Uncertainty, Variability, and Earthquake Physics in Ground‐Motion Prediction Equations

Baltay, A.; Hanks, Thomas C.; Abrahamson, Norm

doi:10.1785/0120160164

Cited by 60 publications

(58 citation statements)

References 47 publications

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“…To demonstrate the effectiveness of velocity heterogeneity, or dVI , we show how it would improve the median values of ground‐motion and path residual standard deviations ( Φ SS , after Baltay et al, and Sahakian et al, ) if it were included as a path‐specific adjustment to every recording in the PGA data set. As there is a distance dependence to this relationship, we fit a quadric surface to the path residual ( δP ij ), gradient metric ( dVI ), and rupture distance ( R rup ), to find a relationship that can be used to predict an adjustment to the median ground‐motion, for any given path:

δ P_{ij} = a italicdV I^{2} + b R_{rup}^{2} + c italicdVI R_{rup} + d italicdVI + e R_{rup} + f

where the coefficients a to f can be used for any path in this region.…”

Section: Discussionmentioning

confidence: 99%

“…The full representation of ground‐motion is written as

ln y_{ij} = f {(M, R_{rup})}_{ij} + δ E_{i} + δ S_{j} + {δWS}_{ij}

where ln y ij is the intensity measure for earthquake i and station j (here taken to be PGA from the aforementioned data set), and f ( M , R rup ) ij is the GMPE functional form described above. δE i and δS j are the random effects, event and site terms for earthquake i and site j , respectively, as defined in Baltay et al (). These are solved for as a part of the mixed effects regression (Sahakian et al, ).…”

Section: Methodsmentioning

confidence: 99%

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Ground Motion Residuals, Path Effects, and Crustal Properties: A Pilot Study in Southern California

et al. 2019

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To improve models of ground motion estimation and probabilistic seismic hazard analyses, the engineering seismology field is moving toward developing fully nonergodic ground motion models, models specific for individual source‐to‐site paths. Previous work on this topic has examined systematic variations in ground‐motion along particular paths (from either recorded or simulated earthquake data) and has not included physical properties of the path. We present here a framework to include physical path properties, by seeking correlations between ground motion amplitudes along specific paths and crustal properties, specifically seismic velocity and anelastic attenuation, along that path. Using a large data set of small‐magnitude earthquakes recorded in Southern California, we find a correlation between the gradient of seismic S wave velocity and the path term residual, after accounting for an average geometric spreading and anelastic attenuation, indicating that heterogeneity in crustal velocity primarily controls the path‐specific attenuation. Even in aseismic regions, details of path‐specific ground motion prediction equations can be developed from crustal structure and property data.

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δ P_{ij} = a italicdV I^{2} + b R_{rup}^{2} + c italicdVI R_{rup} + d italicdVI + e R_{rup} + f

where the coefficients a to f can be used for any path in this region.…”

Section: Discussionmentioning

confidence: 99%

“…The full representation of ground‐motion is written as

ln y_{ij} = f {(M, R_{rup})}_{ij} + δ E_{i} + δ S_{j} + {δWS}_{ij}

Section: Methodsmentioning

confidence: 99%

Ground Motion Residuals, Path Effects, and Crustal Properties: A Pilot Study in Southern California

et al. 2019

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“…In addition to their utility in understanding source dynamics, source parameter estimates may also provide observational constraints for seismic hazard assessment. Because ground motion intensities at high frequencies are controlled primarily by stress drop (Baltay et al, , ; Boore, ; Douglas & Edwards, ; Yenier & Atkinson, ), its characterization is of fundamental interest to studies that aim to develop ground motion prediction equations for induced events (Atkinson & Assatourians, ; Atkinson et al, ; Yenier et al, ). In this study, we observe quantifiable time‐dependent and depth‐dependent variations in stress drop, both of which are in accord with the conclusions of Yenier et al () and Atkinson and Assatourians () for ground motions of recent seismicity in Oklahoma.…”

Section: Discussionmentioning

confidence: 99%

Source Spectral Properties of Small to Moderate Earthquakes in Southern Kansas

et al. 2017

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The source spectral properties of injection‐induced earthquakes give insight into their nucleation, rupture processes, and influence on ground motion. Here we apply a spectral decomposition approach to analyze P wave spectra and estimate Brune‐type stress drop for more than 2,000 ML1.5–5.2 earthquakes occurring in southern Kansas from 2014 to 2016. We find that these earthquakes are characterized by low stress drop values (median ∼0.4 MPa) compared to natural seismicity in California. We observe a significant increase in stress drop as a function of depth, but the shallow depth distribution of these events is not by itself sufficient to explain their lower stress drop. Stress drop increases with magnitude from M1.5 to M3.5, but this scaling trend may weaken above M4 and also depends on the assumed source model. Although we observe a nonstationary, sequence‐specific temporal evolution in stress drop, we find no clear systematic relation with the activity of nearby injection wells.

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“…51 We use a subset of 11 801 records from 117 events at 520 stations, selected to be representative of crustal conditions. 53,54 For each of the two data sets, we estimate a simple GMM using the formulation of Kuehn and Scherbaum, 30 which automatically partitions the residuals into a between-event, station-to-station, and within-event/within-site component. [52][53][54] It consists of 11 777 records of 1891 events at 10 stations.…”

Section: Datamentioning

confidence: 99%

Spatial correlations of ground motion for non‐ergodic seismic hazard analysis

Kuehn

Abrahamson

2019

Earthq Engng Struct Dyn

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Traditional probabilistic seismic hazard analysis (PSHA) uses ground-motion models that are based on the ergodic assumption, which means that the distribution of ground motions over time at a given site is the same as their spatial distribution over different sites. Evaluations of ground-motion data sets with multiple measurements at a given site and multiple earthquakes in a given region have shown that the ergodic assumption is not appropriate as there are strong systematic region-specific source terms and site-specific path and site terms that are spatially correlated. We model these correlations using a spatial Gaussian process model. Different correlations functions are employed, both stationary and nonstationary, and the results are compared in terms of their predictive power. Spatial correlations of residuals are investigated on a Taiwanese strong-motion data set, and ground motions are collected at the ANZA, CA array. Source effects are spatially correlated, but provide a much stronger benefit in terms of prediction for the ANZA data set than for the Taiwanese data set. We find that systematic path effects are best modeled by a non-stationary covariance function that is dependent on source-to-site distance and magnitude. The correlation structure estimated from Californian data can be transferred to Taiwan if one carefully accounts for differences in magnitudes. About 50% of aleatory variance can be explained by accounting for spatial correlation.

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Uncertainty, Variability, and Earthquake Physics in Ground‐Motion Prediction Equations

Cited by 60 publications

References 47 publications

Ground Motion Residuals, Path Effects, and Crustal Properties: A Pilot Study in Southern California

Ground Motion Residuals, Path Effects, and Crustal Properties: A Pilot Study in Southern California

Source Spectral Properties of Small to Moderate Earthquakes in Southern Kansas

Spatial correlations of ground motion for non‐ergodic seismic hazard analysis

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