Ground Motion Prediction Equations 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.

Prediction Equations for Peak-Ground Accelerations and Velocities in Northeast Japan Using the S-net Data

S-net is a seafloor observation network for earthquakes and tsunamis around the Japan Trench, comprising 150 observatories with seismometers and pressure gauges. The region has been known to experience massive earthquakes, and several magnitude 6 and 7 class earthquakes have occurred after the network was established in 2016. This study constructed ground motion prediction equations (GMPEs) for horizontal peak ground accelerations (PGAs) and peak ground velocities (PGVs) using the S-net data and revealed that the GMPEs can be used to predict the PGAs and PGVs at the land stations where measured S-wave velocities are available. We used a relatively short time window of the S-net records from the viewpoint of earthquake early warning but included S waves. Data from earthquakes of magnitudes between Mw 5.5 and Mw 7.4 were used. The construction of the GMPEs was achieved in two steps. First, regression analysis was conducted for each event data, and mean site residual was obtained over the available records at each S-net site. Second, the data were adjusted by the mean site residuals, and stratified regression analysis, which decouples the source and path factors, was performed. Finally, we applied the GMPEs to predict PGAs and PGVs at the KiK-net sites on land. We determined that the residuals at the KiK-net sites were systematically biased with Vs30 (average S-wave velocity in the upper 30 m). We obtained correction factors for the bias and demonstrated that the PGAs and PGVs at the KiK-net sites could be predicted reasonably well.

Earthquake Source database for Chinese strong motion flatfile in 2007-2020

The National Strong-Motion Observation Network System of China has collected over 12000 strong-motion recordings from 2007 to December 2020. This study compiled the source-related metadata of totally 1920 earthquakes associated with assembled well-processed recordings of China. The earthquake basic information, focal mechanisms, and the fault geometry were collected from various institutes and literature. We recommended the MW values for 900 earthquakes, the fault types for 1064 earthquakes, and the fault geometries for 18 large earthquakes. We also performed the statistics analysis for establishing the empirical conversions of MW-MS, and ML, and providing the empirical relationships between MW and ruptured area, aspect ratio, respectively. Moreover, the ruptured fault geometries of large earthquakes were used to preliminarily divide all earthquakes considered into 1141 mainshocks, and 779 aftershocks. The finite-fault distances (RJB and Rrup) of strong-motion recordings from the 18 large earthquakes were calculated, and then used to yield the statistic relationships between the point-source distances (Repi and Rhyp) and finite-fault distances. We finally provided the earthquake source database freely accessible at website. The source-related metadata can be directly applied to develop the ground motion prediction equations of China.

Dependence of horizontal PGA prediction on site conditions at critically short hypocentral distances: an analysis based on Japanese strong motion data

A fundamental element in seismic hazard assessment studies is the prediction of maximum values of ground accelerations. This is achieved through an attenuation relationship, also known as the ground motion prediction equation (GMPE). Seismic design of structures generally assumes the occurrence of a large earthquake on a nearby fault. The predictions from the GMPE are linked to the magnitude, distance, and local site conditions. Critical near‑fault ground motions have unique characteristics such as magnitude saturation and distance saturation. To investigate the impact of local site conditions in the near‑fault region, we analyzed the attenuation relations using the accelerations recorded in Japan by the Kiban Kyoshin network (KiK‑net) from 2000 to 2023. All events with a magnitude equal to or greater than 5.5, and with focal depth and epicentral distance less than 20 km, were considered. In this study, the hypocentral distance ranges from 5 to 23 km. Ordinary least squares regression analysis was conducted for the maximum horizontal vector of ground accelerations. The attenuation relation coefficients for horizontal vector peak ground acceleration were estimated using an effective distance and saturation effect. An additional independent term, the average shear wave velocity of the soil to a depth of 30 meters below the ground surface, and more cost‑effective shallower depths were included in the regression model. The analysis revealed that the site condition is statistically non‑significant at very short hypocentral distances of less than 23 kilometers.

Neural-Network and Multivariate-Normal-Distribution Hybrid Method for Real-Time Ground-Shaking Reconstruction

ABSTRACT Ground-shaking maps provide a spatial representation of the impact of a seismic event in terms of ground-motion parameters (GMPs), especially useful in the context of seismic monitoring and civil protection operations. Algorithms used to compute these maps usually rely on seismic source parameters to steer the interpolation process and consequently are limited to operate in near-real time. The present work introduces a novel algorithm that combines neural networks with the multivariate normal distribution method to reconstruct ground-shaking maps using only data available in real time, improving on previously proposed algorithms. The core idea of the proposed algorithm is to maintain the structure proposed by ShakeMap while removing the dependence on the source parameters, imposed by the use of ground-motion prediction equations, by replacing them with an appropriate neural network working on the GMPs recorded in real time at the seismic stations. The overall workflow of the method and the details of the neural network architecture and training are described. A model trained on synthetic and recorded data to target seismic events affecting the Italian territory is tested using the 2016 Norcia, Italy, earthquake showing the method reconstruction capabilities, its robustness to noise and to network geometry changes, and its real-time potential.

Deep-neural-network model for predicting ground motion parameters using earthquake horizontal-to-vertical spectral ratios

This study proposed a deep-neural-network (DNN) model for seismic ground motion prediction by utilizing a unified strong motion database by the National Research Institute for Earth Science and Disaster Resilience, and earthquake horizontal-to-vertical spectral ratio (EHVR) database in Japan. The model aims to enhance the accuracy of predictions by incorporating the EHVRs for complementing site effects, and utilizing existing ground motion prediction equations (GMPE) as the base model for source and propagation path effects. The hybrid approach enables the prediction of peak ground accelerations (PGAs), peak ground velocities (PGVs), and 5% damped absolute acceleration response spectra (SAs). After classifying the training and test sets from the database, the trained DNN models were applied on the test set to evaluate the performance of the predicted results. The accuracy assessment by the residuals, R-squared ( R2), and root mean square error (RMSE) between the predicted and observed values in the test set revealed the superior performance of the proposed model compared with the traditional GMPE with proxy-based site effects such as V S30s especially in predicting both the spectral amplitude and shape of SAs.

Resolving the Variability Discrepancy between Peak Ground Acceleration and Spectral Stress Drop: Insight from Double-Corner-Frequency Spectra

ABSTRACT This study addresses a challenge in ground-motion prediction, in which the observed variability of spectral stress drop Δσfc estimated from corner frequencies is significantly larger than the between-event variability of peak ground acceleration (PGA) supported by ground-motion prediction equations. To tackle this issue, we performed spectral ratio analyses on 34 crustal earthquakes with Mw 5.0–7.1 in Japan. Initially, we employed the standard spectral ratio method to estimate the corner frequencies fc and the spectral stress drops Δσfc. This method assumes the single-corner-frequency (SCF) spectral model. Next, we introduce a two-stage spectral ratio method to obtain the double-corner-frequency (DCF) spectra. This method first estimates the corner frequency of the small events in advance using further smaller events and the standard method. Then, it computes the spectra of the target event using the spectra of the small events predicted from the SCF model with the estimated corner frequency. We fit the SCF model to the observed spectra to estimate a high-frequency-fitted corner frequency fch and calculate the corresponding spectral stress drops Δσfch, called the stress parameter. Our analyses reveal that the variability of Δσfch aligns with the observed PGA variability, in contrast to the Δσfc variability, which is significantly larger and consistent with findings in previous corner-frequency studies. Thus, at least regarding the spectral ratio approach, the discrepancy between spectral stress drop and PGA variabilities primarily stems from the difference in the Δσfc and Δσfch variabilities, attributed to the diversity in source spectral shapes. This study demonstrates that although source spectra for Mw 5.0 align with the SCF model on average, deviations from the SCF model become increasingly pronounced with larger magnitudes. The results emphasize the significance of implementing the DCF model for improved ground-motion predictions.

Impact of seismotectonic source zoning and seismic parameter: Sensitivity study on probabilistic seismic hazard assessment

The demarcation of seismic area source zones plays a pivotal role in probabilistic seismic hazard assessment (PSHA). In this study, we developed five distinct seismotectonic area source models, integrating large and small areas based on geological, tectonic, and seismological data. Logic tree methodology is utilized in FRISK88M for PSHA. Analysis revealed higher hazards in models with larger area sources compared to those with smaller ones. Hazard deaggregation of seismotectonic model-5 (SM5) is examined and sensitivity analysis of various logic tree nodes is investigated in terms of percent deviation w.r.t mean hazard at various spectral and return periods. The findings underscore that recurrence methods fall into "insensitive level" (≤5%), source mechanism and a-value distribution belong to "low sensitive level" (>5–15%), magnitude distribution lies in "medium sensitive level" (>15–25%), b-value distribution belongs to "high sensitive level" (>25–35%), and ground motion prediction equations (GMPEs) and depth distribution falls into "very high sensitive level" (>35%) in relation to seismic hazard.

Physically adjusted ground motion prediction equations for induced seismicity at Preston New Road, UK

Predicting ground motions due to induced seismicity is a challenging task owing to the scarcity of data and heterogeneity of the uppermost crust. Dealing with this requires a thorough understanding of the underlying physics and consideration of inter-site variability. The most common ground motion model used in practice is the parametric ground motion prediction equation (GMPE), of which hundreds exist in the literature. However, relatively few are developed with a focus on induced seismicity. Developing GMPEs that are specific to an appropriate magnitude-distance range (R<30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R < 30$$\\end{document} km; 2≤M≤6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2 \\le M \\le 6$$\\end{document}) is important for induced seismicity applications. This paper proposes a framework for the development of physically-based GMPEs to provide more accurate and reliable estimates of the potential induced-seismicity ground motion hazard, allowing for better risk assessment and management strategies. To demonstrate this approach, a new set of GMPEs for the 2018-2019 induced seismicity sequence at the Preston New Road (PNR) shale gas site near Blackpool, United Kingdom, is presented. The physically-based GMPE was developed based on a pseudo-finite-fault stochastic ground motion simulation, calibrated with parameters derived from the spectral analysis of weak-motion records from induced seismic events. An optimization-based calibration technique using the area metric (AM) was subsequently performed to calibrate optimal parameters for simulating ground motion at the PNR site. Finally, using a suite of forward simulations for events with 1≤M≤6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1 \\le M \\le 6$$\\end{document} recorded at distances up to 30 km, combined with empirical data, a location-specific GMPE was derived through adjustment of an existing model.

Feasibility of Coseismic Landslide Prediction Based on GNSS Observations: A Case Study of the 2022 Ms 6.8 Luding, China, Earthquake

Abstract On 5 September 2022, an M 6.8 earthquake struck Luding County in Sichuan Province, China, triggering extensive landslides and causing severe damages. In this study, taking this event as an exemplary case study, we test the feasibility of fast earthquake-induced landslide prediction utilizing Global Navigation Satellite System (GNSS) observations. Particularly, we construct finite-slip models based on static offsets and 1 Hz displacement waveforms. Employing these slip models, physics-based simulation (PBS) is applied separately to obtain peak ground velocity (PGV). The PGVs are then integrated into landslide spatial distribution probability prediction based on the Deep Forest algorithm. Our results show that the predicted landslides probability distribution of fast inversion models using static and high-rate GNSS data align well with the landslide catalog. Furthermore, high-rate GNSS data can improve the model performance by providing the evolution information of rupture. In addition, we also derive PGV from the empirically regressed ground-motion prediction equations (GMPEs) and incorporate it into landslide prediction. The GMPEs exhibits an advantage in terms of prediction recall for landslides and a relatively reduced accuracy compared with the PBS. Despite the inherent uncertainties in this study, based on the case study of the 2022 M 6.8 Luding earthquake, we utilize GNSS data and present a set of methods for real-time landslide prediction. The achieved model performance is relatively satisfactory, considering the challenges and uncertainties involved.

Development and use of semi-empirical spectral ground motion models for GPP-induced micro-earthquakes in Southern Germany

This study provides a comprehensive exploration of ground motions associated with micro-earthquakes induced by geothermal power plants (GPP) in Southern Germany and proposes corresponding ground motion prediction equations (GMPE). Initiating with a statistical analysis of recorded seismic data from the GPP in Insheim, the study is extended to the greater Munich area. For the latter, the scarce recorded data are merged with physics-based simulation data. The recorded data in Insheim, Poing, Unterhaching and the simulated data in Munich are compared to existing GMPEs for GPP-induced events, highlighting the need of new region-specific prediction equations. The proposed GMPEs are expressed in terms of peak quantities, spectral accelerations and velocities, separating the horizontal and vertical direction. The regression curves exhibit a good alignment with both recorded and simulated data, within an acceptable range. Notably, the results reveal higher spectral quantities at shorter periods (<0.1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$<0.1$$\\end{document} s), underscoring the importance of this characteristic in seismic assessment. The article shows an exemplary application for a low-rise residential building, located at a hypocentral distance of 3 km. While the building meets serviceability standards for an MW\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$M_W$$\\end{document} up to 2.5, the verification fails at MW=3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$M_W=3$$\\end{document}, emphasizing the need for robust risk assessment. These findings contribute to the understanding of ground motions of GPP-induced events, offering practical implications for serviceability verifications and aiding informed decision-making in geothermal energy projects.Graphical abstract

Probabilistic Seismic Hazard Assessment of the Southwestern Region of Saudi Arabia

In relation to its rapid infrastructure expansion, exemplified by projects like the Najran Valley Dam or the rehabilitation of agricultural terraces, Saudi Arabia stands out among the Arabian Gulf nations. To mitigate the earthquake-related risks effectively, it is imperative to conduct an exhaustive analysis of its natural hazards. The southwesternmost region of Saudi Arabia is the main subject area of this study for the probabilistic seismic hazard assessment (PSHA), which aims to identify the peak ground acceleration (PGA) and spectral acceleration (SA) values. The investigation encompasses a 10% and 5% probability of occurrence over a 50-year exposure time for both B/C and C NEHRP soils. In order to take into account the earthquake activity that takes place in the vicinity of the Red Sea Rift, which in fact may have an impact on the seismic hazard in this active tectonic region, different seismic source zones were especially designed for this evaluation. Various characteristics such as the uncertainties related to the b-value, the expected maximum magnitude, and different ground motion prediction equations (GMPEs) were integrated using a logic tree scheme. Additionally, regression relationships between the computed ground motion values were established, and a novel design response spectrum was developed and recommended for several cities. Regarding the key findings, it is significant to highlight that the seismic hazard decreases towards the northeast, when moving away from the Red Sea Rift, confirming anticipated trends where proximity to the rift corresponds to increased seismic hazard. Notably, cities such as Farasan Island, Jazan, Al Qunfundhah, Al Lith and Al Birk present the highest observed hazard values among all the cities analyzed. For these cities, the obtained maximum SA values for both 475 and 975 years under B/C site conditions are as follows: 0.268 g and 0.412 g, 0.121 g and 0.167 g, 0.099 g and 0.150 g, 0.083 g and 0.135 g, and 0.066 g and 0.118 g, respectively. These results emphasize the crucial necessity of adequately evaluating and thoroughly updating the seismic hazard inherent to these particular areas to enhance the risk reduction and disaster readiness initiatives.

Post-earthquake rapid seismic demand estimation at unmonitored locations via Bayesian networks

Post-earthquake safety assessment of buildings and infrastructure poses significant challenges, often relying on time-consuming visual inspections. To expedite this process, safety criteria based on a demand-capacity model are utilized. However, rapid assessment frameworks require accurate estimations of intensity measures (IMs) to estimate seismic demand and assess structural health. Unfortunately, post-earthquake IM values are typically only available at monitored locations equipped with sensors or monitoring systems, limiting broader assessments. Simple spatial interpolation methods, while possible, struggle to consider crucial physical factors such as earthquake magnitude, epicentral distance, and soil type, leading to substantial estimation errors, especially in areas with insufficient or non-uniform seismic station coverage. To address these issues, a novel framework, BN-GMPE, combining a Bayesian network (BN) and a ground motion prediction equation (GMPE), is proposed. BN-GMPE enables inference and prediction under uncertainty, incorporating physical parameters in seismic wave propagation. A further novelty introduced in this work regards separating the near and far seismic fields in the updating process to attain a clearer understanding of uncertainty and more accurate IM estimation. In the proposed approach, a GMPE is employed for the estimation, and the bias and standard deviation of the prediction error are updated after any new information is entered into the network. The proposed method is benchmarked against a classic Kriging interpolator technique, considering some recent earthquake shocks in Italy. The proposed BN framework can naturally extend for estimating the probability of failure of various structures in a targeted region, which represents the ultimate aim of this research.

Novel approach for energy-spectrum-based probabilistic seismic hazard analysis in regions with limited strong earthquake data

With the rapid development of energy-based seismic design, probabilistic seismic hazard analysis (PSHA) in terms of the input energy spectrum, E I, has become increasingly important. Generally, implementing E I-based PSHA requires a ground-motion prediction equation (GMPE) for E I. However, although a GMPE for E I can be constructed in regions with abundant earthquake data based on regression analyses, it is difficult to obtain in regions lacking strong ground-motion records. Therefore, this study proposes a new approach for performing E I-based PSHA in regions with limited earthquake data. Instead of using a GMPE for E I directly, a model of Fourier amplitude spectrum (FAS) is adopted, which can be determined using a small number of earthquake data with small-to-moderate magnitudes. Then, the E I of the ground motion is obtained from FAS based on the relationship between E I and FAS. Furthermore, to calculate the annual intensity exceedance rate within the proposed framework of adopting the FAS model, a highly efficient method, namely, the moment method, is applied. Several numerical examples indicate that the proposed approach not only is suitable for regions lacking strong ground-motion records but also performs very efficiently in calculating the annual intensity exceedance rate.

Testing the applicability of ground motion prediction equations for the Hainaut region (Belgium) using intensity data

In regions where strong earthquakes occurred before the deployment of dense seismic and accelerometric networks, intensity datasets can help select appropriate ground motion prediction equations (GMPEs) for seismic hazard studies. This is the case for the Hainaut seismic zone, which was one of the most seismically active zones in and around Belgium during the twentieth century. A recent reassessment of the intensity dataset of the area showed that intensities in this region attenuate much faster with distance than in other parts of northwestern Europe. Unfortunately, this characteristic has not yet been taken into account in current hazard maps for Belgium and northern France. Based on this dataset, we evaluate the goodness of fit of published GMPEs with intensities in Hainaut by means of a ground-motion-to-intensity conversion equation (GMICE) and according to different metrics (Likelihood, Log-likelihood and Euclidean-based Distance Ranking) published in literature. We also introduce a new measure to specifically evaluate the distance trend. Our results show that none of the tested GMPEs convincingly fits the intensity dataset, in particular the fast attenuation with distance. Nevertheless, applying the few GMPEs that show a reasonable fit in seismic hazard computations, we observe a decrease of the influence of the Hainaut seismicity on hazard maps for Belgium and northern France. This result is compatible with the earthquake intensity observations for the last 350 years in this part of Europe.

Simulation of pulse-like ground motions with directionality effect for the 2001 Mw 7.6 Bhuj, India earthquake and sensitivity analysis of uncertain model input parameters

Ground motions exhibiting pulse-like (PL) features present a distinct and significant threat to the structural integrity of constructed environments, due to intense pulse, large amplitude, and rapid energy release. The lack of recorded PL ground motion data in the Indian region presents a significant challenge for accurate seismic risk assessment of the built environment. In the absence of recorded PL ground motions, the generation of synthetic PL ground motions can provide earthquake acceleration time histories for seismic response analysis of structures and also to evaluate ground motion prediction equations (GMPEs). Within this framework, the modified stochastic finite-fault approach based on the dynamic corner frequency is used to generate the synthetic ground motions for the 2001 Gujarat earthquake. These artificially generated ground motions are validated using available peak ground acceleration data from thirteen stations. After validation, the ground motions are generated at locations equidistant from the fault in all four directions (i.e., east, west, north, south). The expected acceleration time histories at specific sites, the spatial distribution of ground motion characteristics, and the effect of site non-linearity are investigated. Based on the geographical positioning of sites for the 2001 event, it was observed that 65.15 % of ground motions on the western side, relative to the epicenter, exhibited PL characteristics, whereas 23.64 % of ground motions on the eastern side were observed to have PL properties. The effect of uncertainty in the model input parameters on the simulated ground motions is examined by performing a sensitivity analysis. The reliability and validity of the simulated ground motions are assessed by deriving an empirical equation and compared with the results obtained from available GMPEs for the region.

Predicting the seismic ground-motion parameters: 3D physics-based numerical simulations combined with artificial neural networks

Typically, it is challenging to incorporate near-surface soils into 3D physics-based numerical simulations (PBSs) for ground-motion prediction. The low shear wave speed of near-surface soils, coupled with the complexity of the soil seismic response, poses significant difficulties. To overcome these limitations, a hybrid approach was proposed in this study, combining PBSs with artificial neural networks (ANNs). The essence of the hybrid method can be summarized as follows: (1) development of ANN models, establishing a strong-motion database, training the ANNs on it to predict the ground-motion parameters for East–West (EW), North–South (NS), and Vertical (UD) directions afterward; (2) establishment of 3D PBS model, obtaining the ground-motion parameters of the bedrock face corresponding to a certain shear wave speed; (3) application of the trained ANNs to predict the ground-motion parameters on the ground surface, taking the simulated results and related site parameters as inputs, and the outputs are peak ground acceleration (PGA) and 5% damped spectral accelerations (Sa) at different periods on the ground surface. In this study, ANN models were trained on a strong-motion database based on Kiban–Kyoshin Network (KiK-net). After several verifications of the ANN predictions, a case study of the 21 October 2016 Mw6.2 Central Tottori earthquake was conducted. In addition to the comparison with observations, the broadband (0.1–10 Hz) results of the hybrid method were also compared with the results that obtained by transfer function based on recorded data and Next Generation Attenuation (NGA)-West2 ground-motion prediction equations (GMPEs) to demonstrate the effectiveness and applicability of the proposed method. In addition, the distribution of Sa for four periods in simulated area was presented. The performance of the hybrid method for predicting broadband ground-motion characteristics was generally satisfactory.

Bayesian maximum entropy interpolation analysis for rapid assessment of seismic intensity using station and ground motion prediction equations

In this paper, we explored the combination of seismic station data and ground motion prediction equations (GMPE) to predict seismic intensity results by using Bayesian Maximum Entropy (BME) method. The results indicate that: 1) In earthquake analysis in Japan, soft data has predicted higher values of intensity in disaster areas. BME corrected this phenomenon, especially near the epicenter. Meanwhile, for earthquakes in the United States, BME corrected the erroneous prediction of rupture direction using soft data. 2) Compared with other spatial interpolation methods, the profile results of Japan earthquake and Turkey earthquake show that BME is more consistent with ShakeMap results than IDW and Kriging. Moreover, IDW has a low intensity anomaly zone. 3) The BME method overcomes the phenomenon that the strength evaluation results do not match the actual failure situation when the moment magnitude is small. It more accurately delineates the scope of the disaster area and enriches the post-earthquake processing of disaster area information and data. BME has a wide range of applicability, and it can still be effectively used for interpolation analysis when there is only soft data or few sites with data available.

On-Site Earthquake Early Warning Model for Selected Records in the NGA-West2 Dataset Using S- and P-Wave Spectral Ratios

ABSTRACT On-site earthquake early warning (EEW) requires the best estimates of earthquake magnitude and distance parameters within a few seconds after the P-wave arrival for estimating the subsequent S-wave parameters. The errors in the estimates of the earthquake P-wave parameters will propagate into the estimates of the S-wave parameters. To solve this problem, we used the methodology by Zhao and Zhao (2019), which uses the spectral ratio R3P between the response spectral values of the first 3 s of the S wave and that of the first 3 s of the P wave, referred to as the R3P model. The modeling presented here was based on strong-motion records from the Next Generation Attenuation (NGA)-West2 dataset. We also used the spectral ratio RFP between the response spectral values from the full records from the S-wave arrival time to the end of the record and that of the first 3 s P wave to develop a second EEW model (the RFP model). An advantage of these two models is that the magnitude and hypocentral distance are not required in considerable magnitude and distance ranges. This means that the errors in the estimated source and path parameters from the first 3 s of the P wave will not affect the model predictions. A theoretical justification for these results is that the magnitude and the distance scaling rates for the first 3 s of the P wave are similar to those of the first 3 s of the S wave. This may also apply to the full S-wave window within useful EEW magnitude and distance ranges. We also found that when the estimated magnitude and distance for a record are necessary, the effect of the corresponding errors would be smaller than using a ground-motion prediction equation (GMPE), because the magnitude and distance scaling rates from this study are smaller than those of many GMPEs.

Rapid determination of source parameters of the M6.2 Jishishan earthquake in Gansu Province and its application in emergency response

In this study, we swiftly determined the focal parameters (focal mechanism, seismic imaging process, magnitude) of the Jishishan earthquake, leveraging a solved fault model to assess the intensity field and casualties promptly. The investigation began by retrieving the source mechanism through the P-wave initial motion and W-phase method. This enabled us to chart the spatial and temporal distribution of energy release in the source area via the back-projection technique. Following this, we estimated the earthquake's intensity field by merging the source inversion findings with the ground motion prediction equation. This analysis facilitated the evaluation of earthquake casualties, utilizing the theoretical intensity field and a casualty assessment model. Our findings indicate that the fault type is a thrust fault, characterized by a unilateral rupture in the direction of NW, with a rupture length spanning approximately 10–15 ​km and a duration ranging between 8 and 10 ​s. The earthquake's magnitude varied from M 5.9 to M 6.2. The demarcated high-intensity areas, as per our intensity assessment, align closely with the actual survey results. Furthermore, the predicted total casualties and identified critical rescue zones closely match the real-world casualty figures. These insights offer crucial technical support for governmental emergency command and rescue operations.