Correlations of Horizontal and Vertical Spectral Accelerations for the Chilean Subduction Zone

Reliable synthesis and prediction of seismic waveforms play an important role in evaluating seismic hazards and designing earthquake-resilient structures. However, current methods, such as ground motion models and physics-based simulations, are often limited in fully capturing the complexity of seismic wave propagation, at higher frequencies (>5 Hz). Some of these limitations can potentially be overcome through machine learning techniques. In earthquake engineering, machine learning models have been used for predicting peak ground accelerations and Fourier spectra responses. To model entire waveforms, extensive efforts to generate seismic waveforms have employed advanced machine learning techniques, such as generative models, with most previous approaches relying on generative adversarial networks (GANs). In contrast to these earlier models, this study presents an efficient and extensible generative framework to produce realistic high-frequency seismic waveforms, compared to GANs. Our approach encodes spectrograms of the waveform data into a lower-dimensional sub-manifold using an autoencoder, and a state-of-the-art diffusion model is subsequently trained to generate these latent embeddings. Conditioning is currently performed on key parameters: earthquake magnitude, recording distance, site conditions, and faulting style. The resulting generative model can synthesize waveforms with frequency content up to 50 Hz, from which several scalar ground motion statistics, such as peak ground motion amplitudes, spectral accelerations, or Arias intensity can be directly derived. We validate the quality of the generated waveforms using standard seismological benchmarks and performance metrics from image generation research. Our openly available model produces high-frequency waveforms that align with real data across a wide range of input parameters, including regions where observations are sparse, and accurately reproduces both median trends and variability of empirical ground motion statistics. Our generative waveform model can be potentially used to perform seismic hazard where broadband data are often required such as to train earthquake early warning model. Given the increasing number of generative waveform models, we emphasize that they should be openly accessible and included in community efforts for ground motion model evaluations.

Estimating ground motion parameters at an unsampled site is challenging for seismologists and engineers alike. An attempt is made to apply Kriging interpolation to estimate peak ground accelerations at specific nuclear power plant sites. However, issues such as data quality and Kriging assumptions pose challenges to how practical and reasonable Kriging interpolation results may be in terms of estimating ground motion parameters. Peak ground acceleration data from the 2016 Gyeongju and 2017 Pohang earthquakes were taken from a local seismological agency. Peak ground acceleration, logarithms of the peak ground acceleration, and residuals between the recorded data and global and local ground motion models were used to select and derive empirical variogram models. The leave-one-out cross-validation process suggested estimating peak ground acceleration residuals from a locally developed ground motion model using an Exponential variogram model. Kriging estimates were compared to a site-specific ground motion model. These estimates appeared reasonable at one site but were significantly off at the other site. On the whole, Kriging estimates were lower than ground motion model predictions. When viewed relative to the nearest recordings, Kriging estimates appeared inconsistent across the two earthquake events. A nearest neighbor approach to computing Kriging estimates suggested a minimum of five data points but much more for modeling an empirical variogram. Results also suggest focusing on validation processes more than variogram selection. This suggests caution when applying Kriging for ground motion-related assessments in South Korea.

Artificial neural network-based ground motion model for next-generation seismic intensity measures

The North Sea plays a strategic role in the transition towards the green economy, as it hosts many offshore wind farms and carbon capture and storage (CCS) sites. The North Sea is characterized by moderate seismicity. For example, the 2023 Mw 5.1 North Sea earthquake was the largest earthquake in the region in 33 years, and led to a temporary shutdown of production of Equinor’s oil platform Snorre B.The last comprehensive seismic hazard assessment of the North Sea was performed about twenty years ago (Bungum et al. 2000). Since then, there have been many new ground motions recorded from both onshore and offshore seismic stations in the North Sea region, as well as major advancements in probabilistic seismic hazard assessment (PSHA) methodology. As a result, this study aims to develop the first region-specific ground-motion model for the North Sea, which will be used in an updated PSHA to ensure the safe design of new offshore wind farms and CCS sites.This research forms a part of the SHARP-Storage project, an interdisciplinary Accelerating CCS Technologies project developing improved methods for quantitative assessment of subsurface CO2 storage containment risks. The SHARP project compiled a dataset of North Sea ground-motion records, which comprises data on natural seismicity from broadband seismometers, both onshore and offshore. Moreover, synthetic ground motions are generated to address the paucity of recordings (especially strong motions) in the North Sea region. The associated flat-file, including the metadata and intensity measures (e.g., spectral acceleration and Fourier amplitude) of manually processed waveforms is constructed for the analysis of the ground motion characteristics in the North Sea. An empirical ground-motion model is developed based on the recorded and synthetic data, which is validated and compared with the available observations and models for the North Sea.The ground-motion model is defined for a reference rock condition. Amplification factors to estimate shaking at the soil surface are derived based on a database of one-dimensional site response analyses. The sediments encountered in the North Sea have been deposited in dynamically changing environments ranging from arctic to temperate and reworked by the movement of ice sheets. As a result, a wide range of soil types and properties are found. To capture this variability, representative profiles are developed based on site investigations from locations with different soil conditions encountered in the North Sea. One dimensional non-linear site response analyses are then performed using the recorded and synthetic ground motion database to generate a database of response spectra amplification values.This study presents the preliminary results of the North Sea ground-motion model and highlights the challenges in conducting PSHA for the North Sea region. These results improve the assessment of seismic hazard and risks to CO2 storage security in the North Sea.

With the improvement of the world’s largest seafloor observation network (S-net) and the increase in the quantity and quality of records, the ergodic assumptions can be further relaxed in the modeling of offshore ground motion models (GMMs). This allows accounting for systematic and repeatable source, site, and path effects to further understand the characteristics of offshore ground motion in the Japan Trench region. We developed an offshore ergodic backbone GMM for subduction earthquakes and classified the sites into four categories using horizontal–vertical response spectral ratio to investigate site amplification. The offshore ergodic GMM is applicable for subduction earthquake scenarios with moment magnitudes ranging from 4.0 to 7.4 and rupture distances ranging from 10 to 300 km. Comparing offshore ergodic GMMs with onshore GMMs for subduction earthquakes, we found that offshore GMMs were significantly different from onshore GMMs, especially in the long-period and unburied states. Then a new offshore non-ergodic GMM was developed based on the offshore ergodic GMM. The systematic and repeatable source and site effects were captured by the spatially varying coefficients represented by Gaussian processes, while the systematic and repeatable path effects were captured by cell-specific anelastic attenuation proposed by Dawood and Rodriguez-Marek (2013), calculated with the Cohen-Sutherland computer graphics algorithm. The non-ergodic GMM revealed systematic and repeatable source, site, and path effects that were not captured by the ergodic GMM. Moreover, the non-ergodic GMM showed reduced aleatory variability and epistemic uncertainty on ground motion estimation compared to ergodic GMM. The reduction of aleatory variability and epistemic uncertainty had a significant impact on probabilistic seismic hazard analysis. Quantifications of these results are contributed to conduct reasonable seismic design and seismic risk assessment for marine engineering in offshore regions vulnerable to strong subduction earthquakes.

In engineering applications, the permanent displacement (D) commonly serves as a useful indicator of the seismic performance of slopes. When developing empirical displacement models as a function of ground-motion intensity measures (IMs), the IMs that are best correlated to D are preferred. On the other hand, the predictability of IMs, in terms of the standard deviations using ground motion models, is also of concern in developing D models. This study aims to: (1) investigate the efficiency of IMs in developing D models for a cohesive-frictional slope based on numerical analysis; and (2) compare the means and standard deviations of randomized D by considering uncertainties in predicting both the IMs and D via Monte Carlo simulation (MCS). A total of 10 scalar IMs and 38 vector-IMs, are employed to develop D models. The results indicate that the spectral acceleration at a degraded period of the soil layer (SA(1.5Ts,layer)) and Arias intensity (IA) are the two most efficient scalar IMs. Additionally, the vector-IMs consisting of [IA, spectrum intensity] and [IA, mean period] are the two most efficient vectors. The MCS results illustrate that the rankings for standard deviations of D models and total standard deviations (i.e., including ground motion variability) may be considerably different. The results are also found to be dependent on earthquake magnitudes and site conditions. This study could provide guidance on the development of numerical-based D models especially within a probabilistic seismic slope displacement analysis framework.KeywordsSeismic slope performanceNumerical analysisIntensity measureDisplacement predictionModel variability

A probabilistic seismic hazard analysis of Pakistan is carried out to produce seismic hazard maps showing peak ground acceleration for the 2% and 10% of probability of exceedance in 50 years and spectral acceleration for the 5% and 2% probabilities of exceedance in 50 years. The main objective of this work is to define seismic hazard definition for Pakistan based on the classical approach using updated seismic hazards analysis inputs. A composite earthquake catalogue consisting of 32,700 events was compiled and used in the analysis with a magnitude range of Mw 4.0–8.3. The computations were made at a rectangular grid of 5 km in the OpenQuake engine. Ground motion parameter values have been obtained for flat rock reference seismic site conditions having a shear wave velocity of 760 m/s. Through the logic tree, epistemic uncertainties inherent in ground motion prediction equations and the maximum magnitude potential of seismic sources were estimated. Ground motion prediction equations were assigned equal weights in the logic tree while different weights were assigned to the maximum magnitude potential models. Results of the study were expressed as ground motion contour maps and median uniform hazard spectra for important cities in Pakistan. The results showed that peak ground acceleration values in Pakistan range from 0.16 to 0.54 g for the 10% of probability of exceedance, 0.23–0.72 g for the 5% probability of exceedance and 0.32–1.02 g for the 2% of probability of exceedance in 50 years. Spectral acceleration values at 0.2 s range from 0.67 to 2.19 g for the 2% chance of exceedance in 50 years, respectively, whereas spectral acceleration at 1.0 s ranges from 0.09 to 0.52 g for the 2% chance of exceedance in 50 years. The centre of Pakistan and the southern and the northern region are exposed to high hazard compared to the central-eastern part of the country. Quetta, Chitral, Peshawar, Islamabad Ziarat and Gwadar are exposed to seismic hazard in the PGA range of 0.32–0.45 g. Comparison of the results of this study with those of similar studies suggests that the seismic estimates proposed are reliable. The study provides a demonstration that the classical seismic hazard approach applied here is rational and applicable in Pakistan.

From 1981 to 1989, Lawrence Livermore National Laboratory (LLNL) developed for the Nuclear Regulatory Commission a method for performing Probabilistic Seismic Hazard Analysis (PSHA) in the eastern US with results documented in NUREG/CR-5250. Improvements in the handling of the uncertainties lead to updated results documented in the 1993-EUS-Update study (NUREG-1488.) These results presented substantial differences with the utilities sponsored study performed by the Electric Power Research Institute (EPRI, 1989.) In order to understand the differences between the two studies, the Nuclear Regulatory Commission (NRC), the Department Of Energy (DOE) and EPRI jointly sponsored a study led by the Senior Seismic Hazard Analysis Committee (SSHAC) the task of which was to explain the differences between the two studies and provide guidance on how to perform a state-of-the-art PSHA. The work and conclusions of the SSHAC are documented in NUREG/CR-6372, 1997. As a follow-up to the 1997 SSHAC study, the Trial Implementation Project (TIP), (UCRL-ID-133494, 1998, NUREG/CR-6607) made use of the SSHAC recommendations and developed a set of more detailed guidance for performing PSHA. The TIP project tested the more complicated issue of development of the seismic zonation and seismicity models on two sites: Watts Bar and Vogtle. It was found that the uncertainty generated by artificial disagreements among experts can be considerably reduced through interaction and discussion of the available data and by identifying the elements common to all experts' interpretation. By concentration on those elements, it was possible to develop a consensus of the group on the way to characterize them and eliminate large unnecessary differences. The present study compares the results of the 1993-EUS-Update and the 1998-TIP studies and identifies the reasons for the differences, which were found to be: (1) Differences in the ground motion (GM) attenuation models; (2) The introduction of the Eastern Tennessee Seismic Zone (ETSZ) in the TIP study. We found that these two factors accounted for factor of 6 difference in mean estimates PGA hazard at high GM levels. The agreement between the two studies improved at lower PGA values. The results were in better agreement and only differed by about a factor of two at high ground motion levels, when the same GM model was used with each seismicity model. Finally, it was found that the composite rate of earthquakes around the Watts Bar site was about a factor of two higher for the TIP composite seismicity model than for the composite 1993-EUS-Update seismicity model.

SUMMARY Turkey is characterized by a high level of seismic activity attributed to its complex tectonic structure. The country has a dense network to record earthquake ground motions; however, to study previous earthquakes and to account for potential future ones, ground motion simulations are required. Ground motion simulation techniques offer an alternative means of generating region-specific time-series data for locations with limited seismic networks or regions with seismic data gaps, facilitating the study of potential catastrophic earthquakes. In this research, a local ground motion model (GMM) for Turkey is developed using region-specific simulated records, thus constructing a homogeneous data set. The simulations employ the stochastic finite-fault approach and utilize validated input-model parameters in distinct regions, namely Afyon, Erzincan, Duzce, Istanbul and Van. To overcome the limitations of linear regression-based models, artificial neural network is used to establish the form of equations and coefficients. The predictive input parameters encompass fault mechanism (FM), focal depth (FD), moment magnitude (Mw), Joyner and Boore distance (RJB) and average shear wave velocity in the top 30 m (Vs30). The data set comprises 7359 records with Mw ranging between 5.0 and 7.5 and RJB ranging from 0 to 272 km. The results are presented in terms of spectral ordinates within the period range of 0.03–2.0 s, as well as peak ground acceleration and peak ground velocity. The quantification of the GMM uncertainty is achieved through the analysis of residuals, enabling insights into inter- and intra-event uncertainties. The simulation results and the effectiveness of the model are verified by comparing the predicted values of ground motion parameters with the observed values recorded during previous events in the region. The results demonstrate the efficacy of the proposed model in simulating physical phenomena.

The 2020 oblique normal-faulting M 5.7 Magna mainshock has provided the best dataset of recorded strong ground motions for an earthquake within the Wasatch Front region, Utah, and the larger Basin and Range Province. We performed a preliminary evaluation of the strong motion and broadband data from this earthquake and compared the data with the Next Generation Attenuation - West2 Project (NGA-West2) ground-motion models (GMMs). The highest horizontal peak ground acceleration (PGA) recorded was 0.43g (geometric mean of the two horizontal components) at a station located above the rupture plane at a rupture distance of 8 km. Eleven stations recorded PGAs >0.20g. Most of these stations are located on the deep sedimentary deposits within the Salt Lake Valley, and all are at rupture distances <20 km. The data compare favorably with the NGA-West2 GMMs, although the expected variability was observed. PGAs exceed the GMM predictions at the closest distances for the source model that we used. The area of the strongest ground shaking encompassed the town of Magna, where some of the heaviest damage occurred. A significant implication of the 2020 Magna earthquake for seismic hazards in the Salt Lake Valley arises from the possibility that this earthquake occurred on the Salt Lake City segment of the Wasatch fault. If so, then the dip of this fault segment must decrease with depth to ≤30°–35°, as proposed by Pang et al. (2020)—at least along the northern part of the segment where the earthquake occurred. Because of the lack of information about the subsurface geometry of the Wasatch fault zone, modeling of this fault zone in seismic hazard analyses has assumed a moderate dip of 50°±15°. Assuming a more shallowly dipping fault results in higher estimates of ground shaking in future large earthquakes on this fault. Alternative interpretations of the Magna earthquake are that it occurred (1) on an auxiliary fault within the Wasatch fault zone or (2) on a listric section of the northern Salt Lake City segment that is not representative of the geometry of the whole fault segment.

Iceland is seismically the most active region in northern Europe. Large single earthquakes (~ $$ M_{\text{w}} $$ 7) and seismic sequences of moderate-to-strong earthquakes (~ $$ M_{\text{w}} $$ 6–6.5) have repeatedly occurred during past centuries in the populated South Iceland Seismic Zone (SISZ). The seismic hazard in Iceland has mainly been evaluated using ground motion models (GMMs) developed from strong-motion observations in other countries and only to a very limited extent from Icelandic data, despite a particularly rapid attenuation of ground motions with distance in Iceland. In this study, we evaluate the performance of these GMMs against the Icelandic strong-motion dataset, consisting of peak ground accelerations of moderate-to-strong ( $$ M_{\text{w}} $$ 5–6.5) and local (0–80 km) earthquakes in the SISZ. We find that these GMMs exhibit both a strong bias against the dataset and a relatively large variability, which calls their applicability and earlier hazard analyses into question. To address this issue, we recalibrate each of the GMMs to the dataset using Bayesian regression and Markov Chain Monte Carlo simulations. This approach allows useful prior information of the GMM parameters to be combined with the likelihood of the observed data and provides posterior probability density functions of model residuals and regression parameters. The recalibrated GMMs are unbiased with respect to the data and have a low total standard deviation of around 0.17 (base-10 logarithmic units). The model-to-model variability in the median predictions vary primarily with distance, reaching 0.05 the lowest for $$ M_{\text{w}} $$ 6.3–6.5 at intermediate distances. While the lack of near-fault and far-field data, particularly at large magnitudes, and the different functional forms of the GMMs calibrated to the same dataset may affect the results, the recalibrated GMMs should represent well the ground motions of a typical sequence of moderate-to-strong SISZ earthquakes. We present the recalibrated GMMs of this study as promising candidates for future use in ground motion prediction in Iceland e.g., in the context of either a logic tree or the backbone approach in probabilistic seismic hazard assessment.

Site-specific seismic hazard analysis for Calabrian dam site using regionally customized seismic source and ground motion models

Modelling the spatial correlation of ground motion intensity measures (IMs) has become a keystone in seismic hazard and risk analysis of portfolios of buildings, spatially distributed infrastructures and earthquake-induced phenomena. The quantification of the seismic performance of such systems over a region requires knowledge of the joint probability of occurrence of different ground motion IMs at multiple locations. Therefore, the classical Probabilistic Seismic Hazard Assessment (PSHA) tools, which are based on the hypothesis of independency between IMs at closely spaced sites, are not appropriate. Over the past decade, the spatial correlation of peak ground acceleration (PGA) and spectral acceleration (SA) has been widely studied. Although common findings suggest that the correlation of intra-event residuals decreases quite rapidly with increasing separation distances, these models feature different rates of decay. Among the causes that may lead to inconsistencies between models, with significant impact on hazard and loss estimates, are the multiple techniques used to estimate the correlation structure, the region and local site conditions, as well as the choice of the databases. Furthermore, little effort has been directed towards other IMs suitable to characterize the resulting damage to structures and predict ground failure: peak ground velocity (PGV), peak ground displacement (PGD) and spectral displacement (SD) as well as Arias intensity (퐼") and cumulative absolute velocity (CAV), to name but a few. A proper definition of the seismic action in terms of spectral displacement ordinates has progressively gained importance in performance-based seismic design, and 퐼" and CAV have been found to be adequate for many other earthquake engineering applications, such as evaluating the susceptibility to liquefaction and earthquake-induced landslides. In this study, we use geostatistical tools in order to compute the spatial correlations of such ground motion parameters. We perform comparisons with other existing models with the aim of: (1) identifying factors that most affect the correlation structure, and (2) quantifying the variability of correlation lengths between different events and regions. Moreover, spatial correlation models are usually calibrated on the within-event component of residuals, obtained based on ergodic ground motion prediction equations (GMPEs). Therefore, we also analyse the spatial correlation of event- and site- corrected residuals, retrieved relaxing the ergodic assumption, to further investigate the factors that determine the spatial dependency of IMs. In order to address these issues, we use the 2016-2017 Central Italy seismic sequence database, which includes nine Mw ≥ 5.0 earthquakes that occurred over a time period of five months. These data allow some uncertainties to be removed and an evaluation of the event-to-event variability of the spatial correlation because the same seismic region is considered. Our preliminary results will provide a more accurate picture of ground motions, and thus improve the modelling of earthquake losses for risk model development.

Conditional ground‐motion models (CGMMs) for estimating Arias intensity (IA) for earthquakes in subduction zones are developed. The estimate of IA is conditioned in these models on the estimated peak ground acceleration (PGA), the spectral acceleration at T=1 s (SA1), time‐averaged shear‐wave velocity in the top 30 m (VS30), and magnitude (Mw). Random‐effects regressions are used to develop CGMMs for Japan, Taiwan, South America, and New Zealand. By combining the conditional models of IA with the ground‐motion models (GMMs) for PGA and SA1, the conditional models are converted to scenario‐based GMMs that can be used to estimate the median IA and its standard deviation directly for a given earthquake scenario and site conditions. The conditional scaling approach ensures the estimated IA values are consistent with a design spectrum that may correspond to above‐average spectral values for the controlling scenario. In addition, this approach captures the complex ground‐motion scaling effects found in GMMs for spectral acceleration, such as sediment‐depth effects, soil nonlinearity effects, and regionalization effects, in the developed scenario‐based models for IA. Estimates from the new scenario‐based IA models are compared to those from traditional GMMs for IA in subduction zones.

The Site Specific Spectra Project (SSSP) was a multi-year study funded by the US Nuclear Regulatory Commission to provide estimates of the seismic hazards at a number of nuclear power plant sites in the Eastern US. A key element of our approach was the Peer Review Panel, which we formed in order to ensure that our use of expert opinion was reasonable. We discuss the Peer Review Panel results and provide the complete text of each member's report. In order to improve the ground motion model, an Eastern US Ground Motion Model Panel was formed. In Section 4 we tabulate the responses from the panel members to our feedback questionnaire and discuss the implications of changes introduced by them. We conclude that the net difference in seismic hazard values from those presented in Volume 4 is small and does not warrant a reanalysis. 22 figs.