Estimation Of Ground Motion Research Articles

Estimating ground motion intensities using simulation-based estimates of local crustal seismic response

Abstract. It is estimated that 2 billion people will move to cities in the next 30 years, many of which possess high seismic risk, underscoring the importance of reliable hazard assessments. Current ground motion models for these assessments typically rely on an extensive catalogue of events to derive empirical ground motion prediction equations (GMPEs), which are often unavailable in developing countries. Considering the challenge, we choose an alternative method utilizing physics-based (PB) ground motion simulations and develop a simplified decomposition of ground motion estimation by considering regional attenuation (Δ) and local site amplification (A), thereby exploring how much of the observed variability can be explained solely by wave propagation effects. We deterministically evaluate these parameters in a virtual city named Tomorrowville, located in a 3D-layered crustal velocity model containing sedimentary basins, using randomly oriented extended sources. Using these physics-based empirical parameters (Δ and A), we evaluate the intensities, particularly peak ground acceleration (PGA), of hypothetical future earthquakes. The results suggest that the estimation of PGA using the deterministic Δ−A decomposition exhibits a robust spatial correlation with the PGA obtained from simulations within Tomorrowville. This method exposes an order-of-magnitude spatial variability in PGA within Tomorrowville, primarily associated with the near-surface geology and largely independent of the seismic source. In conclusion, advances in PB simulations and improved crustal structure determination offer the potential to overcome the limitations of earthquake data availability to some extent, enabling prompt evaluation of ground motion intensities.

Broadband Ground-Motion Simulations with Machine-Learning-Based High-Frequency Waves from Fourier Neural Operators

ABSTRACT Seismic hazards analysis relies on accurate estimation of expected ground motions for potential future earthquakes. However, obtaining realistic and robust ground-motion estimates for specific combinations of earthquake magnitudes, source-to-site distances, and site conditions is still challenging due to the limited empirical data. Seismic hazard analysis also benefits from the simulation of ground-motion time histories, whereby physics-based simulations provide reliable time histories but are restricted to a lower frequency for computational reasons and missing information on small-scale earthquake-source and Earth-structure properties that govern high-frequency (HF) seismic waves. In this study, we use densely recorded acceleration broadband (BB) waveforms to develop a machine-learning (ML) model for estimating HF ground-motion time histories from their low-frequency (LF) counterparts based on Fourier Neural Operators (FNOs) and Generative Adversarial Networks (GANs). Our approach involves two separate FNO models to estimate the time and frequency properties of ground motions. In the time domain, we establish a relationship between normalized low-pass filtered and BB waveforms, whereas in the frequency domain, the HF spectrum is trained based on the LF spectrum. These are then combined to generate BB ground motions. We also consider seismological and site-specific factors during the training process to enhance the accuracy of the predictions. We train and validate our models using ground-motion data recorded over a 20 yr period at 18 stations in the Ibaraki province, Japan, considering earthquakes in the magnitude range M 4–7. Based on goodness-of-fit measures, we demonstrate that our simulated time series closely matches recorded observations. To address the ground-motion variability, we employ a conditioned GAN approach. Finally, we compare our results with several alternative approaches for ground-motion simulation (stochastic, hybrid, and ML-based) to highlight the advantages and improvements of our method.

Ground subsidence in major Philippine metropolitan cities from 2014 to 2020

Land subsidence is recognized as one of the hazards that threaten Metro Manila, Philippines, and other major urban areas worldwide. It has become a significant global issue caused by excessive groundwater extraction, rapid urbanization, and natural sediment compaction, exacerbated by climate change through rising sea levels. This paper presents vertical ground motion rates in Philippine metropolitan cities using Sentinel-1 InSAR time series analysis from 2014 to 2020 through UK COMET’s LiCSAR products and the LiCSBAS package of Morishita et al. (2020). The results revealed a maximum subsidence rate of 109 mm/yr in Bulacan Province in Greater Manila. For the first time, this paper also presents ground motion estimates and the following maximum subsidence rates for other Philippine metropolitan cities: 11 mm/yr in Metro Cebu, 38 mm/yr in Metro Davao, 9 mm/yr in Metro Iloilo, and 29 mm/yr in Legazpi City. Areas with remarkably high subsidence rates are observed as circular to elliptical deformation features in vertical motion maps. Sinking areas mostly coincide with industrial and commercial complexes evident as a contiguous distribution of large and expansive man-made structures with distinct radar reflections. Monitoring this hazard is crucial as it increases the risk of floods, building and infrastructure damage, and economic loss, and exposes residents along the coast to worsening tidal incursions and storm surges due to climate change. InSAR is proposed here as a targeted, long-term deformation monitoring tool, especially related to groundwater usage.

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.

Unveiling the Mechanisms of the 1819 M 7.7 Kachchh Earthquake, India: Integrating Physics‐Based Simulation and Strong Ground Motion Estimates

AbstractThis study provided a comprehensive understanding of the source process of the 1819 M 7.7 Kachchh Indian earthquake using physics‐based dynamic rupture modeling and strong ground motion simulations. We successfully simulated the spontaneous dynamic rupture along a curved non‐planar fault using the 3‐D curved‐grid finite‐difference method (CGFDM). The estimated earthquake magnitude is around 7.6, consistent with previous estimations. Our simulations accurately replicated macroscopic rupture patterns and surface deformation, showing agreement with observed data along the Allah Bund fault (ABF) with a maximum displacement ∼5.5 m at the Earth's surface. The maximum modeled coseismic slip on the fault was approximately 7.5 m. Notably, the ABF exhibited characteristics of a weak barrier (leaky barrier) at the bending part, allowing the rupture to propagate further. Despite limitations in surface deformation calculations, the modeled values aligned with the trend of surface fault slip, with a slight deviation in the epicenter toward the east compared to earlier studies. We observed a homogeneous principal stress oriented N25°E, consistent with the present day Indian plate motion. The estimated horizontal peak ground velocities (PGVh) and the maximum value of Intensity X+ aligns well with observations. Furthermore, conducting thorough case studies on significant earthquakes and potential seismic scenarios in stable continental regions is crucial. Such studies play a vital role in validating and improving dynamic rupture models. When combined with statistical methods, this research holds great promise for advancing seismic hazard assessments, earthquake engineering, and strategies for disaster management.

Debris Avalanches in the Northern California Coast Range Triggered by Plate-Boundary Earthquakes

ABSTRACT Determining the timing and cause for ancient hillslope failures is challenging in the western United States, yet it is critical because it can inform ground-motion estimates for large-to-great earthquakes on plate-boundary faults. We identify two preinstumental debris avalanches (3×106 and 4.28×106 m3) in the Coast Ranges of northern California. These debris avalanches are well suited for studying the timing (to the exact year) and trigger, as the densely forested landscape enables effective use of dendrochronology and high-resolution radiocarbon to pinpoint the time of failure, and the legacy of landslide studies in the region provides context for evaluating a seismic trigger as the most likely triggering mechanism. Neither of the debris avalanches was triggered by the largest precipitation event of the instrumental record in 1964 C.E., and the two debris avalanche sites are physiographically suited to experience topographic amplification of seismic shaking. Through a suite of geologic and dendrochronologic evidence, we establish the likely time of failure of the two debris avalanches as 1906 C.E. and 933 C.E. The failure of the younger landslide coincides with the 1906 C.E. San Francisco earthquake on the Pacific/North American plate boundary, whereas the older landslide dates to 933 C.E., within the broad age range (850–966 C.E.) of a likely megathrust earthquake on the Cascadia subduction zone as recorded in coastal marshes in Humboldt Bay, California. If the older landslide was triggered by a subduction earthquake, then the precise age of the 933 C.E. debris avalanche would trim the uncertainty on a Cascadia subduction zone event from 116 yr to 1 yr. Utilizing debris avalanche records from sites suitable to record seismic shaking improves understanding of plate-boundary earthquake timing and extent of shaking.

Evolution of Coastal Environments under Inundation Scenarios Using an Oceanographic Model and Remote Sensing Data

A new methodology to map Italian coastal areas at risk of flooding is presented. This approach relies on detailed projections of the future sea level from a high-resolution, three-dimensional model of the Mediterranean Sea circulation, on the best available digital terrain model of the Italian coasts, and on the most advanced satellite-derived data of ground motion, provided by the European Ground Motion Service of Copernicus. To obtain a reliable understanding of coastal evolution, future sea level projections and estimates of the future vertical ground motion based on the currently available data were combined and spread over the digital terrain model, using a GIS-based approach specifically developed for this work. The coastal plains of Piombino-Follonica and Marina di Campo (Tuscany Region), Alghero-Fertilia (Sardinia), and Rome and Latina-Sabaudia (Lazio Region) were selected as test cases for the new approach. These coastal stretches are important for the ecosystems and the economic activities they host and are relatively stable areas from a geological point of view. Flood maps were constructed for these areas, for the reference periods 2010–2040, 2040–2070, and 2040–2099. Where possible, the new maps were compared with previous results, highlighting differences that are mainly due to the more refined and resolved sea-level projection and to the detailed Copernicus ground motion data. Coastal flooding was simulated by using the “bathtub” approach without considering the morphodynamic processes induced by waves and currents during the inundation process. The inundation zone was represented by the water level raised on a coastal DTM, selecting all vulnerable areas that were below the predicted new water level. Consequent risk was related to the exposed asset.

Graph-learning approach to combine multiresolution seismic velocity models

SUMMARY The resolution of velocity models obtained by tomography varies due to multiple factors and variables, such as the inversion approach, ray coverage, data quality, etc. Combining velocity models with different resolutions can enable more accurate ground motion simulations. Toward this goal, we present a novel methodology to fuse multiresolution seismic velocity maps with probabilistic graphical models (PGMs). The PGMs provide segmentation results, corresponding to various velocity intervals, in seismic velocity models with different resolutions. Further, by considering physical information (such as ray path density), we introduce physics-informed probabilistic graphical models (PIPGMs). These models provide data-driven relations between subdomains with low (LR) and high (HR) resolutions. Transferring (segmented) distribution information from the HR regions enhances the details in the LR regions by solving a maximum likelihood problem with prior knowledge from HR models. When updating areas bordering HR and LR regions, a patch-scanning policy is adopted to consider local patterns and avoid sharp boundaries. To evaluate the efficacy of the proposed PGM fusion method, we tested the fusion approach on both a synthetic checkerboard model and a fault zone structure imaged from the 2019 Ridgecrest, CA, earthquake sequence. The Ridgecrest fault zone image consists of a shallow (top 1 km) high-resolution shear-wave velocity model obtained from ambient noise tomography, which is embedded into the coarser Statewide California Earthquake Center Community Velocity Model version S4.26-M01. The model efficacy is underscored by the deviation between observed and calculated traveltimes along the boundaries between HR and LR regions, 38 per cent less than obtained by conventional Gaussian interpolation. The proposed PGM fusion method can merge any gridded multiresolution velocity model, a valuable tool for computational seismology and ground motion estimation.

Empirical models for Fourier amplitude spectrum of ground-motion calibrated on data from the Iranian plateau

Ground-motion models (GMMs) are frequently used in engineering seismology to estimate ground motion intensities. The majority of GMMs predict the response spectral ordinates (such as spectral acceleration) of a single-degree-of-freedom oscillator because of their common application in engineering design practices. Response spectra show how an idealized structure reacts to applied ground motion; however, they do not necessarily represent the physics of ground motion. The functional forms of the response spectra GMMs are built around ideas taken from the Fourier spectral concept. Assuming the validity of Fourier spectral concepts in the response spectral domain could cause physically inexplainable effects. In this study, using a mixed-effects regression technique, we introduce four models capable of predicting the Fourier amplitude spectrum that investigates the impact of incorporating random-effect event and station terms and variations in using a mixed-effects regression technique in one or two steps using truncated dataset or all data (nontruncated dataset). All data consists of 2581 three-component strong ground motion data resulting from 424 events with magnitude ranging from 4.0 up to 7.4, from 1976 to 2020, and 706 stations. The truncated dataset’s records, events, and stations are reduced to 2071, 408, and 636, respectively. As part of this study, we develop GMMs to predict the Fourier amplitude spectrum for the Iranian plateau within the frequency range of 0.3–30 Hz. We adopted simple, functional forms for four models, and we included a limited number of predictors, namely Mw (moment magnitude), Rjb (Joyner–Boore distance), and VS30 (time-averaged shear-wave velocity in the top 30 m). Due to statistical analyses, the style-of-faulting term was excluded from the final functional forms. The robustness of the derived models is indicated by unbiased residual variation with predictor variables.

Prediction of Fourier Amplitude Spectrum of Ground Motion in Mexico City from Subduction Thrust Earthquakes

Mexico City, one of the largest cities in the world, experiences frequent, devastating interplate earthquakes that originate on the subduction thrust along the Pacific coast of Mexico more than 250 km away. A notorious example is the 19 September 1985 Michoacán earthquake (Mw 8.0) which killed ~ 10,000 persons and caused wide-spread destruction in the city. The main cause of seismic damage in Mexico City is its subsoil that amplifies the ground motion. In view of the seismic hazard faced by the city, a reliable estimation of ground motion during future earthquakes is of vital importance. We present a new ground motion prediction equation (GMPE) for the Fourier amplitude acceleration spectrum (FAS) from subduction thrust earthquakes along the Pacific coast of Mexico at the reference station, CU, located in the firm zone of Mexico City. The GMPE is derived via Bayesian regression analysis and is based on an enlarged set of recordings (1965-2020; 40 earthquakes; 250 ≤ R≤ 500 km; 5 ≤ Mw ≤ 8.0). An important feature of the new GMPE is that it includes a term to model different attenuation along different ray paths. The inclusion of this term leads to a reduction in the aleatory variability of the GMPE, particularly at high frequencies. Since spectral amplification of seismic waves with respect to CU is known at many sites in the city, the FAS at these sites can be computed if it is known at CU. The new GMPE has been incorporated in a fully Fourier-based probabilistic seismic hazard analysis of Mexico City.

Full-field solution from an oblique shock to estimate ground motion from blasting

The full-field solution (FFS) of the seismic radiation from a blasthole is refined with the use of a source function consistent with the shock physics on the detonation shock/rock interface. This source function accounts for the directionality of the flow in the detonation front, that is curved under the influence of the confining rock, inducing on it an oblique shock mostly on axial direction, contrary to current models that consider a radial source. The interaction of the explosive/confiner system is solved with the oblique shock local polar theory. From this analysis, pressure (p) versus deflection angle (Θ) polar curves are constructed and plotted in the p-Θ plane. The intersection of the explosive and confiner polar curves corresponds to the possible shock matching solution across the explosive/rock interface, which leads to a shock incident angle. A code has been written in MATLAB to perform the shock polar analysis. The resulting detonation front angle of the explosive with the confiner is considered to build a combined radial/axial source. For this, the displacement potentials for the axial component of the source are formulated and incorporated to the solution. Experimental data from quarry blasting are used to illustrate the application of the FFS model and the calibration of results in terms of attenuation coefficients. The effect of introducing the radial/axial combined source is also discussed.

Impact of Ergodic and Nonergodic Ground Motion Estimation on the Earthquake Resilience of Shared Distributed Energy Resource Systems

Impact of Ergodic and Nonergodic Ground Motion Estimation on the Earthquake Resilience of Shared Distributed Energy Resource Systems

Regionally adjusted stochastic earthquake ground motion models, associated variabilities and epistemic uncertainties

An optimisation-based calibration technique, using the area metric, is applied to determine the input parameters of a stochastic earthquake-waveform simulation method. The calibration algorithm updates a model prior, specifically an estimate of a region’s seismological (source, path and site) parameters, typically developed using waveform data, or existing models, from a wide range of sources. In the absence of calibration, this can result in overestimates of a target region’s ground motion variability, and in some cases, introduce biases. The proposed method simultaneously attains optimum estimates of median, range and distribution (uncertainty) of these seismological parameters, and resultant ground motions, for a specific target region, through calibration of physically constrained parametric models to local ground motion data. We apply the method to Italy, a region of moderate seismicity, using response spectra recorded in the European Strong Motion (ESM) dataset. As a prior, we utilise independent seismological models developed using strong motion data across a wider European context. The calibration obtains values of each seismological parameter considered (such as, but not limited to, quality factor, geometrical spreading and stress drop), to develop a suite of optimal parameters for locally adjusted stochastic ground motion simulation. We consider both the epistemic and aleatory variability associated with the calibration process. We were able to reduce the area metric (misfit) value by up to 88% for the simulations using updated parameters, compared to the initial prior. This framework for the calibration and updating of seismological models can help achieve robust and transparent regionally adjusted estimates of ground motion, and to consider epistemic uncertainty through correlated parameters.

A modified stochastic finite-fault method for estimating strong ground motion: Validation and application

We developed a modified stochastic finite-fault method for estimating strong ground motions. An adjustment to the dynamic corner frequency was introduced, which accounted for the effect of the location of the subfault relative to the hypocenter and rupture propagation direction, to account for the influence of the rupture propagation direction on the subfault dynamic corner frequency. By comparing the peak ground acceleration (PGA), pseudo-absolute response spectra acceleration (PSA, damping ratio of 5%), and duration, the results of the modified and existing methods were compared, demonstrating that our proposed adjustment to the dynamic corner frequency can accurately reflect the rupture directivity effect. We applied our modified method to simulate near-field strong motions within 150 km of the 2008 MW7.9 Wenchuan earthquake rupture. Our modified method performed well over a broad period range, particularly at 0.04–4 s. The total deviations of the stochastic finite-fault method (EXSIM) and the modified EXSIM were 0.1676 and 0.1494, respectively. The modified method can effectively account for the influence of the rupture propagation direction and provide more realistic ground motion estimations for earthquake disaster mitigation.

Comparison and selection of ground motion prediction equations for the Sichuan–Yunnan area, southwest China

The Sichuan–Yunnan area is one of the most seismically active regions in China. As ground-motion models form a key component of seismic hazard analysis, it is important to select (or develop) appropriate models for this area. The increasing number of digital ground-motion records of earthquakes in this area has allowed the development of local ground motion prediction equations (GMPEs). This study compares and, later, recommends appropriate GMPEs for the Sichuan–Yunnan area. We first evaluate the inherent quality of local GMPEs, with respect to their underlying datasets, the variables used and their functional forms, to determine a set of candidate GMPEs. Then we investigate how well the predictions from the GMPEs match observations computed from strong-motion records of recent earthquakes in this area. The fit between predictions and observations varies significantly amongst the GMPEs. The results suggest that some recent local GMPEs would lead to biased ground-motion estimates due to limitations of their underlying datasets and functional forms. Based on both evaluations of inherent quality and compatibility with observations, only one local GMPE is recommended. A comparison of the predictions from three widely-used non-local GMPEs indicates that ground motions in the Sichuan–Yunnan area appear more variable than those in other regions but that predictions from these non-local GMPEs are generally unbiased. We recommend use of a mixture of robust local and non-local GMPEs within seismic hazard analyses to capture the epistemic uncertainty in ground-motion prediction for this area.

Virtual Shake Robot: Simulating Dynamics of Precariously Balanced Rocks for Overturning and Large-displacement Processes

Understanding the dynamics of precariously balanced rocks (PBRs) is important for seismic hazard analysis and rockfall prediction. Utilizing a physics engine and robotic tools, we develop a virtual shake robot (VSR) to simulate the dynamics of PBRs during overturning and large-displacement processes. We present the background of physics engines and technical details of the VSR, including software architecture, mechanical structure, control system, and implementation procedures. Validation experiments show the median fragility contour from VSR simulation is within the 95% prediction intervals from previous physical experiments, when PGV/PGA is greater than 0.08 s. Using a physical mini shake robot, we validate the qualitative consistency of fragility anisotropy between the VSR and physical experiments. By overturning cuboids on flat terrain, the VSR reveals the relationship between fragility and geometric dimensions (e.g., aspect and scaling ratios). The ground motion orientation and lateral pedestal support affect PBR fragility. Large-displacement experiments estimate rock trajectories for different ground motions, which is useful for understanding the fate of toppled PBRs. Ground motions positively correlate with large displacement statistics such as mean trajectory length, mean largest velocity, and mean terminal distance. The overturning and large displacement processes of PBRs provide complementary methods of ground motion estimation.

On the Use of Instrumental and Macroseismic Data to Evaluate Ground-Motion Models: The 2019 Mw 6.4 Durres, Albania, Earthquake Sequence

Abstract In this study, we analyze the existing ground-motion models (GMMs) applicable in Albania for horizontal peak ground acceleration (PGA) and spectral acceleration (SA) using instrumental ground motions, and also incorporate online citizen responses from “Did you feel it?” (DYFI) to compensate for the sparse distribution of strong-motion stations and provide better constraints for near-fault motions. Our evaluation focuses primarily on the damaging 26 November 2019 Mw 6.4 Durres earthquake, incorporating 1360 DYFI online citizen responses collected after the Durres mainshock event, along with two significant September foreshocks and two large November aftershocks with a moment magnitude Mw>5.0. In general, the DYFI intensities exhibit higher values than instrumentation data, and we find that SA at 0.3 s better represents the observed macroseismic intensities for all events. In the meantime, the reversible relationships between macroseismic intensities and PGA/SA, as established by Oliveti et al. (2022) based on a dataset from the European region (Italy), show a better fit for the converted DYFI observations when compared to instrumental data, in contrast to the fit of the converted DYFI observations by Worden et al. (2012). This underscores the importance of regional characterization when considering the datasets from online citizen responses. The extensive DYFI intensities set, particularly in near-fault regions, significantly improves the evaluation of GMMs due to the sparse distribution of instrumentation data. Moreover, we account for data variance, and applied the log-likelihood approaches to select and rank a candidate set of GMMs. In addition to recommending a set of GMMs suitable for the Albania region, our study highlights the valuable applications of using online citizen responses like DYFI for ground-motion estimations, which are crucial in regions with limited instrumental station coverage. These online citizen response datasets contribute to better constraining the selection of GMMs, although careful consideration is necessary when relating intensity to ground motion for regional characterization. Our study makes a significant contribution to GMM selection and provides a valuable reference for the logic tree structure in subsequent seismic hazard assessments on both national and regional scales.

Rapid Earthquake Rupture Characterization for New Zealand Using the FinDer Algorithm

ABSTRACT Immediately after a significant earthquake, rapid scientific information is critical for response decision-making and estimating secondary hazards, and is a key component of advisories and public communication. Characterization of the fault rupture extent is especially valuable because it strongly controls ground-motion estimates, or tsunami forecasts in offshore settings. The Finite-fault rupture Detector (FinDer) is designed to rapidly estimate location, extent, and orientation of earthquake fault rupture by matching spatial distributions of high-frequency seismic amplitudes with precomputed templates. Under a large public initiative to better prepare for and respond to natural disasters, FinDer is being implemented in New Zealand for rapid source characterization. Here, we report on implementation and performance, including offline and real-time testing using configurations modified for the New Zealand setting. Systematic testing is used to inform guidelines for real-time usage and interpretation. Analysis of rupture parameter recovery when using national network GeoNet stations demonstrates that for moderate (M 6+) onshore earthquakes FinDer can resolve magnitude and location well, and the rupture strike is also well determined for large (M 7+) onshore earthquakes. For near-offshore earthquakes (within 100 km), FinDer can provide reasonable magnitude estimates but cannot determine the location or strike. Real-time testing shows reliable detection for onshore earthquakes of M 4.5+, with reasonable location and magnitude accuracy. First detection times range between 7 and 65 s of earthquake origin, and stable solutions even for large (M 7+) magnitude events are delivered within 2 min. Although the GeoNet seismic network is not optimized for earthquake early warning, this provides a first exploration of network-based capability for New Zealand. Offline testing of significant M 7+ historic earthquakes demonstrates that FinDer’s rupture solutions can be used to improve rapid shaking predictions, and may be used to infer additional directivity and tsunami hazard even for complex events like the 2016 M 7.8 Kaikōura earthquake.

Sediment thickness map of United States Atlantic and Gulf Coastal Plain Strata, and their influence on earthquake ground motions

With the recent successful accounting of basin depth ground-motion adjustments in seismic hazard analyses for select areas of the western United States, we move toward implementing similar adjustments in the Atlantic and Gulf Coastal Plains by constructing a sediment thickness model and evaluating multiple relevant site amplification models for central and eastern United States seismic hazard analyses. We digitize and combine existing sediment thickness data sets into a composite surface that delineates the base of Cretaceous sediments under the Atlantic Coastal Plain and the base of Mesozoic sediments under the Gulf Coastal Plain. Amplification models dependent on sediment thickness, site natural period, and source-to-site path length are compared with data sets of observed ground motions to evaluate the ability of the new models to improve ground motion estimates. We find that the amplification models can account for observed trends in sediment-thickness and period-dependent residuals, but some tuning is required. For example, the model of Chapman and Guo requires a reference VS30, the time-averaged shear-wave velocity within 30 m of the Earth’s surface, for non-Coastal Plain sites, which we estimate to be between about 1 and 2 km/s. Along with our sediment thickness model, we estimate a velocity profile for application to the Harmon et al. site-natural-period-based model in order to best match the Chapman and Guo period dependence for a broad range of sediment thicknesses. The Next Generation of Attenuation models for the eastern United States Gulf Coast path-based adjustment models can also account for seismic attenuation in the Coastal Plain sediments and reduce the standard deviation of total residuals. If enacted in the U.S. Geological Survey National Seismic Hazard Model, these amplification models will reduce predicted short-period (<1 s) and increase predicted long-period (>1 s) ground motions in the Coastal Plains appreciably.

Ground motion models for Fourier amplitude spectra and response spectra using Machine learning techniques

AbstractOne of the main objectives in engineering seismology or in seismic hazard studies is to estimate the possible ground motion for a given earthquake scenario. In the sparse data regions mostly the ground motion models (GMM) are developed using either seismological models or hybrid empirical approaches. However, if these GMMs do not accommodate the regional seismological attributes, a large uncertainty in ground motion estimates is possible. To overcome this concern, scaling 5% damped Pseudo Spectral Acceleration (PSA) from Fourier amplitude spectra (FAS) proves to be physically consistent as it can capture both spatial and temporal characteristics of the ground motion. Hence, the present study aims to develop a GMM for PSA using FAS and significant duration as the predictor variables. However, since there are few GMMs available for FAS, the current study also aims to develop a GMM for FAS using the earthquake parameters as the predictor variables for intraplate regions. This article employs an Artificial Neural Network (ANN) to develop both GMMs using the Next Generation Attenuation (NGA)‐East database for both horizontal (Effective Amplitude spectra for FAS and RotD50 component for PSA) and vertical components. To verify the performance of the developed models, the residuals analysis and parametric studies have been performed. The parametric study shows that the GMMs can capture the magnitude and distance scaling consistent with the observations. Further, the PSA GMM compared with the global GMMs and it is observed that the predictions lie well within the median of all the available models, proving the models’ effectiveness in estimating the ground motion predictions for future data. The developed model can be used only within the considered ranges of the predicted variables such as rupture distances between [19.05–1000] km, the Mw ranges from [3.12–5.74] with Vs30 in the range of [209–2000] m/s.