Regional Ground Motion Prediction Equations Research Articles

Probabilistic seismic hazard analysis of North and Central Himalayas using regional ground motion prediction equations

Probabilistic seismic hazard analysis of North and Central Himalayas using regional ground motion prediction equations

Spatial Correlation of Peak Ground Motions and Pseudo Spectral Acceleration Based on the Iranian Multievent Datasets

ABSTRACT Spatial correlations of peak ground and pseudo spectral acceleration are estimated as a function of intersite separation distance and natural vibration periods. More than 2286 three-component records from 461 Iranian earthquakes are employed to evaluate the intra-event residual correlation based on multiple earthquakes, considering both local and regional ground motion prediction equations (GMPEs). A correlation analysis is carried out through semivariogram as a powerful geostatistical tool. The results show the overall trend of correlation range increases as a function of structural period that appear consistent with previous researches. The average decay of correlation in Iran is slightly more rapid than that of California.

Regional Ground Motion Prediction Equation Developed for the Korean Peninsula Using Recorded and Simulated Ground Motions

ABSTRACT The Korean peninsula is in a stable continental region with moderate seismicity. The first and second largest instrumented earthquakes occurred in the peninsula on September 12, 2016 and November 15, 2017, respectively, which inflicted major damage on structures in nearby populated cities. This study aims to propose a ground motion prediction equation (GMPE) for the peninsula using both ground motions recorded during earthquakes in Korean and those simulated using a regional ground motion simulation (GMS) model. The procedure used in this study can be applied to other low-to-moderate seismic regions with few strong ground motion records.

Merging data and experts’ knowledge-based weights for ranking GMPEs

In this article, Bayes factors (BFs) are used for selecting and weighting the ground motion prediction equations (GMPEs). BFs are defined as the posterior probability of a model being the best model describing data. The Bayesian framework allows for merging information gathered from available seismic data and the experts’ opinion thus allowing for a bridge between data-driven and non-data-driven methods. A multi-dimensional likelihood function is used to account for earthquake-to-earthquake and record-to-record variability. A study is performed to identify the effects of model uncertainty and dataset variations on Bayesian weights by using simulated data. It was found that for a given median prediction, by increasing standard deviation the relative weights increase until it reaches a maximum and then start to decrease. The standard deviation corresponding to the maximum weights corresponds to the scatter of data used for calculating the weights. The method was applied to a local region with nine preselected local and regional GMPEs. The ranking, selection, and weighting are performed using a local dataset and the results are compared with four available ranking methods. While various methods may yield similar or different ranking results, the proposed method is the only one that provides scientific means of selecting appropriate models from a set of initially selected GMPEs.

Next Generation Detailed Seismic Zoning Maps for Southern Sakhalin Island

A probabilistic seismic hazard assessment is applied, for the first time in the case of Sakhalin Island, using a regional ground motion prediction equation in terms of the peak ground acceleration. The seismic hazard maps for 475-year-long and 975-year-long recurrence intervals are made for southern Sakhalin Island.

Strong Motion Data Based Regional Ground Motion Prediction Equations for North East India Based on Non-Linear Regression Models

ABSTRACT Existing Ground Motion Prediction Equations (GMPE) in practice for North East India have been developed using limited or simulated datasets of recorded ground motions. The current study presents the development of a new GMPE based on a well-established model considering actual recorded ground motion data comprising of acceleration, magnitude, and hypocentral distances. A larger dataset with magnitudes ranging from 4.2 to 6.9 and up to 640 kms, with a total of 204 recordings is used in non-linear multiple-regression. The newly developed GMPE could predict ground acceleration realistically over larger ranges of distance and magnitudes, compared to existing GMPEs.

Reconciling Ground Motions and Stress Drops for Induced Earthquakes in the Western Canada Sedimentary Basin

ABSTRACTA regional ground-motion prediction equation (GMPE) is defined for earthquakes in the western Canada sedimentary basin. The stress parameter model that is input to the GMPE, which controls high-frequency amplitudes, is developed based on an empirical Green’s function (EGF) study in the same region (Holmgren et al., 2019). The GMPE is developed using the generic GMPE approach of Yenier and Atkinson (2015a,b); regional parameters, including attenuation and site response, are calibrated using a database of response spectra. The ground-motion database comprises 726 records from 92 earthquakes with magnitudes 2.3–4.4, at distances to 200 km; most events are believed to be related to hydraulic fracturing. To investigate discrepancies between the values of GMPE stress parameter and EGF stress drop for individual earthquakes, stress parameters are computed for each event by fitting the GMPE to observed response spectra. There is a large scatter in the EGF versus GMPE stress estimates, even though the GMPE estimates were implicitly calibrated to equal the EGF values on average. The discrepancies can be attributed to two methodological factors. First, the EGF approach removes the site and path terms through spectral division, whereas the GMPE approach relies on an average regional model as determined from regression of the source and path attenuation. The use of an average regional model results in greater uncertainty, in particular, due to directivity effects (which are better accommodated in the EGF approach). Second, the EGF approach is performed in the Fourier domain, whereas the GMPE fitting is done in the response spectral domain. We conclude that EGF stress-drop models provide useful constraints for GMPE development, when used in combination with calibration to a ground-motion database.

Evaluation of The Seismic Hazard in The Marmara Region (Turkey) Based on Updated Databases

The increase in the wealth of information on the seismotectonic structure of the Marmara region after two devastating earthquakes (M7.6 Izmit and M7.2 Duzce events) in the year 1999 opened the way for the reassessment of the probabilistic seismic hazard in the light of new datasets. In this connection, the most recent findings and outputs of different national and international projects concerning seismicity and fault characterization in terms of geometric and kinematic properties are exploited in the present study to build an updated seismic hazard model. A revised fault segmentation model, alternative earthquake rupture models under a Poisson and renewal assumptions, as well as recently derived global and regional ground motion prediction equations (GMPEs) are put together in the present model to assess the seismic hazard in the region. Probabilistic seismic hazard assessment (PSHA) is conducted based on characteristic earthquake modelling for the fault segments capable of producing large earthquakes and smoothed seismicity modelling for the background smaller magnitude earthquake activity. The time-independent and time-dependent seismic hazard results in terms of spatial distributions of three ground-shaking intensity measures (peak ground acceleration, PGA, and 0.2 s and 1.0 s spectral accelerations (SA) on rock having 10% and 2% probabilities of exceedance in 50 years) as well as the corresponding hazard curves for selected cities are shown and compared with previous studies.

Regional Ground Motion Prediction Equation for Sakhalin Island

The first regional attenuation relationship of peak ground accelerations is developed for Sakhalin Island based on data from a strong motion network and local network of seismometers. The applicability limits and standard error of the relationship are determined. The developed relationship is recommended for use in seismic hazard assessments for Sakhalin Island and the adjacent shelf, including updating of general seismic zoning maps. The ground motion prediction equation models calculated for other Earth regions suitable for Sakhalin are selected.

Scaling earthquake ground motions in western Anatolia, Turkey

In this study, we provide a complete description of the ground-motion characteristics of the western Anatolia region of Turkey. The attenuation of ground motions with distance and the variability in excitation with magnitude are parameterized using three-component 0.25–10.0Hz earthquake ground motions at distances of 15–250km. The data set is comprised of more than 11,600 three-component seismograms from 902 regional earthquakes of local magnitude (ML) 2.5–5.8, recorded during the Western Anatolia Seismic Recording Experiment (WASRE) between November 2002 and October 2003.We used regression analysis to relate the logarithm of measured ground motion to the excitation, site, and propagation effects. Instead of trying to reproduce the details of the high-frequency ground motion in the time domain, we use a source model and a regional scaling law to predict the spectral shape and amplitudes of ground motion at various source-receiver distances. We fit a regression to the peak values of narrow bandpass filtered ground velocity time histories, and root mean square and RMS-average Fourier spectral amplitudes for a range of frequencies to define regional attenuation functions characterized by piece-wise linear geometric spreading (in log–log space) and a frequency-dependent crustal Q(f). An excitation function is also determined, which contains the competing effects of an effective stress parameter Δσ and a high-frequency attenuation term exp(−πκf). The anelastic attenuation coefficient for the entire region is given by Q(f)=180f0.55. The duration of motion for each record is defined as the value that yields the observed relationship between time-domain and spectral-domain amplitudes, according to random process theory. Anatolian excitation spectra are calibrated for our empirical results by using a Brune model with a stress drop of 10MPa for the largest event in our data set (Mw 5.8) and a near-surface attenuation parameter of κ=0.045s.These quantities, together with the effective duration of ground motion in the region, are used to estimate the peak ground motion (PGA, PGV). Using stochastic ground motion simulations, we predict the absolute level of ground shaking and compare them with strong-motion data in the region. The attenuation of simulated ground motion is compared with recent global and regional ground motion prediction equations (GMPEs). The performance of the stochastic model is also tested against small and intermediate-sized earthquakes (the M3.9 11 November 2007, M5.9 17 October 2005 and M5.7 20 October 2005 Izmir-Urla earthquakes) recorded by strong motion stations in the National Strong Ground Motion Network (operated by the Earthquake Department of the Disaster and Emergency Management Presidency, AFAD).

Empirical Evaluation of Aleatory and Epistemic Uncertainty in Eastern Ground Motions

Abstract Horizontal‐component response spectra data for ground motions recorded on hard‐rock sites in eastern North America (ENA) are used to explore the aleatory and epistemic uncertainty in ground‐motion prediction equations (GMPEs). An all‐station sigma, expressing the total calculated scatter of values about a GMPE, ranges from 0.25 to 0.29log 10 unit. Single‐station sigmas, in which the scatter is evaluated station by station relative to a regional GMPE, average in the range of 0.23–0.28. The scatter of observations about site‐specific GMPEs (GMPEs developed from multiple events recorded at a single station), which comes the closest to measuring the actual aleatory variability, has average values of 0.22–0.26. Overall, aleatory variability of ground motions in ENA is no larger than that for California, at least for moderate events recorded on hard‐rock sites. Epistemic uncertainty is considered by looking at the standard deviation of GMPEs developed separately for each station (i.e., the scatter of predictions rather than the scatter of observations). This exercise suggests that the overall epistemic uncertainty in ENAGMPEs should be at least 0.15 log unit (as a standard deviation of the median GMPEs) in the magnitude–distance range in which the prediction equations can be anchored by empirical data (magnitude 50 km). It should be larger than 0.15 unit at large magnitudes and close distances.