Prediction Of Strong Ground Motion Research Articles

A Novel VS30 Prediction Strategy Taking Fluid Saturation into Account and a New VS30 Model of Türkiye

ABSTRACT The averaged shear-wave velocity of the top 30 m (VS30) is widely used in earthquake engineering as a proxy to represent site responses. However, the spatial availability of measured VS30 is rather limited, and, so far, a region-specific VS30 model that would aid prediction of strong ground motions is not yet developed for Türkiye. In this study, a new strategy for predicting VS30 is developed using data from Türkiye and California. At first, VS30 measurements are classified into four sedimentary classes according to their ages (Quaternary–Pliocene, Miocene, Paleogene, and Pre-Paleogene) and three nonsedimentary classes (Intrusive, Extrusive, and Metamorphic). Observations from Quaternary–Pliocene deposits are most abundant and characterized by large data scatter, thus further divided into two major landform groups. Because the reduction of VS with saturation is pronounced in soils due to capillary forces, Quaternary–Pliocene deposits are also differentiated as wet if the water table depth is less than 30 m and dry otherwise. In California, available groundwater measurements are utilized while flat areas with elevation differences less than 30 m from water bodies (sea, lake, and major rivers) are mapped out as wet zones throughout Türkiye. After the elimination of outliers, slope and elevation-based VS30 prediction equations are developed separately for subclasses of Quaternary–Pliocene, Miocene, and Paleogene-aged sedimentary units using multivariable linear regression, whereas VS30 values of Pre-Paleogene sedimentary and nonsedimentary units are fixed to the mean of each subclass. Resultant model misfits and comparisons with measurements from the microzonation study conducted across İstanbul clearly indicate that our proposed VS30 prediction strategy is performing better than the competing models tested, especially in the youngest sedimentary units, and thus provides a new, accurate VS30 model of Türkiye.

Model selection for prediction of strong ground motion peaks in Türkiye

Model selection for prediction of strong ground motion peaks in Türkiye

The spatial properties of the site amplifications of S-waves by generalized spectral inversion technique and the correction method of the site amplifications considering the contribution of later arrivals after major S-waves

Site amplification is an important component of strong ground motion prediction as it differs among sites, reflecting its specific local subsurface geology. Here, we confirm that site amplifications are similar in a neighborhood area over a long period. However, few studies have investigated the spatial properties in a wide region (i.e., the whole of Japan). In this study, we explored the spatial properties of site amplifications based on the generalized inversion technique (GIT) using Fourier amplitude spectra (FAS) as well as pseudo-velocity response spectra (pSv) as the latter is an important index for engineering purposes and the most similar type of response spectra to FAS. The spatial distributions of S-wave site amplifications (SA-S), especially within large sediment basins (e.g., the Kanto and Osaka Basins in Japan), were found to be relatively similar in proximate areas for a long period ranging from 2 to 8 s. This suggests that we could easily predict the site amplifications using an empirical approach through spatial interpolation based on the properties obtained by the GIT. Furthermore, we propose a prediction procedure for site amplification for the whole duration from the SA-S at an arbitrary site. We used the correction function, which converts the SA-S to the site amplification for the whole duration (SA-W), including an S-wave portion and a subsequent portion. This function is called the whole-duration to S-wave spectral ratio (WSR) and is stable in terms of spatial properties. As we could estimate the SA-S either by theoretical transfer functions or observed microtremors, we can easily predict the SA-W based on the proposed WSR concept. We found that SA-S in pSv is more or less similar to SA-S in FAS, however, SA-W in pSv fails to capture the effects of the long duration of ground motions inside a large basin so that we cannot recommend to use pSv for the prediction of whole duration of ground motion.Graphical

DiTing: A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology

In recent years, artificial intelligence technology has exhibited great potential in seismic signal recognition, setting off a new wave of research. Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology research. In this study, based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center, we constructed an artificial intelligence seismological training dataset (“DiTing”) with the largest known total time length. Data were recorded using broadband and short-period seismometers. The obtained dataset included 2,734,748 three-component waveform traces from 787,010 regional seismic events, the corresponding P- and S-phase arrival time labels, and 641,025 P-wave first-motion polarity labels. All waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an earthquake. Each three-component waveform contained a considerable amount of descriptive information, such as the epicentral distance, back azimuth, and signal-to-noise ratios. The magnitudes of seismic events, epicentral distance, signal-to-noise ratio of P-wave data, and signal-to-noise ratio of S-wave data ranged from 0 to 7.7, 0 to 330 km, –0.05 to 5.31 dB, and –0.05 to 4.73 dB, respectively. The dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection, seismic phase picking, first-motion polarity determination, earthquake magnitude prediction, early warning systems, and strong ground-motion prediction. Such research will further promote the development and application of artificial intelligence in seismology.

High‐Resolution Fault‐Rupture Imaging by Combining a Backprojection Method With Binarized MUSIC Spectral Image Calculation

AbstractBackprojection (BP) methods are widely used for estimating the fault rupture processes; however, they are inherently susceptible to noise. Hence, noise suppression is an important research target. In this paper, we develop a fault rupture imaging method by combining beamforming‐based BP and MUltiple Signal Classification (MUSIC), which realizes artificial noise suppression at a high spatial resolution. The stations are grouped into arrays according to the SH wave radiation coefficients, and MUSIC analysis is performed on each array. The MUSIC spectral images of these arrays are binarized and then multiplied by the BP images. Spatial filtering is also applied to the images based on the possible range of the rupture velocity and rise time. When tested using synthetic test data, the proposed method worked as expected. We then applied this method to the 2016 Kumamoto earthquake by interpolating the travel times from the observed travel times of relocated hypocenters using a 3‐D velocity structure model. In the area of large slip and slip rate approximately 30–50 km from the hypocenter on the Futagawa fault, the spatiotemporal evolution of the fault ruptures and waveform inversion results were generally in harmony. The distributions of the low‐ and high‐frequency seismic radiations are complementary, as is understood in the context of fault rupture physics. This method can aid in understanding and modeling the details of seismic radiation sources, enabling the accurate prediction of strong ground motion even in near‐fault areas.

Strong ground-motion prediction equations from induced earthquakes in St. Gallen geothermal field, Switzerland

Abstract Ground shaking, whether it is due to natural or induced earthquakes, has always been a matter of concern since it correlates with structural/non-structural damage and can culminate in human anxiety. Industrial activities such as water injection, gas sequestration and waste fluid disposals, promote induced seismicity and consequent ground shaking that can hinder ongoing activities. Therefore, keeping in mind the importance of timely evaluation of a seismic hazard and its mitigation for societal benefits, the present study proposes specifically designed ground-motion prediction equations (GMPEs) from induced earthquakes in the St. Gallen geothermal area, Switzerland. The data analysed in this study consist of 343 earthquakes with magnitude −1.17 ≤ ML, corr ≤ 3.5 and hypocentral distance between 4 and 15 km. The proposed study is one of the first to incorporate ground motions from negative magnitude earthquakes for the development of GMPEs. The GMPEs are inferred with a two-phase approach. In the first phase, a reference model is obtained by considering the effect of source and medium properties on the ground motion. In the second phase the final model is obtained by including a site/station effect. The comparison between the GMPEs obtained in the present study with GMPEs developed for the other induced seismicity environments highlights a mismatch that is ascribed to differences in regional seismic environment and local site conditions of the respective regions. This suggests that, when dealing with induced earthquakes, GMPEs specific for the study should be inferred and used for both monitoring purposes and seismic hazard analyses.

Near-Real-Time Strong Motion Acquisition at National Scale and Automatic Analysis.

A strong motion monitoring network records data that provide an excellent way to study how source, path, and site effects influence the ground motion, specifically in the near-source area. Such data are essential for updating seismic hazard maps and consequently building codes and earthquake-resistant design. This paper aims to present the Italian Strong Motion Network (RAN), describing its current status, employment, and further developments. It has 648 stations and is the result of a fruitful co-operation between the Italian government, regions, and local authorities. In fact, the network can be divided into three sub-networks: the Friuli Venezia Giulia Accelerometric Network, the Irpinia Seismic Network, and all the other stations. The Antelope software automatically collects, processes, and archives data in the data acquisition centre in Rome (Italy). The efficiency of the network on a daily basis is today more than 97%. The automatic and fast procedures that run in Antelope for the real-time strong motion data analysis are continuously improved at the University of Trieste: a large set of strong motion parameters and correspondent Ground Motion Prediction Equations allow ground shaking intensity maps to be provided for moderate to strong earthquakes occurring within the Italian territory. These maps and strong motion parameters are included in automatic reports generated for civil protection purposes.

Evaluation of horizontal ground motion waveforms at Sedongpu Glacier during the 2017 M6.9 Mainling earthquake based on the equivalent Green's function

The terrain along the Tibet Railway is primarily steep mountains and valleys with complex geological structures. The occurrence of the Mainling earthquake indicates that the intensity of seismicity in the eastern structural junction area has strengthened, and the potential risk of earthquake-induced disasters such as landslides and debris flows is also increasing. The earthquake engineering community generally recognizes that the prediction of near-field strong ground motion can reduce the disasters caused by future large earthquakes and can guide earthquake prevention, disaster reduction and seismic design along the Sichuan–Tibet railway. The M6.9 Mainling earthquake affected the stability of Sedongpu Glacier near the epicentre. A glacier avalanche occurred under the coupling of multiple factors, including post-earthquake and precipitation events. The waveform and response spectra of a strong earthquake are key input parameters for investigating the seismic triggering mechanisms of landslides and glacier avalanches and accurately reproducing the dynamics of such disasters. In this paper, the concept of the equivalent Green's function is introduced, and various source parameter uncertainties are fully considered. The waveform and response spectra of the Mainling earthquake at Sedongpu Glacier are simulated, and the most likely seismic waveform is comprehensively assessed from multiple perspectives. This work can provide reliable acceleration waveform input for the analysis of ground motion damage and the study of earthquake-induced disaster mechanisms at important locations along the Sichuan–Tibet railway and provide a scientific reference for earthquake prevention and disaster reduction along the railway.

Predictive Simulation for Surface Fault Occurrence Using High-Performance Computing

Numerical simulations based on continuum mechanics are promising methods for the estimation of surface fault displacements. We developed a parallel finite element method program to perform such simulations and applied the program to reproduce the 2016 Kumamoto earthquake, where surface rupture was observed. We constructed an analysis model of the 5 × 5 × 1 km domain, including primary and secondary faults, and inputted the slip distribution of the primary fault, which was obtained through inversion analysis and the elastic theory of dislocation. The simulated slips on the surface were in good agreement with the observations. We then conducted a predictive simulation by inputting the slip distributions of the primary fault, which were determined using a strong ground motion prediction method for an earthquake with a specified source fault. In this simulation, no surface slip was induced in the sub-faults. A large surface slip area must be established near a sub-fault to induce the occurrence of a slip on the surface.

Rapid Prediction of Strong Ground Motions from Major Earthquakes: An Example in the Wudu Basin, Sichuan, China

ABSTRACT Rapid prediction of ground motions of major earthquakes can assist emergency response and postseismic damage assessment. Numerical simulations can perform such prediction. However, they need a large number of calculations based on real data and depend on large-scale computation resources, limiting practice of real-time prediction. To solve this problem, this article proposes a new prediction method based on a 3D transfer function, which is obtained by solving the site seismic response to three impulse function excitations that can be calculated by different pre-event 3D physics-based numerical simulations. This approach only needs one seismic record in the study area and can quickly (near-real time) predict ground motions in the whole study area. We verify the proposed approach with an Mw 5.7 aftershock of the 2008 Mw 7.9 Wenchuan earthquake in the Wudu basin, Sichuan Province, China. The results show that this method is feasible and has sufficient accuracy in nearly real-time prediction of seismic ground motions.

Strong Ground Motion Prediction Model for PGV and Spectral Velocity for the Chilean Subduction Zone

ABSTRACTGround-motion prediction models (GMPMs) are a critical tool in performing seismic hazard analyses; in turn, these studies condition structural designs. Consequently, new research has appeared not only with a regionalization focus but has also explored the prediction of intensities other than acceleration. We present a GMPM for peak ground velocity (PGV) and spectral velocity (Sv) for the Chilean subduction zone. Because of the limitations of VS30 as site proxy, the proposed model adds the site’s fundamental frequency (f0) as an explanatory variable for the site term in the GMPM. We developed the model for PGV and spectral response periods between 0.06 and 10 s. The total error (σ) of the model shows a slight reduction with the inclusion of the fundamental frequency (f0) compared with a similar model for the pseudoacceleration response spectrum in the same zone. We used the proposed model to predict structural damage during the 2010 Mw 8.8 Maule earthquake, showing a good fit with the geographical distribution of damage, and this creates an opportunity to characterize the seismic behavior of soil deposits, including basins, for urban planning.

Inversion of Love waves in earthquake ground motion records for two-dimensional S-wave velocity model of deep sedimentary layers

We propose a new waveform inversion method to estimate the 2D S-wave velocity structure of deep sedimentary layers using broadband Love waves. As a preprocessing operation in our inversion scheme, we decompose earthquake observation records into velocity waveforms for periods of 1 s each. Then, we include in the inversion only those periods for which the assumption of 2D propagation holds, which we propose to determine through a principal component analysis. A linearized iterative inversion analysis for the selected Love wave segments filtered for periods of 1 s each allows a detailed estimation of the boundary shapes of interfaces over the seismic bedrock with an S-wave velocity of approximately 3 km/s. We demonstrate the effectiveness of the technique with applications to observed seismograms in the Kanto Plain, Japan. The differences between the estimated and existing velocity structure models are remarkable at the basin edges. Our results show remarkable differences from previous existing structural models, particularly near the basin edges while being in good agreement with the surface geology. Since a subsurface structure at a basin edge strongly affects the earthquake ground motions in a basin with the generation of surface waves, our method can provide a detailed model of a complex S-wave velocity structure at an edge part for strong ground motion prediction.

Modeling of Subsurface Velocity Structures from Seismic Bedrock to Ground Surface in the Tokai Region, Japan, for Broadband Strong Ground Motion Prediction

For sophistication of strong ground motion prediction in terms of disaster mitigation, one of the principal issues is to model subsurface velocity structures so that characteristics of earthquake ground motions can be reproduced in the broadband range 0.1 Hz to 10 Hz. In recent years, subsurface structures have been modeled in sedimentary layers on seismic bedrock for a few regions of Japan, in a national project. In this study, subsurface velocity structures were modeled from seismic bedrock to the ground surface for the Tokai region. These models were constructed in accordance with the subsurface velocity structure modeling scheme published by the Headquarters for Earthquake Research Promotion. To begin with, initial models were constructed based on existing bore-hole data, geological information, etc. Next, they were improved based on results of microtremor explorations which had been conducted in recent years. It was found that the new model had different characteristics to the conventional model. This paper will present the modeling process and characteristics of distribution maps for velocity structures and amplification index.

Site Classification Method Based on Geomorphological and Geological Characteristics and Its Application in China

Abstract Earthquake insurance has become a topic of major importance in mainland China ever since the Mw 7.9 Wenchuan earthquake in 2008. Site‐condition amplification is one of the essential factors that influences the prediction of strong ground motion and the assessment of damages of buildings and structures. The site effects should hence be included in calculating earthquake damage and determining the insurance rate for insurance companies. In particular, large‐scale site classification is required for the implementation of earthquake insurance. This study first used a slope method for site classification in China through the VS30 values from the topographic slope data and the correlation between VS30 and the topographic slope. A novel site classification method, called the slope–geological method, was then proposed in this study to improve the slope method by combining geological characteristics, including geological age, the depositional environment, and soil properties. Four hundred and fifty three pieces of borehole data in Northern Plain and Sichuan of China were used to verify the effectiveness and difference of the slope method and the slope–geological method proposed in this study. Site‐condition classification maps of China were drawn using the slope method and slope–geological method, respectively.

Development of Dynamic Asperity Models to Predict Surface Fault Displacement Caused by Earthquakes

Asperity models for ground motion prediction is widely used in Japan. Here we expand the application of these asperity models to predict fault displacement caused by surface rupture. The proposed approach is rather simple and practical for the use in fault displacement hazard analysis in nuclear installations and other critical infrastructures, as it is the emphasis of the current topical issue. The proposed method mainly consists of two steps. The first step consists in the characterization of asperities at the seismogenic zone based on the kinematic asperity source model following Irikura’s recipe (Irikura and Miyake in Pure Applied Geophysics, 168:85–104, 2011) for strong ground motion prediction. Since the kinematic model does not take into account surface rupture mechanism, this first step assumes that the fault is buried, and then by trial and error procedure the stress drop on the asperities are estimated, so that the average slip at each asperity be consistent with the ones from the kinematic model. At this stage, the dynamic model predicts strong ground motion consistent with those from the kinematic model. In the second step, the surface rupture is included by calibrating the shallow layer (SL) with stress drop, strength excess and critical slip distance, so that the final fault displacement along the fault be consistent with observations. The 2010 Mw 7.0 Darfield (New Zealand) earthquake is used to test the proposed method. Surface-rupturing was observed in several sites along the main fault reaching values of fault displacement larger than 5 m. The main fault of this earthquake is strike-slip, almost vertical. Therefore, a simplified planar fault asperity model to capture the main features of the fault displacement is assumed. The fault dimensions are assumed to have a length of 60 km and a width of 24 km with three asperities. The preferred model of the first step predicts average slip for each asperity of 2.7, 2.7, and 2 m corresponding, respectively, to stress drops of 6.0 MPa, 8.5 MPa, and 7.0 MPa. In the second step, the surface rupture is calibrated assuming a SL zone of 3 km depth. We found that negative stress drop is not necessary in the SL, because this strongly inhibits surface rupturing. Our preferred model produces fault displacement distribution closer to the observed ones, but average slip at each asperity increases to 3.4 m, 3.2 and 2.8 m. This increase in average slip is due to the contribution of surface rupturing. Ground motion differences between fault-surface rupturing and buried models are negligible, except at the very near-source. These differences are attributed to the SL rupture that mainly affect the ground motion at the very near-source. Overall, our simple asperity model captures the main features of the observed fault displacement and near-source ground motion, proving that the proposed simple and practical two step-procedure provides meaningful estimate of fault displacement and near-source ground motion consistent with observations, as such, this method has the potential to be used in practical fault displacement hazard analysis.

Source Modeling for Predicting Ground Motions and Permanent Displacements Very Close to the Fault Trace

A conventional “recipe” for strong ground motion prediction has been applied to the seismic fault (deep fault; located within seismogenic layer). In order to perform assessments of strong ground motions and permanent displacements at sites very close to the fault trace, we proposed the method of modeling that takes the entire ruptured fault from the ground surface to the seismic fault into account. Our approach was validated by the simulation of observed records obtained at stations very close to the fault trace of the mainshock of the 2016 Kumamoto Japan, earthquake (Mw7.1). Also, through the ground motion assessment performed for a hypothetical strike-slip fault with a 90[Formula: see text] dip angle, we found that adding the shallow fault had virtually no effect on acceleration time history, but it had a clear effect on the fault-parallel component of velocity and displacement time histories in the area close to the fault trace.

Sensitivity analysis study of the source parameter uncertainty factors for predicting near-field strong ground motion

Uncertainty factors have substantial influences on the numerical simulations of earthquakes. However, most simulation methods are deterministic and do not sufficiently consider those uncertainty factors. A good approach for predicting future destructive earthquakes that is also applied to probabilistic hazard analysis is studying those uncertainty factors, which is very significant for improving the reliability and accuracy of ground-motion predictions. In this paper, we investigated several uncertainty factors, namely the initial rupture point, stress drop, and number of sub-faults, all of which display substantial influences on ground-motion predictions, via sensitivity analysis. The associated uncertainties are derived by considering the uncertainties in the parameter values, as those uncertainties are associated with the ground motion itself. A sensitivity analysis confirms which uncertainty factors have large influences on ground motion predictions, based upon which we can allocate appropriate weights to those uncertainty factors during the prediction process. We employ the empirical Green function method as a numerical simulation tool. The effectiveness of this method has been previously validated, especially in areas with sufficient earthquake record data such as Japan, Southwest China, and Taiwan, China. Accordingly, we analyse the sensitivities of the uncertainty factors during a prediction of strong ground motion using the empirical Green function method. We consequently draw the following conclusions. (1) The stress drop has the largest influence on ground-motion predictions. The discrepancy between the maximum and minimum PGA among three different stations is very large. In addition, the PGV and PGD also change drastically. The Arias intensity increases exponentially with an increase in the stress drop ratio of two earthquakes. (2) The number of sub-faults also has a large influence on various ground-motion parameters but a small influence on the Fourier spectrum and response spectrum. (3) The initial rupture point largely influences the PGA and Arias intensity. We will accordingly pay additional attention to these uncertainty factors when we conduct ground-motion predictions in the future.

S-wave attenuation of the shallow sediments in the North China basin based on borehole seismograms of local earthquakes

S-wave velocity and attenuation structures of shallow sediments play important roles in accurate prediction of strong ground motion. However, it is more difficult to investigate the attenuation than velocity structures. In this study, we developed a new approach for estimating frequency-dependent S-wave attenuation (⁠|$Q_S^{ - 1}$|⁠) structures of shallow sediments based on multiple time window analysis of borehole seismograms from local earthquakes. Multiple time windows for separating direct and surface-reflected S-waves in local earthquake waveforms at borehole stations are selected with a global optimization scheme. With respect to different time windows, the transfer functions between direct and surface-reflected S-waves are achieved with a weighted averaging scheme, based on which frequency dependent |$Q_S^{ - 1}$| values are obtained. Synthetic tests suggest that the proposed method can restore robust and reliable |${\rm{\ }}Q_S^{ - 1}$| values, especially when the data set of local earthquakes is not abundant. We utilize this method for local earthquake waveforms at 14 borehole seismic stations in the North China basin, and obtain |$Q_S^{ - 1}$| values in 2–10 Hz frequency band, as well as average |${V_P}$|⁠, |${V_S}$| and |${V_P}/{\rm{\ }}{V_S}$| ratio for shallow sediments deep to a few hundred metres. Results suggest that |$Q_S^{ - 1}$| values are to 0.01–0.06, and generally decrease with frequency. The average attenuation structure of shallow sediments within the depth of a few hundred metres beneath 14 borehole stations in the North China basin can be modelled as |$Q_S^{ - 1} = {\rm{\ }}0.056{f^{ - 0.61}}$|⁠. It is generally consistent with the attenuation structure of sedimentary basins in other areas, such as Mississippi Embayment sediments in the United States and Sendai basin in Japan.

Prediction of Strong Ground Motion Using Fuzzy Inference Systems Based on Adaptive Networks

Prediction of Strong Ground Motion Using Fuzzy Inference Systems Based on Adaptive Networks

Real‐Time Detection of Rupture Development: Earthquake Early Warning Using P Waves From Growing Ruptures

AbstractLarge earthquakes with long rupture durations emit P wave energy throughout the rupture period. Incorporating late‐onset P waves into earthquake early warning (EEW) algorithms could contribute to robust predictions of strong ground motion. Here I describe a technique to detect in real time P waves from growing ruptures to improve the timeliness of an EEW algorithm based on seismic wavefield estimation. The proposed P wave detector, which employs a simple polarization analysis, successfully detected P waves from strong motion generation areas of the 2011 Mw 9.0 Tohoku‐oki earthquake rupture. An analysis using 23 large (M ≥ 7) events from Japan confirmed that seismic intensity predictions based on the P wave detector significantly increased lead times without appreciably decreasing the prediction accuracy. P waves from growing ruptures, being one of the fastest carriers of information on ongoing rupture development, have the potential to improve the performance of EEW systems.