtain the new hazard maps.

2014 Update to the National Seismic Hazard Model in California

2015 ◽  
Vol 31 (1_suppl) ◽  
pp. S177-S200 ◽  
Author(s):  
Peter M. Powers ◽  
Edward H. Field
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Hazard Model ◽  
Strike Slip ◽  
Reverse Fault ◽  
Rate Model ◽  
Motion Models ◽  
Moderate Earthquake ◽  
Source Models ◽  
Ground Motion Models

The 2014 update to the U. S. Geological Survey National Seismic Hazard Model in California introduces a new earthquake rate model and new ground motion models (GMMs) that give rise to numerous changes to seismic hazard throughout the state. The updated earthquake rate model is the third version of the Uniform California Earthquake Rupture Forecast (UCERF3), wherein the rates of all ruptures are determined via a self-consistent inverse methodology. This approach accommodates multifault ruptures and reduces the overprediction of moderate earthquake rates exhibited by the previous model (UCERF2). UCERF3 introduces new faults, changes to slip or moment rates on existing faults, and adaptively smoothed gridded seismicity source models, all of which contribute to significant changes in hazard. New GMMs increase ground motion near large strike-slip faults and reduce hazard over dip-slip faults. The addition of very large strike-slip ruptures and decreased reverse fault rupture rates in UCERF3 further enhances these effects.

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.

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.

Ground-Motion Prediction Model Based on Neural Networks to Extract Site Properties from Observational Records

2021 ◽  
Author(s):  
Tomohisa Okazaki ◽  
Nobuyuki Morikawa ◽  
Asako Iwaki ◽  
Hiroyuki Fujiwara ◽  
Tomoharu Iwata ◽  
...  
Keyword(s):  
Ground Motion ◽  
Ground Acceleration ◽  
Site Specific ◽  
Motion Data ◽  
Motion Models ◽  
Proposed Model ◽  
Single Station ◽  
Input Variables ◽  
Ground Condition ◽  
Ground Motion Models

ABSTRACT Choosing the method for inputting site conditions is critical in reducing the uncertainty of empirical ground-motion models (GMMs). We apply a neural network (NN) to construct a GMM of peak ground acceleration that extracts site properties from ground-motion data instead of referring to ground condition variables given for each site. A key structure of the model is one-hot representations of the site ID, that is, specifying the collection site of each ground-motion record by preparing input variables corresponding to all observation sites. This representation makes the best use of the flexibility of NN to obtain site-specific properties while avoiding overfitting at sites where a small number of strong motions have been recorded. The proposed model exhibits accurate and robust estimations among several compared models in different aspects, including data-poor sites and strong motions from large earthquakes. This model is expected to derive a single-station sigma that evaluates the residual uncertainty under the specification of estimation sites. The proposed NN structure of one-hot representations would serve as a standard ingredient for constructing site-specific GMMs in general regions.

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