Ground Motion Prediction Research Articles

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.

Site response analysis by generating a new 3d mesh design with surface topography: a 3d site response analysis of northwest Turkey

Predicting soil behavior under dynamic load due to earthquakes is pivotal for engineering structures and human life. Due to various limitations, such as insufficient computers and difficulties in generating models, the third-dimension effect is generally neglected in many studies. Conversely, the third-dimension effect in regions with high topographic differences, deep basins, three-dimensional heterogeneous and anisotropic environments, and alluvium is at a level that cannot be neglected. This study created a three-dimensional model of the northwest of Turkey for the first time by including surface topography. Soil properties were added to this model, and dynamic analysis was performed. This new model aims to increase the accuracy of ground motion predictions in Northwest Turkey. The accuracy of this model was analyzed using real earthquake data recorded in the study area. In addition, a new software (SiteEffect3D) with various features has been developed to create a three-dimensional mesh with topography using digital elevation model data and to perform dynamic analysis more effectively. This software has been tested comparatively with “Plaxis 3D” software using synthetic terrain models. The importance of this study is that in addition to its contributions to site response analysis and seismic hazard assessment, new software has been developed that can be used in similar studies. The findings will provide valuable information for seismic design and construction practices and facilitate the development of more effective strategies to reduce the potential damage from earthquakes in the region.

High-resolution 3D ambient noise tomography around the Meishan-Chiayi active fault system of western Taiwan

The Meishan-Chiayi area of western Taiwan has a large probability of producing a major earthquake in the near future. Historically, one of the largest and most damaging of Taiwan’s earthquakes occurred there. It is, therefore, important to have a well-constrained upper crustal 3-D shear-wave velocity model that can be used to accurately determine ground motion predictions and fault geometry models used in seismic hazard and risk modelling. In this study, we carried out an ambient noise tomography experiment using 100 seismometers deployed with a ∼2 km spacing on a 20 by 20 km grid. The reliable periods of phase velocity from Rayleigh waves are 0.6 to 6.8 s, providing a well-resolved Vs structure from the surface to a depth of around 4 km. The velocity model displays a prominent, roughly northeast-striking change in Vs that follows the projected surface trace of the blind Chiayi thrust. The uplift of relatively higher Vs rocks in its hanging wall, together with a negative to positive change in dVs suggests that it dips gently eastward across the study area. A northward thickening of the lower Vs crust, together with a high negative dVs in the north of the study area is related to an increased thickness of foreland basin rocks across the Meishan fault. The Vs and dVs models provide reasonable evidence that the Meishan fault can be traced at a high angle from its surface rupture to the base of the model at 4 km depth. It cuts the Chiayi thrust.

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.

Nationwide frequency-dependent seismic site amplification models for Iceland

Seismic wave amplification due to localized site conditions or site-effects, is an important aspect of regional seismic hazard assessment. In the absence of systematic studies of frequency-dependent site-effects during strong Icelandic earthquakes, various local site proxies of large-scale studies in other seismic regions have been used and/or proposed for application in Iceland. Recently, however, earthquake site-effects were rigorously quantified for 34 strong-motion stations in Southwest Iceland for the first time and correlated to distinct Icelandic geological units of hard rock, rock, lava rock, and sedimentary soil. These units are prevalent throughout Iceland and in this study therefore, we present 1) nationwide maps of proxies (e.g. slope, Vs30, geological units) that may contribute to a better estimation of site effects and associated, 2) frequency-dependent site-amplification maps of Iceland relative to the median ground-motion prediction of Rahpeyma et al. (2023) [55]. We particularly focus on the two transform zones of the country, on the basis of digital elevation models and detailed geological maps. Specifically, the frequency-dependent site factors for each geological unit are presented at 1, 2, 5, 7, 10–30 Hz, and PGA. Finally, for comparison, we generate site amplification maps based on recent large-scale models developed in other seismic regions and/or applied in other studies (e.g., ESRM20) as well as various site proxies they are based on (e.g., geology- and slope-based inferred VS30, geomorphological sedimentary thickness). We compare site-proxy maps and amplification maps from both Icelandic and large-scale, non-Icelandic, models. Specifically, neither spatial patterns nor amplification levels in either proxy or amplification maps from large-scale non-Icelandic studies resemble those observed from local quantitative strong-motion research as presented in this study. We attribute the discrepancy primarily to the young geology of Iceland and its formation history. Additionally, this study compares model performance across frequencies by assessing the bias of model predictions against empirical site amplifications in the South Iceland Seismic Zone, accounting for site-to-site variability of residuals indicating the superior performance of the local amplification model. The results presented in this study now allow a more informed estimation of earthquake ground motion amplitudes on various geological units that are expected to result in more reliable and information-based seismic hazard estimates in Iceland.

Assessing seismic hazard and uncertainty in Büyükçekmece using ground motion simulations

This study presents a comprehensive seismic hazard assessment for Büyükçekmece, a district in Istanbul, Turkey, situated near the seismically active North Anatolian Fault (NAF). The study utilizes stochastic ground motion simulations with the validated EXSIM algorithm to understand the potential impact of medium to large-magnitude earthquakes (ranging from MW 6.3 to 7.42) on this vulnerable community. The research employs a site-specific approach, considering unique amplification factors for each location. By conducting 50 scenario-based simulations, the study assesses the seismic hazard, highlighting the importance of comprehending variations in ground motion, even when they are small, for a more precise hazard assessment. Furthermore, this study addresses the critical issue of uncertainty, particularly concerning stress parameters and hypocenter locations. The researchers demonstrate that variability in these factors can introduce substantial uncertainty in ground motion predictions. The study provides insights into the range of potential ground motion outcomes through probabilistic assessments involving multiple scenarios and stress drop values. Notably, the results indicate that ground motion levels vary with earthquake magnitudes and underscore the significance of accounting for this variability. This research emphasizes the seismic vulnerability of Büyükçekmece and the importance of accurate ground motion simulations, offering valuable insights for earthquake preparedness and mitigation efforts in the region.

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.

Prediction of Regional Broadband Strong Ground Motions Using a Teleseismic Source Model of the 18 April 2014 Mw 7.3 Papanoa, Mexico, Earthquake

ABSTRACT To estimate predicted ground motion from a teleseismic slip model, we use a low- and high-frequency hybrid method to simulate the regional, strong ground motions observed following the 18 April 2014 moment magnitude (Mw) 7.3 Papanoa, Mexico, earthquake. To generate the regional ground motion at low frequencies (<1 Hz), a teleseismically derived, finite-fault, kinematic model is used to define the earthquake source, taking into account slip-model variations identified with a parameter sampling approach that considers possible errors in the fault geometry, the hypocenter depth, and the rupture velocity. A 3D crustal model is used to calculate the low-frequency ground motions using a finite-element calculation that includes topography and considers variations in the source model to estimate the uncertainty in the calculations. High frequencies (>1 Hz) are added using a 1D full-wave propagation code that estimates uncertainties by considering multiple random distributions of slip with different spatial correlation lengths. The synthetic, broadband (0.05–10.0 Hz) ground motions are obtained by combining the low- and high-frequency portions match filtered at 1 Hz. These synthetic ground motions are compared with the regional observations using velocity records, peak ground acceleration, and medians of the orientation-independent response spectra of the horizontal components (RotD50) calculated at periods of 0.2, 0.3, 0.5, 1.0, 2.0, 3.0, 5.0, 7.5, and 10.0 s. The results indicate that ground motions estimated at these periods using our hybrid approach based primarily on a teleseismically derived source model are comparable to the values observed for the 2014 Papanoa earthquake at regional distances. The approach could be used to estimate strong-motion spectral levels expected for regions with limited local and regional recordings and could also fill in magnitude or distance gaps in ground-motion prediction relations utilized in the assessment of seismic hazard.

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.

Seismic Features Predict Ground Motions During Repeating Caldera Collapse Sequence

AbstractApplying machine learning to continuous acoustic emissions, signals previously deemed noise, from laboratory faults and slowly slipping subduction‐zone faults, demonstrates hidden signatures are emitted that describe physical details, including fault displacement and friction. However, no evidence currently exists to demonstrate that similar hidden signals occur during seismogenic stick‐slip on earthquake faults—the damaging earthquakes of most societal interest. We show that continuous seismic emissions emitted during the 2018 multi‐month caldera collapse sequence at the Kı̄lauea volcano in Hawai'i contain hidden signatures characterizing the earthquake cycle. Multi‐spectral data features extracted from 30 s intervals of the continuous seismic emission are used to train a gradient boosted tree regression model to predict the GNSS‐derived contemporaneous surface displacement and time‐to‐failure of the upcoming collapse event. This striking result suggests that at least some faults emit such signals and provide a potential path to characterizing the instantaneous and future behavior of earthquake faults.

Ground-motion models for earthquakes occurring in the United Kingdom

This article presents models to predict median horizontal elastic response spectral accelerations for 5% damping from earthquakes with moment magnitudes ranging from 3.5 to 7.25 occurring in the United Kingdom. This model was derived using the hybrid stochastic-empirical method based on an existing ground-motion model for California and a stochastic model for the UK that was developed specifically for this purpose. The model is presented in two consistent formats, both for two distance metrics, with different target end-users. Firstly, we provide a complete logic tree with 162 branches, and associated weights, capturing epistemic uncertainties in the depth to the top of rupture, geometric spreading, anelastic path attenuation, site attenuation and stress drop, which is more likely to be used for research. The weights for these branches were derived using Bayesian updating of a priori weights from expert judgment. Secondly, we provide a backbone model with three and five branches corresponding to different percentiles, with corresponding weights, capturing the overall epistemic uncertainty, which is tailored for engineering applications. The derived models are compared with ground-motion observations, both instrumental and macroseismic, from the UK and surrounding region (northern France, Belgium, the Netherlands, western Germany and western Scandinavia). These comparisons show that the model is well-centred (low overall bias and no obvious trends with magnitude or distance) and that the branches capture the body and range of the technically defensible interpretations. In addition, comparisons with ground-motion models that have been previously used within seismic hazard assessments for the UK show that ground-motion predictions from the proposed model match those from previous models quite closely for most magnitudes and distances. The models are available as computer subroutines for ease of use.

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.

GRAPES: Earthquake Early Warning by Passing Seismic Vectors Through the Grapevine

AbstractEstimating an earthquake's magnitude and location may not be necessary to predict shaking in real time; instead, wavefield‐based approaches predict shaking with few assumptions about the seismic source. Here, we introduce GRAph Prediction of Earthquake Shaking (GRAPES), a deep learning model trained to characterize and propagate earthquake shaking across a seismic network. We show that GRAPES’ internal activations, which we call “seismic vectors”, correspond to the arrival of distinct seismic phases. GRAPES builds upon recent deep learning models applied to earthquake early warning by allowing for continuous ground motion prediction with seismic networks of all sizes. While trained on earthquakes recorded in Japan, we show that GRAPES, without modification, outperforms the ShakeAlert earthquake early warning system on the 2019 M7.1 Ridgecrest, CA earthquake.

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.

Evaluation of variability of shear wave velocity in India

The shear wave velocity is a significant component in seismic hazard studies. This involves standard practice in seismic hazard analysis through ground motion prediction equations (GMPEs), ground response analyses (GRAs), or the computation of amplification factors. In practice, the shear wave velocity may be obtained through invasive methods, noninvasive methods, transformation models, or correlation-based methods. Currently, the uncertainty component of shear wave velocity has either been overlooked or oversimplified. The aim was to quantify the range of variations in shear wave velocity across India. The database included data of reported shear wave velocities in 20 states collected from various project reports and publications. A brief review of the methods used to obtain the data is given, along with a systematic highlight of the range of uncertainties encountered and their statistics. Base-case profiles for response analyses are recommended, and their statistics are examined. These observations aid computation of amplification factors. The transformation uncertainties of the shear wave velocity and the standard penetration test (SPT) were examined. The outcomes of the research will guide reliable seismic hazard studies across countries. Furthermore, this study paves the way for future development toward addressing uncertainties in shear wave velocity and facilitates the use of reliability-based design procedures.

Machine learning ground motion models for a critical site with long-term earthquake records

Ground motion models derived using data from widespread stations and earthquakes induce significant aleatory variability when applied to predict ground motions for a single site. This issue can be even worse when the sites of interest show complex site response characteristics and do not have enough similar sites in the ground motion model development dataset. This work provides a machine-learning-based ground motion modeling framework for sites with numerous earthquake recordings. The Onahama Port Array, a liquefiable site in Japan that has recorded more than 1200 earthquakes in the last 25 years, was selected as a case study. The gradient boosting method was employed to predict response spectra with periods from 0.01 to 10 s for earthquakes with Japan Meteorological Agency (JMA) magnitude ranging from 2.4 to 9.0. Different predictor combinations were tested, and two gradient boosting models were recommended: the basic model that requires earthquake magnitude and epicentral distance as inputs, and the optimal model that requires three additional inputs, focal depth, azimuth, and rainfall. The basic and optimal gradient boosting models have the root mean square error of 0.004 and 0.001 g, average r2 of 5-fold cross validation of 0.915 and 0.983, respectively. The results shed light on seismic hazard assessment for critical sites with long-term earthquake recordings.

Ground motion prediction using Artificial Neural Network in Pakistan

The goal of this research project is to design, build, and validate an artificial neural network (ANN) model that predicts ground motion from the previous data of earthquake for seismic incidents in Pakistan. The prediction of ground motion is essential for determining the seismic risks and consequently providing measures to mitigate them. The ANN model implements activation function in hidden neurons to represent those relationships the seismic data holds and get logarithmic PGA values. The model performance evaluation metrics which are like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Correlation Coefficients prove the accuracy and robustness of the ANNPGA model. The ANNPGA model displays the best predictive power of all PGA values through the validation dataset. The MSE value is 0.00264 which model's accuracy in capturing ground motion variability. A comparative analysis with already created empirical and physics-based models will demonstrate that the ANPGA model gives more accurate predictions in most cases and especially in situations where nonlinear relationships are involved.

Vertical Ground Motion Prediction Equations for Interplate and Intermediate-Depth Intraslab Earthquakes at Mexico City’s Hill Zone

ABSTRACT We present ground motion prediction models for spectral pseudo acceleration (5% damping), peak ground velocity, and peak ground acceleration values from interplate and intermediate-depth intraslab earthquakes at the so-called hill zone of Mexico City for the vertical component and the vertical-to-horizontal ratio case. There are no previous models for the vertical component from these events in Mexico City. The prediction equations were developed as a function of magnitude and also as a function of distance to the fault surface of earthquakes by using 30 and 26 seismic recordings from interplate (1965–2022) and intermediate-depth intraslab (1964–2017) earthquakes at the hill zone of the city by using Bayesian regression analyses. The attenuation models are developed for interplate events for distances ranging from 280 km to over 500 km and Mw from 6 to 8.1, and for intraslab events for distances to the fault surface ranging from 100 km up to about 600 km, focal depths from 40 km to over 120 km and Mw from 5 to 8.2. Nevertheless, since at large distances ground motions are generally weak, we recommend the applicability of the proposed models to be restricted to up to 400 km. The models are adequate to estimating the seismic hazard at the hill zone of Mexico City, as proved by contrasting analytical versus empirical seismic exceedance rates for the vertical component.

Analysis of impulsive ground motions from February 2023 Kahramanmaraş earthquake sequence

AbstractOn the 6th of February 2023, a large magnitude earthquake (Pazarcık earthquake), $$M_{w}=$$ M w = 7.7, occurred in southeast Türkiye, which caused significant destruction in Türkiye and Syria. Relatively large magnitude aftershocks followed the main shock, and after 9 hours of the main event, another large magnitude earthquake (Elbistan earthquake) occurred, $$M_{w}=$$ M w = 7.6, on a nearby fault. This study analyzes the near-fault seismic signals from earthquakes larger than 5.5 recorded between the main shock and the 31st of March 2023. More than 60 impulsive motions are detected in 3 earthquakes, mostly concentrated in the Pazarcık and Elbistan earthquakes. In the Pazarcık earthquake, many impulsive motions are recorded in near-fault stations with periods of up to 14 s. In contrast, in the Elbistan earthquake, impulsive motions are spatially distributed, with pulse periods of up to 11 s and at distances greater than 150 km. Pulse periods mostly correlate with the magnitude of the earthquake, but pulse probability models do not predict impulsive motions over long distances. The presence of strong impulsive motions in vertical components is also observed. For both earthquakes, peak ground velocities (PGVs) are larger than predicted by ground motion prediction equations. The observation of long-period, large amplitude signals may indicate the presence of a directivity effect for both earthquakes. In some stations, spectral periods exceed the 2018 Turkish building design codes for long periods ($$\ge$$ ≥ 1 s).