Estimated Peak Ground Acceleration Research Articles

Impact of ground motion uncertainty evolution from post-earthquake data on building damage assessment

Accurate damage assessment after an earthquake is crucial for effective emergency response. Using ground motion information enables rapid building damage assessment when detailed damage data are unavailable. While uncertainty in earthquake parameters plays a significant role in the accuracy of rapid estimations, it is usually treated as a constant parameter rather than as a dynamic parameter that considers the amount of ground motion data collected that evolve over time. This work investigates the impact of incorporating evolving ground motion uncertainty in ground motion estimations from US Geological Survey’s (USGS) ShakeMap on post-disaster damage assessments from two methodologies: the revised Thiel–Zsutty (TZR) model and Federal Emergency Management Agency’s (FEMA) Hazus. Using data from the 2020 Indios earthquake in Puerto Rico and the 2014 Napa earthquake, we find that changes in uncertainty in estimates of peak ground acceleration reach 65% between early and late versions of the ShakeMap. We propose a process to integrate this evolution with the two damage assessment methodologies through a Monte Carlo simulation-based approach, demonstrating that it is critical to introduce dynamic ground motion uncertainty in the damage assessment process to avoid propagating unreliable measures. Both methodologies show that resulting damage estimates can be characterized by narrower distributions, indicative of reduced uncertainty and increased precision in damage estimates. For the TZR model, an improved estimate of post-disaster loss is achieved with narrower bounds in distributions of expected high scenario loss. For Hazus, the results show potential changes in the most probable damage state with an average change of 13% in the most probable damage state. The described methodology also demonstrates how uncertainty in the resulting damage state distributions can be reduced compared with the use of the current Hazus methodology.

Predicting Peak Ground Acceleration of Strong-Motion Earthquakes Using Variable Snapshots of P-Wave Data with Long Short-Term Memory Neural Network

Abstract Conventional earthquake early warning systems (EEWS) rely on mathematical functions that utilize P-wave parameters extracted over a 3 s window to estimate peak ground acceleration (PGA). Advancements in the capabilities of deep neural networks to approximate universal functions, coupled with the availability of strong seismic event data, offer an unprecedented opportunity to evaluate the relationship between variable snapshots of P-wave data and the PGA of strong-motion earthquakes. This convergence of technology and data opens new avenues for research into utilizing smaller snapshots of P-wave in EEWS. Our study centers on the utilization of a long short-term memory (LSTM) neural network to model long dependencies within the P-wave of 1839 earthquakes recorded by the Kiyonshin Network (K-NET) for the prediction of PGA of S waves. Our methodology involves experiments that ultimately evaluate the network’s performance on 4, 3, and 2 s of P-wave snapshots. Our findings indicate that there is sufficient information in 2 s of temporal accelerometer readings after the onset of P waves to predict PGA accurately with an LSTM network.

Ground-Motion Model for Small-to-Moderate Potentially Induced Earthquakes Using an Ensemble Machine Learning Approach for CENA

ABSTRACT Ground-motion models (GMMs) are vital in assessing probabilistic seismic hazards and uncertainties. This study develops new GMMs benefiting from nonparametric machine learning algorithms, including artificial neural networks, kernel ridge, random forest, and gradient boosting regression techniques for small-to-moderate potentially induced earthquakes in central and eastern North America (CENA). As part of this study, we evaluate the performance of different machine learning models in estimating peak ground acceleration (PGA) and 17 spectral accelerations based on the moment magnitude (Mw), hypocentral distance (Rhypo), and the timed-average shear-wave velocity of the upper 30 m of soil (VS30). To train the algorithms, we have utilized a database of nearly 31,000 ground motions with small and moderate moment magnitudes ranging from 3.0 to 5.8, recorded within a hypocentral distance of less than 200 km in CENA. Typically, for GMM development, analysts employ linear regression-based models with predefined functional forms. The requirement for predefined functional forms can restrict the use of complicated and nonlinear equations to improve performance. Although the conventional regression model is more interpretable, machine learning can achieve a better result given sufficient training data. The results of error metrics reveal that gradient-boosting regression provides a better performance. Furthermore, a machine learning ensemble method is used to combine the regression results of four machine learning algorithms. The ensemble method improves the GMM performance and provides smoother results.

Probabilistic Assessment of Seismic Hazard for Uttarakhand State of India

Abstract Uttarakhand state of India, being located in the seismically active Himalayan mountain range, is highly prone to seismic hazards. This study presents assessment of seismic hazard of Uttarakhand through probabilistic approach. For conducting this study, a homogenous earthquake catalogue for moment magnitude during the period 1953 - 2020 with magnitude range of 2.0 - 6.9 consisting of 522 events was used. Various geological structures viz. folds, faults and lineament as well as thrust, have been identified in the associated regions. The state was divided into several grid points and the seismic hazard was evaluated for each of the grid points through probabilistic method. Finally, based on the analysis results, bedrock level hazard maps were developed for the state for contingency level earthquake (CLE) and maximum credible earthquake (MCE). The estimated peak ground acceleration (PGA) of the state is in the range of 0.16g-0.52g for CLE and the PGA varies for 0.20g-0.61g for MCE level earthquake. Main Central Thrust (MCT), Sunder Nagar fault and Ropar fault were observed to be more hazardous in the northern region of the state.

Application of grey wolf optimizer to develop new global GMPE for estimating peak ground acceleration

Application of grey wolf optimizer to develop new global GMPE for estimating peak ground acceleration

A Study on Site Specific Ground Response Analysis in Bihar

The influence of local soil conditions on earthquake earthquakes is assessed using seismic site response analysis. Earthquakes are natural phenomena that have claimed the lives and property of millions of people over the centuries. As a result, classifying these strong ground motions, predicting their damage potential, and suggesting mitigation measures become critical. The present study area is Kanti (Muzaffarpur), which is located in north Bihar and covers a geographical area of 3,132 square kilometres and lies between North Latitude 250 54’00”-260 23’00” and East Longitude 84053’00”- 85045’00” which falls in seismic zone IV of IS 1893: 2016. The geotechnical characterization of the site has been investigated using borehole data collected from various locations. The bedrock depth could not be determined due to a lack of deep boreholes, but traces of rock found in the collected data suggest rock outcrops in this region. According to geotechnical characterization, the soil profile in the region is dominated by silty sands, clayey silts, and poorly graded soils. DEEPSOIL software was used to estimate peak ground acceleration. Peak ground acceleration has been estimated using equivalent linear analysis with soil layer depth, bulk density, shear wave velocities, and damping as inputs.

Regional Finite-Fault Source Model for Development of Ground Motion Attenuation Relationship in Sichuan, China

The attenuation relationship of ground motion based on seismology has always been a front subject of engineering earthquake. Among them, the regional finite-fault source model is very important. In view of this point, the general characteristics of regional seism-tectonics, including the dip and depth of the fault plane, are emphasized. According to the statistics of regional seism-tectonics and focal mechanisms in Sichuan, China, and the sensitivity of estimated peak ground acceleration (PGA) attenuation is analyzed, and the dip angle is taken as an average of 70°. Based the statistics of the upper crustal structure and the focal depth of regional earthquakes, the bottom boundary of the sedimentary cover can be used as the upper limit for estimating the depth of upper-edge. The analysis shows that this value is sensitive to PGA. Based on the analysis of geometric relations, the corresponding calculation formula is used, and a set of concepts and steps for building the regional finite-fault source model is proposed. The estimation of source parameters takes into account the uncertainty, the geometric relationship among parameters and the total energy conservation. Meanwhile, a set of reasonable models is developed, which lay a foundation for the further study of regional ground motion attenuation based on seismology.

A Probabilistic Physics-Based Seismic Hazard Model for the Alborz Region, Iran

ABSTRACTThe seismic activity rate is one of the most significant factors in seismic hazard modeling. Although it is usually estimated from observed seismicity, a complete picture of the possible earthquakes is not always available since catalogs of the observed earthquakes are short and incomplete. Long-term physics-based numerical simulations, providing a comprehensive range of earthquakes, are a decent way to overcome such deficiency. With this contribution, we built a seismic hazard model for the Alborz region, Iran, using a long-term physics-based synthetic earthquake catalog, enriched with the additional consideration of background seismicity derived from a deformation model. 200,000 yr synthetic catalogs for the Alborz region, Iran, are used and validated by considering the recurrence time of large-magnitude events estimated from the paleoseismological investigation on individual faults. The magnitude–frequency distribution (MFD) from the synthetic earthquake catalog is then compared with the MFD based on observation, which overall indicates good compatibility, although there are discrepancies for some faults. The estimated peak ground acceleration (PGA) for the Alborz region varies in the ranges of 0.16–0.52g and 0.27–1.0g for 10% and 2% probability of exceedance in 50 yr, respectively. The absolute natural logarithm differences averaged across the region are ∼0.21, corresponding to an average of 23% difference in PGA values in comparison with the most up-to-date observed-based hazard model. Hazard curves for several populated cities are also presented and compared with the other independent estimates. The proposed procedure could be an alternative approach to evaluate seismic hazard for a seismically active region, in particular for those without a complete catalog of observed earthquakes.

Data-Driven Synthesis of Broadband Earthquake Ground Motions Using Artificial Intelligence

ABSTRACTRobust estimation of ground motions generated by scenario earthquakes is critical for many engineering applications. We leverage recent advances in generative adversarial networks (GANs) to develop a new framework for synthesizing earthquake acceleration time histories. Our approach extends the Wasserstein GAN formulation to allow for the generation of ground motions conditioned on a set of continuous physical variables. Our model is trained to approximate the intrinsic probability distribution of a massive set of strong-motion recordings from Japan. We show that the trained generator model can synthesize realistic three-component accelerograms conditioned on magnitude, distance, and VS30. Our model captures most of the relevant statistical features of the acceleration spectra and waveform envelopes. The output seismograms display clear P- and S-wave arrivals with the appropriate energy content and relative onset timing. The synthesized peak ground acceleration estimates are also consistent with observations. We develop a set of metrics that allow us to assess the training process’s stability and to tune model hyperparameters. We further show that the trained generator network can interpolate to conditions in which no earthquake ground-motion recordings exist. Our approach allows for the on-demand synthesis of accelerograms for engineering purposes.

New conditional, scenario-based, and non-conditional cumulative absolute velocity models for subduction tectonic settings

The PEER NGA-Sub ground-motion intensity measure database is used to develop new conditional ground-motion models (CGMMs), a set of scenario-based models, and non-conditional models to estimate the cumulative absolute velocity ([Formula: see text]) of ground motions from subduction zone earthquakes. In the CGMMs, the median estimate of [Formula: see text] is conditioned on the estimated peak ground acceleration ([Formula: see text]), the time-averaged shear-wave velocity in the top 30 m of the soil ([Formula: see text]), the earthquake magnitude ([Formula: see text]), and the spectral acceleration at the period of 1 s ([Formula: see text]). Multiple scenario-based [Formula: see text] models are developed by combining the CGMMs with pseudo-spectral acceleration ([Formula: see text]) ground-motion models (GMMs) for [Formula: see text] and [Formula: see text] to directly estimate [Formula: see text] given an earthquake scenario and site conditions. Scenario-based [Formula: see text] models are capable of capturing the complex ground-motion effects (e.g. soil non-linearity and regionalization effects) included in their underlying [Formula: see text]/[Formula: see text] GMMs. This approach also ensures the consistency of the [Formula: see text] estimates with a [Formula: see text] design spectrum. In addition, two non-conditional [Formula: see text] GMMs are developed using Bayesian hierarchical regressions. Finally, we present comparisons between the developed models. The comparisons show that if non-conditional GMMs are properly constrained, they are consistent with scenario-based GMMs. The [Formula: see text] GMMs developed in this study advance the performance-based earthquake engineering practice in areas affected by subduction zone earthquakes.

Fragility estimation for global building classes using analysis of the Cambridge earthquake damage database (CEQID)

This paper describes CEQID, a database of earthquake damage and casualty data assembled since the 1980s based on post-earthquake damage surveys conducted by a range of research groups. Following 2017–2019 updates, the database contains damage data for more than five million individual buildings in over 1000 survey locations following 79 severely damaging earthquakes worldwide. The building damage data for five broadly defined masonry and reinforced concrete building classes has been assembled and a uniform set of six damage levels assigned. Using estimated peak ground acceleration (PGA) for each survey location based on USGS Shakemap data, a set of lognormal fragility curves has been developed to estimate the probability of exceedance of each damage level for each class, and separate fragility curves for each of five geographical regions are presented. A revised set of fragility curves has also been prepared in which the bias in the curve resulting from the uncertainty in the ground motion parameter has been removed. The uncertainty in the fragility curves is evaluated and discussed and the curves are compared with those from other studies. A resistance index for each class of building is developed and cross-regional comparisons using this resistance index are presented.

Destructive M6.2 Petrinja Earthquake (Croatia) in 2020—Preliminary Multidisciplinary Research

On 28 December 2020, seismic activity in the wider Petrinja area strongly intensified after a period of relative seismological quiescence that had lasted more than 100 years (since the well-known M5.8 Kupa Valley earthquake of 1909, which is known based on the discovery of the Mohorovičić discontinuity). The day after the M5 foreshock, a destructive M6.2 mainshock occurred. Outcomes of preliminary seismological, geological and SAR image analyses indicate that the foreshocks, mainshock and aftershocks were generated due to the (re)activation of a complex fault system—the intersection of longitudinal NW–SE right-lateral and transverse NE–SW left-lateral faults along the transitional contact zone of the Dinarides and the Pannonian Basin. According to a survey of damage to buildings, approximately 15% of buildings were very heavily damaged or collapsed. Buildings of special or outstanding historical or cultural heritage significance mostly collapsed or became unserviceable. A preliminary analysis of the earthquake ground motion showed that in the epicentral area, the estimated peak ground acceleration PGA values for the bedrock ranged from 0.29 to 0.44 g. In the close Petrinja epicentral area that is characterized by the superficial deposits, significant ground failures were reported within local site effects. Based on that finding and building damage, we assume that the resulting peak ground acceleration (PGAsite) values were likely between 0.4 and 0.6 g depending on the local site characteristics and the distance from the epicentre.

A Study on the Applicability of Amplification Factor to Estimate Peak Ground Acceleration of Pohang Area

A Study on the Applicability of Amplification Factor to Estimate Peak Ground Acceleration of Pohang Area

Earthquake Potential Hazard Analysis of Palembang City, Sumatra Island

DOI: 10.17014/ijog.8.1.1-9Most of the destructive earthquakes in Sumatra are dominated by thrust mechanisms that occur due to the process of subduction and some earthquakes with strike-slip fault sources such as the Sumatra fault and northwestern Sumatra. The subduction zones along western Sumatra and Sumatran fault zones are active seismic sources of earthquake events. The seismotectonics of South Sumatra can be affected by earthquakes triggered by these seismic sources. In this study, an estimation and analysis of the potential for earthquake hazard curves were carried out in Palembang City due to the influence of subduction zone sources, strike-slip faults, and intermediate to deep earthquake sources. The algorithm of the seismicity smoothing was applied to estimate the seismicity rate for megathrust sources, active faults, and intermediate to deep earthquake sources. The results of the earthquake hazard potential curve showed that the estimated Peak Ground Acceleration (PGA) in the bedrock of the subduction earthquake sources was greater than the estimated ground shaking due to strike-slip fault sources as well as intermediate to deep earthquake sources. To understand better the potential ground shaking, the evaluation of PGA at the surface was then estimated by including the amplification factor. The amplification factor was calculated using the Horizontal-Vertical Spectral Ratio (HVSR) method. Based on the PGA estimated at the surface of 10% probability exceedance level during 50 years, the Palembang City has a potential shaking of around 35 gal, which is likely to be caused by a megathrust earthquake source.

New Scenario-Based Cumulative Absolute Velocity Models for Shallow Crustal Tectonic Settings

ABSTRACT The Pacific Earthquake Engineering Research Center Next Generation Attenuation-West2 database is used to derive a new conditional ground-motion model (CGMM) and a set of scenario-based models for estimating cumulative absolute velocity (CAV) for earthquakes in shallow crustal tectonic settings. Random-effects regressions were performed to develop the conditional model, with random effects across different earthquakes. The estimate of CAV is conditioned on the estimated peak ground acceleration (PGA), the time averaged shear-wave velocity in the top 30 m (VS30), the earthquake magnitude (Mw), and the rupture distance (Rrup). By combining the conditional CAV model with ground-motion models (GMM) in shallow crustal earthquake zones for PGA, new scenario-based models are developed for estimating the median CAV and its standard deviation, directly from an earthquake scenario and site conditions. A scenario-based CAV model captures inherently the complex ground-motion scaling effects included in the GMMs for spectral accelerations on which it is based on, such as, sediment-depth effects, soil nonlinearity effects, and regionalization effects. This approach also ensures consistency between the estimated CAV values and a design spectral acceleration response spectrum. The conditional and scenario-based models to estimate CAV are presented, and trends of the developed scenario-based models and previous traditional models for CAV are compared. Interestingly, we found a remarkable consistency between scenario-based and traditional nonconditional CAV models, when the underlain spectral GMM used in the implementation of the scenario-based model is properly constrained. Finally, we provide examples for the use of the conditional and scenario-based models in performance-based earthquake engineering.

Comparison of source-base estimate of peak ground acceleration (Amax) in Zagros by Bayesian method with non-source approach

Regarding Bayesian probability theory is an appropriate and useful method to estimate parameters in seismic hazard analysis. The analysis in Bayesian approaches is based on a posterior belief, also their special ability is to take into account the uncertainty of parameters in probabilistic relations and a priori knowledge. In this study, we used the program for seismic hazard Bayesian estimate which was elaborated by Alexey Lyubushin. Our study is the next in the sequence of applications of this software to seismic hazard assessment in different regions of the world. In this study, Bayesian approach has been used to obtain estimated seismic parameters. In order to reach this aim, 30 different source regions in Zagros seismotectonic province have been considered. The main assumptions are Poissonian character of the seismic events flow and properties of the Gutenberg-Richter distribution law. The a posteriori probability distribution functions of Mmax(T) and the tail probabilities P(Mmax(T) > M), that will occur in future time intervals of 10, 20, 50, 100 and 475 years are illustrated for source regions. The map of peak ground acceleration (PGA) zonation by probability level of 90% (in g) in rock bed for average return period of 50, 100 and 475 years is presented. According to the results, the maximum acceleration is estimated for the cities of Kermanshah, Ilam, Khorram Abad and Bandar Abbas which are related to NWZ1, NWZ2, NWZ3, NWZ7, NWZ8, SZ3, SH1, PG1 and PG2 sources. Finally, the results of this study are compared with obtained results of non-source approach.

Re-assessment of peak ground accelerations for large magnitude Vrancea intermediate-depth earthquakes

In this short paper, the peak ground acceleration estimates of four major Vrancea intermediate-depth earthquakes with moment magnitudes MW ≥ 6.9 which occurred in the twentieth century are re-assessed using a recently proposed procedure combining macroseismic intensity data with a ground motion model derived for this seismic source. An empirical relation between macroseismic intensity and peak ground acceleration is proposed in this study for Vrancea intermediate-depth earthquakes. The results show that the largest median updated peak ground accelerations are obtained in the case of the Vrancea MW 7.7 earthquake of November 1940. Moreover, for some cities, the updated peak ground accelerations are higher than the ones given in the current Romanian seismic design code. In addition, peak ground acceleration values in excess of 0.1 g are noticed for almost half of the Romanian territory in the case of the November 1940 and March 1977 Vrancea intermediate-depth seismic events.

Building Damage Caused by the 2017 M5.4 Pohang, South Korea, Earthquake, and Effects of Ground Conditions

ABSTRACT The 2017 M5.4 Pohang, South Korea, earthquake damaged numerous buildings. Immediately after the main shock event, 1,880 professional engineers inspected the damaged buildings and assigned three damage grades (“Usable,” “Restricted use,” and “Unusable”). We investigate factors influencing the building damage. The built year, number of stories, and estimated peak ground acceleration are strongly related with the frequency of building damage. We also prove that the damage caused to the building is influenced by the basin geometry, based on site parameters such as N value, shear wave velocity, depth to rock, and site fundamental period.

Comparative PGA-driven probabilistic seismic hazard assessment (PSHA) of Turkey with a Bayesian perspective

While there has been significant research on probabilistic seismic hazard analysis (PSHA) using several different seismic sources, this paper focuses particularly on understanding the spatially varying seismic hazard controlled only by earthquakes. In that vein, regarding Turkish seismicity, this study is the first of its kind to explore this conundrum from a Bayesian point of view and offer new estimates to compare with the existing ones. In this study, a national-extent peak ground acceleration (PGA)–driven hazard map (upon 90% quantile of maxima, VS30 = 760 m/s, and a return period of 475 years) was created and then compared both with the old and new versions of the officially recognized seismic hazard maps of Turkey. Regarding 10 earthquake-prone cities, the new PGA estimates were compared with those picked from these two maps. Next, individual site-based hazard estimates were drawn for these city centers considering the return periods of 43, 72, 140, and 475 years. The present hazard map was in compliance with the seismotectonic setup of Turkey and its PGA estimates were slightly high compared with the last two hazard maps for some specific regions, most of which are located in major active fault zones with a history of intense seismic activity, albeit the figures for low seismic zones were relatively low. With this study, it becomes clear that the process of PSHA, which innately requires a long and tiresome effort, can instantaneously be performed against the changing of catalog data over time, and thence prompt evaluations on variations can consequently be made.

Localizing Ground-Motion Models in Volcanic Terranes: Shallow Events at Mt. Etna, Italy, Revisited

ABSTRACTWe present a set of revised ground-motion models (GMMs) for shallow events at Mt. Etna Volcano. The recent occurrence of damaging events, in particular two of the strongest earthquakes ever instrumentally recorded in the area, has required revising previous GMMs, as these failed to match the observations made for events with local magnitude ML>4.3, above all for sites situated close to the epicenter. The dataset now includes 49 seismic events, with a total of 1600 time histories recorded at distances of up to 100 km, and ML ranging from 3.0 to 4.8. The model gives estimates of peak ground acceleration (both horizontal and vertical), peak ground velocity (both horizontal and vertical), and 5% damped horizontal pseudoacceleration response spectral ordinates up to a period of 4 s. GMMs were developed using the functional form proposed by Boore and Atkinson (2008). Furthermore, with a slightly modified approach, we also considered a regression model using a pseudodepth (h) depending on magnitude according to the scaling law by Azzaro et al. (2017). Both models were applied to hypocentral distance ranges of up to 60 km and up to 100 km, respectively. From the statistical analysis, we found that reducing the maximum distance from the event up to 60 km and introducing a magnitude-dependent pseudodepth improved the model in terms of total error. We compared our results with those derived using the GMMs for shallow events at Mt. Etna found by Tusa and Langer (2016) and for volcanic areas by Lanzano and Luzi (2019). The main differences are observed at short epicentral distances and for higher magnitude events. The use of variable pseudodepth avoids sharp peaks of predicted ground-motion parameters around the epicenter, preventing instabilities when using a GMM in probabilistic seismic hazard analysis.